Beneath every step we take, soils hold one of the planet’s largest reservoirs of carbon, a vast, slow-moving bank of organic matter that quietly regulates how much of the world’s carbon stays locked away and how much escapes into the atmosphere as greenhouse gas. Now a sweeping new review warns that climate-driven extremes, droughts, floods, and wildfires, are rewriting the rules of that subterranean economy in ways that conventional models and land-management practices have not yet caught up with. The analysis, led by environmental scientist Nanthi Bolan of the University of Western Australia and colleagues, including a corresponding authorship by Bolan himself, synthesizes a substantial body of global evidence on how extreme weather reshapes carbon inputs to soil, microbial processing, erosion, and the sequestration of carbon that underpins soil health, water retention, nutrient cycling, and agricultural productivity.

The work, published in the journal Carbon Research, is notable for the scale of its underlying evidence base. On 14 January 2026, the authors searched Google Scholar, PubMed, and Web of Science using terms spanning climate change, extreme weather, soil carbon, microbial activity, decomposition, erosion, dissolved organic matter, and leaching. The initial sweep returned 14,218 records; after removing duplicates and screening for eligibility, 3,754 articles remained for bibliometric and qualitative analysis. From those, 187 publications were selected for detailed synthesis based on relevance, methodological rigor, and the substantive quality of their findings. Keyword co-occurrence patterns were mapped with VOSviewer, a bibliometric tool that reveals the intellectual architecture of a research field. The result is a critical review grounded in structured literature screening rather than new experimental data, but its synthesis offers one of the most comprehensive maps yet of how extremes rearrange the soil carbon cycle.

The review’s central finding is that no two extremes act alike. Drought, the most widespread disturbance, operates largely through the plant side of the equation. When rainfall fails, photosynthesis slows, plant biomass production falls, and the total flux of carbon entering soils through roots, residues, and litter declines. There is a nuance here: under moderate water stress, plants may temporarily shift a greater proportion of their fixed carbon belowground, investing in roots to chase moisture. But prolonged drought overwhelms that compensatory response. Microbial biomass shrinks, litter decomposition stalls, and the living engine of the soil carbon cycle idles. Then comes the deceptive twist: when rain finally returns, rewetting triggers a rapid pulse of carbon dioxide as starved soil microbes seize upon the accumulated substrates left behind by months of suspended activity. That post-drought CO₂ flush, the review emphasizes, can erase much of the apparent “savings” that accumulated during the dry period. Across the assembled evidence, repeated and multi-year drought was consistently associated with declining soil organic carbon, with grasslands and arable croplands showing particularly pronounced losses, ecosystems that happen to anchor much of the world’s food production.

Flooding tells the opposite story, but it ends in many of the same places. Water saturation chokes off oxygen diffusion through soil pores, forcing microbial metabolism into anaerobic pathways. That shift changes everything downstream. Prolonged waterlogging can drive the chemical reduction of iron oxyhydroxides, minerals that in oxygenated conditions act as microscopic glues binding organic carbon to soil particles. When those minerals dissolve, the carbon they held is released as dissolved organic carbon, which can then be stripped away by erosion and leaching and exported to rivers, lakes, and ultimately the ocean. The review stresses, however, that flooding is not unambiguously a carbon loss story. Wetlands and other periodically inundated ecosystems can accumulate and retain enormous carbon stocks precisely because water constrains decomposition. Whether a flood event releases carbon or banks it depends on the interplay between plant inputs, oxygen availability, and hydrological transport, a delicate balance that shifting rainfall regimes are actively destabilizing.

Wildfire is the most dramatic and immediately visible of the three forces, and its effects cascade on multiple timescales. Combustion directly strips vegetation, litter, and soil organic matter, transferring carbon to the atmosphere in hours. But fire also rearranges the physical and biological landscape of the soil in subtler ways: it can increase erosion by removing protective ground cover, alter soil wettability in ways that change hydrological behavior, restructure microbial communities, and expose previously protected carbon, once shielded inside aggregates or mineral associations, to fresh decomposition. There is a paradoxical bright spot. Pyrogenic carbon, the charcoal-like residue of incomplete combustion, is chemically recalcitrant and can persist in soils for centuries or millennia, functioning as a long-term carbon sink. Whether a fire leaves a landscape as a net carbon source or stabilizes some of its carbon in pyrogenic form depends on fire severity, soil moisture, fuel load, ecosystem type, and the trajectory of post-fire recovery. The review is clear that this balance cannot be predicted from fire alone; it is contingent, site by site, on conditions that vary widely across the globe.

Perhaps the most consequential theme running through the synthesis is that carbon responses cannot be inferred from the type of extreme event alone. Land use, soil mineralogy, hydrology, salinity, vegetation composition, microbial community makeup, and the timing of events within a season all determine whether carbon ends up stored, mobilized, or released. Iron and aluminum oxyhydroxides and clay minerals, for instance, provide mineral protection that can shield organic matter even under stress, while sandy or low-clay soils offer little such refuge. Managed croplands respond differently from native grasslands; saline coastal soils behave differently from acidic forest soils. And compound events, drought followed by wildfire, or alternating drought and flooding in rapid succession, may produce effects that are qualitatively different from those of isolated disturbances, as when a dried landscape burns more intensely, or when post-fire soils, stripped of vegetation, wash away in the first heavy rain. The review argues that the climate science and soil science communities have largely studied these disturbances one at a time, while the real world increasingly delivers them in combination.

The implications for climate mitigation and adaptation are substantial. Soil organic carbon is frequently invoked in national climate pledges and voluntary carbon markets as a bankable sink, yet the review suggests that the security of that bank depends heavily on how often and how severely extremes strike. If multi-year droughts steadily drain carbon from grasslands and croplands, or if fires repeatedly reset accumulation cycles, the effective permanence of soil carbon storage becomes far more fragile than many accounting frameworks assume. Conversely, protecting and rebuilding soil carbon delivers benefits well beyond climate: it improves water retention in drought-prone regions, supports nutrient cycling, stabilizes soil structure against erosion, and sustains the microbial diversity on which fertility depends. Carbon stewardship, in other words, is not separable from food security or ecosystem resilience; it is the same problem viewed from different angles.

The authors are candid about the limits of the current evidence base. The article does not report newly generated datasets and does not present a dedicated limitations section, and its conclusions rest on a qualitative synthesis of selected publications. Consequently, they call for improved and more consistent evidence across soil types, land uses, and both terrestrial and aquatic environments, and across multiple temporal scales, from the instantaneous CO₂ pulse after rewetting to the multi-decadal trajectory of carbon storage. Standardizing measurements of carbon pools, microbial functions, and event intensity, they argue, would make results comparable across ecosystems and enable more confident generalization, something the field currently struggles to achieve given heterogeneous methods and site-specific reporting.

Looking forward, the review sketches a research agenda that is as technologically ambitious as it is mechanistically focused. The authors recommend integrating high-resolution remote sensing with ground observations, advanced spectroscopy, molecular analyses, and integrated modeling to track carbon as it moves through soils and into waterways. They single out several priorities: characterizing microbial genomic and functional diversity as it responds to stress, quantifying carbon stoichiometry, probing the interactions between organic matter and iron and aluminum oxyhydroxides, understanding clay-mineral protection mechanisms, tracking long-term storage outcomes, and systematically studying the sequencing of compound extreme events. Each of these threads addresses a gap in the mechanistic chain that links a weather event in the atmosphere to a molecule of carbon, stabilized or released, in the soil.

What emerges from the review is a picture of the soil carbon cycle not as a stable background process but as a dynamic, contested system whose behavior under climate extremes is far less predictable than once assumed. The same storm that floods a rice paddy and exports carbon to a river delta may, a few hundred kilometers away, end a drought and briefly green a grassland, with opposite consequences for the atmosphere. As extremes grow more frequent and more compound, the review makes the case that understanding, and safeguarding, the carbon held beneath our feet will require science that is as interconnected and as restless as the weather itself.

