As a wound heals, it goes through several stages: clotting to stop bleeding, immune system response, scabbing, and scarring.

A wearable device called "a-Heal," designed by engineers at the University of California, Santa Cruz, aims to optimize each stage of the process. The system uses a tiny camera and AI to detect the stage of healing and deliver a treatment in the form of medication or an electric field. The system responds to the unique healing process of the patient, offering personalized treatment.

The portable, wireless device could make wound therapy more accessible to patients in remote areas or with limited mobility. Initial preclinical results, published in the journal npj Biomedical Innovations, show the device successfully speeds up the healing process.

Designing a-Heal

A team of UC Santa Cruz and UC Davis researchers, sponsored by the DARPA-BETR program and led by UC Santa Cruz Baskin Engineering Endowed Chair and Professor of Electrical and Computer Engineering (ECE) Marco Rolandi, designed a device that combines a camera, bioelectronics, and AI for faster wound healing. The integration in one device makes it a "closed-loop system" -- one of the firsts of its kind for wound healing as far as the researchers are aware.

"Our system takes all the cues from the body, and with external interventions, it optimizes the healing progress," Rolandi said.

The device uses an onboard camera, developed by fellow Associate Professor of ECE Mircea Teodorescu and described in a Communications Biology study, to take photos of the wound every two hours. The photos are fed into a machine learning (ML) model, developed by Associate Professor of Applied Mathematics Marcella Gomez, which the researchers call the "AI physician" running on a nearby computer.

"It's essentially a microscope in a bandage," Teodorescu said. "Individual images say little, but over time, continuous imaging lets AI spot trends, wound healing stages, flag issues, and suggest treatments."

The AI physician uses the image to diagnose the wound stage and compares that to where the wound should be along a timeline of optimal wound healing. If the image reveals a lag, the ML model applies a treatment: either medicine, delivered via bioelectronics; or an electric field, which can enhance cell migration toward wound closure.

The treatment topically delivered through the device is fluoxetine, a selective serotonin reuptake inhibitor which controls serotonin levels in the wound and improves healing by decreasing inflammation and increasing wound tissue closure. The dose, determined by preclinical studies by the Isseroff group at UC Davis group to optimize healing, is administered by bioelectronic actuators on the device, developed by Rolandi. An electric field, optimized to improve healing and developed by prior work of the UC Davis' Min Zhao and Roslyn Rivkah Isseroff, is also delivered through the device.

The AI physician determines the optimal dosage of medication to deliver and the magnitude of the applied electric field. After the therapy has been applied for a certain period of time, the camera takes another image, and the process starts again.

While in use, the device transmits images and data such as healing rate to a secure web interface, so a human physician can intervene manually and fine-tune treatment as needed. The device attaches directly to a commercially available bandage for convenient and secure use.

To assess the potential for clinical use, the UC Davis team tested the device in preclinical wound models. In these studies, wounds treated with a-Heal followed a healing trajectory about 25% faster than standard of care. These findings highlight the promise of the technology not only for accelerating closure of acute wounds, but also for jump-starting stalled healing in chronic wounds.

AI reinforcement

The AI model used for this system, which was led by Assistant Professor of Applied Mathematics Marcella Gomez, uses a reinforcement learning approach, described in a study in the journal Bioengineering, to mimic the diagnostic approach used by physicians.

Reinforcement learning is a technique in which a model is designed to fulfill a specific end goal, learning through trial and error how to best achieve that goal. In this context, the model is given a goal of minimizing time to wound closure, and is rewarded for making progress toward that goal. It continually learns from the patient and adapts its treatment approach.

The reinforcement learning model is guided by an algorithm that Gomez and her students created called Deep Mapper, described in a preprint study, which processes wound images to quantify the stage of healing in comparison to normal progression, mapping it along the trajectory of healing. As time passes with the device on a wound, it learns a linear dynamic model of the past healing and uses that to forecast how the healing will continue to progress.

"It's not enough to just have the image, you need to process that and put it into context. Then, you can apply the feedback control," Gomez said.

