❌

Normal view

How Zebrafish Could Hold the Key to Developing Treatments for CADASIL

24 June 2026 at 12:44

In a groundbreaking advancement in neurological research, scientists have unveiled a novel animal model that elucidates the complex pathophysiology of CADASIL—a hereditary cerebral small vessel disease responsible for recurrent strokes and progressive cognitive decline. This innovative model leverages the unique advantages of zebrafish, an emerging organism in biomedical investigations, to faithfully replicate cardinal features of CADASIL that have eluded previous studies relying on traditional rodent systems.

CADASIL, an acronym for cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy, is a debilitating inherited condition characterized by the deterioration of the brain’s microvasculature. This deterioration precipitates repetitive ischemic strokes and gradually destroys white matter integrity, culminating in premature dementia. Despite recognition of mutations in the NOTCH3 gene as the underlying genetic cause, the molecular mechanisms by which these mutations accelerate vascular damage and neurological decline remain inadequately understood, stalling the development of effective therapeutic interventions.

The limitations of current animal models have largely impeded progress in CADASIL research. While mouse models reproduce some pathological features, they fall short of mimicking critical clinical aspects such as notable cognitive impairment, brain atrophy, and hallmark white matter lesions observed in patients. Seeking to overcome these barriers, a research team at Chiba University in Japan strategically harnessed the zebrafish system to create a genetically engineered model expressing the CADASIL-associated NOTCH3 mutation p.C680S. This approach taps into zebrafish’s translational potential due to their genetic tractability, transparent embryos, and highly conserved neurological architecture.

Over a meticulously designed longitudinal study, the zebrafish harboring the NOTCH3 mutation were followed through key developmental milestones. Utilizing live cerebral blood flow imaging, behavioral assays to assess memory and learning, advanced magnetic resonance imaging for detecting brain structural changes, and molecular profiling techniques, the researchers captured a multidimensional portrait of disease progression. Notably, at approximately 11 months, mutant zebrafish exhibited initial declines in cerebral perfusion, a precursor to subsequent neurological dysfunction.

At 14 months, behavioral testing revealed a marked deterioration in learning capacity, with significant deficits in working memory emerging by 17 months, mirroring the temporal pattern of cognitive decline in human CADASIL patients. Structural brain assessments detected tissue loss predominantly in the cerebral regions analogous to those affected by the human disease. Electron microscopy further substantiated the model’s fidelity by identifying granular osmiophilic material deposits—abnormal protein aggregates universally recognized as a pathological hallmark of CADASIL.

Delving deeper into the molecular underpinnings, the team uncovered a pronounced reduction in type IV collagen expression surrounding cerebral vasculature in the mutant zebrafish. Type IV collagen is a vital component of the basement membrane, providing structural integrity, tensile strength, and resilience to blood vessels. The deficiency of this crucial extracellular matrix protein likely destabilizes the vascular basement membrane, rendering the vasculature susceptible to damage and disruption.

This reduction in type IV collagen offers a compelling mechanistic link between NOTCH3 mutations and microvascular fragility, providing a fresh perspective on the molecular etiology of CADASIL. The findings suggest that therapeutic strategies aimed at restoring or preserving type IV collagen integrity may hold promise for halting or reversing vascular degeneration in patients afflicted with this disease.

Highlighting the scientific significance of this work, Professor Motoyuki Itoh remarked that their study opens new avenues for exploring the interplay between genetic mutations and extracellular matrix components in age-related cerebrovascular disorders. The zebrafish model thereby stands as a versatile platform for dissecting disease mechanisms and screening potential therapeutics in a cost-effective and expedited manner.

Furthermore, the successful recapitulation of progressive vascular and neurological symptoms in this model sets the stage for the generation of future zebrafish lines harboring diverse CADASIL mutations. Such developments will expand the research toolkit, facilitating nuanced investigations into genotype-phenotype correlations and accelerating the translation of laboratory findings to clinical applications.

Beyond CADASIL, this work underscores the broader utility of zebrafish in neurodegenerative disease research. Their rapid development, amenability to high-resolution imaging, and genetic manipulability position them as invaluable in vivo models for unraveling the complexities of brain aging and dementia.

The implications of this breakthrough resonate profoundly for patients and families affected by CADASIL, particularly in East Asia where certain gene mutations are highly prevalent. The zebrafish model not only advances scientific understanding but also injects hope for the discovery of effective treatments that have, until now, remained elusive.

Looking ahead, collaborative efforts integrating zebrafish modeling with molecular genetics, pharmacology, and clinical studies promise to accelerate the pace of therapeutic innovation. This pioneering study marks a paradigm shift, showcasing how thoughtful adoption of non-traditional model organisms can surmount longstanding challenges in understanding human neurological disease.

As research continues to unravel the intricate biology of CADASIL and related disorders, it is anticipated that the zebrafish will play an increasingly central role in drug discovery pipelines, offering a window into disease processes and a testbed for novel interventions aimed at preserving brain health and function in aging populations.

The integrated research conducted by the team at Chiba University exemplifies the power of interdisciplinary collaboration, combining expertise in molecular biology, neuroscience, imaging technology, and animal modeling to break new ground in a previously intractable field.

This compelling advance heralds a new chapter in neurovascular research, with the zebrafish emerging not only as a model organism but as a beacon of hope for unlocking the secrets of devastating genetic brain diseases like CADASIL.


Subject of Research: Animals

Article Title: Age-dependent vascular and neurological characteristics of CADASIL are recapitulated in Notch3 mutant zebrafish, implicating a role for type IV collagen in disease progression

News Publication Date: 3-Jun-2026

Web References:
http://doi.org/10.1186/s40478-026-02333-8
https://www.cn.chiba-u.jp/en/news/

References:
DOI: 10.1186/s40478-026-02333-8

Image Credits:
Professor Motoyuki Itoh from Chiba University, Japan

Keywords: CADASIL, NOTCH3 mutation, zebrafish model, cerebral small vessel disease, type IV collagen, vascular basement membrane, neurodegeneration, cerebral blood flow, cognitive decline, genetic modeling, MRI, neuropathology

Epigenetic Locking Drives H. pylori Gastric Cancer

24 June 2026 at 12:02

In a groundbreaking study published in Cell Death Discovery, researchers have unveiled a novel mechanism by which Helicobacter pylori, the notorious gastric pathogen, drives the formation and persistence of gastric cancer through a process termed the “hit-and-run” legacy. This research elucidates how transient infection by H. pylori delivers a lasting epigenetic imprint on gastric epithelial cells, effectively locking these cells into a survival mode that fosters oncogenesis. The implications of these findings are profound, offering fresh insights into the molecular interplay between infection-induced epigenetic modification and cancer development, which could pave the way for revolutionary diagnostic and therapeutic strategies.

Helicobacter pylori has long been established as a principal carcinogen associated with gastric cancer, a malignancy ranking among the world’s deadliest. Traditionally, the carcinogenic potential of H. pylori has been attributed to chronic inflammation and the persistent presence of bacterial virulence factors that continuously disrupt gastric mucosa homeostasis. However, the current study challenges this paradigm by demonstrating that even transient exposure to H. pylori can induce stable, heritable epigenetic alterations that effectively “lock” the cellular genome into an aberrant state, independent of ongoing infection. This “hit-and-run” mechanism underscores the importance of early, possibly even brief, pathogen-host interactions in cancer genesis.

At the heart of this discovery is the concept of epigenetic locking—a process through which transient signals induce durable modifications in DNA methylation and histone patterns, which subsequently enforce gene expression profiles conducive to cell survival and proliferation. The group led by Sun, Hu, and Traub employed state-of-the-art genome-wide methylation profiling alongside chromatin immunoprecipitation sequencing (ChIP-seq) to map the intricate epigenetic landscapes shaped by H. pylori infection. Their findings revealed a marked enrichment of repressive histone marks on tumor suppressor gene loci and a reduction in methylation at key oncogenic enhancers, a dual mechanism that effectively enables gastric epithelial cells to bypass apoptosis and adopt a resilient phenotype.

Intriguingly, the authors identified a “survival switch” embedded within the epigenetic circuitry, which perpetuates cell viability in the hostile gastric environment despite the absence of the pathogen. This survival pathway hinges on the modulation of several critical transcription factors and downstream effectors that drive resistance to oxidative stress and immune checkpoint evasion. The persistence of this switch presents a formidable barrier to therapeutic intervention, explaining the often-observed resistance and relapse seen in conventional anti-H. pylori and chemotherapeutic regimes.

The study further explores the timing and sequence of epigenetic alterations following H. pylori infection. Temporal analysis demonstrated that initial infection triggers a wave of epigenetic remodeling within days, with key changes becoming fixed in the genome over weeks, thus confirming the durability of the modifications. This rapid “imprinting” emphasizes the need for early detection and intervention post infection to prevent the establishment of irreversible oncogenic programs.

Mechanistically, the team delved into the role of bacterial virulence factors such as CagA and VacA in orchestrating these epigenetic changes. They postulate that CagA, upon translocation into host cells, interacts with host chromatin modifiers and signaling pathways like NF-kB and STAT3, initiating cascades that culminate in altered chromatin architecture. VacA further modulates cellular metabolism and stress responses, cumulatively fixing the epigenetic landscape conducive to cancer progression.

The implications of the “hit-and-run” model extend beyond H. pylori-induced gastric cancer, provoking reconsideration of how transient infections may contribute to other epigenetically driven malignancies. This paradigm shift advocates for a broader investigation into the long-term impact of infectious insults at the epigenome level, potentially revolutionizing the standard view of tumorigenesis associated with microbial pathogens.

Therapeutically, the study stimulates the development of epigenetic-targeted approaches aimed at “unlocking” these stable aberrations. By reversing methylation patterns or disrupting the survival networks sustained by epigenetic modifications, it may become possible to sensitize tumor cells to existing chemotherapies or to immune-mediated clearance. The prospect of integrating epigenetic therapy into the clinical management of H. pylori-associated gastric cancer represents a promising frontier.

Moreover, diagnostic innovation could also benefit from these findings. Epigenetic biomarkers identified through this research may serve as early indicators of cancer risk in patients with a history of H. pylori infection, enabling stratification and personalized surveillance. The early detection of epigenetic locking signatures in gastric mucosa could dramatically improve prognosis by facilitating timely therapeutic intervention.

The study’s comprehensive exploration of epigenetic changes was bolstered by the use of gastric organoids and in vivo models, which authentically recapitulated the cellular and tissue contexts of H. pylori infection. This multi-faceted experimental design lent robust physiological relevance to the findings and highlighted the dynamic interplay between pathogen factors and host epigenomic machinery.

Collectively, this research not only deciphers the cryptic molecular legacy left by H. pylori but also refines our understanding of infection-associated carcinogenesis. The “hit-and-run” legacy paradigm sheds light on the complex, often covert, mechanisms through which transient pathogen interactions exert long-lasting oncogenic effects, shifting both scientific insight and clinical approach.

In summary, the work by Sun, Hu, Traub, and colleagues heralds a new era in gastric cancer biology, emphasizing the critical role of infection-driven epigenetic programming. As further investigations build upon this foundation, multidisciplinary efforts encompassing microbiology, epigenetics, and cancer therapeutics will be essential to translate these insights into tangible clinical applications.

The future of combatting H. pylori-associated gastric cancer lies not only in eradicating the bacteria but also in decoding and reversing the epigenetic codes it inscribes. This endeavor carries the promise of transforming patient outcomes, potentially mitigating one of the world’s most lethal cancers with strategies born from the understanding of the pathogen’s epigenetic “hit-and-run” legacy.


Subject of Research: Epigenetic mechanisms underlying the persistence and oncogenic transformation in Helicobacter pylori-associated gastric cancer.

Article Title: The “Hit-and-Run” legacy: epigenetic locking and the survival switch in H. pylori-associated gastric cancer.

Article References:
Sun, J., Hu, P., Traub, B. et al. The “Hit-and-Run” legacy: epigenetic locking and the survival switch in H. pylori-associated gastric cancer. Cell Death Discov. (2026). https://doi.org/10.1038/s41420-026-03210-y

Image Credits: AI Generated

DOI: https://doi.org/10.1038/s41420-026-03210-y

Fusobacterium periodonticum Spurs Colorectal Cancer via Decanoic Acid

24 June 2026 at 11:02

In a groundbreaking study published in Nature Communications, researchers have illuminated a critical link between Fusobacterium periodonticum and the progression of colorectal cancer, unveiling a complex biochemical pathway that involves decanoic acid-mediated neutrophil chemotaxis. This discovery adds a significant piece to the puzzle of how microbial influences within the gut microenvironment contribute to tumorigenesis, potentially opening new avenues for targeted therapies against colorectal cancer.

