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Category: health

Fungus-powered farming delivers higher yields and better-tasting crops, says study

Can we have higher yields and better taste? Using a natural extract from the fungus Pseudozyma aphidis, this method improves the firmness and natural sugar content of crops like tomatoes and melons while significantly boosting production. This discovery offers a practical path to meeting global food demands without compromising the health of the planet or produce quality. Furthermore, because the approach uses stable microbial secretions instead of live cultures, it ensures consistent and reliable performance across various agricultural environments and climates.

Researchers at the Hebrew University of Jerusalem have identified a natural, eco-friendly way to significantly increase agricultural yields while also improving the quality and taste of produce. The study, led by Professor Maggie Levy alongside researchers Anton Fennec and Neta Rotem, focuses on an extract derived from the yeast-like fungus Pseudozyma aphidis.

As the global population continues to grow, the demand for higher agricultural output has historically led to the heavy use of synthetic fertilizers and pesticides. These chemicals often contribute to soil and water pollution and increase greenhouse gas emissions. The new research, published in the journal Plant Physiology, suggests that beneficial micro-organisms can offer a sustainable alternative to these traditional agricultural inputs.

Outside the Safe Operating Space of a New Planetary Boundary for Per- and Polyfluoroalkyl Substances (PFAS)

It is hypothesized that environmental contamination by per-and polyfluoroalkyl substances (PFAS) defines a separate planetary boundary and that this boundary has been exceeded. This hypothesis is tested by comparing the levels of four selected perfluoroalkyl acids (PFAAs) (i.e., perfluorooctanesulfonic acid (PFOS), perfluorooctanoic acid (PFOA), perfluorohexanesulfonic acid (PFHxS), and perfluorononanoic acid (PFNA)) in various global environmental media (i.e., rainwater, soils, and surface waters) with recently proposed guideline levels. On the basis of the four PFAAs considered, it is concluded that levels of PFOA and PFOS in rainwater often greatly exceed US Environmental Protection Agency (EPA) Lifetime Drinking Water Health Advisory levels and the sum of the aforementioned four PFAAs (Σ4 PFAS) in rainwater is often above Danish drinking water limit values also based on Σ4 PFAS; levels of PFOS in rainwater are often above Environmental Quality Standard for Inland European Union Surface Water; and atmospheric deposition also leads to global soils being ubiquitously contaminated and to be often above proposed Dutch guideline values. It is, therefore, concluded that the global spread of these four PFAAs in the atmosphere has led to the planetary boundary for chemical pollution being exceeded. Levels of PFAAs in atmospheric deposition are especially poorly reversible because of the high persistence of PFAAs and their ability to continuously cycle in the hydrosphere, including on sea spray aerosols emitted from the oceans. Because of the poor reversibility of environmental exposure to PFAS and their associated effects, it is vitally important that PFAS uses and emissions are rapidly restricted.

Biodegradable sensors attached to plants detect pesticides in 3 minutes

Researchers at the São Carlos Institute of Physics at the University of São Paulo (IFSC-USP) in Brazil, led by Paulo Augusto Raymundo-Pereira, have created biodegradable, “wearable” sensors for plants to monitor their health, including the presence of pesticides. The sensors are made from carbon ink and are screen-printed onto transparent cellulose acetate bioplastics.

The study was published in Biosensors and Bioelectronics: X. The World Economic Forum selected wearable sensor engineering as one of the top ten emerging technologies of 2023 for its potential to improve plant health and increase agricultural productivity.

However, most wearable devices today are made from nonrenewable plastic polymers derived from petroleum and have poor adhesion to uneven, wavy, and curved surfaces.

AI is starting to beat doctors at making correct diagnoses

Researchers show that a type of AI known as a large language model often outperformed physicians at diagnosing complex and potentially life-threatening conditions, including decreased blood flow to the heart, even in the fast-moving stages of real ER care when information is limited.

In early ER cases, the model identified the correct or a very close diagnosis in about 67% of cases, compared with roughly 50% to 55% for physicians. And the technology is only getting better.

