A new study led by Aalto University examines how AI companions impacted people’s mental health and social lives over a two-year period. Combining large‑scale data from the discussion platform Reddit with in-depth interviews, it showed that while interacting with an AI companion can support users, it also coincided with increased signs of distress in their online language. The work offers one of the first causal, long-term examinations of AI companions’ mental health impact at scale, grounded in first‑hand accounts of users’ everyday lives.

In a multicenter retrospective Chinese ICU cohort (5 hospitals, 2012–2023; 9,221 pediatric suspected-infection admissions; 13.4% mortality), the Phoenix Sepsis Score showed only moderate discrimination for in-hospital death (AUROC 0.60). Using XGBoost/SHAP-guided, clinically feasible predictors, the authors created PSS+, presented as a logistic regression nomogram, improving discrimination in internal (AUROC 0.75) and external (AUROC 0.71) validation versus PSS-4/8 and pSOFA.

Nanofilm electrodes capable of detecting stress in plants through bioelectric potentials could pave the way for more resilient agriculture, report researchers from Institute of Science Tokyo. Thanks to the electrode’s small thickness, leaf surface hairs can easily pierce through it, enabling stable and long-term electrical contact without compromising the leaf’s natural processes. This work could help improve crop yields by enabling early detection of stress in plants.

Microbial metabolites influence health far beyond the intestinal tract. Yet, a systematic understanding of how these molecules precisely control specific immune cell functions and regulate disease has remained elusive. A comprehensive review by the team of Professor Changtao Jiang and Dr. Kai Wang at Peking University addresses this gap. The article provides a critical theoretical foundation for understanding the gut microbiota-metabolite-immune axis in disease pathogenesis and for developing targeted intervention strategies.

Artificial intelligence (AI) is increasingly being explored as a tool to support clinical decision-making, yet its real-world performance in pediatric diagnosis remains unclear. Now, a Pediatric Investigation study using authentic clinical cases reports that advanced AI models outperform clinicians in diagnostic accuracy, particularly for rare diseases, while a combined human-AI approach achieves the highest overall success. The findings highlight the potential of AI as a complementary tool to improve diagnostic precision and patient outcomes.

MIT researchers have developed a “living implant” that uses muscle to restore function to weakened organs—potentially offering an alternative to mechanical devices and transplants.