Colorectal cancer is among the leading causes of cancer-related mortality worldwide, and although genetic factors and lifestyle influences have been extensively studied, the role of the gut microbiota has only recently come to the forefront. The study conducted by Jia, Jiang, Gong, and colleagues delves deeply into the mechanistic interactions between a specific bacterial species, Fusobacterium periodonticum, and the host immune landscape, highlighting a novel metabolic crosstalk that accelerates tumor development.

Fusobacterium periodonticum, traditionally associated with oral biofilms and periodontal disease, has increasingly been detected in colorectal cancer tissues, but its precise role in cancer biology remained unclear until now. The researchers demonstrate that this bacterium secretes decanoic acid, a medium-chain fatty acid, which acts as a chemoattractant, mobilizing neutrophils to the site of tumor inception. Neutrophils, while pivotal in innate immunity, paradoxically can induce pro-tumorigenic inflammation under certain conditions.

By employing an integrative approach combining metagenomic analyses, metabolomics, and in vivo murine models, the team traced the cascade from bacterial colonization to immune recruitment and revealed that decanoic acid engages specific chemokine receptors on neutrophils. This interaction fosters a microenvironment rich in inflammatory mediators, growth factors, and oxidative stress, all of which potentiate malignant transformation and tumor growth.

The significance of neutrophils in cancer progression is increasingly recognized, but the finding that a bacterial metabolite directly drives neutrophil chemotaxis in the colorectal milieu establishes a new paradigm. Unlike conventional chemoattractants, decanoic acid appears to uniquely modulate neutrophil behavior, enhancing their recruitment and activation state, which in turn exacerbates tissue remodeling and genomic instability within epithelial cells.

This intricate host-microbe dialogue also implicates metabolic dysregulation as a driving force. The elevated levels of decanoic acid in the tumor microenvironment underscore how bacterial metabolism intersects with host pathways, creating conditions unfavorable for normal tissue homeostasis and favorable for neoplastic transformation. The researchers adeptly demonstrate that blocking the receptors responsive to decanoic acid attenuates neutrophil infiltration and reduces tumor burden in experimental models, underscoring therapeutic potential.

Further dissection of the immune milieu revealed that neutrophils recruited by decanoic acid secrete a spectrum of cytokines and chemokines that recruit additional immune and stromal cells, contributing to a chronic inflammatory state. This inflammation not only supports tumor cell proliferation but also promotes angiogenesis and suppresses adaptive immune responses, thereby facilitating immune evasion.

The clinical implications of these findings are manifold. Understanding the role of Fusobacterium periodonticum and its metabolic products in colorectal tumorigenesis invites consideration of microbiota-targeted interventions. Strategies such as selective antibiotics, probiotics to restore microbial balance, or small molecules that inhibit decanoic acid signaling might serve as adjuncts to conventional chemotherapy or immunotherapy.

Indeed, the intersection between microbial ecology and cancer biology posits a compelling narrative wherein microorganisms are not mere bystanders but active participants orchestrating complex pathophysiological processes. The decanoic acid-driven recruitment of neutrophils exemplifies how microbial metabolites can hijack immune mechanisms, turning protective responses into facilitators of malignancy.

Moreover, the identification of decanoic acid as a pivotal mediator expands the landscape of oncogenic metabolites beyond the commonly studied short-chain fatty acids, introducing medium-chain fatty acids as critical players in tumor-immune dynamics. This underscores the importance of integrating metabolomic profiling in cancer research to uncover previously underappreciated biochemical pathways.

From a diagnostic perspective, elevated decanoic acid or markers of neutrophil activation within colorectal tissues or patient serum samples might serve as biomarkers for early detection or risk stratification. The specificity imparted by the involvement of Fusobacterium periodonticum offers a microbiota-based signature that could refine personalized medicine approaches.

The authors highlight the translational potential of targeting the decanoic acid-neutrophil axis. For instance, developing receptor antagonists or neutralizing antibodies that mitigate neutrophil chemotaxis may diminish pro-tumor inflammation without compromising host defense. Such therapies could synergize with immunomodulatory agents to reinvigorate anti-tumor immunity.

Beyond colorectal cancer, these findings prompt reevaluation of Fusobacterium periodonticum’s role in other inflammatory or neoplastic conditions associated with mucosal surfaces. The paradigms unveiled here may be applicable to understanding microbiota-driven pathogenesis in other cancers or chronic inflammatory diseases, hinting at broader biomedical relevance.

The robust experimental framework of this study, which ranges from bacterial culture to patient-derived samples and genetically engineered mouse models, strengthens the causal link and positions it as a cornerstone for future investigations. The detailed mechanistic insights provide a platform for exploring combinatorial therapies that integrate microbial manipulation and immune modulation.

In sum, Jia et al. have charted a novel territory in cancer research by elucidating how a bacterial species long recognized for its role in oral health can influence colorectal carcinogenesis through a metabolite-mediated immune recruitment mechanism. This work exemplifies the power of interdisciplinary research bridging microbiology, immunology, and oncology to unravel complex disease processes.

As research continues to unravel the intricate dance between microbiota and host immunity, the elucidation of pathways like the decanoic acid-driven neutrophil chemotaxis reaffirms the critical role of the microbiome in health and disease. Ultimately, this knowledge holds promise for innovative cancer prevention strategies that target the microbial ecosystem alongside human cells.

The implications of this research resonate beyond the laboratory, raising compelling questions about dietary modulation, microbiome management, and personalized interventions that could transform cancer prognosis. Understanding the forces that drive tumorigenesis at the intersection of microbes and immune cells is not just an intriguing scientific pursuit but a critical step toward conquering a major human health burden.

Subject of Research:
The study focuses on the interaction between Fusobacterium periodonticum and neutrophil chemotaxis mediated by decanoic acid, with implications for colorectal tumorigenesis.

Article Title:
Fusobacterium periodonticum promotes colorectal tumorigenesis via decanoic acid-driven neutrophil chemotaxis.

Article References:
Jia, X., Jiang, L., Gong, Y. et al. Fusobacterium periodonticum promotes colorectal tumorigenesis via decanoic acid-driven neutrophil chemotaxis. Nat Commun (2026). https://doi.org/10.1038/s41467-026-74591-y

Image Credits: AI Generated

Using Visual Studio Code’s ‘air-gapped’ AI model mode

24 June 2026 at 10:00

Microsoft has been pushing hard to make Visual Studio Code a major way to consume its AI services, mostly in the form of GitHub Copilot. GitHub Copilot’s deep integration with VS Code brings many conveniences — inline autocomplete, for instance — but it’s frustrating for those, like me, who would rather use another model provider, or even a locally hosted LLM, for those functions.

Visual Studio Code 1.122 introduced a new feature, “Use BYOK [Bring Your Own Key] without a GitHub sign-in,” that allows you to “use chat, tools, and MCP servers in air-gapped or restricted environments where GitHub sign-in isn’t possible.” More importantly, it “enables fully offline workflows with local models like Ollama.”

In other words, you can now use locally hosted LLMs for chat, tools, and Model Context Protocol servers inside Visual Studio Code. The one thing you still can’t do is use a local LLM for inline and next-edit suggestions — at least, not without additional tooling.

Choosing a model for BYOK mode

If you want to use a local LLM with VS Code’s bring-your-own-model system, the first thing you need is a way to host the model. VS Code lacks a model-hosting mechanism of its own, although it’s conceivable that a VS Code extension may offer something like that in the future. That said, hosting models is complicated enough that a dedicated app is really needed for the job.

One easy way to host models is via a product like LM Studio, a convenient GUI for standing up, serving, and managing LLMs on one’s own hardware. The model host does not have to be the same system you run VS Code on, either. It can be on a server box you control, or on a cloud instance.

The choice of model is also important. Many models are powerful but won’t run well on commodity hardware because they’re simply too big. A good rule of thumb is to choose a model that fits into existing VRAM, along with the memory needed for a sizable token context (the more, the better). Also, the model should be suited to coding and development work. Some models in this vein that fit comfortably into 8GB VRAM include:

Setting up BYOK mode in VS Code

Once you have a model up and running, you can integrate it with Visual Studio Code. If you’ve disabled VS Code’s AI features, you will need to turn them on. Make sure the setting chat.disableAIFeatures is turned off. You can find it in Settings | Chat | Miscellaneous.

Third-party language models are managed through Visual Studio Code’s language model list. Press Ctrl-Shift-P and type Manage Language Models to open the list of existing language models.

Managing VS Code's AI language model list

Managing VS Code’s AI language model list. The models available by default are only models available as external APIs, not models that run locally.

Foundry

First you will see a list of the built-in models, which are all externally hosted. To add a new model, select Add Models at the top right and select Custom Endpoint.

You’ll then get a series of prompts:

  • Group Name: This is “Custom Endpoint” by default, but you can choose any name you want. The name is strictly for organizing the model list and doesn’t affect things like model recognition or connectivity.
  • API Key: If you’ve configured LM Studio to use an API key for serving models, provide it here. If you’re hosting the model locally and you haven’t explicitly set up API keys, you can leave this blank.
  • API Type: The options here are Chat Completions, Responses, and Messages. Most of the time you’ll want to use Responses, as it’s the most general-purpose option of the three.

Once you finish providing those answers, you’ll be dropped into a modal editor for a JSON file that holds the details about the endpoint you’re configuring.

A newly created custom endpoint for a locally hosted model

A newly created custom endpoint for a locally hosted model. The ID, name, and URL still need to be defined for this model to be useful.

Foundry

You’ll need to provide a few more details by typing them into the labeled fields:

  • id: A text field that uniquely identifies this particular entry. The choice of ID is pretty much arbitrary; if you’re using only a single model, the ID could be the model name.
  • name: The name of the model that is used to identify it on the model server. In LM Studio, you can get this name by clicking on My Models in the main interface, then selecting the three-dot icon for the model in question and clicking Copy Default Identifier. For Qwen 2.5, for instance, name might be something like qwen2.5-coder-7b-instruct.
  • url: The URL to the server’s endpoint. On LM Studio, this defaults to something like http://127.0.0.1:1234/v1. The /v1 at the end is important because that endpoint is used for autodiscovery of models and their capabilities.

The other fields generally don’t need editing. Most models have tool calling functionality. If you know for a fact that the model you’re using doesn’t have vision support, then set vision to false.

Once you have these fields filled in, you can close the modal editor to save the changes. If you reload the Manage Language Models page, you’ll now see your new endpoint:

A newly created local endpoint

A newly created local endpoint. The choice of name and group is arbitrary. “Custom Endpoint” is the default name for a newly created group of endpoints.

Foundry

You should now be able to launch the chat window and use the defined model for conversation and utilities:

Conversing with the local model using VS Code's chat window

Conversing with the local model using VS Code’s chat window. Note the selected code block in the left pane that is being used as the context for the conversation.

Foundry

One current, and major, limitation of Visual Studio Code’s BYOK functionality is that it only works for chat and utility tasks. It doesn’t allow you to use a local model for inline suggestions or code completions. The only way to take advantage of local models for expanded functionality with VS Code is to use a third-party tool like Continue.

It isn’t clear if Microsoft will eventually lift this restriction. GitHub Copilot integration in VS Code is a large part of how Copilot as a service reaches its target audience. For the time being, you can certainly use third-party and local models for a significant part of your AI-assisted development work in VS Code, and you can close the functionality gap with additional tooling.

Open source grapples with agentic coding

24 June 2026 at 10:00

Unless you’ve been living under an old woodpile in your backyard, you have certainly seen how agentic coding is rocking the software development world. Things are happening fast and furious, and keeping up is practically a full-time job. 

The latest area that is catching the attention of developers is how agentic coding is affecting the open source community. The open source movement has been defending the rights of folks to use, change, and contribute to software for many years. And of course, agentic coding is starting to become part of that process.

On the one hand, maintainers of open source projects rightfully are frustrated as they become overwhelmed with pull requests of dubious quality and usefulness being submitted by coding agents. On the other hand, as David Heinemeier Hansson notes, maintainers are starting to get a little snooty about accepting AI-written code, viewing it as somehow not worthy of being included. Some organizations have explicitly banned AI-generated submissions.

I get that they don’t want AI slop overwhelming their input queues. But I think it is a huge mistake to ban AI-written code outright.

Whose code?

Before I dig deeper into that notion, it’s important to look at another issue that arises from all of this: Who actually owns the code that AI writes? 