Keywords

soil organic carbon, extreme weather, drought, flooding, wildfire, carbon sequestration, microbial activity, dissolved organic carbon, erosion, pyrogenic carbon, climate change, soil health

Idiopathic pulmonary fibrosis, a relentlessly progressive and fatal scarring disease of the lungs, has long frustrated clinicians with only two approved drugs that slow but cannot reverse its course. Now, a team of researchers in Taiwan has identified a surprising new player in the disease’s destructive machinery: doublecortin-like kinase 1, or DCLK1, a kinase better known for its role in cancer stem cells. Their study, published in the Journal of Biomedical Science, demonstrates that DCLK1 drives fibroblast activation in the lung and that blocking it—either genetically or with an oral inhibitor—slows fibrosis and preserves lung function in mice. The findings position DCLK1 as a promising therapeutic target in one of medicine’s most stubborn diseases.

The research, led by Lee-Yuan Lin, Wun-Hao Cheng, Chien-Huang Lin and Bing-Chang Chen of Taipei Medical University, together with collaborators at National Taiwan University Hospital and Universitas Gadjah Mada, began with a question that has lingered in the literature. Gene expression analyses had previously hinted that DCLK1 might be implicated in IPF, and earlier work reported that the kinase is markedly enhanced in the alveolar epithelial cells of patients with pulmonary fibrosis and in mice with bleomycin-induced fibrosis. But its function in fibroblast activation—the cellular engine of fibrosis—had remained unexplained.

To begin untangling that role, the team mined a publicly available single-cell RNA sequencing data set containing more than 312,000 cells from healthy lungs, IPF lungs, and lungs with chronic obstructive pulmonary disease. After stringent quality control filtering, they focused on roughly 240,000 cells from the control and IPF groups and mapped DCLK1 expression across the major lung cell lineages: epithelial, endothelial, lymphoid, myeloid, and stromal cells. The signal was unmistakable. DCLK1 was expressed across many cell populations, but the highest expression appeared in the stromal compartment, particularly in myofibroblasts—the specialized, contractile fibroblasts that churn out the extracellular matrix and stiffen lung tissue in IPF.

Human tissue told the same story. The researchers examined lung samples from eleven patients with IPF, obtained during lung transplantation or surgery, alongside fourteen nonfibrotic control samples. Histological staining with hematoxylin and eosin and Masson trichrome confirmed the expected picture of interstitial consolidation and collagen deposition in the IPF specimens. More importantly, immunohistochemistry revealed that both total DCLK1 and its phosphorylated, activated form were significantly upregulated in the fibrotic lung interstitium compared with control tissue. DCLK1, in other words, was not merely present in scarred lungs—it was switched on.

DCLK1 is an intriguing molecule to find at the center of a fibrotic process. It is a serine/threonine kinase belonging to the calcium/calmodulin-dependent kinase family, distinguished by two microtubule-binding domains. In oncology, DCLK1 overexpression has been documented in colorectal, pancreatic, gastric, renal, and hepatocellular carcinomas, where it correlates with poor prognosis, high recurrence, and metastasis, promoting tumor initiation and the epithelial–mesenchymal transition in cancer stem cells. It has also been linked to inflammatory signaling in SARS-CoV-2 infection, where inhibiting it blocks proinflammatory caspase-1/interleukin-1β signaling. The new study extends this portfolio of mischief to the fibrotic lung.

To test causality rather than mere correlation, the researchers turned to mice. They crossed animals carrying loxP sites flanking exon 3 of the DCLK1 locus with Pgk1-RFP-Cre/ERT2 transgenic mice, generating a system in which tamoxifen administration triggers global deletion of the Dclk1 gene. When these DCLK1 knockout mice and littermate controls were given intratracheal bleomycin, a chemotherapy drug used to induce pulmonary fibrosis in laboratory animals, the difference was striking. Knockout mice showed far less lung infiltration and collagen deposition, reduced numbers of cells staining positive for DCLK1 together with fibroblast surface protein and alpha-smooth muscle actin, and significantly lower levels of the profibrotic proteins fibronectin, α-SMA, and connective tissue growth factor, or CTGF.

The protective effect extended well beyond molecular markers. Using the FlexiVent FX system to measure lung mechanics, the team found that bleomycin treatment significantly reduced respiratory system compliance and pressure–volume performance and increased the normalized work of breathing in control mice—hallmarks of a stiffening, fibrotic lung. In the DCLK1 knockout animals, these functional deficits were substantially attenuated. In vivo microcomputed tomography, acquired at 34.75-micrometer voxel resolution, painted the morphological counterpart: bleomycin caused parenchymal destruction and volume loss in control lungs, while knockout mice retained lung structure and volume. Density histograms shifted toward fibrotic tissue in wild-type animals but stayed largely preserved in the knockouts, and peripheral blood neutrophil percentages, which climbed after bleomycin injury, were significantly reduced in the knockout group.

With the in vivo evidence established, the investigators dissected the molecular wiring in cultured normal human lung fibroblasts. Transforming growth factor beta, the master profibrotic cytokine, induced DCLK1 phosphorylation within 30 minutes and DCLK1 expression within 8 hours. Chromatin immunoprecipitation assays showed that after TGF-β stimulation, both Smad3—the canonical transcriptional mediator of TGF-β signaling—and the nuclear factor kappa B subunit p65 enriched at the DCLK1 promoter, indicating that TGF-β switches on DCLK1 through both Smad-dependent and noncanonical NF-κB pathways. Silencing DCLK1 with small interfering RNA then blunted the fibroblast’s profibrotic response, reducing TGF-β-induced expression of fibronectin, collagen 1A1, α-SMA, and CTGF.

The pathway, however, does not stop there. The team found that TGF-β also phosphorylated Akt, and that transfecting fibroblasts with a dominant-negative Akt construct reduced TGF-β-induced DCLK1 phosphorylation and the expression of fibronectin and CTGF—placing Akt upstream of DCLK1. Downstream, DCLK1 knockdown reduced Smad3 phosphorylation while DCLK1 overexpression enhanced it. Coimmunoprecipitation revealed a physical association between DCLK1 and Smad3, and immunofluorescence microscopy showed the two proteins colocalizing in the nucleus within 30 minutes of TGF-β treatment. A ChIP assay using a DCLK1 antibody pulled down DNA from the Smad3-binding region of the CTGF promoter, and pretreatment with the selective DCLK1 inhibitor DCLK1-IN-1 reduced TGF-β-induced CTGF expression. Together, the results sketch an Akt/DCLK1/Smad3 axis through which TGF-β drives the production of connective tissue growth factor, a potent profibrotic mediator.

The clinical payoff came in the pharmacological arm of the study. Mice receiving oral DCLK1-IN-1 at 10 milligrams per kilogram daily, beginning on day 7 after bleomycin instillation and continuing until day 28, showed visibly reduced fibrotic progression on histology, lower expression of fibronectin, α-SMA, and CTGF in lung tissue and lysates, preserved pulmonary compliance, improved pressure–volume loops, and reduced work of breathing. Serial microCT scans on days 7, 17, and 27 documented less severe fibrotic remodeling in the treated animals, with lung attenuation histograms showing a smaller fraction of dense, nonaerated voxels. Blood neutrophil counts were also lower. In short, a pill-like oral inhibitor measurably slowed the disease in a model that mirrors the human condition.

The implications are significant because the current therapeutic landscape for IPF is so thin. Only nintedanib, a tyrosine kinase inhibitor targeting growth factor receptors, and pirfenidone, which suppresses TGF-β production and activity, are approved by the US Food and Drug Administration, and both merely slow progression. The recent failure of pamrevlumab, a monoclonal antibody against CTGF, in the phase 3 ZEPHYRUS-1 trial suggested that targeting downstream fibrotic end products alone may be insufficient—pointing to the value of upstream signaling regulators such as DCLK1. Adding to the appeal, a DCLK1 inhibitor has already been shown to prevent inflammatory responses in acute lung injury, hinting at dual anti-fibrotic and anti-inflammatory potential.

The authors are careful to note limitations. DCLK1 is expressed across multiple lung cell types, and the tamoxifen-inducible knockout used here was global rather than cell-type-specific, so the contributions of fibroblasts versus epithelial, endothelial, and immune cells could not be fully separated. Human control tissue, as in most fibrosis studies, came from heterogeneous sources, including tumor-adjacent lung. The precise subcellular mechanics of the DCLK1–Smad3 interaction, whether DCLK1 inhibition can promote fibrosis resolution rather than merely slow progression, and the systemic safety of long-term DCLK1 blockade—all relevant given the kinase’s role in cancer and other processes—remain open questions.

