In a groundbreaking advancement that could reshape the landscape of bioelectronic medicine, researchers have developed a wirelessly powered, bioresorbable electrical stimulation system with unprecedented programmable control over stimulation waveforms. This innovation integrates tissue-penetrating near-infrared (NIR) light to modulate electrical currents with precision, harnessing a novel bioresorbable silicon phototransistor as the cornerstone of its operation. Pioneering beyond earlier monophasic pulse devices, this technology allows for the delivery of monophasic, biphasic, and polyphasic stimulation patterns, significantly enhancing the versatility and therapeutic potential of implantable electrical stimulators designed to dissolve harmlessly after use.

Traditional electrical stimulators implanted in biological tissues have largely relied on wired connections or bulky batteries, impeding patient mobility and often necessitating additional surgeries for device removal. Bioresorbable devices emerged as a solution to these challenges by safely degrading in the body over time, eliminating the risks associated with permanent implants. However, these earlier systems primarily delivered monophasic pulses, limiting therapeutic precision and adaptability in complex treatments. The innovation reported by Kim, Seo, Wang, and colleagues introduces a new paradigm by integrating wireless power transfer via NIR light and programmable waveform control within a fully bioresorbable platform, thus extending the functional range and easing the clinical management of bioelectronic therapies.

Central to this system is the incorporation of bioresorbable silicon phototransistors engineered to serve as optoelectronic modulators within the electrical circuitry. When illuminated by externally applied near-infrared light that penetrates deep into biological tissues, these phototransistors effectively adjust current flows at critical circuit nodes. This optical modulation capability replaces conventional electronic control elements, which often rely on persistent power sources or invasive wiring, thereby enabling the device to operate autonomously while still allowing fine-tuning of stimulation parameters through non-contact optical inputs.

The adoption of near-infrared light as the wireless power and control mechanism is particularly significant. NIR light possesses the ability to penetrate through several centimeters of tissue without causing damage, providing a safe and efficient transmission channel between the external controller and the implanted device. This feature supports programmability in situ, empowering clinicians to tailor electrical stimulation waveforms dynamically after device implantation. Precisely controlling the shape, amplitude, and timing of the electrical pulses is crucial for optimizing therapeutic outcomes in applications such as neuroregeneration and cardiac pacing.

The research team demonstrated the remarkable versatility of their bioresorbable system by successfully delivering a variety of stimulation waveforms. Monophasic pulses, which are unidirectional current flows, serve as a baseline therapy. More complex biphasic and polyphasic waveforms, which alternate current direction and comprise multiple phases respectively, have been shown to minimize tissue damage and improve efficacy in various biosignaling contexts. The ability to generate these sophisticated patterns expands the therapeutic range of the device, potentially enabling treatments that were previously inaccessible using bioresorbable technologies.

In demonstrating practical utility, the device was tested in both small and large animal models, validating its capacity for clinically relevant electrical stimulation regimes. The system was effectively employed in single- and dual-chamber cardiac pacing, a critical intervention for managing arrhythmias and heart block. Through controlled electrical stimulation, the device could synchronize cardiac contractions, a feat crucial for restoring efficient cardiac output. These trials highlight the translational potential of the technology for future human applications, situating it as a promising alternative to current electronic pacemakers that require permanent implantation and battery replacements.

Further extending the functional scope, the researchers utilized the bioresorbable stimulator to activate phrenic neuromuscular pathways to either induce or block diaphragmatic movements. The phrenic nerve controls diaphragmatic excursion, essential for respiration, and its modulation has significant implications for respiratory therapies, such as in patients with spinal cord injuries or central hypoventilation syndromes. By enabling wireless, programmable neurostimulation of the phrenic nerve, the technology offers a minimally invasive approach to manage life-sustaining respiratory functions, with the added benefit of device degradation after the therapeutic window.

