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Smart Bandage Takes Another Step Forward: Revolutionizing Chronic Wound Care with Real-Time Monitoring and Treatment

The iCares bandage uses innovative microfluidic components, sensors, and machine learning to sample and analyze wounds and provide data to help patients and caregivers make treatment decisions.

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Smart Bandages have long been envisioned as a “lab on skin” that could monitor and treat chronic wounds in patients. Caltech Professor of Medical Engineering Wei Gao and his colleagues are now one step closer to achieving this goal. After successfully demonstrating the efficacy of their smart bandage, iCares, in animal models, they have now cleared another hurdle by showing its ability to continually sample fluid from human patients with chronic wounds.

The improved version of the smart bandage, which integrates three microfluidic components, is designed to clear excess moisture from wounds while providing real-time data about biomarkers present. The innovative microfluidics system ensures that only fresh samples are analyzed, allowing for accurate measurements of biomarkers such as nitric oxide and hydrogen peroxide.

Gao’s team has demonstrated the potential of their smart bandage to detect signs of inflammation and infection in patients up to three days before symptoms appear. Furthermore, they have developed a machine-learning algorithm that can accurately classify wounds and predict healing time with a level of accuracy comparable to expert clinicians.

The iCares system consists of a flexible, biocompatible polymer strip that can be 3D printed at low cost. It integrates nanoengineered biomarker sensor arrays for single-use applications and reuses signal processing and wireless data transmission through a user interface like a smartphone. The triad of microfluidic modules includes a membrane that draws wound fluid from the surface, a bioinspired component that shuttles it to the sensor array where analysis takes place, and a micropillar module that carries the sampled fluid away from the bandage.

The implications of this innovation are vast, with potential applications extending beyond chronic wound care. By integrating real-time monitoring and treatment capabilities into wearable devices, we may soon see significant improvements in patient outcomes and quality of life.

Artificial Intelligence

Accelerating Evolution: The Power of T7-ORACLE in Protein Engineering

Researchers at Scripps have created T7-ORACLE, a powerful new tool that speeds up evolution, allowing scientists to design and improve proteins thousands of times faster than nature. Using engineered bacteria and a modified viral replication system, this method can create new protein versions in days instead of months. In tests, it quickly produced enzymes that could survive extreme doses of antibiotics, showing how it could help develop better medicines, cancer treatments, and other breakthroughs far more quickly than ever before.

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The accelerated evolution engine known as T7-ORACLE has revolutionized the field of medicine and biotechnology by allowing researchers to evolve proteins with new or improved functions at an unprecedented rate. This breakthrough was achieved by Scripps Research scientists who have developed a synthetic biology platform that enables continuous evolution inside cells without damaging the cell’s genome.

Directed evolution is a laboratory process where mutations are introduced, and variants with improved function are selected over multiple cycles. Traditional methods require labor-intensive steps and can take weeks or more to complete. In contrast, T7-ORACLE accelerates this process by enabling simultaneous mutation and selection with each round of cell division, making it possible to evolve proteins continuously and precisely inside cells.

T7-ORACLE circumvents the bottlenecks associated with traditional approaches by engineering E. coli bacteria to host a second, artificial DNA replication system derived from bacteriophage T7. This allows for continuous hypermutation and accelerated evolution of biomacromolecules, making it possible to evolve proteins in days instead of months.

To demonstrate the power of T7-ORACLE, researchers inserted a common antibiotic resistance gene into the system and exposed E. coli cells to escalating doses of various antibiotics. In less than a week, the system evolved versions of the enzyme that could resist antibiotic levels up to 5,000 times higher than the original.

The broader potential of T7-ORACLE lies in its adaptability as a platform for protein engineering. Scientists can insert genes from humans, viruses, or other sources into plasmids and introduce them into E. coli cells, which are then mutated by T7-ORACLE to generate variant proteins that can be screened or selected for improved function.

This could help scientists more rapidly evolve antibodies to target specific cancers, evolve more effective therapeutic enzymes, and design proteases that target proteins involved in cancer and neurodegenerative disease. The system’s ease of implementation, combined with its scalability, makes it a valuable tool for advancing synthetic biology.

The research team is currently focused on evolving human-derived enzymes for therapeutic use and tailoring proteases to recognize specific cancer-related protein sequences. In the future, they aim to explore the possibility of evolving polymerases that can replicate entirely unnatural nucleic acids, opening up possibilities in synthetic genomics.

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Artificial Intelligence

Google’s Deepfake Hunter: Exposing Manipulated Videos with a Universal Detector

AI-generated videos are becoming dangerously convincing and UC Riverside researchers have teamed up with Google to fight back. Their new system, UNITE, can detect deepfakes even when faces aren’t visible, going beyond traditional methods by scanning backgrounds, motion, and subtle cues. As fake content becomes easier to generate and harder to detect, this universal tool might become essential for newsrooms and social media platforms trying to safeguard the truth.