Even so, the convergence of evidence is striking: DCLK1 is upregulated in human IPF, amplified in experimental fibrosis, mechanistically coupled to the TGF-β–Smad3 engine of fibroblast activation, and its suppression by both genetics and a small molecule preserves lung structure and function. What began as a cancer stem cell marker has now emerged, unexpectedly, as a central node in the scarring of the lung—offering fresh hope that a disease long considered irreversible may one day be stopped at its molecular source.

In a finding that could reshape how ophthalmologists think about one of the most delicate complications of corneal surgery, a team of computational engineers has built the first detailed computer model of a human eye in which the Descemet membrane—the paper-thin basement layer at the back of the cornea—has become detached. By simulating the fluid–structure interaction between the aqueous humor sloshing inside the eye’s anterior chamber and the flexible, detached membrane itself, the researchers have produced quantitative predictions about the mechanical stresses such a detachment imposes, and about how different transplanted corneas might alter those stresses during post-operative thermal therapy.

The study, published in the Annals of Biomedical Engineering, addresses a long-standing blind spot in ocular biomechanics. Detachment of the Descemet membrane is a recognized complication of cataract surgery, corneal transplantation and certain corneal dystrophies, and it can cause significant corneal swelling and vision loss if the membrane does not reattach. Yet most previous computational examinations of the anterior chamber have treated the eye’s interior as rigid-walled plumbing, focusing on fluid flow or heat transfer while excluding the complex two-way coupling between the intraocular fluid and the fragile ocular structures suspended within it. The new work changes that by modeling the detached Descemet membrane as a deformable, elastic solid that bends, stretches and deforms under the pressure and shear of the aqueous humor flowing around it.

The researchers, led by Ammar I. Alsabery of the Islamic University in Najaf, Iraq, together with colleagues from the University of Basrah, King Khalid University in Saudi Arabia, Saveetha School of Engineering in India, and Universiti Kebangsaan Malaysia, employed a coupled numerical scheme that solves simultaneously for the buoyancy-driven flow of aqueous humor and for the structural deformation of the membrane. The geometry represents the anterior chamber—the fluid-filled space between the cornea and the iris—with a warm region corresponding to the lens contact zone held at an isothermal hot temperature, mimicking the thermal gradients that arise in ocular warming therapies and in normal physiological conditions.

At the heart of the analysis is the Rayleigh number, a dimensionless parameter that governs the strength of natural convection: warmer, less dense fluid rises while cooler fluid sinks, generating circulating currents within the chamber. The team varied the Rayleigh number across three orders of magnitude, from 10³ to 10⁶, which corresponds to temperature differences spanning from a fraction of a degree to 250 Kelvin. This range deliberately encompasses both the mild thermal gradients present in the living eye and the much more aggressive temperature differences applied during clinical thermal therapy of the cornea, allowing the same model to speak to both resting physiology and active treatment.

Equally central to the study is the elasticity modulus of the detached membrane. The researchers swept this property across nine orders of magnitude, from 10⁸ to 10¹¹, a range designed to represent everything from highly compliant, freshly detached tissue to the much stiffer mechanical character of donor corneal tissue used in transplantation. Because the Descemet membrane’s stiffness varies with age, disease state and surgical history, this parametric approach lets the model answer a question that clinicians genuinely care about: does the type of cornea grafted into a patient change the mechanical load experienced by a detached membrane during recovery? The answer, according to the simulations, is nuanced. The cornea type does affect the stress imparted on the detached Descemet membrane, but it does not affect the thermal characteristics of the anterior chamber or the overall displacement behavior within it. In other words, stiffer transplanted tissue changes the mechanical environment at the membrane itself without appreciably altering the convective heat transfer patterns of the aqueous humor circulating beneath it.

The fluid dynamics of the anterior chamber turned out to be far more sensitive to geometry than to tissue stiffness. One of the most striking results concerns the iris, the pigmented diaphragm whose length in the model determines the extent of the hot, isothermal contact region associated with the lens. When the team simulated a shrinkage of the iris—effectively reducing the length of the contacting part of the lens held at the hot temperature—they found that the convective thermal characteristic of the anterior chamber rose by approximately 78 percent. This dramatic enhancement arises because the reduced iris length reshapes the buoyancy-driven circulation cells, allowing warmer fluid to travel more freely and exchange heat more efficiently with its surroundings. For clinicians, the implication is that the geometry of the anterior chamber, shaped in part by the iris and the lens, is a first-order determinant of how heat is distributed during thermal therapy, potentially more important than the material properties of the graft itself.

The temperature difference applied during thermal therapy also proved to be a powerful lever. Raising the applied thermal gradient from 2.5 Kelvin to 25 Kelvin—a tenfold increase—promoted heat transfer within the anterior chamber by 47.6 percent. While the scaling is sublinear rather than directly proportional to the temperature difference, the result confirms that modest increases in therapeutic temperature can yield meaningful gains in convective heat delivery without requiring a hundredfold jump in applied thermal load. The finding offers a quantitative framework for calibrating thermal treatments of the anterior segment: therapy protocols could, in principle, be tuned to achieve target heat transfer rates while respecting the narrow thermal safety margins of living ocular tissue.

Technically, the simulations belong to a class of problems known as fluid–structure interaction, or FSI, in which the fluid equations and the solid mechanics equations are coupled and solved together rather than in isolation. In this case, the aqueous humor—an essentially water-like fluid—drives natural convection under the influence of gravity, while the detached Descemet membrane responds to the pressure and viscous stresses exerted by that flow. The membrane’s deformation, in turn, modifies the shape of the fluid domain, feeding back into the flow field. This two-way coupling is computationally demanding but essential, because a rigid-wall approximation would entirely miss the bending and displacement of the detached membrane, along with the localized stress concentrations that could determine whether the membrane reattaches, tears further, or remains stable during healing. The methodology builds on the team’s earlier FSI studies of flexible fins, baffles and partitions in convective systems, as well as their prior work applying FSI modeling to blood flow in abdominal aortic aneurysms under thermal treatment, adapting those engineering tools to the peculiar geometry and physiology of the human eye.

The choice to represent the detached Descemet membrane as a flexible structure rather than a fixed boundary is what gives the study its clinical bite. Prior models of aqueous humor flow, including foundational work on fluid flow in the anterior chamber of the human eye published decades ago, captured the buoyancy-driven currents but assumed static, impermeable walls. By letting the membrane move, the new simulations capture the interplay between hemodynamics-like fluid loading and tissue compliance—precisely the interplay that governs how a detached membrane flutters against the aqueous humor, how stresses concentrate at its edges and attachments, and how different graft stiffnesses redistribute those stresses across the corneal structure. The team’s results suggest that surgeons and biomaterials scientists selecting corneal grafts for patients prone to Descemet membrane detachment could use stress predictions of this kind to weigh graft stiffness as part of the decision, even though the overall thermal environment of the chamber will remain largely unchanged.

Beyond transplantation, the model has direct relevance to thermal therapy of the eye, a class of treatments in which controlled warming is applied to manage conditions of the anterior segment. Because the simulations connect an easily controlled clinical variable—the applied temperature difference—to a measurable outcome in convective heat transfer, and because they quantify the role of iris geometry in shaping the thermal flow field, they provide a template for patient-specific treatment planning. A clinician armed with imaging of a patient’s anterior chamber geometry could, in principle, use models of this type to predict how a planned thermal protocol will distribute heat in the presence of a detached membrane, and whether the resulting stresses on the membrane fall within tolerable limits.

The work is not without its simplifications, as the authors themselves frame the study as an evaluation of corneal transplantation processes rather than a complete clinical simulator. The anterior chamber geometry is idealized, the aqueous humor is treated with standard transport properties, and the elasticity modulus range, while broad, samples the problem in parametric steps rather than matching every individual patient’s tissue. Real eyes also feature aqueous production and drainage, blinking, saccadic motion and ocular pulse effects that a steady natural-convection FSI model does not capture. Nonetheless, the paper’s central contribution—demonstrating that corneal stiffness governs membrane stress while iris geometry and thermal gradient govern heat transfer—stands as a clean separation of effects that future, more detailed models will need to reproduce.

