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Category: robotics/AI

Agenus Closes Strategic Immunotherapeutic Collaboration with Zydus Lifesciences

As part of the collaboration, Agenus has granted Zydus exclusive rights to develop and commercialize BOT and BAL in India and Sri Lanka, with Agenus eligible to receive royalties on net sales in those territories.

“Closing this collaboration with Zydus strengthens our balance sheet and, critically, secures dedicated U.S. manufacturing capacity at a pivotal moment for Agenus,” continued Armen. “With these foundations in place, our focus in 2026 is disciplined execution—advancing our Phase III program, broadening paid patient access through authorized pathways, and progressing toward regulatory submission supported by one of the most substantial clinical datasets generated in [microsatellite stable] MSS colorectal cancer.”

Following the closing, the Emeryville and Berkeley, CA, biologics manufacturing facilities will be transferred to Zydus and housed under a newly formed subsidiary named Zylidac Bio. Agenus has secured committed manufacturing capacity at these U.S. sites to support BOT+BAL supply needs for its clinical trials, global access programs, and future commercialization.

AI model detects prediabetes using ECG data without need for blood tests

DiaCardia, a novel artificial intelligence model that can accurately identify individuals with prediabetes using either 12-lead or single-lead electrocardiogram (ECG) data, has been developed. This breakthrough holds promise for future home-based prediabetes screening using consumer wearable devices, without requiring invasive blood tests.

Type 2 diabetes occurs when the human body either cannot make enough insulin or does not use insulin well, resulting in high blood glucose levels. This condition is a growing global health burden that can reduce the quality of life and life expectancy.

Before type 2 diabetes develops, many people go through a prolonged stage called prediabetes, where blood glucose levels are above normal but not high enough to be diagnosed as diabetes. Prediabetes is an important window wherein lifestyle changes can reduce the progression to diabetes.

Benchmarking framework reveals major safety risks of using AI in lab experiments

While artificial intelligence (AI) models have proved useful in some areas of science, like predicting 3D protein structures, a new study shows that it should not yet be trusted in many lab experiments. The study, published in Nature Machine Intelligence, revealed that all of the large-language models (LLMs) and vision-language models (VLMs) tested fell short on lab safety knowledge. Overtrusting these AI models for help in lab experiments can put researchers at risk.

LabSafety Bench for AI use in labs

The research team involved in the new study initially sought to answer whether LLMs can effectively identify potential hazards, accurately assess risks and make reliable decisions to mitigate laboratory safety threats. To help answer these questions, the team developed a benchmarking framework, called “LabSafety Bench.”

Year 2100: Future Technologies that Will Rule the World

The androids of the future will be the distant results of synthetic biology and not silicon.

🚀 Step into a world of boundless innovation as we take you on a journey through the awe-inspiring technologies that await humanity in the 22nd century! 🌌 From advancements in space exploration to mind-boggling leaps in artificial intelligence, this captivating video offers a glimpse into the cutting-edge breakthroughs that will redefine the very fabric of human existence.

🌐 Witness the birth of extraterrestrial civilizations as humans venture further into space, exploring distant planets and establishing self-sustaining colonies. Experience the seamless integration of artificial intelligence into our daily lives, transforming how we interact with technology and creating new possibilities for societal progress. Prepare to be amazed by quantum computing’s extraordinary power, revolutionizing problem-solving and opening doors to scientific discoveries previously deemed impossible.

🌿 Delve into the world of sustainable marvels, where eco-friendly innovations mend our relationship with the environment and pave the way for a greener, more harmonious future. Explore the ethical implications of biotechnology advancements, which offer insights into longevity and human potential. This video paints an inspiring picture of the limitless possibilities and profound transformations that lie ahead in the remarkable world of 22nd-century technologies. Like, share, and subscribe to our channel for more captivating glimpses of the ever-evolving world of tomorrow. 🌟🔮🌠 #FutureTechnologies #22ndCenturyInnovations #EmbracingTomorrow

How AI and Quantum, And Space Are Redefining Cybersecurity

Sharing my latest Forbes article: by Chuck Brooks.

Thanks for reading and sharing!

#cybersecurity #tech #ai #quantum #space Forbes

Artificial intelligence and quantum computing are no longer speculative technologies. They are reshaping cybersecurity, economic viability, and managing risk in real time.

Deep Learning in Otolaryngology: A Narrative Review

Deep learning models have achieved diagnostic accuracy rates up to 92% for nasopharyngeal carcinoma and over 95% for otologic pathology, matching expert performance in otolaryngology.

This review summarizes recent deep learning advances in otolaryngology, including diagnostic models with expert-level accuracy for nasopharyngeal carcinoma and otologic pathology.

This narrative review synthesizes recent deep-learning applications and proposes a framework for their integration into otolaryngology.

Machine learning can predict patients’ responses to antidepressants—while disentangling drug and placebo effects

Depression is one of the most widespread mental health disorders worldwide, affecting approximately 4% of the global population. It is characterized by a persistent low mood, disruptions in typical sleeping and/or eating habits, a lack of motivation, a loss of interest in daily activities and unhelpful thought patterns.

There are now various treatments for depression, including psychotherapy-based interventions and different types of antidepressant medications. Identifying the best treatment strategy, however, is not always easy, and many patients try different medications before they find one that works for them.

Researchers at Stanford University, Lehigh University, the University of Texas at Austin and other institutes explored the potential of machine learning techniques, computational models that can identify patterns in data, for predicting the responses of individual patients to two different antidepressants and to a placebo (i.e., a pill that contains no active chemicals).

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