A century-old mystery in Venus’s clouds just became more intriguing, as scientists calculated how strongly the unidentified material behind its dark ultraviolet patterns must absorb light. The results sharply narrow the possibilities and could help future spacecraft determine whether the mysterious substance is organic, inorganic, or something unexpected.

Ancient rocks from Western Australia suggest water was reaching deep inside Earth more than three billion years ago, long before modern plate tectonics may have fully developed. Researchers propose that water-rich pieces of crust periodically sank into the mantle through a process they call “dripduction.” The buried water then helped produce magma and volcanic eruptions.

A natural compound found in foods such as spinach, almonds, and sweet potatoes may aggravate gut inflammation in people with Crohn’s disease and ulcerative colitis because their intestines appear to handle it differently. Researchers say the finding could open the door to lower-oxalate diets or microbiome treatments tailored specifically to people with IBD.

GLP-1 drugs such as Ozempic, Wegovy, and Zepbound may increase hair-loss risk by 7% in men who are already genetically susceptible to male-pattern baldness. The findings suggest thinning hair may not be caused only by rapid weight loss and could also reflect a biological link between GLP-1 activity and hair follicles.

Scientists have uncovered a natural braking system that helps the immune system switch off inflammation and prevent harmful immune cells from building up. Boosting this pathway in people reduced inflammation-related immune changes and sped up pain relief, pointing toward potential new treatments for chronic inflammatory diseases.

Nature Medicine, Published online: 13 September 2026; doi:10.1038/s41591-026-04587-0

A study shows that integrating numerous multimodal, patient-level biomarkers enhances the prediction of cancer immunotherapy response, but generalizability is still a challenge.

Nature Medicine, Published online: 13 September 2026; doi:10.1038/s41591-026-04488-2

In a large international real-world study of non-small cell lung cancer, a multimodal explainable AI model outperformed established biomarkers for immunotherapy outcome prediction and improved physician decision-making.

Physicists at CERN have found a new way to peer deep inside atomic nuclei and distinguish between two competing explanations for how gluons behave. Using the ALICE experiment at the Large Hadron Collider, researchers measured particle production at unprecedented spatial resolution, revealing structures as small as about one-quarter the size of a proton. At the smallest scales, they saw a surprising drop in J/ψ production that conventional “nuclear shadowing” struggles to explain.

A 10-year study of more than 6,000 adults found that popular coronary calcium scans may add little to standard heart disease risk estimates for many people. But for patients with borderline or intermediate risk, the scans could make a meaningful difference by revealing who is actually more likely to develop heart disease.

Researchers at Harvard have demonstrated a way to protect quantum information using microscopic sound waves. By continuously surrounding a diamond-based qubit with mechanical vibrations, they extended its coherence time by roughly threefold. The same phonons could eventually both transmit and protect quantum information, opening the door to compact sound-based quantum networks on chips.

Semaglutide helped older healthy mice live longer while improving memory, muscle function, blood sugar control, and several biological signs of aging. Its effects went beyond those seen with calorie restriction, raising the possibility that GLP-1 drugs could tap into a separate biological pathway linked to longevity.

Researchers at Binghamton University have developed a mathematical strategy that can solve Wordle with a 99% success rate. Instead of simply guessing words packed with common letters or choosing the most likely answer, the method uses Shannon entropy, a measure from information theory, to identify guesses that reveal the most useful information. Each guess is designed to rapidly shrink the pool of possible answers, even when the guessed word itself seems unlikely to be correct.

Researchers found that blocking IL1RAP could disrupt a powerful inflammatory network that helps pancreatic cancer survive treatment. In preclinical experiments, the approach reduced tumor-protecting cells and fibrosis while boosting the activity of cancer-fighting T cells. That could make chemotherapy and immunotherapy more effective.

Scientists have discovered that Mercury’s surface contains far less silicon dioxide than previously thought, pointing to an unexpectedly hot volcanic past. The finding suggests ancient lava may have originated from deeper, more extensively melted parts of the planet’s mantle.

Long-lived bats appear to combine powerful immune defenses with unusual strategies for eliminating damaged cells, potentially helping them avoid cancer and disease for decades. Researchers hope these genetic tricks could eventually reveal new ways to protect human health during aging.

A feather discovered inside 66-million-year-old fossilized dinosaur poop may help explain why modern birds survived the asteroid catastrophe. The extinct bird it came from had more primitive insulating feathers, suggesting superior plumage may have helped the ancestors of living birds endure the deadly impact winter.