The research was supported by the Universiti Kebangsaan Malaysia Research Grant GP-2024-K006388 and by the Deanship of Research and Graduate Studies at King Khalid University through a large Research Group Project. As computational biomechanics continues to migrate from engineering journals into clinical planning, studies like this one mark a shift toward treating the eye not as a static optical instrument but as a living, deformable, fluid-filled machine—one in which a membrane thinner than a human hair can dominate the mechanical conversation, and where the difference between a compliant and a stiff cornea may one day inform the surgeon’s graft choice.

Google is one of the major players in AI (meaning machine learning) weather forecast model space. The models it and others generate have their strengths and weaknesses, but the main advantage is that they can have forecast performance similar to traditional models while requiring far less computing horsepower to run. That means they can be run more frequently.

Google recently released version 3 of its WeatherNext model, with the biggest change being that it now ingests some satellite weather data, shortening the lag time between current weather conditions and generating a new forecast. The update is detailed in a white paper.

Reanalysis

Many weather models make use of what’s called a “reanalysis,” which is a sort of model of its own. Reanalyses take in all kinds of weather data and combine them into a single, consistent global snapshot of the atmosphere. That requires that they provide estimates for conditions over locations without real-world measurements, because weather forecast models need to work with a global picture.

Read full article

Comments

Google is one of the major players in AI (meaning machine learning) weather forecast model space. The models it and others generate have their strengths and weaknesses, but the main advantage is that they can have forecast performance similar to traditional models while requiring far less computing horsepower to run. That means they can be run more frequently.

Google recently released version 3 of its WeatherNext model, with the biggest change being that it now ingests some satellite weather data, shortening the lag time between current weather conditions and generating a new forecast. The update is detailed in a white paper.

Reanalysis

Many weather models make use of what’s called a “reanalysis,” which is a sort of model of its own. Reanalyses take in all kinds of weather data and combine them into a single, consistent global snapshot of the atmosphere. That requires that they provide estimates for conditions over locations without real-world measurements, because weather forecast models need to work with a global picture.

Read full article

Comments

The United States Department of Defense has funded an effort by SUNY Polytechnic Institute scientists to use artificial intelligence to evaluate complex defense scenarios before they unfold, including real-time predictive analysis of multiple potential scenarios in a concerted effort to predict the future on the battlefield.

DoD and SUNY Poly officials said the ‘landmark’ $61.8 million award, which will directly support the U.S. Army Transformation Decision Analysis Center (TDAC), will use advanced, data-driven analytical tools to offer the U.S. Army an unprecedented tactical advantage, improving preparedness and reducing uncertainty in high-stakes decision-making battlefield environments

“The initiative aims to provide commanders with a decisive strategic advantage in an increasingly volatile global landscape by integrating high-velocity AI into the Army’s core decision-making frameworks,” a statement announcing the new award explained.

Using AI to Predict the Future Helps Army “Respond to Evolving Threats”

The first phase of the effort to use AI to predict the future will focus on synchronizing military operations in several critical domains, including land, air, space, and cyberspace. The team, which includes researchers from the University of South Florida and the University System of Maryland, said this phase will emphasize integrating generative AI, foundation models, and advanced inference systems to “simulate complex operational scenarios and provide predictive insights.”

“These tools will enable the Army to better assess risks, evaluate strategies, and respond to evolving threats in both competitive and crisis environments,” they explain.

As the SUNY Poly-led team moves into future phases, it will also explore several “advanced” capabilities not covered in the initial phase. For example, the team will evaluate synthetic data generation and anomaly detection. The announcement said the team also plans to explore large language models (LLMs) and time-series foundation models to “enhance situational awareness.”

Other areas of focus include AI system security and reliability, including the ability to operate in “adversarial and resource-constrained environments.” Critically, this effort also includes ensuring robust AI performance “even under cyber threats or disrupted conditions.”

Evaluating Complex Scenarios Before They Unfold

One of the more intriguing components of the new award involves simulating battlefield environments to predict the future. Specifically, the team said it hopes to use battlefield simulations to enable “spatial reasoning through the integration of generative AI and active inference models.”

“These innovations will allow military planners to evaluate complex scenarios before they unfold, improving preparedness and reducing uncertainty in high-stakes decision-making,” the statement explains, adding that the effort will “contribute to the Army’s broader modernization strategy, supporting the development of next-generation analytical tools that inform readiness, capability development, and operational planning.”

Dr. Michael Carpenter, Vice President for Research at SUNY Poly, said the work represents a major step forward for the integration of AI and machine learning to “support resilient and adaptive decision-making for the Army.”

“Our team is developing sophisticated models that can synthesize vast amounts of data, generate realistic scenarios, and provide actionable intelligence,” Dr. Carpenter explained. “These capabilities will be critical in helping the Army anticipate challenges and maintain a strategic edge.”

After noting their honor at receiving the award, SUNY Poly President Dr. Winston Soboyejo said the DoD’s confidence in their program “reflects SUNY Poly’s research excellence and our commitment to solving the nation’s most complex security challenges.”

“By working closely with our partners, we will deliver transformative technologies that ensure the Army remains agile, informed, and ready for any multi-domain conflict,” Dr. Soboyejo concluded, adding that “we are proud to contribute to technologies that advance science and engineering, and strengthen national security and global stability.”

Christopher Plain is a Science Fiction and Fantasy novelist and has spent the last six years as Associate News Editor and Head Science Writer at The Debrief. Follow and connect with him on X, learn about his books at plainfiction.com, or email him at christopher@thedebrief.org

Animal testing has shaped modern drug development for decades, but its limitations become especially visible when scientists evaluate cancer immunotherapies. These treatments are designed to stimulate the immune system to recognize and destroy malignant cells, yet the same powerful immune activation can trigger life-threatening complications in patients. Researchers at the University of Rochester are developing a human-cell-based alternative: miniature tissue models known as organs-on-a-chip that could help predict these toxicities before experimental medicines reach clinical trials.

The work is being led through the university’s Translational Center for Barrier Microphysiological Systems, or TraCe-bMPS. The center is building drug-development tools around modular µSiM tissue chips, devices that contain ultrathin membranes populated with human cells. Unlike conventional laboratory cultures, these systems are engineered to reproduce key features of biological barriers, including the physical separation between tissues, controlled exposure to drugs, and measurable inflammatory responses. Their modular construction also allows the devices to be manufactured in large quantities with consistent dimensions and materials.

The platform is based primarily on acrylic components and incorporates membranes designed to mimic interfaces found in the human body. Such barriers regulate the movement of molecules and immune cells between compartments, making them central to drug absorption, toxicity, and inflammation. By assembling reproducible chips with human cells, researchers can study how a therapy affects a tissue under conditions that more closely approximate human biology than many animal models. The design also makes it possible to modify individual components for different organs or disease settings.

A major feature of the Rochester system is the integration of sensors directly into the chips. Developed in part through the work of biomedical engineering professor Benjamin Miller, these sensors can track barrier function and inflammatory signaling in real time. Instead of waiting until the end of an experiment to examine damaged cells, researchers can observe how a tissue responds as a drug is introduced. Changes in electrical or molecular signals may reveal that a barrier is becoming more permeable or that inflammatory pathways are being activated, providing an early warning of toxicity.

The immediate focus is on two severe complications associated with some cancer immunotherapies. Cytokine release syndrome, commonly called CRS, occurs when activated immune cells release large quantities of signaling proteins known as cytokines. The resulting systemic inflammation can cause fever, low blood pressure, breathing difficulties, and, in the most serious cases, organ failure. Immune effector cell-associated neurotoxicity syndrome, or ICANS, can affect the nervous system, producing confusion, seizures, impaired speech, or other neurological symptoms. Both conditions are difficult to predict reliably with animal testing because species differ in immune signaling networks and cell-surface receptors.

“The goal is to predict these toxicities from human cells on a chip, before a drug ever reaches a patient, and to do it without relying on animal models that have repeatedly failed to predict CRS in people,” says James McGrath, director of TraCe-bMPS and the William R. Kenan Jr. Professor of Biomedical Engineering. The approach does not simply attempt to reproduce a whole human body in miniature. Instead, it focuses on specific biological barriers and immune interactions that are directly relevant to the safety question being studied.