Copyright requires that a human produce the thing being copyrighted. If you prompt Claude Code with “Write me a CMS system” and then Claude writes you a CMS system that you check into a public GitHub repository unchanged, it’s not quite clear if that code is protected by copyright. However, if you prompt Claude Code with a specification and guidelines and then you work with Claude to refine the initial result, reviewing the code and making changes as part of an iterative process, then it could be argued that a human did produce that code. But it is not at all clear-cut legally. (Please note that I am not a lawyer.)

The current thinking is that the result of accepting verbatim the output of a simple prompt is not copyrightable, and that no one actually owns the code — an interesting notion in and of itself. 

But then the ethical question comes into play. If I find a bug in an open source project, I ask GitHub Copilot to fix it, and Copilot writes a clever and effective fix, then who cares who owns the code? Should a maintainer of the project reject such a pull request just because it was AI-generated? That seems silly to me, yet it is happening today. 

Our code

There is, too, the issue of license compliance for AI-generated code. As a general rule, LLMs generate code rather than copying it. They don’t copy and paste code directly from repositories. However, there have been cases where AI-produced code has resembled open source code so closely that the claim could be made that it is a copy. If this happens with GPL code, it could be a violation of the license to use it without the receiving code base being “infected.” Open source maintainers naturally should be concerned about this happening.

In the end, an open source maintainer should care about the quality and license compliance of submissions, not how those submissions were derived. Gatekeeping based on the source of code doesn’t seem like a good path towards project success. Good code is good code, no matter where it comes from.

Agentic coding is here, and the open source community needs to realize — and embrace — that inevitability.

OpenAI rolls out AI-led push to fix open-source software flaws

23 June 2026 at 11:35

OpenAI has launched a program with cybersecurity firm Trail of Bits to use AI to find and fix vulnerabilities in widely used open-source software, as enterprises face growing risks from flaws buried deep in their software supply chains.

The initiative, called Patch the Planet, uses AI-assisted vulnerability research alongside human review to help turn security findings into tested fixes that can be disclosed through existing project channels.

Initial participants include Python, Go, cURL, Sigstore, NATS Server, aiohttp, freenginx, pyca/cryptography, and python.org. These projects support software development, networking, cryptography, and supply chain infrastructure used across a wide range of enterprise applications and services.

OpenAI said each engagement will begin with consultation with maintainers to identify where security support is most needed. Researchers will then investigate potential vulnerabilities, validate meaningful issues, develop or refine patches, support testing, and coordinate disclosure through the project’s existing channels.

Participating security researchers will use the company’s models and Codex Security to analyze code and help move fixes toward release. Trail of Bits engineers will review findings before they are sent to maintainers, a step meant to filter out false positives and duplicate reports before they add to the workload of open-source projects.

The company is also working with HackerOne and Calif to support vulnerability triage, coordinated disclosure, and additional discovery work as the program expands.

OpenAI said work under the program has already identified “hundreds of security issues and merged dozens of patches, with many more still undergoing coordinated disclosure.”

The work has also produced tools for fuzzing, historical CVE analysis, and differential testing, along with systems to filter inaccurate findings before patches are generated, OpenAI added.

The focus on open-source security follows incidents such as Log4Shell and the XZ Utils backdoor, which showed how quickly a flaw in a shared component can move through enterprise software.

Analysts said Patch the Planet changes the risk equation only if enterprises treat AI-assisted vulnerability research as an input to a broader software supply chain risk program, not as a substitute for one.

“The key shift is speed: AI-assisted research can help find, validate, patch, test, and document issues faster, while human reviewers reduce false positives before maintainers are burdened,” said Biswajeet Mahapatra, principal analyst at Forrester. “But the dependency on scarce expertise does not go away; it moves to triage, exploitability judgment, patch safety, disclosure timing, and production rollout.”

Guardrails before deployment

CISOs should put governance controls in place before using AI-assisted vulnerability research in enterprise security pipelines, to ensure unverified findings do not overwhelm engineering teams, said Devashri Datta, an open-source cybersecurity architect.

“CISOs should demand a Safety Relevance Layer in their risk modeling, a structured framework that requires every AI-generated finding to pass automated verification, including dynamic proof-of-concept validation and strong false-positive filtering, before it reaches a human analyst,” Datta said.

Those controls should also cover disclosure, particularly when AI tools identify flaws in third-party open-source components that the enterprise does not control, Datta said. Organizations need predefined escalation paths, notification timelines, and role assignments that take effect once a confirmed issue is found in an external dependency.

“Ad hoc disclosure in an AI-accelerated environment isn’t just a process gap; it’s a liability,” Datta said. “Trusting AI in the production pipeline requires verifiable auditability: organizations must be able to trace why the AI flagged a line of code, how it validated the exploit, and how it determined that the patch would not break downstream production systems.”

Continuous exposure reduction

AI-assisted vulnerability research could force enterprises to move away from periodic patching cycles and toward more continuous risk assessment, analysts said. If variant analysis and differential testing can be compressed from weeks to days, security teams may need faster ways to decide which findings matter most in their own environments.

That shift also means enterprises can no longer rely only on generic CVSS scores to prioritize remediation, Datta said. Findings will need to be assessed against the affected system, its business role, runtime exposure and the likelihood that a flaw can be exploited.

“We have to move toward context-aware, safety-critical prioritization,” Datta said. “Enterprise SBOM and VEX programs must evolve from passive compliance spreadsheets into live, machine-readable data feeds. For AI-assisted pipelines specifically, that means extending the VEX model to cover AI-introduced risk surfaces.”

Mahapatra said vulnerability management programs will also need to become more closely tied to software ownership, supplier response, and business impact.

“Security teams should move from periodic vulnerability handling to continuous exposure reduction,” Mahapatra said.

That means SBOMs should be treated as live inventories tied to runtime exposure and supplier response, rather than static compliance documents. Patch decisions should also account for asset criticality, exploitability, compensating controls, and business impact.

The article originally appeared on CSO.

How fuzzy APIs are remaking the web

23 June 2026 at 10:00

For nearly as long as the web has existed, web development has wrestled mightily with the right way to connect components over the network. This is the question of the remote API. It influences every aspect of the software we build. We sort of arrived at a tolerable compromise with JSON APIs. While these have their limitations, you have to appreciate their underlying simplicity.

But the advent of AI-enabled endpoints that can mediate intent is changing the basic workings of the internet. This change is gradually reawakening an old dream, the service-oriented architecture (SOA). This time around, with luck, we’ll finally gain the flexible, discoverable, and maintainable automated service discovery we’ve longed for. Fingers crossed.

Why old-school SOA failed

Let’s call this burgeoning influence of AI on web architecture SOA 2.0.

To understand why SOA 2.0 is different from SOA 1.0, we have to remember the trauma of the 2000s. (This may be painful but also cathartic.) The original dream of SOA was beautiful: a world where disparate business services—inventory, billing, shipping, you name it—could automatically discover each other, understand capabilities, and orchestrate complex tasks without human intervention.

To achieve this, we built a monument to complexity. We had SOAP (Simple Object Access Protocol) for messaging, WSDL (Web Services Description Language) to define contracts, and UDDI registries for service discovery. At the center of it all sat the Enterprise Service Bus (ESB), a massive piece of middleware that was supposed to route everything gracefully, seamlessly. In case you young’uns are confused, that is all based on XML.

By the time you were done understanding the infrastructure well enough to know how to do something, you had forgotten what you set out to do.

It failed. It was egregiously heavy. Just to do some simple thing like create a “New Item” endpoint, you immediately had to begin scaling a wall of rigid definitions.

Because computers historically required absolute, deterministic perfection, if a single XML tag in a SOAP envelope was missing, or if a service updated its WSDL without every client re-generating its stubs, the entire multi-million-dollar pipeline would violently unravel. Some of us may be familiar with a similar challenge in containerized microservices (like Kubernates), where trying to determine where in the mesh a problem originated is… awkward.

Classic SOA was a house of cards, too brittle to survive the fuzzy reality of the internet.

The typical JSON API of today is a reaction against SOA. (It may be an overreaction.) We abandoned SOA for the relative simplicity of REST, giving up on the dream of autonomous service orchestration in exchange for manual integrations that just work.

The new intention-to-execution middleware

A sea change is already happening with app-level architecture. 

The effect of AI endpoints in an app’s service profile goes beyond just a new capability. It changes how the rest of the services work together. The overall effect is something like the app gaining an understanding of itself, and what it can do. This is not dissimilar to what WSDL was supposed to accomplish. But instead of a hard-coded descriptor, where some person had to keep what was available and what was described in sync, you now have a layer that can accept dynamically produced descriptors and unite them with fuzzy user intention and produce meaningful action.

You tie in AI endpoints to bridge between what the user is trying to accomplish, with the various strict capabilities available. These capabilities may exist within the app at the back end, at the front end, or at another service layer. The main thing is that there is a flexible AI layer that mitigates the need to hard-code the links between services.

In classic SOA, the contract was a rigid, unforgiving WSDL document. In modern common practice, the contract is a strongly coupled RESTful endpoint. In SOA 2.0, the contract has a hitherto unknown degree of flexibility, thanks to the natural language capabilities of an LLM.

When a user or a system expresses an intent—say, “Provision a new staging environment for the billing service”—the AI middleware doesn’t look for a hard-coded, point-to-point integration. Instead, it digests the intent and performs semantic routing, consulting a registry and selecting the appropriate tools. That registry, rather than a heavy UDDI, might be a vector database of available internal API endpoints, or a collection of available functions.

Modern LLMs equipped with function-calling capabilities act as the ultimate dynamic orchestrators. They read the JSON schema of a target REST API, understand its parameters, and dynamically map the user’s fuzzy, unstructured intent into a perfectly formatted JSON payload. If a field is missing, the LLM can either infer it from context or pause execution to ask the user for clarification.

The brittleness of SOA 1.0 is replaced by a shock absorber. If the target API changes a parameter name from customerID to clientId, the AI middleware can read the updated schema and adjust its mapping on the fly. No client code needs to be recompiled. No stubs need to be regenerated. The multi-million-dollar pipeline survives.

When software becomes smart

These are not just abstract ideas. I recently did my taxes, using a popular mainstream service that I will not name. I had several unusual and grumpy areas to deal with, including the new crypto regulations. It was not pretty.

But what I was most struck by was how dumb the software was, compared to the AI chatbot I was using to help guide me. I wanted to be able to tell the (stupid) software what I was trying to do. Such as “Carry my NOL from last year!” Or “I don’t know if I need a schedule K, you tell me!”

I don’t want another chatbot. I mean, I already have a good chatbot. I want the application to be well-integrated with AI services that understand the app, understand my current situation within the app, and meet me at the level of intent, applying the lessons learned by others who have used the same tools.

This kind of targeted, intelligent leveling up of intention is, from all I can see, the next stage of software development, and it is going to be massive.

Latency, non-determinism, and other challenges

We are trading the deterministic brittleness of classic SOA for the probabilistic fuzziness of SOA 2.0. And that trade is going to be demanded with ever more insistence by users. But it comes with a new set of trade-offs.

First, there is the latency tax. The old enterprise service bus was heavy to configure, but at run time, the messaging was just routed XML. Injecting an LLM into the critical path of an application adds hundreds of milliseconds, if not seconds, of latency. For asynchronous tasks or complex orchestrations, this is a welcome trade-off. For real-time, high-throughput microservices, it is a deal-breaker.

Second, there is the problem of non-determinism. We spent decades training ourselves (and our systems) to expect that given input A, a system will always produce output B. That deterministic equation was our bottom line faith. The intent layer doesn’t work that way. An LLM might route a request beautifully 99 times, then hallucinate a parameter on the 100th. Or it might choose an entirely different execution path based on a subtle shift in the user’s phrasing.

A third fly in the ointment is the so-called non-functional requirements, or NFRs. These are your pesky sidebar issues that refuse to be ignored, like security and reliability. 

Security concerns are magnified by model capabilities like function calling (or “function passing”). If you pair a user’s desires with what the AI can do, and you then let the AI decide, what happens next is clearly an act of faith unless guardrails are put in place. These guardrails must go beyond typical web security (i.e., make sure important function calls are hardened on the server, not exposed on the client) and must be internalized by the AI or (more likely) imposed from a layer outside the AI. There are a number of ways to do this, varying in degree of power and complexity. 

We certainly will continue to use standard practices (like RBAC and SSO) to enforce authentication. We will continue to implement standard authorization techniques (like OAUTH and JWT). But we will bring these to bear in the context of that intent layer and its capabilities.

Reliability is another challenge. For example, I recently hit a snag with Google’s Imagen API. Everything was working beautifully, then suddenly, some of the images stopped generating. There were no errors in the client or server logs; however, there were 500 errors in the network. Upon deeper examination, the prompting had morphed (between app context and user content) to include what the Imagen API rules deemed to be dangerous content. This was not obviously flagged prompting. It was fairly pedestrian creative writing, along the lines of “A dark, surreal, and glitchy cyberpunk landscape with menacing figures….” That kind of thing.