This technique makes it possible for the algorithm to learn in real-time the impact of the drug or electric field on healing, and guides the reinforcement learning model's iterative decision making on how to adjust the drug concentration or electric-field strength.

Now, the research team is exploring the potential for this device to improve healing of chronic and infected wounds.

Additional publications related to this work can be found linked here.

This research was supported by the Defense Advanced Research Projects Agency and the Advanced Research Projects Agency for Health.

Read more …AI-powered smart bandage heals wounds 25% faster

"This concerns the biosynthesis of a molecule that has a very long history with humans," explains Prof. Dirk Hoffmeister, head of the research group Pharmaceutical Microbiology at Friedrich Schiller University Jena and the Leibniz Institute for Natural Product Research and Infection Biology (Leibniz-HKI). "We are referring to psilocybin, a substance found in so-called 'magic mushrooms', which our body converts into psilocin - a compound that can profoundly alter consciousness. However, psilocybin not only triggers psychedelic experiences, but is also considered a promising active compound in the treatment of therapy-resistant depression," says Hoffmeister.

Two paths, one molecule

The study, which was conducted within the Cluster of Excellence 'Balance of the Microverse', shows for the first time that fungi have developed the ability to produce psilocybin at least twice independently of each other. While Psilocybe species use a known enzyme toolkit for this purpose, fiber cap mushrooms employ a completely different biochemical arsenal - and yet arrive at the same molecule. This finding is considered an example of convergent evolution: different species have independently developed a similar trait, but the 'magic mushrooms' have gone their own way in doing so.

Searching for clues in fungal genomes

Tim Schäfer, lead author of the study and doctoral researcher in Hoffmeister's team, explains: "It was like looking at two different workshops, but both ultimately delivering the same product. In the fiber caps, we found a unique set of enzymes that have nothing to do with those found in Psilocybe mushrooms. Nevertheless, they all catalyze the steps necessary to form psilocybin."

The researchers analyzed the enzymes in the laboratory. Protein models created by Innsbruck chemist Bernhard Rupp confirmed that the sequence of reactions differs significantly from that known in Psilocybe. "Here, nature has actually invented the same active compound twice," says Schäfer.

However, why two such different groups of fungi produce the same active compound remains unclear. "The real answer is: we don't know," emphasizes Hoffmeister. "Nature does nothing without reason. So there must be an advantage to both fiber cap mushrooms in the forest and Psilocybe species on manure or wood mulch producing this molecule - we just don't know what it is yet."

"One possible reason could be that psilocybin is intended to deter predators. Even the smallest injuries cause Psilocybe mushrooms to turn blue through a chemical chain reaction, revealing the breakdown products of psilocybin. Perhaps the molecule is a type of chemical defense mechanism," says Hoffmeister.

More tools for biotechnology

Although it is still unclear why different fungi ultimately produce the same molecule, the discovery nevertheless has practical implications: "Now that we know about additional enzymes, we have more tools in our toolbox for the biotechnological production of psilocybin," explains Hoffmeister.

Schäfer is also looking ahead: "We hope that our results will contribute to the future production of psilocybin for pharmaceuticals in bioreactors without the need for complex chemical syntheses." At the Leibniz-HKI in Jena, Hoffmeister's team is working closely with the Bio Pilot Plant, which is developing processes for producing natural products such as psilocybin on an industry-like scale.

At the same time, the study provides exciting insights into the diversity of chemical strategies used by fungi and their interactions with their environment. It thus addresses central questions of the Collaborative Research Center ChemBioSys and the Cluster of Excellence ׅ'Balance of the Microverse' at Friedrich Schiller University Jena, within the framework of which the work was carried out and funded by the German Research Foundation (DFG), among others. While the CRC ChemBioSys investigates how natural compounds shape biological communities, the Cluster of Excellence focuses on the complex dynamics of microorganisms and their environment.

Read more …Mushrooms evolved psychedelics twice, baffling scientists

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