The project has now reached an important regulatory stage. The team was accepted into the US Food and Drug Administration’s Innovative Science and Technology Approaches for New Drugs, or ISTAND, pilot program. ISTAND is intended to help evaluate emerging methods that could improve drug development and eventually support regulatory decisions. Acceptance does not mean that the tissue-chip platform has already been approved for routine use, but it creates a formal pathway for the researchers to work with the FDA on defining the evidence needed for qualification.

The Rochester group is preparing a detailed qualification plan that will describe clinical considerations, timelines, data-sharing procedures, and statistical methods for evaluating the technology. If the platform ultimately receives FDA qualification, pharmaceutical companies could use its results as part of the evidence submitted in support of new drug applications. Such recognition would represent a significant step for organs-on-a-chip research, which has expanded rapidly but has only rarely progressed into formal regulatory programs.

The effort comes as US regulators seek alternatives to animal testing in preclinical safety studies. The FDA Modernization Act 2.0, enacted in 2022, opened the door for non-animal methods, while the agency’s 2025 roadmap outlined further efforts to reduce reliance on animal models. Researchers say pharmaceutical companies are showing strong interest because many immunotherapies are already in clinical development and carry risks of CRS or ICANS. The Rochester team collaborated with Graham Marsh of the Critical Path Institute and consulted with scientists at Pfizer about how the tool could fit into real-world drug development. If successful, the technology could give researchers a more human-relevant way to identify dangerous immune reactions early, helping developers eliminate unsafe candidates and refine promising treatments before patients are exposed.

Subject of Research: Human-cell tissue chips for predicting cancer immunotherapy toxicities.

Web References: University of Rochester; Translational Center for Barrier Microphysiological Systems; University of Rochester Biomedical Engineering; FDA ISTAND pilot program; FDA Modernization Act 2.0.

Image Credits: URochester photo / J. Adam Fenster.

Keywords: Drug discovery, tissue chips, organs-on-a-chip, cancer immunotherapy, cytokine release syndrome, CRS, ICANS, neurotoxicity, animal testing alternatives, biomedical engineering, microphysiological systems, human-cell models.

The University of Houston has launched UH Health, a cross-disciplinary academic health enterprise intended to reshape how healthcare professionals are educated, how medical research is conducted and how care reaches communities across Texas. The initiative brings together medicine, nursing, pharmacy, optometry, psychology, social work, population health, biomedical research and health systems science under a single interprofessional framework. University leaders say the model is designed to confront a rapidly intensifying combination of population growth, healthcare worker shortages, chronic disease and unequal access to medical services.

Rather than treating healthcare disciplines as separate academic and clinical systems, UH Health is built around team-based care. In this approach, physicians, nurses, pharmacists, optometrists, behavioral health specialists, social workers and public health experts contribute to a shared understanding of each patient’s needs. The model reflects the growing recognition that health outcomes are shaped not only by disease biology, but also by mental health, housing, income, nutrition, transportation and access to preventive services. By training students to work across professional boundaries, the University hopes to prepare graduates for a healthcare system increasingly focused on coordinated and personalized care.

“Healthcare is no longer delivered in isolation, and complex health challenges require coordinated solutions,” said Jonathan McCullers, vice president for health affairs at UH and dean of the Tilman J. Fertitta Family College of Medicine. The university’s academic health strategy is intended to place students in an environment where clinical reasoning, behavioral science, population health and scientific discovery interact. Such integration can help future clinicians identify the underlying drivers of illness, select more appropriate interventions and communicate more effectively with specialists and community organizations.

The launch comes as Texas faces a widening gap between healthcare demand and workforce capacity. Rapid population growth, an aging population and shortages of trained professionals have placed pressure on hospitals and clinics throughout the state. The Texas Hospital Association has reported that 64% of hospitals were operating with fewer beds or reduced services because of staffing shortages. UH Health aims to expand the pipeline of physicians, nurses, pharmacists, optometrists, social workers and other professionals while emphasizing workforce readiness, collaborative practice and the ability to manage complex cases.

UH’s location near the Texas Medical Center gives the initiative access to one of the world’s largest concentrations of hospitals, research institutes and specialized clinical programs. Partnerships with organizations including HCA Healthcare, Memorial Hermann Hospital, Baylor College of Medicine and MD Anderson Cancer Center are expected to provide students and researchers with expanded opportunities for clinical training, translational research and collaboration. Translational research—the process of moving discoveries from laboratories into diagnostics, treatments and public health programs—depends heavily on this type of interaction between universities, hospitals and industry.

The university is also positioning UH Health as a platform for research that reaches beyond traditional medical departments. Scientists in chemistry, physics and biomedical engineering are working alongside health researchers on problems ranging from cancer and infectious disease to substance-use disorders and neurodegeneration. UH researchers have received more than $11 million from the Cancer Prevention and Research Institute of Texas for work involving cancer prevention, diagnosis and treatment. A fentanyl vaccine developed at the university is advancing toward human trials, although its clinical effectiveness and safety will need to be established through carefully controlled studies.

Brain health is another major focus. Texas ranks fourth nationally in the number of Alzheimer’s cases and second in Alzheimer’s-related deaths, according to the Texas Department of State Health Services. More than 100 faculty members across 12 of UH’s 16 colleges are involved in research related to prevention, early detection, caregiving and treatment, supported by $62.5 million in active grants. Their work may involve diverse technologies and methods, including biological markers, computational analysis, behavioral interventions, biomedical engineering and the study of social factors that influence cognitive decline and caregiver health.

To strengthen this research ecosystem, UH is investing $77 million in a new 55,000-square-foot medical research building. The facility is expected to support interdisciplinary laboratories and shared scientific infrastructure, allowing investigators from different fields to exchange data, equipment and expertise more efficiently. In modern biomedical science, discoveries often emerge at the boundaries between disciplines—for example, when engineering approaches are applied to drug delivery, physical sciences are used to study disease mechanisms or data science improves early diagnosis.

UH Health’s community programs are intended to connect these academic and research activities with practical healthcare needs. The UH Health Family Care Center provides primary care and mental health services to the university and surrounding communities, including Third Ward, East End and South Houston. The Household-Centered Care program gives students experience through home visits with community caregivers, while the 3rd Ward Place of Wellness offers screenings, preventive services and health education. The College of Optometry extends access to vision care through community clinics and outreach programs serving children and families.

The enterprise is also expanding beyond Houston through a partnership with DHR Health in the Rio Grande Valley, a rapidly growing region that continues to face significant medical access challenges. University officials say the collaboration could support healthcare education, workforce development and research tailored to local needs. By linking professional training, scientific discovery and community partnerships, UH Health is presenting an academic health model designed for a state where medical demands are growing faster than conventional systems can respond.

Subject of Research: Academic healthcare education, biomedical research, healthcare workforce development and community health access

Article Title: University of Houston Launches UH Health to Integrate Education, Research and Community Care

Web References: https://mediasvc.eurekalert.org/Api/v1/Multimedia/ed42117d-36c9-45cf-b2e3-443e4e08b680/Rendition/low-res/Content/Public

References: Texas Hospital Association; Texas Department of State Health Services; University of Houston

Image Credits: University of Houston and UH Health

Keywords: UH Health, University of Houston, healthcare workforce shortage, academic health, team-based care, biomedical research, cancer research, fentanyl vaccine, Alzheimer’s disease, community healthcare, nursing, optometry, pharmacy, medical education, Texas Medical Center

A new study has brought scientists closer to observing how inflammation during pregnancy may alter the earliest stages of human brain development. Using three-dimensional tissue models made from human fetal brain samples, researchers modeled maternal immune activation and identified the signaling molecule interleukin-17A, or IL-17A, as a major driver of abnormal cortical development. The findings, published in Nature Neuroscience, offer a human-based experimental system for investigating how immune signals outside the brain can influence the formation of neural circuits before birth.

Maternal immune activation refers to the biological response triggered when a pregnant individual experiences infection or significant inflammation. Epidemiological studies have associated certain prenatal inflammatory exposures with an increased likelihood of neurodevelopmental conditions, including autism spectrum disorder, schizophrenia, and intellectual disability. Yet the mechanisms behind these associations have remained difficult to study. Animal models have provided important clues, but differences between species can limit their ability to reproduce the timing, cellular organization, and molecular environment of the developing human cortex.