The bioresorbable nature of the silicon phototransistors and associated electronics assures that after fulfilling their therapeutic role, the devices gradually dissolve into biocompatible byproducts. This process circumvents the complications related to device explantation surgeries, reducing patient risk and healthcare costs. Moreover, the materials chosen minimize inflammatory responses and tissue irritation, facilitating seamless integration with host biology during the operational phase.

The engineering behind this system represents an intersection of materials science, optoelectronics, and biomedical engineering. The architecture leverages the semiconducting properties of silicon in thin-film, bioresorbable form, combined with circuit design optimized for minimal power consumption and maximal efficiency during NIR irradiation. These multidisciplinary innovations collectively overcome longstanding barriers in the fabrication of transient electronic implants capable of sophisticated programmable functionality.

Controlling the stimulation waveforms via light permits remote adjustment without physical connections, paving the way for real-time therapeutic modulation responsive to patient status or physiological signals. Future iterations could incorporate feedback mechanisms by integrating bioresorbable sensors that monitor tissue response, creating closed-loop systems for personalized medicine. This dynamic adaptability could be transformative in managing chronic conditions requiring variable and responsive electrical stimulation protocols.

This research also addresses one of the fundamental challenges of implantable devices: power delivery. The optical wireless power transfer circumvents the need for bulky batteries or explant procedures, offering renewed device miniaturization potential. Coupled with programmable phototransistors, these innovations enable multifunctional implants that conform to soft tissues and degrade harmlessly, a trifecta crucial for implantation in sensitive organs like the heart and nervous system.

The ability to deliver polyphasic waveforms through a single bioresorbable receiver unit is particularly noteworthy. Conventionally, generating these complex waveforms requires sophisticated circuitry and stable power sources, neither of which are trivial to implement in resorbable devices. The research showcases how optical control unlocks this capability without sacrificing bioresorbability or wireless operation, representing a paradigm shift in transient biomedical electronics.

The successful application of this device in large animal cardiac models bridges a critical gap toward clinical translation. Such models replicate human physiological scales and complexities far more closely than small animals, validating both the efficacy and safety of the system under conditions analogous to human therapies. This demonstration is an essential milestone on the path toward human clinical trials and eventual regulatory approval.

Moreover, the phrenic nerve stimulation experiments illustrate the broader applicability beyond cardiac pacing. Neuromuscular control via electrical stimulation has vast clinical relevance, from restoring motor function to modulating organ systems. The biodegradable, wireless nature of this system makes it especially suited for temporary therapeutic interventions, such as post-surgical recovery or transient disease states, where permanent implants would be unnecessary or undesirable.

While this work represents a major leap forward, ongoing challenges remain, including further optimization of device longevity relative to treatment durations, refinement of light delivery systems to maximize tissue penetration and minimize off-target effects, and integration with advanced biocompatible materials to tailor resorption timelines. Addressing these will be key to broadening the range of clinical applications and ensuring patient safety across diverse scenarios.

Overall, this wirelessly powered, light-controlled, bioresorbable stimulation system emerges as a versatile platform poised to transform bioelectronic therapies. Its elegant combination of programmable polyphasic waveform delivery, wireless operation via near-infrared light, and full bioresorbability addresses critical limitations of current implantable stimulators. By enabling sophisticated, minimally invasive, and transient electrical stimulation paradigms, this technology advances the frontiers of regenerative medicine, cardiac therapy, and neuromodulation, marking a significant milestone in the evolution of biomedical devices.

Subject of Research: Wireless bioresorbable electrical stimulation device with programmable waveform control using near-infrared light

Article Title: A wirelessly powered, light-controlled, bioresorbable stimulation system with programmable polyphasic waveforms

Article References:
Kim, J.U., Seo, S.G., Wang, H. et al. A wirelessly powered, light-controlled, bioresorbable stimulation system with programmable polyphasic waveforms. Nat Electron (2026). https://doi.org/10.1038/s41928-026-01655-8

Image Credits: AI Generated

DOI: https://doi.org/10.1038/s41928-026-01655-8

"Anyone who still thinks that Lucy Letby is guilty should have their head examined," says Professor Shoo Lee, whose research was used in court to help prosecute the nurse. Julian Mann reports on a new book on the case.