If you walk into an emergency room (ER) in 10 years, you’ll encounter a new type of caregiver: an artificial intelligence (AI) system designed to get you a diagnosis faster and help your care team make more informed decisions. While you sit in the waiting room, you’ll be hooked up to a blood pressure cuff that’s constantly and autonomously monitored. All the while, an AI agent will be listening in while you and your doctor talk about your symptoms, ready to flag any mistakes your physician makes or suggest next steps.

This vision of AI-assisted emergency health care may soon be reality. In a new study, researchers show that a type of AI known as a large language model (LLM) often outperformed physicians at diagnosing complex and potentially life-threatening conditions, including decreased blood flow to the heart, even in the fast-moving stages of real ER care when information is limited, they report today in Science. In early ER cases, the model identified the correct or a very close diagnosis in about 67% of cases, compared with roughly 50% to 55% for physicians. And the technology is only getting better.

“Evaluating AI in medicine demands both depth and breadth across different clinical tasks and settings,” and these authors were able to incorporate both in this study, says Shreya Johri, a computer scientist at the Dana-Farber Cancer Institute who was uninvolved with the new research. Still, she notes, wide adoption of these AI systems in health care will hinge on knowing the contexts in which they’re most reliable.

How an HIV/AIDS tragedy spurred human evolution

Researchers show that a type of AI known as a large language model often outperformed physicians at diagnosing complex and potentially life-threatening conditions, including decreased blood flow to the heart, even in the fast-moving stages of real ER care when information is limited.

In early ER cases, the model identified the correct or a very close diagnosis in about 67% of cases, compared with roughly 50% to 55% for physicians. And the technology is only getting better.

Before antiretroviral (ARV) drugs started to become widely available in KwaZulu-Natal in 2005, there was “kind of the perfect storm,” with several unusual factors coalescing to drive a devastating epidemic, says Philip Goulder, an immunologist at the University of Oxford who led the study, which appears today in the Proceedings of the National Academy of Sciences. HIV had made few inroads into South Africa until the early 1990s, when an epidemic exploded in the heterosexual population, infecting about 40% of pregnant women in KwaZulu-Natal. (That astonishingly high prevalence persists today.) Because of a mix of genetics, limited health care, and possibly the viral subtype in circulation, infected people developed AIDS—when the destruction of the immune system threatens survival—exceptionally quickly, within about 4.5 years versus 10 years in North America.

Other studies have shown how infectious diseases, including malaria and tuberculosis, have altered the human genome. But those changes took thousands of years. “That’s what is quite exciting about this: You can see how rapidly evolution actually can occur,” Goulder says.

Similar evolutionary forces may have been at work in North America and Europe, but they are more difficult to see—and less likely to affect future generations. HIV prevalence in those regions is below 1%, and the hardest-hit group is men who have sex with men. “They are generally not a population that’s leaving behind as many offspring,” Worobey notes.

A DNA-organizing protein offers new insight into infertility, IVF and generational health

The causes of male infertility can be hard to diagnose, with many tests failing to detect genetic defects. Sometimes, infertility doesn’t even involve the genes themselves. It can arise from improper folding of the father’s DNA in the sperm. If a couple conceives, this mispackaged DNA can damage the lifelong health of the child.

“Paternal health is critical to sperm quality and the health of the offspring,” said Satoshi Namekawa, a professor of microbiology and molecular genetics. “Understanding the packing and folding of DNA in sperm cells is a fundamental question in modern biology.”

Namekawa and Ph.D. student Yu-Han Yeh have now unveiled an important new piece of this puzzle. They have identified a protein, called DAXX, that guides how sperm DNA is organized. DAXX silences thousands of genes so they don’t interfere with reproduction. It also keeps a handful of crucial genes turned on—shaping the delicate, early stages of embryonic development. The work was published recently in Genes & Development.

Performance of a large language model on the reasoning tasks of a physician

What if every scientific paper you read was just the “highlight reel” of a much longer, messier, and more complicated movie? You see the breakthrough, but you never see the hundreds of hours of footage showing what didn’t work.