The researchers addressed this challenge with “brain cerebroids,” three-dimensional, ex vivo models that preserve features of developing human fetal brain tissue. Unlike simplified cultures composed of a single cell type, these structures retain a more complex arrangement of neural progenitors, immature neurons, and supporting cells. Their three-dimensional architecture allows cells to communicate and organize in ways that more closely resemble the fetal cortex, including the production and movement of neurons that ultimately establish the layered structure of the brain’s outer region.

To recreate aspects of maternal immune activation, the team exposed the fetal brain cerebroids to inflammatory conditions associated with immune signaling during pregnancy. The central focus was IL-17A, a cytokine produced primarily by activated immune cells, particularly a subset of T cells known as T helper 17 cells. Cytokines are small signaling proteins that coordinate immune responses, but they can also affect tissues beyond the immune system. In the developing brain, inappropriate or excessive exposure to such signals may influence cell proliferation, migration, differentiation, and the formation of connections between neurons.

The study found that IL-17A exposure was linked to disruptions in cortical development within the human cerebroids. These effects are described as “IL-17A-driven,” indicating that the cytokine was not merely associated with the developmental changes but played a functionally important role in producing them. The disturbances affected the organization and behavior of developing neural cells, processes that are essential for building the cortex. During normal development, neural progenitor cells generate new neurons in tightly regulated sequences, while young neurons migrate to their appropriate positions. Interference with either process can alter the structure and connectivity of later-forming brain tissue.

The results are especially significant because they were observed in human fetal brain material rather than inferred only from animal studies. Previous research in mice has implicated maternal immune activation and IL-17A in changes to cortical organization and behavior in offspring. However, the human cerebroid model provides an opportunity to examine whether similar immune mechanisms operate in human tissue, where the timing of neurogenesis, cell-type composition, and cortical architecture differ from those of rodents. The approach may also help reveal human-specific responses that conventional laboratory animals cannot capture.

The findings do not mean that IL-17A exposure during pregnancy inevitably causes a neurodevelopmental condition, nor do they establish that prenatal inflammation is a single explanation for such conditions. Brain development is shaped by a combination of genetic factors, immune signals, environmental influences, and developmental timing. The experiments also took place in an ex vivo model, outside the full physiological environment of a living pregnancy. Cerebroids cannot reproduce the placenta, maternal circulation, hormonal changes, or the complete immune system, all of which influence how inflammatory signals reach the fetus and how long they remain active.

Nevertheless, the model could become a powerful platform for testing mechanisms and potential interventions. Researchers may use it to determine how IL-17A changes gene activity in neural progenitors, whether it alters communication between neurons and glial cells, and which molecular pathways make some developing cells more vulnerable than others. Because the tissue is human and three-dimensional, it may also support studies of patient-specific genetic risk, allowing investigators to compare how different backgrounds respond to the same inflammatory signal.

The work also highlights a broader shift in neuroscience toward experimental systems that combine human tissue with controlled laboratory manipulation. Understanding prenatal inflammation is difficult in clinical studies because immune exposure cannot be ethically assigned or precisely timed in pregnant people. Brain cerebroids offer a way to isolate individual components of that complex biology while preserving more of the developing tissue’s natural organization than conventional two-dimensional cell cultures. As these models improve, they may help distinguish temporary developmental changes from persistent alterations in cortical circuitry and identify when immune signaling is most consequential.

By showing that IL-17A can disrupt human cortical development in a three-dimensional ex vivo system, the study gives researchers a clearer biological target in the search for links between prenatal inflammation and later neurodevelopment. Its most immediate contribution is not a new diagnosis or treatment, but a more precise experimental framework: one that connects maternal immune signaling to cellular events in the developing human cortex. That connection could guide future work on prevention, therapeutic timing, and the complex origins of developmental differences long before the first symptoms become visible.

Subject of Research: Maternal immune activation and IL-17A-driven disruption of human fetal cortical development in 3D ex vivo brain cerebroids.

Article Title: Modeling maternal immune activation in 3D ex vivo human fetal brain cerebroids reveals IL-17A-driven disruption of cortical development.

Article References: Assir, M.Z.K., Yanakiev, M., Gim, D.H. et al. Modeling maternal immune activation in 3D ex vivo human fetal brain cerebroids reveals IL-17A-driven disruption of cortical development. Nature Neuroscience (2026). https://doi.org/10.1038/s41593-026-02400-2

Image Credits: AI Generated

DOI: https://doi.org/10.1038/s41593-026-02400-2

Keywords: maternal immune activation, IL-17A, fetal brain development, cortical development, brain cerebroids, neurodevelopment, prenatal inflammation, human brain models, cytokines, neuroscience

Foundation models are promising to become the “universal translators” of biomedical data—but a new perspective argues that medicine should resist treating them as all-knowing digital doctors. In a review published in Nature Biomedical Engineering, researchers describe how large, adaptable artificial intelligence systems are reshaping biomedical imaging while warning that impressive benchmark scores can conceal serious weaknesses in clinical environments. Their central message is direct: foundation models are most likely to improve healthcare by augmenting specialists, not replacing them.

Foundation models are designed to learn broad representations from enormous and diverse datasets before being adapted to specific tasks. In biomedical imaging, those data may include magnetic resonance imaging, computed tomography, X-rays, ultrasound, digital pathology slides and ophthalmic images. The same underlying model could theoretically identify tumors, segment organs, estimate disease risk, retrieve similar cases and generate clinical reports. The vision becomes even broader when imaging is combined with pathology, electronic health records and genomic information, creating a composite system intended to analyze a patient across multiple biological scales.

That ambition reflects a major change from traditional medical AI. Earlier systems were generally built for one narrowly defined task, such as detecting pneumonia on chest radiographs or outlining a brain lesion on MRI scans. Foundation models instead attempt to create a reusable backbone that can be transferred across diseases, hospitals and imaging technologies. Technically, they learn statistical patterns in high-dimensional data, often through self-supervised training in which the system predicts missing, transformed or associated information. Afterward, developers fine-tune or prompt the model for particular clinical applications.

But biomedical imaging is not a single, uniform world. Images differ according to scanner manufacturer, acquisition protocol, patient population, disease prevalence and local clinical practice. A model trained primarily on data from large academic hospitals may perform very differently in community clinics, rural settings or countries with limited equipment. This problem, known as domain shift, can occur even when images appear visually similar. Small changes in image quality, contrast, hardware or patient demographics may alter the statistical distribution learned by the model and undermine its predictions.

The authors introduce a framework called real-world evaluation and assessment of foundation models, or REAL-FM, to examine whether these systems are ready for practical use. Rather than focusing only on accuracy scores, REAL-FM considers several dimensions at once, including the quality and representativeness of training data, technical readiness, clinical value, integration into existing workflows and responsible artificial intelligence. The framework is intended to help clinicians interpret claims about new models and to push developers toward evaluations that resemble actual medical practice.

One of the sharpest distinctions in the perspective is between pattern recognition and causal reasoning. Foundation models can become extraordinarily skilled at recognizing visual associations—for example, linking a particular texture or anatomical feature with a diagnosis present in their training data. Yet association is not the same as understanding why a disease occurs, how it will progress or whether a treatment will benefit an individual patient. A model may identify a correlation that is valid in one hospital but reflects a hidden confounder, such as a scanner type, reporting convention or patient-selection pattern, rather than a biological signal.

This limitation becomes especially dangerous when models are moved beyond simplified benchmarks. Many benchmark datasets provide carefully curated images, clear labels and narrowly defined tasks. Clinical care is messier: scans can be incomplete, diagnoses can be uncertain, records may contain contradictory information and several conditions may coexist. The perspective highlights a lack of verified generalization across such conditions, along with a shortage of prospective, outcome-based validation. In a prospective study, an AI system would be evaluated while care is actually being delivered, with researchers measuring whether it improves diagnostic accuracy, treatment decisions, patient outcomes or workflow efficiency.

Data scarcity is another obstacle. The largest foundation models require vast quantities of data, but medical information is difficult to collect, standardize and share. Patient records are fragmented across institutions, imaging data may lack reliable annotations and rare diseases are inherently underrepresented. Privacy regulations and governance requirements further complicate the creation of centralized datasets. As a result, a model may appear broadly capable while remaining poorly tested in the populations and clinical circumstances where errors could have the greatest consequences.