These are some of the ways that even simple, direct use of LLM APIs can surprise you. The question I am mulling is, what will be the unexpected outcomes on software writ large?

Dawn of a probabilistic web

Since its inception, the unpredictable, probabilistic nature of the internet came primarily from the humans using it (and background radiation flipping transistors, network failures, geopolitical effects on the ground, and the like). But AI-mediated APIs introduce an intentional, semantically controlled form of probability.

As developers, we will naturally discover the techniques that make consuming AI endpoints more effective. Here I am thinking about practices like structured responses and function calling. But the larger question is, what will the nature of software become?

In a world of binary states, strict protocols, and rigid URIs, if you send a GET request to a specific endpoint, you expect an exact, predictable response. We have spent the last 40 years treating the web like a vast, unimaginably complex state machine.

But as LLM-mediated APIs permeate our architecture with stochastics, the very fabric of the internet begins to change. By injecting AI into the routing and discovery layers, we are introducing a massive dose of probability into the foundation of our networks. When a request is no longer a hard-coded URI call but a natural language intent parsed by an LLM, the connection between node A and node B ceases to be a rigid wire. It becomes a weighted probability.

In essence, we are remaking the internet to mirror the architecture of the AI models we are deploying. Just as a neural network relies on the probabilistic firing of synapses rather than deterministic if/then statements, the next iteration of the web will rely on fluid, semantic discovery. Services won’t just “link” to each other; they will gravitate toward one another based on the conceptual proximity of their capabilities within a shared latent space.

This alters the character of software engineering. We lose (the illusion) of being entirely in control. Its strange paradox is that engineering using explicitly probabilistic components may make for a more resilient system. There is a longstanding debate about the best metaphor for software development. For the longest time, the construction of a building always seemed to be an apt analogy, or perhaps the mechanics of a vehicle. But these days, the gardening or cultivation metaphor is looking ever more relevant.

Despite the challenges posed by inserting AI in the stack, we are finally circling back to the original promise of the early 2000s. This time, fingers crossed, we are equipped with the right tools for the job.

We tried to build autonomous service discovery using rigid logic and deterministic XML, and it collapsed under its own weight. Now, we are building it with neural networks that understand the “intent” behind the integration. We are still building middleware, but instead of an enterprise service bus, we are building an enterprise reasoning bus.

The era of manually hard-coding every integration between every microservice may be coming to a close.

The missing layer in enterprise agentic AI

23 June 2026 at 10:00

In the past year, the enterprise AI ecosystem has gained enormous capability and zero consensus.

Developers now have a remarkable set of tools for building AI agents: OpenAI’s frameworks, Anthropic’s Claude tooling, LangChain, LangGraph, CrewAI, Microsoft AutoGen, and a growing list of alternatives. Each promises to coordinate reasoning loops, manage multi-step task execution, and connect agents to tools and APIs. For experimentation, the progress has been substantial. Teams can now assemble sophisticated agent workflows in days that would have taken months two years ago.

But I’ve watched this pattern before. In over two decades of building and selling distributed systems platforms, I’ve seen the same dynamic play out across nearly every major infrastructure shift: the tools for consuming a new capability arrive before the infrastructure for governing it does. The gap that emerges isn’t immediately obvious in development environments. It becomes obvious in production.

That’s exactly where enterprise AI stands today.

What agent frameworks don’t handle

Modern agent frameworks are fundamentally coordination systems. They determine what a system should do: which tools to call, how to sequence tasks, how to delegate work across agents. That’s hard work, and they’ve gotten quite good at it.

What they rarely address is where those tasks are allowed to run, and under what conditions.

Take a seemingly simple workflow: summarize customer support transcripts using an LLM. In a development environment, the implementation is clean. The agent calls a model API, passes the transcript, and returns a summary. In production at an enterprise, the same request may involve a dataset that can’t cross a specific geographic boundary, a model that isn’t approved for regulated data, and an audit requirement that demands a traceable record of what happened.

Those aren’t planning problems the agent framework was designed to solve. They’re execution governance problems. Most frameworks quietly assume they’re handled somewhere else in the stack. In many enterprise environments, they’re not handled at all. Gartner predicts more than 40% of agentic AI projects will be canceled by the end of 2027, citing inadequate risk controls as a primary driver of failure—a number that reflects exactly this gap.

What the missing layer actually does

Addressing these governance problems requires an additional layer between agent logic and execution: one that evaluates every agent action against policies governing where data can reside, which models may process it, who authorized the request, and how the action fits within the organizational context. The agent framework determines what the system should do. The orchestration layer determines whether and where it’s allowed to happen. Keeping those responsibilities separate allows both layers to evolve independently. It also means you can adopt new agent frameworks without rebuilding your governance model from scratch.

This separation will feel familiar to anyone who has worked through the Kubernetes era. Kubernetes doesn’t care what’s inside your container. It finds capacity, allocates resources, and ensures things run. The orchestration layer for agentic AI plays an analogous role: it doesn’t care which agent framework generated the request. It enforces the conditions under which that request can execute.

Richer authorization models

Traditional enterprise access control is built around a simple question: can user X access resource Y? That’s insufficient for autonomous agents.

A realistic authorization decision for an agent request might look more like this:

request = {
    "agent": "support-summary-agent",
    "task": "summarize",
    "dataset": "customer_support_logs",
    "model": "external_llm_api",
    "delegated_by": "user_4821"
}

policy = evaluate_policy(request)

if policy.allowed:
    route_to_execution(policy.execution_environment)
else:
    raise AuthorizationError(policy.reason)

The policy engine here evaluates dataset classification, model approval status, geographic processing rules, and the delegation chain that initiated the request. That might mean redirecting the task to an internal inference cluster instead of a public API endpoint, or blocking the request if no compliant execution environment exists. From the agent’s perspective, the task still executes. The orchestration layer ensures it runs in an environment that satisfies enterprise policy.

Why ontologies are load-bearing infrastructure

For the orchestration layer to make good decisions, it needs to do more than label data. It needs to understand how the entities involved in a request relate to each other, and reason over those relationships to determine what’s allowed.

Consider the customer support transcript example again. Metadata tells you the dataset contains PII (personally identifiable information). An ontology lets the system reason across a connected chain: the task operates on a dataset containing personal data; that data is governed by GDPR; the organization’s policy requires processing within an approved EU environment; the selected model runs outside that boundary. From those four connected facts, the orchestration layer can infer the request must be rerouted or blocked. The system reasoned over the relationships rather than matching against a hardcoded rule tied to a specific dataset.

This is what makes policy enforcement, execution routing, data locality, and audit decisions computable at runtime. An ontology can be built around virtually any entity-relationship set the enterprise needs to govern: datasets, models, agents, users, regulations, tasks, environments. The relationships that matter are the ones that drive the decisions the governance layer needs to make. Access control lists can restrict who touches a resource, but they can’t reason across a connected set of entities. That reasoning is what the orchestration layer depends on.

Decision provenance as a first-class requirement

Enterprise systems also require auditability. When automated agents trigger actions across multiple systems, organizations must be able to reconstruct the decision path that produced the outcome. Compliance depends on it. So does incident response and basic operational trust.

An orchestration layer generates records describing the initiating identity, the agent, the model, the data sources, the policies evaluated during authorization, and virtually anything else the organization chooses to capture in its ontology. That chain of custody allows teams to investigate incidents and validate compliance without treating production AI systems as operational black boxes.

Regulators and auditors are no longer satisfied with knowing what an AI system was designed to do. They want a factual record of what it did in a specific instance, under what authorization, and with what effect—something dashboards can’t provide, but a well-designed orchestration layer can. The EU AI Act makes this explicit: under Article 12 and Article 17, high-risk AI systems must maintain documentation that makes decisions traceable and auditable, with records sufficient to support investigation after the fact.

Where this leaves enterprise teams

Agent frameworks will keep improving. The coordination problems they solve are real, and the ecosystem will continue to mature. But the architectural challenge for enterprises has shifted. It’s no longer primarily about coordinating agents. It’s about governing how those agents interact with real infrastructure, real data, and real compliance obligations.

The patterns for doing that exist today: contextual authorization, data locality enforcement, ontology-aware policy evaluation, decision provenance. What most organizations are missing is the recognition that these capabilities belong in a distinct layer that operates independently of whichever agent framework sits above it. Build that layer, and the rest becomes manageable.

—

New Tech Forum provides a venue for technology leaders—including vendors and other outside contributors—to explore and discuss emerging enterprise technology in unprecedented depth and breadth. The selection is subjective, based on our pick of the technologies we believe to be important and of greatest interest to InfoWorld readers. InfoWorld does not accept marketing collateral for publication and reserves the right to edit all contributed content. Send all inquiries to doug_dineley@foundryco.com.

GitHub Actions hardens checkout security to block ‘pwn request’ attacks

23 June 2026 at 00:43

Stung by a surge in cyberattacks that have run amok in developer environments, GitHub has strengthened the security of actions/checkout to block ‘pwn request’ attacks that exploit insecure use of the pull_request_target workflow trigger to run an attacker’s code with the workflow’s full privileges.

Announced on June 18, actions/checkout v7 now automatically blocks and fails workflows when used inside pull_request_target or workflow_run events when attempting to fetch unreviewed fork pull request code.

From now on, the only away around these checks will be for developers to implement an opt out by adding an explicit allow-unsafe-pr-checkout to actions/checkout, GitHub said in its V7 changelog.

The change signals the beginning of a new ‘secure by default’ era in which security will be defined by the GitHub system rather than being left to discretion of developers. As part of that effort, on July 16, the new defaults will be backported to all supported major versions.

“Workflows pinned to a floating major tag (e.g., actions/checkout@v4) will automatically pick up the change. Workflows pinned to a specific SHA, minor, or patch version aren’t affected by the backport and will need to upgrade using Dependabot or through established upgrade processes,” GitHub explained.

However, because pwn request attacks can happen in other ways, “further hardening of additional events may be explored in future releases,” the changelog added.

Blind spot

If there’s a criticism that can be levelled at GitHub over this, it’s that it has taken so long to address a weakness that’s been known about for years.

The issue is with GitHub Actions, which allows triggers to run workflows, including pull_request, which processes third-party forks without giving access to secrets such as API keys, service tokens, and credentials. The downside is that this restriction prevents some automations from working, which is why developers turn to an alternative trigger, pull_request_target, which grants the required access.

At some point, attackers realized that where pull_request_target was configured carelessly with actions/checkout to pull in untrusted fork code, it offered a back door into repositories and their secrets.

In other words, the weakness in pull_request_target isn’t the trigger itself, which is legitimate and secure when correctly used, but in its  incorrect use. As GitHub’s changelog puts it: “Checking out the head of an unreviewed pull request from a fork inside one of these workflows typically lets attacker-controlled code execute with the workflow’s full privileges.”

The arrival of actions/checkout v7, however, should make this harder, automatically blocking risky workflows regardless of their configuration.

Unfortunately, a lot of damage has already been done. Open source repositories have recently come under sustained attacks from the TeamPCP hacking group, using a variety of techniques, including pwn requests.

A notable example was its attack last month, which compromised 170 node package manager (npm) packages, including the TanStack Router ecosystem, thanks to a pwn request exploit. Embarrassingly, in a separate incident not involving a pwn request, GitHub itself was breached and the attackers exfiltrated source code from around 3,800 of the company’s internal repositories.

Better late than never, GitHub has sprung into action, plotting a series of security reforms on the platform, including, earlier this month, limiting automatic install script execution in npm.

Why open infrastructure will define the AI era

22 June 2026 at 10:00

A new form of vendor lock-in is here. And it’s not proprietary languages or rigid enterprise software suites — it’s something more fundamental. It’s the very thing that writes the code.

JetBrains Research found that 74% of developers worldwide use AI tools. Claude Code, available only since May 2025, is now the most popular AI coding tool, followed by Gemini Code Assist and GitHub Copilot, according to Jellyfish’s 2026 State of Engineering Management Report.

The latter study also found that 91% of developers say their productivity has increased in the past 12 months. As coding output expectations are rewritten daily, the engineering world is becoming heavily reliant on paid external AI services.

Gartner predicts that by 2028 spending on AI coding tokens could exceed developer salaries. Yet, tokenmaxxing while vibe coding through a vendor’s cloud-based API feels like a far cry from the open foundations of free programming languages and open models, which many of today’s AI platforms now abstract.

