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.

A multi-channel wearable scent display developed at Institute of Science Tokyo allows a user to experience multiple scents while exploring virtual environments. Based on virtual scenes, the device can blend up to eight fragrances in real time and deliver them with precise control of odor intensity. By synchronizing smell with virtual reality content, the device enables better immersion and realism—opening new possibilities for enhanced digital entertainment, realistic simulation training, and future digital scent technologies.

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.

A new study shows that systems designed to capture methane from cow manure, called dairy digesters, are highly effective. But on the rare occasions they fail, the leaks are large enough to offset their climate benefits.

Researchers at The University of Manchester have created a groundbreaking physics‑informed machine‑learning model that can run molecular simulations for unprecedented lengths of time, even at temperatures as high as 1000 Kelvin.

Both Pd single atoms (Pd₁) and clusters (Pd_c) were constructed in three-dimensional ordered macroporous (3DOM) In₂O₃ for photocatalytic CO₂ reduction with H₂O. The large surface area and abundant pore channels of 3DOM-In₂O₃ facilitate mass transfer and intermediate enrichment. The synergisticPd₁ and Pd_c active sites enhance the adsorption and activation of CO₂ and H₂O. The localized surface plasmon resonance of Pd clusters induces a photothermal effect, further accelerating the reaction kinetics.