The researchers argue that human oversight therefore remains indispensable. In practical terms, foundation models may be most useful as clinical assistants that prioritize images for review, highlight suspicious regions, summarize longitudinal records or offer a second opinion that specialists can interrogate. Safe deployment will require transparent reporting of uncertainty, monitoring for performance drift, mechanisms for correcting errors and clear accountability when recommendations influence care. The future envisioned by the authors is not a single monolithic medical oracle, but a coordinated ecosystem of specialized AI systems, each evaluated for a defined clinical role and connected to expert-led workflows.

The perspective arrives as the biomedical AI field races toward increasingly general-purpose systems. Its warning is not that foundation models lack value, but that technical scale alone cannot establish clinical reliability. Before these models can be trusted with consequential decisions, they must demonstrate robustness across domains, usefulness in real workflows, safety under unexpected conditions and measurable benefits for patients. The proposed REAL-FM framework offers a way to separate viral demonstrations from durable medical progress—by asking not only what a model can recognize, but where it works, why it works and whether it makes care better.

Subject of Research: Foundation models and their real-world evaluation in biomedical imaging and clinical medicine.

Article Title: Foundation models in biomedical imaging: turning hype into reality

Article References: Muneer, A., Zhang, K., Hamdi, I. et al. Foundation models in biomedical imaging: turning hype into reality. Nature Biomedical Engineering 10, 1557–1575 (2026). https://doi.org/10.1038/s41551-026-01762-z

Image Credits: AI Generated

DOI: 10.1038/s41551-026-01762-z

Keywords: foundation models, biomedical imaging, medical artificial intelligence, clinical AI, multimodal AI, domain shift, causal reasoning, clinical validation, responsible AI, REAL-FM

Meta has announced its intention to focus on open-weight large language models. Additionally, the company announced the release of an open model called Muse Glimmer and a promise to open the weights for Muse Spark 1.2, its more powerful model, in the next few weeks.

Alongside these announcements, Meta CEO Mark Zuckerberg published a more than 6,000-word essay outlining the company's philosophy about AI systems and governance moving forward. The essay aims to differentiate Meta from companies like OpenAI and Anthropic, which develop proprietary models and have lobbied the US government for help competing against large-scale distillation—which involves using an existing model to train a new one—or open-weight models by Chinese labs.

Read full article

Comments

Last week’s unprecedented security event in which two OpenAI security hacking models trespassed into the network of fellow AI company Hugging Face was enabled by exploiting one or more zero-day vulnerabilities in Artifactory, JFrog, the product’s developer, said Monday.

In an incident mimicking a dystopian sci-fi novel, two OpenAI models broke out of the restricted environment meant to keep them from accessing the Internet during an internal test, the AI company revealed last week. The models went on to breach Hugging Face’s network and steal confidential information and credentials. OpenAI said its agent achieved the feat by exploiting a previously unknown vulnerability. The company called the event “unprecedented,” and outsiders largely agreed.

Not the triumph it was made out to be

OpenAI said the models exploited multiple attack vectors, including stolen credentials and zero-days, to gain remote code execution capabilities, but until now, the vulnerable software was unknown. JFrog’s Monday disclosure said the product was a self-managed instance Artifactory, a repository management system that secures and streamlines customers’ software development operations. JFrog says Artifactory is used by more than 7,500 developer Teams, 80 percent of which work for Fortune 100 companies.

Read full article

Comments

Age-related remodeling of the immune system leaves older adults increasingly susceptible to sudden infections and cancer, while also promoting chronic, misdirected inflammation and impaired wound repair. These effects help drive a wide spectrum of long-term diseases. Yet despite rapid progress in immunology and gerontology, key mechanistic links—how specific immune-cell programs degrade over time and how that degradation shapes early response decisions—remain incompletely understood.

A major obstacle has been the limited availability of immune-aging models that are both predictive and experimentally tractable. Conventional in vitro systems often fail to capture the tissue context, cellular crosstalk, and microenvironmental cues that govern when and how immune responses ignite. As a result, “aging phenotypes” can be difficult to reproduce across laboratories and difficult to connect to causal pathways.

In a 2026 Nature Aging discussion, Nikolich and colleagues argue that the field needs a shift from descriptive comparisons toward engineered, mechanistically grounded platforms. They propose multidisciplinary strategies spanning immunology, gerontology, and engineering to interrogate immune aging at the point of initiation—when innate sensors, tissue barriers, and early signaling networks collectively determine the trajectory of downstream immunity.

The authors highlight drug delivery technologies as a route to more precise interventions. By tuning where, when, and at what dose immunomodulatory agents act, researchers could better test how age-altered signaling thresholds change the balance between protective responses and harmful inflammation.

They also emphasize extracellular matrix (ECM) and biomaterials, noting that aging reshapes the physical and biochemical scaffold surrounding immune cells. Biomaterials that recapitulate age-associated stiffness, ligand density, and degradation dynamics could reveal how microenvironmental cues reprogram immune recruitment, activation, and effector function.

Microphysiological systems and organoid-like models are presented as complementary engines for causality. These platforms can integrate multiple cell types, enable controlled perturbations, and better mimic tissue architecture than traditional culture formats.

Together, these approaches aim to deliver validated in vitro tools for immune-aging research—tools designed not only to measure differences, but to reproduce them reliably and connect them to specific upstream drivers.

The ultimate ambition is precision diagnostics and therapies that can “reprogram and rejuvenate” immune function in older adults, extending both lifespan and healthspan. By targeting immune-response initiation with engineered models, the field may finally bridge the gap between observational aging biology and interventions that work.

Subject of Research: Aging immune system; immune response initiation models and modulation

Article Title: Bioengineering strategies to interrogate and modulate the aging immune system

Article References: Nikolich, J.Ž., Wolf, M.T., Pompano, R.R. et al. Bioengineering strategies to interrogate and modulate the aging immune system. Nat Aging (2026). https://doi.org/10.1038/s43587-026-01187-y

Image Credits: AI Generated

DOI: https://doi.org/10.1038/s43587-026-01187-y

A new study reporting in Translational Psychiatry suggests that a traditional medicinal formulation called Rannasangpei—and particularly its constituent crocin-1—may help blunt autism-like behaviors triggered by prenatal exposure to valproic acid (VPA). The work frames autism-related impairments not only as behavioral phenomena, but also as downstream consequences of disrupted redox balance and chronic neuroinflammatory signaling in the brain.

The researchers used a VPA-induced model to mimic key aspects of autism-like neurodevelopmental disruption. Within this framework, they assessed whether Rannasangpei components could restore biological stability and translate into measurable improvements in behavior. The experimental logic is straightforward: if oxidative stress and neuroinflammation are causal amplifiers, then reducing them should lead to behavioral rescue.

A central finding is that treatment with Rannasangpei correlated with reduced oxidative stress markers. Oxidative stress is increasingly viewed as a bridge between genetic/environmental risk and neuronal dysfunction, because reactive oxygen species can disturb synaptic integrity, neuronal maturation, and signaling cascades essential for social and cognitive behaviors.

Equally important, the study reports a suppression of neuroinflammation. Neuroinflammation can reshape neural circuits through glial activation and altered cytokine profiles, potentially worsening developmental trajectories. By dampening inflammatory responses, crocin-1–linked effects appear to protect the brain environment during a sensitive developmental window.

The authors also emphasize that crocin-1’s contribution is not merely supportive but functionally significant, consistent with the bioactive chemistry of crocins that have been studied for antioxidant and anti-inflammatory activity. In this sense, the paper positions crocin-1 as a mechanistic candidate within a multi-component formulation.

Importantly for translational enthusiasm, the results connect molecular readouts to behavior, strengthening the argument that the observed changes are not cosmetic. Instead, they suggest an integrated pathway: oxidative imbalance and inflammatory tone shift in parallel with autism-like phenotype severity.

Overall, the study adds to a growing viral science-news narrative in neurodevelopment: natural compounds may modulate the biological “stress–inflammation” axis that shapes risk models like VPA. While animal data cannot be directly generalized to humans, the mechanistic coherence makes crocin-1 and Rannasangpei an attention-worthy direction for future preclinical and clinical exploration.