“Open infrastructure will be the backbone of the AI era,” says Peter Farkas, CEO of Percona, a provider of open-source database solutions. “Right now, too many companies are building their entire AI strategy on top of proprietary platforms because the convenience is seductive.”

“It’s ‘three clicks’ to stand up a database or an AI service in a hyperscaler, and that convenience blinds people to the lock-in they’re signing up for,” he adds. “As AI workloads mature, organizations will realize that depending on one vendor for their data, models, runtime, and pricing is not a strategy.”

AI-assisted coding is democratizing software engineering for non-engineers and accelerating top performers. But if teams are always working within the confines of how one platform thinks the world should work, it could create locked-in toolsets at scale. And as AI platform costs rise, a fundamental question arises: will software developers consume AI on their own terms, or on someone else’s?

There’s a strong case that the long-term winners in tech will be built on open-source standards and foundations, similar to the history of cloud-native computing and the internet itself.

“Open always wins,” says Brian Alvey, CTO at WordPress VIP, a managed WordPress hosting platform. “Not because it’s a fancy ideology, but because it gives you total freedom to adapt, evolve, and stay in control.”

Open infrastructure avoids a future where developers perpetually rent. “For AI to be useful to people at large, it can’t be something you’re paying rent for the rest of your life,” says Manik Surtani, CTO and co-founder of the Agentic AI Foundation (AAIF), a vendor-neutral home for open-source agentic AI technologies. “And it can’t be concentrated in one particular corporation or a small handful of corporations, because we know how that goes.”

Pricey, closed, proprietary AI

AI development today is traveling two parallel paths. On one path, open-source AI is thriving and fueling tremendous growth in the number and variety of AI models and tools. Just take the thousands of open-weight models on HuggingFace, the community around the OpenClaw AI agent, or the many academic institutions publishing new breakthroughs.

“Open-source models and tooling are hot on the heels of state-of-the-art, with interesting and boundary-pushing work being shared by labs and researchers across the world,” says Austin Parker, director of AI strategy at Honeycomb, an observability platform provider, citing frontier open-source models like Mistral, DeepSeek, and Ai2’s OLMo as examples.

Others agree. “There’s unprecedented openness at the model and tooling layer, with open-source models, frameworks, and orchestration advancing at remarkable speed,” says Mark Collier, general manager of AI and infrastructure at the Linux Foundation.

On the other path, we’re seeing heavy reliance on proprietary AI systems controlled by Anthropic, Cursor, Google, Microsoft, OpenAI, and others. As Collier says, “Many platforms are wrapping those open components in closed, opinionated interfaces that trade short-term speed for long-term constraints.”

Open source and the AI tooling market don’t always mix well. LangChain’s Open Agent Platform, for instance, was open-sourced to much fanfare in 2025, but by 2026 had been deprecated, with the repository now recommending fully managed alternatives.

For Roman Shaposhnik, co-founder and CTO of Ainekko, provider of an open-source, composable AI stack, the current AI platform landscape is reminiscent of low-code and no-code platforms, which promised democratization of software development but often failed to deliver, becoming synonymous with platform lock-in and inflexibility.

“Honestly, it feels familiar,” Shaposhnik says. “We have incredibly powerful AI tools right now, but most of them come bundled as tightly controlled platforms.” This is a risk for AI, he says, because the infrastructure, models, and hardware are tightly coupled. “If those layers are closed, you lose flexibility fast.”

Some abstractions that sit on top of models, like routing and agent frameworks, tend to be tightly coupled and optimized for certain models. Other platforms take the walled garden concept quite literally. Anthropic, for instance, has repeatedly made headlines for blocking access to its Claude models over vague policy violations. The company recently shut off competitor xAI’s use and stonewalled OpenCode, drawing community backlash.

Moves toward increasingly closed systems don’t bode well for an AI economy already built on shaky economics. As Vikram Srivats, head of product experience at WaveMaker, provider of an agentic application development platform, adds, “Given the unit economics of AI tooling and pace of accelerated change to keep up, it seems obvious that some will evolve to more of a closed system to be able to monetize and gain ROI.”

Why openness matters in the AI era

Reliance on proprietary AI platforms can create long-term operational dependencies. As systems become less interoperable, organizations may be forced to standardize on a single stack across data pipelines, models, and decision logic, says the Linux Foundation’s Collier.

“As infrastructure consolidates, enterprises become more exposed when platforms change direction, raise prices, or fall behind technically,” he says. “If you can’t change platforms without re-architecting your AI systems, you’ve already given up too much control.”

“When you build on someone else’s platform, you have to live by their rules and those rules always change,” adds WordPress VIP’s Alvey. “We’ve all seen this before, businesses wasting time and money building to serve Google, Facebook, YouTube, and the App Store, instead of building to serve their customers.”

Platform lock-in can also create direct business risk. As Ainekko’s Shaposhnik says, “It usually shows up as higher costs, fragile systems, and growing risk when it’s time to change direction.”

At Ainekko, an internal group called the AI Plumbers focuses on back-end AI infrastructure like inference, scheduling, memory, and hardware integration. “Their view is simple,” says Shaposhnik. “If those layers are closed, everything above them becomes fragile.”

Open standards, interfaces, and infrastructure provide a necessary hedge against closed systems to prevent this sort of fragility. “In the AI era, open infrastructure gives enterprises control, portability, and choice at exactly the time they need it most,” says Percona’s Farkas.

It can cost upwards of $100,000 to migrate enterprise software, according to Cloudaware, making portability a major enterprise concern. From this perspective, procuring closed systems can become a costly architectural dependency.

Others argue that openness is a critical hedge against vendor concentration risks at large, especially if AI replaces human labor en masse. “If all of that economic value is now being concentrated in the hands of one or two companies,” says the AAIF’s Surtani, “that’s an order of magnitude bigger problem than we’ve seen in any other wave of computing.”

Instead, open foundations allow adaptability to evolving conditions so enterprises can swap out models, agents, data, hardware, and orchestration, as needed. “Open standards let those components change independently without breaking the system,” says Collier. 

Openness can also help future-proof businesses against economic upheaval. “Open everything will help build a cushion for businesses and users to survive and thrive after the almost-certain correction in the current hype cycle,” says WaveMaker’s Srivats.

Momentum toward open AI infrastructure

At the industry level, momentum toward open AI infrastructure is growing. The establishment of the Agentic AI Foundation, Anthropic’s donation of Model Context Protocol (MCP), and Block’s donation of its Goose agent are significant ecosystem-wide moves toward openness. Other advances include the donation of llm-d, a Kubernetes framework for LLM inference, to the Cloud Native Computing Foundation (CNCF).

For Parker, donations like this help ensure long-term support and care. “Open standards aren’t just the foundation of the internet, they’re the foundation of the AI space,” he says. “I predict that we’ll see these practices continue, especially as enterprise adoption increases in earnest,” he adds.

Still, some question whether this level of stewardship is enough for a rapidly evolving ecosystem. “The internet benefited early on from groups that helped keep vendors aligned,” says Shaposhnik. “In AI infrastructure, we don’t really have that yet.”

“All of us open source veterans are hopeful,” he says, “but we also need to adapt to this new reality in what we do regarding AI infrastructure.”

Beyond industry governing bodies, companies themselves are also spearheading open AI initiatives. Warp, an agentic development environment, recently went open source amid closed-source rivals. Arcade.dev, meanwhile, is pushing an open-source Agent Library for agentic memory.

Where openness matters most in the AI stack

While AI infrastructure can be open in many ways, a few layers stand out as especially important. First is the openness of the model itself. “Open-source models must be the foundation of future trust and value,” says WaveMaker’s Srivats.

“The forms of open infrastructure that reduce integration friction and accelerate adoption stand out,” adds Neeraj Abhyankar, VP of data and AI at R Systems, a global digital solutions provider. For him, open model representation formats, open orchestration and execution layers, open agentic protocols, and open governance and metadata standards are all essential for enterprise flexibility.

Others place more value on the connective tissue between AI components. “The most important forms of open infrastructure are the ones that connect systems together,” says Collier. “That includes open APIs, metadata standards, identity and policy frameworks, and protocols for how models and agents communicate.” 

Arguably, MCP has become the connective tissue between AI agents and the broader API ecosystem. “If we get MCP right we unlock the same level of interoperability between entities on the web and models driving them as we came to enjoy during the Web 2.0 era and the API-first boom,” says Shaposhnik. “If we don’t we risk massive proprietary lock-ins.”

Parker agrees that open protocols will underlie future AI progress. “We’ll see continued development and progress on AI agents which will rely on protocols like MCP and ACP [Agent Client Protocol] to interoperate with various clients and each other,” he says. Yet a gap remains around API conventions for models. “It would be nice if we could get a commitment from model providers to use a standard here.”

For the AAIF’s Surtani, opening up the protocol layer is the most important aspect. “I think it’s really important for interoperability, for choice,” he says. “It means you can bring your own agent, you can bring your own framework, you can bring your own harness, and pick what model you want.”

Open standards may also play a significant role within inference architecture. “As AI expands to the edge, developers need visibility into how models run, how memory is used, and how performance scales,” says Shaposhnik. Open systems could make it easier to optimize, debug, and adapt while helping enterprises avoid observability fragmentation.

Lastly, cloud-native architectural standards are a key ingredient for open AI infrastructure. “We’re seeing Kubernetes become the missing link for people who want the hyperscaler-style convenience without hyperscaler lock-in,” says Percona’s Farkas. For him, Kubernetes has become the de facto hybrid enterprise deployment option for data, workloads, and AI components.

History repeats itself

The 2026 State of Open Source Report found avoiding vendor lock-in to be the primary driver of open source adoption. But beyond being a strategic decision for a single company, open infrastructure provides a layer for entire industries to be built upon.

Arguably, the internet itself is evidence of this, where groups like the IETF and the IEEE were instrumental in defining the fundamental protocols. “Without open protocols we would’ve been in telco hell and without phenomenons like Google or Facebook,” says Shaposhnik.

Or, take the history of Linux as a parallel. “Linux became the default operating system because it offered a common, vendor-neutral foundation that everyone could build on,” says Collier. “In the AI era, open infrastructure will define the layers that organizations rely on for long-term continuity.”

At the infrastructure level, open standards have repeatedly underpinned major platform shifts, from Docker to Kubernetes. The question now is whether AI will develop a similarly durable standards layer.

For Parker, it’s too early to say, but the current growth of AI mirrors the early cloud. “Remember that it took many years before we saw the development and popularization of the open source cloud-native ecosystem,” he says. “I think it would be a mistake to extrapolate from the current trajectory towards a closed, proprietary future.”

Others agree the future must be rooted in openness. “I see open infrastructure becoming the foundation of enterprise AI,” says R Systems’s Abhyankar. “As systems become more distributed and agent‑driven, closed ecosystems simply won’t scale.”

The groundwork is being laid through open agentic protocols, open frameworks, and industry support intended to reduce fragmentation around proprietary standards.

“Ironically, the AI movement has mostly seemed to learn from the mistakes of the past and is starting off on a more open foot,” says Parker. “Over time, I believe we’ll see innovation and openness thrive.”

Governmental Efficacy Drives Climate Action Psychology

21 June 2026 at 11:35

In the urgent and multifaceted battle against climate change, understanding the psychological pathways that drive individual and collective action is crucial. Recent research underscores governmental efficacy—not merely as a political concept but as a psychological catalyst—as a pivotal determinant in galvanizing public engagement with climate initiatives. This insight shifts the paradigm from traditional economic or policy-driven explanations to a deeper recognition of how people’s belief in government competence fundamentally shapes climate-related behaviors and attitudes.

Climate change, as a global crisis, demands robust cooperation between citizens, governments, and institutions. Governments worldwide often attempt to implement policies and programs aimed at reducing carbon emissions, promoting renewable energy, and enhancing sustainability. However, such measures frequently encounter public skepticism or apathy. The research conducted by Goldwert, Sabherwal, and Vlasceanu highlights that the perception of government efficacy—that is, the public’s confidence in the government’s ability to enact meaningful change—acts as a critical psychological mechanism enabling or hindering climate action.

At its core, governmental efficacy is a psychological construct reflecting the belief that governmental bodies possess the competence, resources, and willpower to address climate crises effectively. This belief influences individuals’ propensity to participate in climate activism, adopt environmentally friendly behaviors, and support pro-environmental policies. The study reveals that when people trust that their government can and will perform decisively and competently on environmental issues, they feel empowered and are more motivated to contribute to the collective effort.

Moreover, the research draws on extensive psychological theory and behavioral science frameworks to explain how perceptions of governmental efficacy impact motivation. Notably, the role of self-efficacy—the belief in one’s own ability to effect change—is intertwined with collective efficacy regarding governments. When governments demonstrate efficacy, they enhance citizens’ sense of personal and collective agency, creating a feedback loop that fosters more dynamic participation in climate solutions.