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The Transient Artifact and Continuous Learning System (TACLS) leverages data from continuously operating satellite networks coupled with machine learning models to help meteorologists at the National Weather Service forecast flash floods more efficiently. This new software is the result of a collaboration between NASA’s Jet Propulsion Laboratory, the University of California, San Diego (UCSD), and the National Oceanic and Atmospheric Administration (NOAA) National Weather Service (NWS).

Created with support from NASA’s Earth Science Technology Office (ESTO), TACLS leverages machine learning to automatically locate evidence (unusual increases in atmospheric moisture) of impending flash flooding that meteorologists may otherwise miss as they analyze large amounts of data. TACLS flags that evidence, indicates where flash flooding could likely occur, and displays that information via a user-friendly visualization for human analysts to interpret. Those analysts can then decide whether to issue a flash flood warning or weather advisory.

This novel framework for tracking extreme weather events and predicting imminent flash floods operates in near real-time, producing forecasts in as little as fifteen minutes.

“That’s really what we wanted to do, to give meteorologists a tool to help decision making for flash flood warnings,” said Yehuda Bock, Distinguished Researcher at the UCSD Scripps Institution of Oceanography and principal investigator for TACLS.

In simulations testing, TACLS used data from diverse severe weather events—including atmospheric rivers, monsoonal convection, and tropical cyclone remnants—between 2017 and 2023 and successfully captured 93% of the issued flash-flood warnings. Meteorologists from the National Weather Service are currently working to incorporate TACLS into their existing systems for forecasting flash floods in Southern California.

This learning system has two main components. First, an analytic back-end software suite uses machine learning algorithms to process satellite data and determine areas at risk for flooding. Second, user-friendly visualization software highlights those areas for further analysis by humans.

The ACLS back-end software analyzes data from satellites in the Global Navigation Satellite System (GNSS), a constellation of satellite networks that drive navigation services around the world. Water vapor in the troposphere delays signals from these satellites as they travel to Earth. This signal delay can be analyzed to calculate the amount of water vapor in the atmosphere over a particular location on Earth.

The TACLS analytic back-end software suite features a machine learning model trained using more than 30 years of past GNSS data. This model is an anomaly detector that tracks unusual increases in atmospheric moisture. The model then carefully examines that atmospheric moisture data and determines whether it’s either an artifact (a false feature or distortion in the data) or a transient (a time-sensitive physical event, like heavy precipitation) that requires interpretation by human analysts.

If TACLS determines the data represents a transient, such as an extreme weather event that warrants a flash flood warning, it will forward that data to the TACLS visualization software (MGViz) for further evaluation by humans. The analysts use their judgement and experience to interpret these events and determine whether the flagged data indicates a flash flood is likely, and, if necessary, issue a flash flood warning.

Several past innovations developed at JPL are leveraged by TACLS to process GNSS data and present the results. The analytic back-end software suite incorporates elements from JPL’s Domain-agnostic Outlier Ranking Algorithms program and the Time-series Forecasting, Evaluation, and Deployment program. The TACLS visualizer is based on the Multi-Mission Geographic Information System, originally developed at JPL for NASA’s Mars missions.

The TACLS software binds all these components within a novel system that enhances existing methods to reduce the amount of time it takes for a human analyst to determine whether to issue a flash flood warning.

Both the TACLS software and the data used to train it will be open-source, allowing scientists to either tailor this model in response to their unique research needs or create their own model from scratch.

For additional details, see the entry for this project on NASA TechPort. 

Project Lead: Dr. Yehuda Bock, University of California, San Diego. 

Sponsoring Organization(s): NASA’s Earth Science Technology Office Advanced Information Systems Technology Program; JPL; NOAA; National Weather Service. 