In the meantime, the headline is clear: Rannasangpei and crocin-1 show promise in reducing VPA-induced autism-like behaviors by calming oxidative stress and neuroinflammation, bringing a traditional medicine ingredient into modern neurobiological spotlight.

Subject of Research: Autism-like behaviors induced by valproic acid; oxidative stress and neuroinflammation
Article Title: Rannasangpei and its constituent crocin-1 ameliorate valproic acid–induced autism-like behaviors accompanied by reduced oxidative stress and neuroinflammation.
Article References: Qiu, R., Li, L., Yao, T. et al. Transl Psychiatry (2026). https://doi.org/10.1038/s41398-026-04283-0
Image Credits: AI Generated
DOI: https://doi.org/10.1038/s41398-026-04283-0

Apple Intelligence on-device processing remains Apple’s most convincing argument in artificial intelligence. Rivals may lead in public model rankings, chatbot adoption and the speed of releasing experimental features, but Apple is building AI inside devices people already use for their most personal work.

That distinction changes the definition of winning.

Apple does not need every iPhone owner to open a separate chatbot app. It can place intelligence inside Messages, Mail, Photos, Notes, Shortcuts, Siri and third-party apps. Local models can operate on private information without automatically transferring that information to an external server.

The strategy appeared slow while the AI market measured progress through chatbot launches and model size. It looks more competitive as the industry confronts cloud costs, privacy concerns, unreliable connectivity and the difficulty of turning general-purpose AI into useful daily actions.

In his video “Apple Lost the AI Race,” Marques Brownlee captures the contradiction in one concise line:

“Apple lost the AI race. Actually, Apple won the AI race.”

The apparent reversal describes Apple’s current position well. Apple may have lost the first race for public perception. It is increasingly prepared for the longer contest over where AI runs, how it reaches users and who controls the personal data required to make it useful.

Apple Intelligence On-Device Changes the AI Relationship

Most popular generative AI products began as cloud services. A user sends a request to a remote model, the provider processes it in a data center and the answer returns through an app or browser.

That structure supports enormous models but creates dependence on internet access, server capacity and continuing infrastructure expense. It also means personal prompts leave the device, even when the provider uses strong security and privacy policies.

Apple Intelligence on-device begins from the opposite direction. The iPhone, iPad or Mac first attempts to handle the request locally using Apple Foundation Models optimized for Apple silicon.

That local model does not need to know everything available on the internet. It needs to understand the user’s request, work with relevant personal context and complete common tasks efficiently.

The difference is similar to owning a capable computer rather than renting every calculation from a distant server. Cloud systems remain useful for difficult workloads, but they no longer need to participate in every interaction.

For Apple, local processing is also a product advantage. The company controls the chip, operating system, model architecture, memory system and apps. Each layer can be designed around the others rather than assembled from unrelated services.

Privacy Becomes a Technical Feature

Technology companies frequently describe privacy as a policy. Apple is trying to make it part of the AI architecture.

When a request runs entirely on an iPhone, the associated data does not need to leave the device. A model can work with messages, appointments, photos or app content without uploading that personal context to a conventional cloud account.

That structure is especially relevant to Siri AI. A truly personal assistant needs access to information that users may never place willingly inside an ordinary chatbot: private conversations, travel plans, health-related details, work documents and relationships between people.

An assistant becomes more useful as it gains context. It also becomes more invasive when the same context is stored, analyzed or used for model training outside the device.

Apple Intelligence on-device allows Apple to expand personal context while maintaining a stronger boundary around it. The assistant can understand information locally, perform an action and discard temporary processing data without building a permanent advertising or training profile.

This is difficult for competitors whose businesses depend on cloud services, large-scale data collection or engagement inside separate AI products. Apple’s hardware business gives it different incentives.

Private Cloud Compute Extends the Model

Not every AI task can fit within the power and memory limits of an iPhone. Apple uses Private Cloud Compute when a request requires a larger model.

Private Cloud Compute is designed to extend the privacy architecture of Apple devices into the cloud. User information is processed only for the request, is not stored and is not made available to Apple for unrelated purposes.

Independent researchers can inspect software images used by the system, creating an unusual level of outside visibility into the cloud environment. Apple has also expanded the architecture while maintaining control over the software and privacy protections used for processing.

This creates a layered system rather than a simple choice between local and cloud AI.

Apple Intelligence first determines whether the device can handle the request. More complex work can move to Private Cloud Compute. External models may be offered separately when the user chooses to involve them.

The user does not need to understand the technical path behind every answer. The system chooses the smallest and most private environment capable of completing the task.

Apple Silicon Was an AI Investment

Apple’s control of its processors has become one of its largest advantages in the AI era.

The Neural Engine first appeared years before generative AI became a consumer obsession. It supported Face ID, photography, speech recognition and other machine-learning tasks. Apple continued expanding that hardware across the iPhone, iPad and Mac.

Those investments now provide a large installed base of devices designed to run machine-learning workloads locally. Recent Apple chips combine CPU, GPU, Neural Engine and unified memory in one architecture that can move information efficiently between different types of processing.

The same design helps foundation models run without requiring the power consumption or separate memory systems associated with conventional data-center hardware.

Apple also controls the operating systems that schedule those workloads. A model can be optimized for the exact processors, memory limits and energy behavior of supported devices.

Competitors can build excellent models, but they often need to support hardware from several manufacturers with inconsistent capabilities. Apple can decide that an AI feature requires a specific generation of silicon and design the complete experience around it.

Developers Gain a Local Model

The Foundation Models framework extends Apple Intelligence on-device beyond Apple’s own apps. Developers can use the system model through native Swift tools rather than shipping a separate large model or paying for every cloud request.

An app can generate structured content, interpret natural language or use tool calling while keeping supported processing on the device. Apple handles model delivery and system integration.

This can reduce operating costs for developers. A cloud-based AI feature creates an ongoing expense each time a user submits a request. Local inference uses hardware the customer already owns.

It can also improve reliability. Features may remain available without an internet connection and respond without waiting for a remote service.

The framework encourages a quieter form of AI adoption. Apps do not need to add a prominent chatbot tab or redesign themselves around a conversational interface. Intelligence can appear inside existing workflows as a useful capability.

That is where Apple’s strategy may reach scale. Millions of apps can incorporate small AI functions that users experience as normal software behavior rather than as visits to an AI destination.

The Installed Base Is Apple’s Distribution Advantage

The AI race is often discussed as a contest between models. Consumer adoption also depends on distribution.

Apple can deliver new intelligence features through operating-system updates across the iPhone, iPad, Mac and Vision Pro. Users do not need to create another account, choose a provider or transfer their personal information into a new service.

Siri already occupies the system interface. Apple Intelligence can work across first-party apps. Developers can connect their own features through Apple frameworks.

The scale is difficult to reproduce. A model provider may have stronger benchmark scores but still need users to download an app and consciously open it. Apple can introduce AI during actions people already perform: replying to a message, locating a photo, organizing a note or completing a shortcut.

This does not guarantee success. Apple must make Siri AI reliable, keep feature availability consistent and prove that local models can deliver enough capability to satisfy users accustomed to powerful cloud assistants.

The distribution advantage only becomes meaningful when the experience works.

Winning Without Building the Loudest Chatbot

Apple has made mistakes during its AI transition. Siri improvements arrived later than promised, regional regulations delayed features and competitors established powerful consumer brands before Apple completed its new system.

Those setbacks explain why the claim that Apple lost the AI race became persuasive.

They do not settle the outcome.

The first stage rewarded companies that released chatbots quickly and demonstrated increasingly large models. The next stage may reward systems that can integrate AI into ordinary life without demanding constant cloud access, high subscription costs or broad collection of personal information.

Apple Intelligence on-device is built for that stage. It uses hardware already in the user’s hand, protects more personal context locally and gives developers access to intelligence without requiring every interaction to become a server transaction.

Apple does not need to win every benchmark or release the most talkative assistant. It needs AI to become a reliable part of the devices people already trust.

Brownlee’s title works because both statements can be true. Apple lost the visible sprint that defined the early generative AI market. By concentrating on local processing, silicon, privacy and operating-system integration, it may already be positioning itself to win the race that follows.

The post Apple Intelligence On-Device Is Winning the AI Race appeared first on AppleMagazine.