The authors intricately explore how different dimensions of governmental efficacy influence climate engagement. These include the perceived transparency of government actions, accountability mechanisms, the clarity of communication regarding climate policies, and the tangible outcomes of implemented measures. Each of these dimensions works collectively to nurture a sense of trust and effectiveness, which is indispensable for sustaining long-term climate action.

Additionally, the research proffers that diminished confidence in government efficacy can have a demobilizing effect, breeding cynicism and disengagement. This dynamic is particularly salient in regions or countries where political instability, corruption, or historical failures undermine trust in public institutions. In such contexts, grassroots movements and non-governmental organizations often struggle to compensate for the psychological vacuum left by ineffective governance structures.

In transcending the usual discourses surrounding climate action, this research brings psychological determinants to the forefront. It suggests that increasing governmental efficacy perception is not solely a task for policymakers but also for communicators, educators, and community leaders. Strategic public engagement that transparently showcases government capabilities and ethical commitments can foster this efficacy perception, transforming passive observers into active participants.

The implications of this study extend to international climate policy frameworks, where nations’ commitments and track records are scrutinized by citizens and global observers alike. High-profile failures to meet targets or opaque governance breeds disillusionment. Conversely, governments that effectively communicate both their successes and challenges—and engage citizens in problem-solving—can inspire broader public confidence and support.

Furthermore, this psychological perspective has ripple effects on innovation and technology acceptance in climate mitigation. When citizens trust their governments’ ability to manage the complexities of new technologies—such as carbon capture, geoengineering, or renewable infrastructure—they are more likely to endorse and adopt these innovations. This psychological endorsement is crucial for scaling emerging climate solutions from experimental phases to widespread societal integration.

The authors also delve into the nuanced relationship between governmental efficacy and media narratives. Media representations not only shape public perception but also reflect and influence political accountability. Transparent and factual media coverage that highlights government efficacy in climate matters can amplify motivation for individual and collective action, while sensationalism or misinformation can erode these perceptions.

From a policy standpoint, the research advocates for governance models that prioritize transparency, inclusivity, and responsiveness. Collaborative governance, which involves stakeholders across sectors and levels, tends to enhance perceptions of efficacy by distributing responsibility and fostering shared ownership of climate goals. Such models not only bolster democratic legitimacy but also improve policy resilience through diversified input and adaptive capacities.

This study also explores the temporal dynamics of governmental efficacy. Short-term successes in climate policy can provide critical psychological boosts, fostering sustained engagement. Yet, it is the consistency of governmental performance and communication over time that cements long-range trust and motivation. This insight supports sustained investment not only in technical capacity but also in institutional stability and ethical governance.

Given the rising tide of climate anxiety and eco-paralysis among populations worldwide, the authors emphasize the transformative power of reinforcing governmental efficacy. In psychological terms, this approach combats helplessness and despair by cultivating hope and agency, critical emotions necessary for lasting behavioral change.

As climate change continues to present complex and evolving challenges, the integration of psychological perspectives like governmental efficacy into environmental strategies can significantly amplify their effectiveness. By recognizing and nurturing this vital pathway, societies can unlock new levels of climate engagement, ensuring that policies and initiatives do not languish in public disinterest but instead become powerful catalysts for collective transformation.

Ultimately, the research by Goldwert, Sabherwal, and Vlasceanu offers a compelling, empirically grounded narrative that elevates the psychological mechanisms underpinning climate action. It opens fresh avenues for research and practice, suggesting that tackling climate change is as much about fostering belief in collective competence as it is about technical innovation or legislative frameworks. This paradigm encourages a holistic understanding that intertwines governance, psychology, and social dynamics in the deeply interconnected quest for a sustainable future.

Subject of Research: Psychological pathways linking governmental efficacy to climate action

Article Title: Governmental efficacy is a key psychological pathway to climate action

Article References:
Goldwert, D., Sabherwal, A.C., & Vlasceanu, M. Governmental efficacy is a key psychological pathway to climate action. Commun Earth Environ (2026). https://doi.org/10.1038/s43247-026-03743-1

Image Credits: AI Generated

SHR8554: New Biased Opioid Trial Shows Safety

20 June 2026 at 15:28

In a groundbreaking clinical development set to reshape the landscape of pain management, a recent phase I trial has unveiled the safety profile, pharmacokinetics, and pharmacodynamics of SHR8554, a novel biased µ-opioid receptor agonist. This investigational compound stands at the forefront of opioid research, offering the tantalizing prospect of effective analgesia with potentially reduced adverse effects, addressing one of the most pressing challenges in medicine today.

The µ-opioid receptor (MOR) is well-known as the primary target for opioid analgesics, which remain the cornerstone of treatment for moderate to severe pain. However, traditional opioids activate multiple signaling pathways upon receptor binding, leading not only to pain relief but also to common and serious side effects such as respiratory depression, constipation, tolerance, and the risk of addiction. SHR8554 distinguishes itself by its biased agonism, selectively activating beneficial signaling cascades while sparing pathways responsible for unwanted side effects.

The trial, conducted on healthy volunteers, represents the initial in-human assessment of SHR8554, focusing on evaluating its safety and tolerability alongside detailed pharmacokinetic and pharmacodynamic profiling. Such early-phase studies are critical in determining if a candidate drug can proceed through the rigorous pipeline toward therapeutic approval and widespread clinical use.

Safety was meticulously assessed across a range of doses, with continuous monitoring for adverse events and vital signs. Encouragingly, SHR8554 displayed a favorable safety profile, with no serious adverse effects reported at any tested dose. Mild and transient symptoms noted were consistent with expected opioid-like profiles but occurred less frequently and with lower intensity compared to traditional µ-opioid receptor agonists.

Pharmacokinetic analysis revealed that SHR8554 is characterized by rapid absorption and a predictable dose-dependent systemic exposure. The compound exhibited a half-life compatible with standard dosing regimens, potentially allowing for flexible administration schedules tailored to patient needs. Importantly, metabolic pathways were identified, showing minimal involvement of cytochrome P450 enzymes, which may reduce drug-drug interaction risks.

From a pharmacodynamic perspective, the trial confirmed that SHR8554 engages the µ-opioid receptor effectively, achieving dose-dependent analgesia-related biomarker modulation without substantial activation of the β-arrestin pathway typically linked to side effects like respiratory depression. This biased signaling profile aligns with the molecule’s design and preclinical promise.

The implications of these findings are substantial, given the opioid crisis that has underscored the urgent need for safer analgesics. By selectively harnessing the MOR pathways associated with pain relief while minimizing those tied to adverse reactions, SHR8554 could represent a new class of opioid therapies with enhanced safety margins.

Mechanistically, biased agonism takes advantage of the receptor’s ability to couple to multiple intracellular signaling proteins. Traditional opioids indiscriminately activate both G protein-mediated pathways, which generally mediate analgesia, and β-arrestin pathways, which are implicated in adverse effects. SHR8554’s selective activation shifts this balance, offering potent analgesic effects while reducing the potential for tolerance development and respiratory complications.

Preclinical studies previously demonstrated SHR8554’s efficacy in animal models of pain with a reduced side-effect profile, supporting its translation to human trials. The phase I study reported here builds on this foundation, offering crucial human data that reinforce the molecule’s promise and informing dose selection for future efficacy trials.

In discussing the broader pharmacological context, biased agonists like SHR8554 may herald a paradigm shift in drug design for receptor systems exhibiting functional selectivity. Beyond pain management, this approach could be applicable to a range of neurological and psychiatric conditions where receptor signaling outcomes need to be finely tuned.

Despite the enthusiastic results, the researchers emphasize that longer and larger trials in patients with pain conditions are necessary to fully understand SHR8554’s therapeutic potential, including its efficacy, longer-term safety, and impact on quality of life. Subsequent phase II and III studies will need to rigorously examine these parameters and compare SHR8554’s performance against existing opioid and non-opioid analgesics.

The investigation also opens avenues for combination therapies, where biased MOR agonists might be paired with other analgesic agents to enhance outcomes and further reduce opioid-related risks. Moreover, it invites exploration into patient subgroups, such as those with opioid tolerance or chronic pain syndromes, to see whether SHR8554 can offer distinct advantages.

This pioneering research comes at a time when precision medicine approaches gain traction, emphasizing the tailoring of treatments not only to the disease but also to receptor-level signaling dynamics. If SHR8554 continues to demonstrate a superior therapeutic index, it could redefine standards of care and influence regulatory frameworks regarding opioid approval and use.

The promise of biased agonism embodied by SHR8554 underscores the power of modern pharmacology to dissect receptor behaviors intricately and design molecules that exploit subtle signaling nuances. This finesse could mitigate one of medicine’s oldest dilemmas—balancing efficacy against toxicity in powerful drugs like opioids.

Ultimately, SHR8554 exemplifies innovation borne out of necessity, responding to opioid misuse and overdose epidemics with science-driven solutions. While challenges remain ahead, this phase I trial sets a hopeful precedent, illuminating a path toward safer, more effective pain management strategies for patients worldwide.

As the scientific community eagerly awaits the results of forthcoming studies, SHR8554’s development journey will no doubt inspire further research into biased agonism and its vast therapeutic implications, potentially transforming the treatment landscape for millions suffering from pain.


Subject of Research: Safety, tolerability, pharmacokinetics, and pharmacodynamics of SHR8554, a biased µ-opioid receptor agonist.

Article Title: Safety, tolerability, pharmacokinetics, and pharmacodynamics of SHR8554, a biased µ-opioid receptor agonist: a phase I trial in healthy volunteers.

Article References: Zhang, L., Luo, G., Wang, H. et al. Safety, tolerability, pharmacokinetics, and pharmacodynamics of SHR8554, a biased µ-opioid receptor agonist: a phase I trial in healthy volunteers. BMC Pharmacol Toxicol (2026). https://doi.org/10.1186/s40360-026-01172-6

Image Credits: AI Generated

Trigger-Day hCG Impacts DNA Methylation, Neurodevelopment

20 June 2026 at 14:14

In the rapidly advancing field of assisted reproductive technologies (ART), subtle biochemical cues during treatment cycles can have profound and lasting impacts on offspring health. A groundbreaking study by Jiang, Wei, Liu, and colleagues, set to appear in Nature Communications in 2026, reveals intricate links between human chorionic gonadotropin (hCG) exposure on the trigger day of ART protocols and enduring epigenetic modifications that influence neurodevelopmental trajectories in children conceived via these methods. This research shines a spotlight on the complex interplay between hormonal interventions during ART and the epigenetic landscape shaping long-term neurodevelopmental outcomes.

At the core of this investigation lies hCG, a glycoprotein hormone traditionally recognized for its critical role in early pregnancy maintenance. It is clinically administered as a trigger to induce final oocyte maturation and subsequent ovulation in ART cycles. Despite its widespread use, the broader biological consequences of hCG administration timing and dosage, particularly regarding the epigenome of resulting embryos and offspring, remain poorly understood. Jiang and colleagues address this knowledge gap by systematically evaluating how hCG exposure on trigger day affects DNA methylation patterns—a key epigenetic mechanism regulating gene expression—in offspring conceived via ART.

Utilizing a large cohort of ART offspring and sophisticated epigenomic profiling techniques, the researchers uncovered that variations in trigger-day hCG levels corresponded to distinct DNA methylation signatures detectable in peripheral tissues of the children years post-birth. These methylation changes were not random but localized to genes with established roles in neurodevelopment and synaptic function. Notably, altered methylation was observed in loci involved in neuronal proliferation, differentiation, and synaptic plasticity, processes fundamental to cognitive development and neurological health.

The implications of these findings are considerable given the rising prevalence of ART-conceived individuals globally. Epigenetic modifications such as DNA methylation are dynamic yet heritable through cell division, and perturbations during critical windows of embryonic development can have cascading effects on gene regulation. Jiang et al.’s data suggest that the timing and hormonal milieu created by hCG administration on trigger day may inadvertently shape the epigenetic program of the embryo with consequences extending into postnatal brain development.

To unravel the potential neurodevelopmental consequences, the team correlated their epigenetic data with standardized neuropsychological assessments performed on the ART offspring. They reported significant associations between specific methylation patterns and measures of cognitive function, executive processing, and social behavior, domains frequently implicated in neurodevelopmental disorders. This links the biochemical intervention in ART directly to behavioral phenotypes observed in the children, underscoring the functional relevance of epigenetic imprinting driven by hCG exposure.