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In an era where manipulated videos can spread disinformation, bully people, and incite harm, researchers at the University of California, Riverside (UCR), have created a powerful new system to expose these fakes. Amit Roy-Chowdhury, a professor of electrical and computer engineering, and doctoral candidate Rohit Kundu, teamed up with Google scientists to develop an artificial intelligence model that detects video tampering – even when manipulations go far beyond face swaps and altered speech.

Their new system, called the Universal Network for Identifying Tampered and synthEtic videos (UNITE), detects forgeries by examining not just faces but full video frames, including backgrounds and motion patterns. This analysis makes it one of the first tools capable of identifying synthetic or doctored videos that do not rely on facial content.

“Deepfakes have evolved,” Kundu said. “They’re not just about face swaps anymore. People are now creating entirely fake videos – from faces to backgrounds – using powerful generative models. Our system is built to catch all of that.”

UNITE’s development comes as text-to-video and image-to-video generation have become widely available online. These AI platforms enable virtually anyone to fabricate highly convincing videos, posing serious risks to individuals, institutions, and democracy itself.

“It’s scary how accessible these tools have become,” Kundu said. “Anyone with moderate skills can bypass safety filters and generate realistic videos of public figures saying things they never said.”

Kundu explained that earlier deepfake detectors focused almost entirely on face cues. If there’s no face in the frame, many detectors simply don’t work. But disinformation can come in many forms. Altering a scene’s background can distort the truth just as easily.

To address this, UNITE uses a transformer-based deep learning model to analyze video clips. It detects subtle spatial and temporal inconsistencies – cues often missed by previous systems. The model draws on a foundational AI framework known as SigLIP, which extracts features not bound to a specific person or object. A novel training method, dubbed “attention-diversity loss,” prompts the system to monitor multiple visual regions in each frame, preventing it from focusing solely on faces.

The result is a universal detector capable of flagging a range of forgeries – from simple facial swaps to complex, fully synthetic videos generated without any real footage. It’s one model that handles all these scenarios,” Kundu said. “That’s what makes it universal.”

The researchers presented their findings at the high-ranking 2025 Conference on Computer Vision and Pattern Recognition (CVPR) in Nashville, Tenn. Their paper, led by Kundu, outlines UNITE’s architecture and training methodology.

While still in development, UNITE could soon play a vital role in defending against video disinformation. Potential users include social media platforms, fact-checkers, and newsrooms working to prevent manipulated videos from going viral.

“People deserve to know whether what they’re seeing is real,” Kundu said. “And as AI gets better at faking reality, we have to get better at revealing the truth.”

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Artificial Intelligence

The Quantum Drumhead Revolution: A Breakthrough in Signal Transmission with Near-Perfect Efficiency

Researchers have developed an ultra-thin drumhead-like membrane that lets sound signals, or phonons, travel through it with astonishingly low loss, better than even electronic circuits. These near-lossless vibrations open the door to new ways of transferring information in systems like quantum computers or ultra-sensitive biological sensors.

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The Niels Bohr Institute at the University of Copenhagen has made a groundbreaking discovery that could revolutionize the way we transmit information. Researchers, in collaboration with the University of Konstanz and ETH Zurich, have successfully sent vibrations through an ultra-thin drumhead, measuring only 10 mm wide, with astonishingly low loss – just one phonon out of a million. This achievement is even more impressive than electronic circuit signal handling.

The drumhead, perforated with many triangular holes, utilizes the concept of phonons to transmit signals. Phonons are essentially sound waves that travel through solid materials by vibrating atoms and pushing each other. This phenomenon is not unlike encoding a message and sending it through a material, where signal loss can occur due to various factors like heat or incorrect vibrations.

The researchers’ success lies in achieving almost lossless transmission of signals through the membrane. The reliability of this platform for sending information is incredibly high, making it a promising candidate for future applications. To measure the loss, researchers directed the signal through the material and around the holes, observing that the amplitude decreased by only about one phonon out of a million.

This achievement has significant implications for quantum research. Building a quantum computer requires super-precise transfer of signals between its different parts. The development of sensors capable of measuring the smallest biological fluctuations in our own body also relies heavily on signal transfer. As Assistant Professor Xiang Xi and Professor Albert Schliesser explain, their current focus is on exploring further possibilities with this method.

“We want to experiment with more complex structures and see how phonons move around them or collide like cars at an intersection,” says Albert Schliesser. “This will give us a better understanding of what’s ultimately possible and what new applications there are.” The pursuit of basic research is about producing new knowledge, and this discovery is a testament to the power of scientific inquiry.

In conclusion, the quantum drumhead revolution has brought us one step closer to achieving near-perfect signal transmission. As researchers continue to explore the possibilities of this method, we can expect exciting breakthroughs in various fields, ultimately leading to innovative applications that will transform our understanding of the world.

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