Ultimately, the ARA marks a shift toward a future where “The Last Human-Written Paper” isn’t the end of science, but the beginning of a much deeper, machine-readable conversation.

However, this shift toward radical transparency comes with its own set of hurdles. While ARAs make AI agents more efficient, the study found a “prior-run box” effect where seeing a human’s past failures actually limited an AI’s ability to think outside the box and find creative new solutions. There is also a significant cultural and technical gap to bridge: the system relies on researchers being willing to expose their “messy” unfinished work, and even with better data, the jump in actual experiment reproduction was relatively modest. Furthermore, the reliance on “compilers” to translate old papers into this new format risks baking in errors or “hallucinations” if the original source was vague, proving that while machine-readable data is powerful, it isn’t a magic fix for the inherent complexities of scientific discovery.

We systematically evaluated the medical reasoning abilities of an LLM across six diverse experiments, comparing the model with hundreds of expert physicians. Overall, the model outperformed physicians across experiments, including in cases utilizing real and unstructured clinical data taken directly from the health record in an emergency department. These diagnostic touchpoints mirror the high-stakes decisions taken in emergency medicine departments, where nurses and clinicians make time-sensitive choices with limited information. Our results showed that humans, GPT-4o, and o1 all improved their diagnostic abilities as more information was available; o1 outperformed humans at multiple touchpoints, with the widest gap at initial ER triage, where there is the least information available.

The rapid pace of improvement in LLMs has substantial implications for the science and practice of clinical medicine. Although applying AI to assist with clinical decision support is sometimes viewed as a high-risk endeavor (22, 23), greater use of these tools might serve to mitigate the human and financial costs of diagnostic error, delay, and lack of access (24, 25). Our findings suggest the urgent need for prospective trials to evaluate these technologies in real-world patient care settings and for health care systems to prepare for investments for computing infrastructure and design for clinician-AI interaction that can facilitate the safe integration of AI tools into patient-care workflows. This includes the development of robust monitoring frameworks to oversee the broader implementation of AI clinical decision support systems (22), monitoring not just final diagnostic accuracy but other metrics crucial for successful deployment, including safety, efficiency, and cost.

We emphasize that our study addresses only text-based performance for both humans and machines; clinical medicine is multifaceted and awash with nontext inputs, including auditory (such as the patient’s level of distress) and visual information (for example, interpretation of medical imaging studies) that clinicians routinely use. Existing studies suggest that current foundation models are more limited in reasoning over nontext inputs (26, 27); future work is needed to assess how humans and machines may effectively collaborate (28) in use of nontext signals. This requires new benchmarks, trials, and technological solutions to more faithfully measure clinical encounters. Existing investment in increasingly pervasive ambient AI scribes and other passive monitoring technologies holds promise to serve as the basis for such investigations.

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Metastatic cancer detection and management with artificial intelligence and augmented reality (Review)

Metastatic cancer remains a significant global health challenge, contributing to the majority of cancer-related mortality due to late detection, therapeutic resistance and the complexity of disseminated disease. Recent advances in artificial intelligence (AI) and augmented reality (AR) are transforming the landscape of metastatic cancer detection and management. AI-driven tools, including radiomics, deep learning models, and predictive analytics, enhance early identification of metastatic lesions, improve diagnostic accuracy, and support personalized treatment strategies by integrating multimodal clinical, imaging and molecular data. At the same time, AR technologies are increasingly applied in image-guided surgery, real-time tumor visualization and patient education, enabling more precise interventions and improved clinical decision-making.

Common asthma drug may turn off tumor ‘switch’ tied to immunotherapy resistance

A drug widely used to treat asthma and allergies may also help fight aggressive cancers, reports a new Northwestern Medicine study that uncovered how tumors hijack common white blood cells to evade immunotherapy.

The findings in mice and human tissues point to a practical, new way to improve treatment for tough tumors, such as triple-negative breast cancer, where immunotherapy often fails.

The study is published in Nature Cancer.

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