Mechanistically, the study posits that hCG triggers a cascade of intracellular signaling events including modulation of DNA methyltransferase (DNMT) activity and chromatin remodelers during oocyte maturation and early embryo development. These processes may alter the establishment or maintenance of methylation marks at neurodevelopmentally critical genes. Interestingly, variations in the hCG dose and timing created distinct epigenomic landscapes, emphasizing the fine balance required in ART hormone protocols to avoid unintended epigenetic dysregulation.

This research not only advances understanding of ART-associated neurodevelopmental risks but also offers actionable insights for clinical practice. Optimizing hCG administration parameters could mitigate adverse epigenetic and developmental outcomes, enhancing the safety profile of ART. The notion that a single hormonal manipulation at a specific time point can induce persistent epigenomic and neurodevelopmental alterations demands a reevaluation of ART protocols incorporating epigenetic endpoints.

The findings may also have broader implications beyond ART, revealing fundamental principles of how transient hormonal milieus during early development can imprint long-lasting changes on gene regulation and phenotype. This could inform developmental biology, neuroepigenetics, and endocrine research, potentially identifying biomarkers predictive of neurodevelopmental risk across diverse prenatal exposures.

Furthermore, the study leverages cutting-edge epigenomic technologies such as whole-genome bisulfite sequencing and single-cell methylation analysis, coupled with rigorous neurobehavioral phenotyping in a longitudinal cohort framework. This integrative, multi-omics approach sets a new standard for assessing the safety and long-term effects of reproductive interventions, blending molecular precision with clinical relevance.

Ethically, these insights prompt a dialogue about the need for comprehensive monitoring of ART offspring, integrating epigenetic screening and neurodevelopmental surveillance to detect and potentially intervene in early deviations. The epigenetic alterations detected by Jiang et al. are reversible in principle, raising the prospect of targeted epigenetic therapies or nutritional interventions to restore typical developmental trajectories.

Moreover, the study encourages further exploration into paternal contributions and environmental factors interacting with trigger-day hCG effects, recognizing that ART outcomes emerge from a complex interplay of genetics, epigenetics, and external exposures. Identifying modifiers of hCG-induced epigenetic programming could refine personalized ART approaches tailored to individual risk profiles.

Overall, this landmark research by Jiang and colleagues opens a new frontier in reproductive medicine, illustrating that the endocrinology of ART cycles extends beyond immediate gamete maturation to sculpt the epigenomic and neurodevelopmental fate of future generations. It reinforces the imperative for precision medicine in reproductive technologies and highlights the power of epigenetics in mediating early-life environmental influences on human development.

As ART continues to bring hope to millions struggling with infertility, understanding and controlling the subtle molecular ripples of treatment interventions like hCG administration becomes paramount. Jiang et al.’s study sets a vital precedent, demonstrating that optimizing hormone triggers is not merely a matter of improving pregnancy rates but safeguarding the neurological health and well-being of ART-conceived individuals throughout their lives.

Subject of Research: Human chorionic gonadotropin (hCG) effects on DNA methylation and neurodevelopment in offspring conceived via assisted reproductive technologies (ART).

Article Title: Trigger-day hCG effects on DNA methylation and neurodevelopment in ART offspring.

Article References:
Jiang, Y., Wei, X., Liu, X. et al. Trigger-day hCG effects on DNA methylation and neurodevelopment in ART offspring. Nat Commun (2026). https://doi.org/10.1038/s41467-026-74712-7

Image Credits: AI Generated

Brain and Behavior Predict Early Autism Vocabulary Growth

20 June 2026 at 13:32

In a groundbreaking study poised to redefine early interventions in autism spectrum disorder (ASD), researchers have unveiled new insights into the intricate interplay between brain development and behavioral patterns that forecast spoken vocabulary acquisition in young children with autism. Published in Translational Psychiatry in 2026, this pioneering work merges multimodal neuroimaging with detailed behavioral assessments, offering a nuanced understanding of how early childhood brain and behavioral markers converge to shape language outcomes in this population.

The study tackles one of the most perplexing facets of autism—the vast variability in language development, particularly spoken vocabulary. Language acquisition in children with ASD is notoriously heterogeneous, with some children progressing to age-appropriate speech while others remain nonverbal or develop only limited vocabularies. The research team, led by Surgent, Naigles, Dakopolos, and colleagues, conceptualized a multimodal predictive framework that integrates neurological data and observable behaviors, an approach that promises to yield more precise individual prognoses.

Central to their methodology was the utilization of advanced neuroimaging techniques during early childhood to evaluate brain structure and connectivity. These imaging modalities included diffusion tensor imaging (DTI), functional magnetic resonance imaging (fMRI), and volumetric analyses that together painted a comprehensive picture of the neural substrates critical for language processing. Specifically, measures of white matter integrity in tracts such as the arcuate fasciculus, known for its role in language networks, offered predictive value regarding the trajectory of vocabulary growth.

Parallel to imaging, the researchers conducted rigorous behavioral observations, focusing on early communicative attempts, joint attention, and social engagement metrics. These behavioral indices serve as functional readouts of the developing brain’s linguistic capacity and its interaction with the child’s environment. The amalgamation of brain imaging metrics with behavioral data created a rich dataset enabling machine learning algorithms to identify patterns that standard clinical assessments might overlook.

One of the most compelling revelations of this research came from the combined predictive power of brain-behavior models as opposed to isolated measures. While previous studies have often considered either neurobiological markers or behavioral indicators in silos, this study demonstrated that an integrative model significantly enhances prediction accuracy for spoken vocabulary outcomes months to years in advance. This finding signals a paradigm shift in early diagnosis and intervention planning, suggesting that multimodal data collection should become standard practice.

The implications for clinical application are profound. Accurate early prediction of language development trajectories using this multimodal approach could allow clinicians and caregivers to tailor intervention strategies with unprecedented specificity. For instance, children flagged as likely to experience more significant language delays might receive intensified speech and language therapies, while those predicted to gain vocabularies at typical rates could benefit from less intensive, yet still supportive, programs. This targeting aligns resources more efficiently and helps optimize developmental outcomes.

Moreover, the study illuminated potential neurobiological mechanisms underlying the language profiles seen in autism. White matter pathways supporting auditory-motor integration, semantic processing, and syntactic construction were shown to exhibit differing maturation trajectories in children with more favorable vs. impaired vocabulary development. These findings hint at critical windows during which neural plasticity interventions might be most effective, informing future trials of neurotherapeutics or brain stimulation techniques.

Beyond therapeutic considerations, the research also advances fundamental understanding of autism itself by underscoring how language acquisition is modeled through complex brain networks and shaped by early communicative behaviors. The integration of structural and functional brain data with real-world social and language behaviors accentuates the bidirectional and dynamic nature of neurodevelopment in ASD, challenging static conceptualizations that have predominated in the field.

The authors further caution that while predictive models are promising, ethical considerations around prognostic information for families must be carefully managed. Delivering predictions about language outcomes is profoundly impactful, necessitating sensitive communication, counseling support, and ongoing monitoring to adjust interventions as children grow and contexts change. The study emphasizes that predictive algorithms are tools to augment, not replace, clinical judgment.

Complementing their scientific contributions, the study’s open-access dissemination via a reputed neuroscience journal ensures rapid accessibility to clinicians, researchers, and policy makers worldwide. The transparency of methods and availability of anonymized data for secondary analyses encourage replication and cross-validation, fostering accelerated progress in autism research ecosystems.

Looking forward, the research team envisions expanding their multimodal approach to encompass other domains of development affected in autism, such as social cognition, executive function, and adaptive behavior. Integrating additional biomarker modalities like genomics and metabolomics could further refine individualized predictions and catalyze precision medicine initiatives in neurodevelopmental disorders.

In sum, this seminal study marks a critical advance in unraveling the complex nexus between brain maturation, behavioral development, and language acquisition in autism. Its innovative multimodal framework sets a new standard for early prediction and personalized intervention, offering hope for improving long-term communicative outcomes for children with ASD worldwide. As science continues to decode the developing autistic brain, such integrative approaches will be instrumental in bridging biology and behavior to transform lives.


Subject of Research: Early childhood brain and behavior predictors of spoken vocabulary development in autism

Article Title: Multimodal predictors of spoken vocabulary development in autism: the role of early childhood brain and behavior

Article References:
Surgent, O., Naigles, L., Dakopolos, A. et al. Multimodal predictors of spoken vocabulary development in autism: the role of early childhood brain and behavior. Transl Psychiatry (2026). https://doi.org/10.1038/s41398-026-04168-2

Image Credits: AI Generated

DOI: https://doi.org/10.1038/s41398-026-04168-2

Shared Auditory Maps Predict Human and Mouse Decisions

20 June 2026 at 05:47

In a groundbreaking study poised to redefine our understanding of auditory perception across species, researchers have unveiled strikingly similar auditory perceptual maps in both humans and mice. This discovery not only bridges a crucial gap between animal models and human sensory processing but also offers predictive insight into how perceptual decisions are formed during auditory discrimination learning. The implications ripple across neuroscience, psychology, and artificial intelligence, promising new avenues for studying sensory perception and developing advanced hearing-related technologies.

Auditory perception—the way organisms interpret and make sense of sound stimuli—is a complex process that involves integrating multifaceted sound attributes such as frequency, intensity, and temporal patterns. While mouse models have long been foundational in auditory research due to their genetic malleability and experimental accessibility, skepticism has persisted about the extent to which their perceptual frameworks parallel those of humans. This novel research offers compelling evidence that these frameworks are not only comparable but also exhibit shared underlying structures.

The study employed a sophisticated experimental design in which both human participants and mice were exposed to a wide array of tonal stimuli varying systematically across multiple auditory dimensions. Using advanced computational modeling and neural decoding techniques, the researchers were able to construct perceptual maps—conceptual frameworks representing how different sounds are internally organized within the brain’s auditory space. These maps enabled a quantifiable comparison of perceptual similarity and distance between sounds for both species, revealing a remarkable congruence in their auditory representations.

What makes these findings particularly compelling is the demonstration that these perceptual maps are predictive of behavioral outcomes during discrimination tasks. In other words, the way an individual’s brain groups different sounds predicts how well that individual can distinguish between them in learning scenarios. This link between neural representation and perceptual decision-making underlines a fundamental cognitive mechanism shared across mammalian species, offering crucial insights into the principles guiding sensory learning processes.

Beyond the fundamental science, the implications for translational research are profound. Understanding shared auditory perceptual structures facilitates the development of more accurate animal models for studying hearing impairments and neurological disorders affecting auditory processing. It also guides the design of auditory prosthetics, such as cochlear implants, by informing the algorithms that mimic natural sound perception. By aligning animal and human perceptual frameworks, therapies can be tailored with greater precision and efficacy.

A key technical achievement of the study was the use of multidimensional scaling techniques combined with machine learning algorithms to decode auditory neural responses with unprecedented granularity. The researchers collected high-resolution neural data using electrophysiological recordings in mice and non-invasive neuroimaging in humans. This dual approach allowed cross-species alignment of perceptual space, overcoming previous methodological challenges that hindered direct comparisons between human and rodent sensory processing.

Moreover, the study explored dynamic aspects of perceptual learning by measuring how auditory maps evolve through repeated training sessions. The maps exhibited plasticity reflective of learning-induced changes, emphasizing the adaptive nature of auditory perception. This plasticity was mirrored across both species, suggesting an evolutionarily conserved mechanism for tuning sensory systems based on environmental demands and experience.

One of the study’s most innovative aspects lies in its predictive modeling of discrimination performance. By mapping perceptual distances within the auditory space, the researchers could forecast individual differences in learning speed and accuracy, an advancement that holds enormous promise for personalized auditory training protocols. Such protocols could optimize rehabilitation strategies for individuals with auditory processing deficits by targeting specific perceptual aspects most relevant to their unique neural patterns.

The shared structure of auditory perceptual maps also raises intriguing questions about the evolutionary trajectory of sensory systems. The findings suggest that despite divergent evolutionary paths, fundamental auditory processing architectures remain conserved, supporting efficient sound discrimination necessary for survival and communication. This conservation across species underpins the generalizability of animal research findings to human sensory neuroscience.

Furthermore, these insights pave the way for advancing artificial sensory systems, especially in the realm of auditory artificial intelligence. By modeling computational auditory perception after natural neural maps, engineers can design more sophisticated speech recognition systems and assistive devices that better emulate human-like sound processing. The study’s integrative framework thus acts as a blueprint for creating tonal perceptual algorithms with enhanced contextual understanding.

This research also contributes importantly to theoretical debates surrounding sensory representation. By empirically demonstrating a shared, structured perceptual space, the study challenges views that sensory maps are strictly idiosyncratic or species-specific. Instead, it supports a paradigm that sensory systems leverage common organizational principles, rooted perhaps in fundamental neural coding constraints and environmental pressures shaping auditory perception.