The Transient Artifact and Continuous Learning System (TACLS) leverages data from continuously operating satellite networks coupled with machine learning models to help meteorologists at the National Weather Service forecast flash floods more efficiently. This new software is the result of a collaboration between NASA’s Jet Propulsion Laboratory, the University of California, San Diego (UCSD), and the National Oceanic and Atmospheric Administration (NOAA) National Weather Service (NWS).

Created with support from NASA’s Earth Science Technology Office (ESTO), TACLS leverages machine learning to automatically locate evidence (unusual increases in atmospheric moisture) of impending flash flooding that meteorologists may otherwise miss as they analyze large amounts of data. TACLS flags that evidence, indicates where flash flooding could likely occur, and displays that information via a user-friendly visualization for human analysts to interpret. Those analysts can then decide whether to issue a flash flood warning or weather advisory.

This novel framework for tracking extreme weather events and predicting imminent flash floods operates in near real-time, producing forecasts in as little as fifteen minutes.

“That’s really what we wanted to do, to give meteorologists a tool to help decision making for flash flood warnings,” said Yehuda Bock, Distinguished Researcher at the UCSD Scripps Institution of Oceanography and principal investigator for TACLS.

In simulations testing, TACLS used data from diverse severe weather events—including atmospheric rivers, monsoonal convection, and tropical cyclone remnants—between 2017 and 2023 and successfully captured 93% of the issued flash-flood warnings. Meteorologists from the National Weather Service are currently working to incorporate TACLS into their existing systems for forecasting flash floods in Southern California.

This learning system has two main components. First, an analytic back-end software suite uses machine learning algorithms to process satellite data and determine areas at risk for flooding. Second, user-friendly visualization software highlights those areas for further analysis by humans.

The ACLS back-end software analyzes data from satellites in the Global Navigation Satellite System (GNSS), a constellation of satellite networks that drive navigation services around the world. Water vapor in the troposphere delays signals from these satellites as they travel to Earth. This signal delay can be analyzed to calculate the amount of water vapor in the atmosphere over a particular location on Earth.

The TACLS analytic back-end software suite features a machine learning model trained using more than 30 years of past GNSS data. This model is an anomaly detector that tracks unusual increases in atmospheric moisture. The model then carefully examines that atmospheric moisture data and determines whether it’s either an artifact (a false feature or distortion in the data) or a transient (a time-sensitive physical event, like heavy precipitation) that requires interpretation by human analysts.

If TACLS determines the data represents a transient, such as an extreme weather event that warrants a flash flood warning, it will forward that data to the TACLS visualization software (MGViz) for further evaluation by humans. The analysts use their judgement and experience to interpret these events and determine whether the flagged data indicates a flash flood is likely, and, if necessary, issue a flash flood warning.

Several past innovations developed at JPL are leveraged by TACLS to process GNSS data and present the results. The analytic back-end software suite incorporates elements from JPL’s Domain-agnostic Outlier Ranking Algorithms program and the Time-series Forecasting, Evaluation, and Deployment program. The TACLS visualizer is based on the Multi-Mission Geographic Information System, originally developed at JPL for NASA’s Mars missions.

The TACLS software binds all these components within a novel system that enhances existing methods to reduce the amount of time it takes for a human analyst to determine whether to issue a flash flood warning.

Both the TACLS software and the data used to train it will be open-source, allowing scientists to either tailor this model in response to their unique research needs or create their own model from scratch.

For additional details, see the entry for this project on NASA TechPort. 

Project Lead: Dr. Yehuda Bock, University of California, San Diego. 

Sponsoring Organization(s): NASA’s Earth Science Technology Office Advanced Information Systems Technology Program; JPL; NOAA; National Weather Service. 

The post NASA Uses Machine Learning to Enhance Flash Flood Warnings appeared first on NASA Science.

Hurty words on social media will bring the police to your door. But when a landlord discovered a cannabis farm in her property, officers didn't want to know. Welcome to two-tier Britain.

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