The study’s methodology and results were meticulously detailed in a recent publication, where the authors delineated the computational techniques, behavioral paradigms, and statistical analyses that underscored their conclusions. This transparency fosters reproducibility and invites further interdisciplinary collaboration between computational neuroscientists, experimentalists, and cognitive psychologists aiming to unravel the complexities of sensory perception.

Importantly, these findings hold societal relevance beyond academic circles. Hearing loss and auditory processing disorders affect millions worldwide, often hindering communication and quality of life. Technologies and therapies inspired by a unified perceptual mapping approach could revolutionize auditory rehabilitation, offering hope for more effective interventions grounded in a thorough understanding of perceptual neuroscience.

In conclusion, this pioneering work illuminates the common auditory perceptual code shared by humans and mice, demonstrating that the brain’s representation of sound is not merely a byproduct of species-specific adaptations but a fundamental, conserved neural language. This discovery not only furthers scientific knowledge but also catalyzes innovation in medicine and technology, underscoring the power of cross-species research in unraveling the mysteries of the sensory brain.

As this field develops, future investigations are likely to explore how these perceptual maps interact with other cognitive systems such as memory and attention, and how factors such as age, experience, and pathology modulate the structure and function of auditory perception. With burgeoning advances in neurotechnologies and computational methods, the confluence of cross-species neuroscience and perceptual science promises to unlock new dimensions of understanding sensory experience.

Ultimately, the revelation that humans and mice share auditory perceptual maps that predict learning efficacies serves as a testament to nature’s optimization strategies, propelling a unified framework for deciphering sensory perception. This convergence of biology, computation, and psychology heralds a new chapter in auditory neuroscience with far-reaching implications for science and society alike.


Subject of Research: Auditory perceptual maps and discrimination learning in humans and mice

Article Title: Auditory perceptual maps in humans and mice share common structures and predict perceptual decisions in discrimination learning

Article References:
Seiler, J.PH., Cazzetta, G., Ghobadi, A. et al. Auditory perceptual maps in humans and mice share common structures and predict perceptual decisions in discrimination learning. Commun Psychol 4, 98 (2026). https://doi.org/10.1038/s44271-026-00485-w

Image Credits: AI Generated

DOI: https://doi.org/10.1038/s44271-026-00485-w

Researchers grow a hypothesis tree for AI coding agents

19 June 2026 at 23:16

AI coding agents can tend to isolate research, running experiments and generating ideas that are then forgotten when context windows reset. This can waste tokens, as models then repeat the same mistakes and hit the same dead ends.

But new research argues that it’s not the model itself, but the overarching ‘tree,’ that needs tweaking. To that end, data scientists from the Gaoling School of Artificial Intelligence, Renmin University of China, and Microsoft Research have introduced Arbor, a “persistent hypothesis tree” that helps agents remember and refine learnings over long research sessions.

A long-lived coordinator manages research strategy across the tree, while short-lived executors spin up isolated worktrees to test different hypotheses. As results come back, the tree updates, narrowing and refining throughout experimentation.

In practical tests, this technique delivered more than two-fold performance gains over standard AI coding agents across real-world engineering tasks, for the same budget.

This is because, said Mahmoud Ramin, a research director at Info-Tech Research Group, “Arbor accumulates information over time and allows agents to build upon prior discoveries just as humans do, through learning, adaptation, and eventually building upon what they have learned in the past.”

How Arbor grows

Arbor’s builders argued that longer execution on its own does not guarantee research progress. The challenge is maintaining a state that turns many individual attempts into “cumulative hypothesis refinement.”

Further, progress should not depend on human overseers regularly stepping in to dictate logical next steps or interpret the meaning of previous trials, they noted. To be truly autonomous, agentic research frameworks must maintain connections between experiments, data, results, and failures over time.

Arbor is built to fulfill three system requirements. First, it must be able to branch as sub-trees test out competing hypotheses that are all potentially plausible. At the same time, unrestricted branching can degenerate the whole framework, so that must be controlled to remain organized. The researchers call this “branching with coherence.”

Second, the infrastructure must separate local execution from overarching strategy. Testing out single hypotheses requires short-horizon tasks like editing, debugging, and evaluation. But these should not “obscure” the larger tree making decisions based on evidence gathered across the whole run.

Finally, the systems must be able to distinguish exploratory improvement from verified improvement. This prevents AI from overfitting during trial-and-error instead of iteratively learning from underlying patterns.

Persistence is at the core; the tree links hypotheses and ideas, the code or configuration artifacts used to test them, experimental evidence (results, metrics), and distilled insights (such as “this data filter helped, but this learning rate scheduler didn’t”).

Once a project kicks off, shorter-execution work trees run code, log their work, and collect metrics. The long-lived coordinator above them serves as the de-facto head of research, keeping an eye on the process, updating nodes, selecting “promising leaves,” pruning or merging branches, propagating reusable lessons, and deciding which hypotheses to pursue next.

“The tree therefore acts as the operational research state of the system,” Arbor’s builders wrote. “It is simultaneously the search frontier, the memory of past attempts, and the audit trail for verified artifact improvement.”

Outperforming Codex and Claude on new data

To test how well this process works, the researchers evaluated Arbor in an autonomous optimization (AO) setting: the agent was given an initial research artifact (a data pipeline, harness, or training script) and was tasked with improving its “held-out performance” through iterative experimentation, without human steering. Held-out performance is a machine learning (ML) metric that evaluates how well models are able to generalize on data they haven’t seen before.

The tree-based architecture was tested on several real research tasks across model training (its ability to improve training recipes and hyperparameters), harness engineering (how well it can upgrade evaluation or training harnesses), and data synthesis (its capacity to generate better data for training or evals).

Ultimately, Arbor outperformed the average held-out gains of Codex and Claude Code by 2.5x, for the same resource budget.

The takeaway, said the researchers: Keeping a structured, evolving hypothesis tree yields greater performance improvements than running the same models as ‘memoryless’ coding agents.

Arbor’s most innovative feature is its ability to maintain the agent’s memory and retain relevant data from prior attempts and hypotheses, Info-Tech’s Ramin pointed out, and, he said, “the next step for autonomous agents may be accumulating evidence over time.”

However, this does raise concerns about the auditability of robust research environments at a large scale, he noted. “As autonomous agents become more capable of performing work without human operators overseeing them, enterprises will need transparency into how and/or why an agent took a specific action or reached a certain conclusion.”

Tiny Genetic Fragments Crucial for Signaling Brain Rest Identified

19 June 2026 at 20:51

In a groundbreaking study shedding light on the molecular intricacies of arousal regulation, researchers from Pompeu Fabra University (UPF) and the Centre for Genomic Regulation (CRG) have unveiled the profound influence of neuronal microexons on behavioral states in zebrafish. This study elucidates how subtle alterations in these tiny genetic fragments can trigger hyperarousal—a state marked by heightened neural excitability and pronounced insomnia-like symptoms—which echoes the pathophysiological features observed in various neurodevelopmental disorders. The implications extend far beyond aquatic models, offering a window into the conserved mechanisms of arousal that potentially inform conditions such as autism spectrum disorder and schizophrenia in humans.

Arousal represents a fundamental neurophysiological process essential for survival, enabling organisms to respond to external and internal stimuli with appropriate behavioral and neural adaptations. This highly conserved mechanism across species ensures a meticulously balanced state, modulating responsiveness between lethargy and sensory hypersensitivity. Dysregulation of this balance manifests clinically as either diminished responsiveness or excessive wakefulness and sensory overload, typical hallmarks of stress and various neurodevelopmental pathologies.

Fundamental to this regulatory system is the diversity of proteins synthesized during development and adulthood through alternative splicing—a sophisticated post-transcriptional gene editing process. Alternative splicing enables the generation of multiple functionally distinct protein isoforms from a single gene, often mediated by the inclusion or exclusion of microexons. Microexons are exceptionally short exonic sequences within neuronal genes that profoundly influence protein function and neuronal circuit dynamics despite their minuscule size.

The investigative team employed zebrafish larvae, leveraging their optical transparency and genetic tractability to scrutinize the behavioral consequences of neural microexon misregulation. Larvae exhibiting abnormal microexon patterns demonstrated conspicuous hyperarousal behaviors including disrupted swim patterns and shortened sleep duration. “These larvae not only sleep less frequently but also take considerably longer to initiate sleep,” remarks first author Tahnee Mackensen. This behavioral hyperactivity parallels neural hyperexcitability, suggesting microexon regulation as a pivotal determinant of neurobehavioral states.

At the cellular signaling level, the researchers identified dysregulated cyclic adenosine monophosphate (cAMP) cascades as a key mediator of the observed hyperactive state. cAMP is a ubiquitous second messenger involved in modulating neuronal excitability and synaptic plasticity. The altered splicing of microexons modulates cAMP synthesis and degradation pathways, leading to an aberrant excitation of forebrain neurons in hyperaroused larvae. Notably, this altered cAMP signaling manifests as heightened cAMP-dependent protein kinase A (PKA) activity and subsequent phosphorylation of the transcription factor CREB, implicating the canonical cAMP-PKA-CREB pathway in the regulation of arousal.

The study’s pharmacological interventions underscore the centrality of cAMP regulation in arousal control. Application of cAMP inhibitors normalized the elevated neural activity and behavioral hyperarousal in mutant fish, whereas artificially elevating cAMP in wild-type fish recapitulated the hyperactive phenotype. This bidirectional modulation fortifies the concept that neuronal cAMP levels function as a ‘thermostat’ for arousal states, fine-tuning neuronal excitability and behavioral responsiveness.

Beyond the immediate findings in zebrafish, this research builds upon prior observations in drosophila models demonstrating that microexon disruption similarly impairs sleep and elevates arousal. “The parallel between species indicates an evolutionarily conserved arousal mechanism,” explains Manuel Irimia, senior author. This conservation implies that microexon misregulation, while mechanistically nuanced, may contribute to the neuropsychiatric symptomatology observed in mammals, including humans.

Human neurological disorders such as autism and schizophrenia are often accompanied by sleep disruption and sensory processing anomalies attributed, in part, to aberrant microexon splicing. While microexon alterations are unlikely to be sole causative factors, they may exacerbate or modulate disease phenotypes by disturbing neural excitability homeostasis. These insights prompt a reevaluation of therapeutic strategies aimed at restoring microexon splicing fidelity or modulating cAMP signaling pathways to alleviate neurodevelopmental symptomatology.

Moreover, the link between this microexon-cAMP pathway and mood disorders such as anxiety and depression opens compelling avenues for future research. The cAMP-PKA-CREB axis has well-documented roles in synaptic plasticity and mood regulation, suggesting that microexon-associated dysregulation could contribute to broader neuropsychiatric conditions. “This discovery might just scratch the surface of a complex regulatory network influencing brain function,” notes Mackensen.

The transparency and genetic accessibility of the zebrafish model provided unparalleled opportunities to visualize and quantify internal states through behavioral readouts. Advanced imaging of larval swimming patterns and direct measurement of neurochemical parameters furnished robust correlative evidence linking genetic alterations to functional outcomes. These technical advancements highlight the integrative power of model organisms in neuroscience.

Importantly, this research received support from an array of prestigious funding bodies, including the Horizon 2020 Framework Programme, the Marie Sklodowska-Curie Actions, and the Wellcome Trust, emphasizing the global significance and collaborative nature of this work. The study’s publication in Science Advances confirms its high impact and relevance to the scientific community.

As the team pursues translational studies, the prospect of correcting arousal imbalances by manipulating cAMP pathways or restoring microexon expression presents a promising frontier. This is especially critical given that aberrant arousal and sleep disturbances profoundly impair quality of life in neurodevelopmental disorders. The findings pave the way for multidisciplinary approaches integrating molecular genetics, neurobiology, and pharmacology to develop targeted interventions.

In summation, the identification of neuronal microexons as key modulators of arousal states via the cAMP-PKA-CREB pathway in zebrafish represents a seminal advance in our understanding of the molecular substrates governing complex behavioral phenotypes. This research not only deciphers fundamental biological mechanisms but also holds translational potential to inform therapeutic avenues for neuropsychiatric conditions marked by disrupted arousal and sleep.


Subject of Research: Animals

Article Title: Neuronal microexons modulate arousal via the cAMP-PKA-CREB pathway in zebrafish

News Publication Date: 19-Jun-2026

Web References: 10.1126/sciadv.ady8291

Image Credits: UPF – CRG

Keywords: Exons, Gene splicing, Developmental neuroscience, Anxiety, Sleep disorders, cAMP signaling, Zebrafish

❌