Background: Wearable devices are increasingly used for sleep monitoring and as adjunctive treatment. Existing meta-analyses mostly pool composite digital therapies and rarely isolate stand-alone wearables or distinguish between objective and subjective end points. Whether stand-alone wearable interventions improve sleep outcomes in adults with insomnia, and which factors moderate treatment heterogeneity, remains unclear. Objective: This study aims to evaluate the effectiveness of wearable digital interventions on sleep outcomes in adults with insomnia versus control strategies and explore moderators of effectiveness, including device-wearing position, intervention duration, and control type, using meta-regression. Methods: This systematic review and meta-analysis was conducted in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta‑Analyses) 2020 statement and the PRISMA-S (Preferred Reporting Items for Systematic Reviews and Meta‑Analyses Literature Search Extension) guideline. Five electronic databases and clinical trial registries were searched from inception to May 18, 2026. Eligible studies were randomized controlled trials (RCTs) evaluating wearable digital interventions in adults with insomnia compared with sham, waitlist, usual care, or active control conditions and had an intervention duration of at least 1 week. Study screening, data extraction, and risk-of-bias assessment were carried out independently by 2 reviewers. Pooled estimates were calculated using a restricted maximum likelihood random-effects model with the Hartung-Knapp-Sidik-Jonkman correction. Heterogeneity was assessed using the ² statistic, and 95% prediction intervals (PIs) were calculated for the primary analyses. The certainty of evidence was rated using the GRADE (Grading of Recommendations, Assessment, Development, and Evaluation) approach. Results: Sixteen RCTs (N=910) were included. Wearable digital interventions were associated with a significant reduction in objective sleep-onset latency (SOL; mean difference [MD] −4.52, 95% CI −8.38 to −0.67, PI −9.52 to 0.47 min) and a significant improvement in subjective sleep efficiency (SE; MD 2.00%, 95% CI 1.90%‐2.11%, PI 1.85%‐2.15%). Subjective total sleep time (TST) also showed a significant increase (MD 19.11, 95% CI 2.98‐35.24, PI −16.20 to 54.43 minutes). Meta-regression showed that control type, intervention duration, and device location did not explain the heterogeneity of the insomnia severity index (ISI) (=0). Sensitivity analysis confirmed the robustness of pooled ISI estimates, and an Egger test indicated no small-study effects (=.07). Certainty of evidence ranged from moderate to high. Conclusions: Wearable digital interventions provide selective benefits for objective SOL, subjective SE, and subjective TST in adults with insomnia, with no improvement in overall ISI. Despite statistically significant effects on several sleep parameters, wide PIs, substantial heterogeneity, and limited study numbers indicate preliminary, nonconclusive findings. Wearables should be viewed as affordable adjunctive tools requiring further validation, not substitutes for first-line cognitive behavioral therapy for insomnia. Large-scale, long-term RCTs with standardized protocols and patient-level external validation are required to consolidate the evidence base. Trial Registration: PROSPERO CRD420251038603; https://www.crd.york.ac.uk/PROSPERO/view/CRD420251038603

Digital assessment technologies, such as optical motion capture and inertial measurement units, enable detailed kinematic analysis and continuous monitoring of upper limb activity in persons with neurological conditions. While such digital metrics of functioning are increasingly recognized in research, their uptake in clinical neurorehabilitation is limited. It remains unclear which digital metrics of functioning clinicians perceive as most meaningful and how these are integrated into patient-centered care. Understanding clinicians’ information needs and reasoning processes is a prerequisite for implementing digital assessment technology. To characterize how rehabilitation professionals perceive, prioritize, and integrate digital metrics of functioning into clinical reasoning and to identify features that would support their routine use. Three 90-minute focus groups were conducted in 3 Swiss neurorehabilitation centers, involving 11 clinicians with diverse professional backgrounds (5 physiotherapists, 4 occupational therapists, 1 movement scientist, and 1 medical practitioner). Participants discussed essential parameter domains and individually rated the relevance and meaningfulness of 17 kinematic metrics for the well-studied drinking task and 10 established arm use performance metrics. Verbatim transcripts were analyzed using reflexive thematic analysis, and rating data were summarized descriptively. Five main themes were identified. (1) Functional requirements to interpret movement quality and performance (active/passive range of motion (ROM), strength, selective muscle control, grasp) form the basis for interpreting movement. (2) Essential aspects of movement quality (smoothness, efficiency, compensatory movement) are valued when aligned with observable task execution. (3) Added value of real-world performance (hourly activity profiles, arm-use symmetry, functional workspace) represents the reference for patient-centered reasoning. (4) Individualizing what matters, including diagnosis-specific preferences, shapes assessment selection. (5) Blending clinical eye and reference data reflects clinicians’ reliance on visual judgment complemented by normative values. Intuitive metrics such as task duration, number of movement units, and ROM were favored, whereas confidence was lower in more complex metrics (e.g., jerk, inter-joint coordination). Clinicians value intuitive digital metrics of functioning when they are clearly linked to patient-centered outcomes and supported by normative references. The findings highlight the need for targeted educational strategies and digital competency training that help clinicians interpret digital metrics and integrate them with contextual information and clinical reasoning.

Background: Large language models (LLMs) are increasingly being explored for clinical decision support, but their performance in audiology has not been systematically benchmarked using clinically grounded case materials and rubric-based safety evaluations. Objective: This study aimed to develop and evaluate AUDIOLOGYBENCH, a 3-tier benchmark for characterizing frontier LLM capability in clinical audiology along (1) curated domain knowledge, (2) literature-derived evidence, and (3) clinical reasoning under multimodal case input, with an explicit human audit of the automated adjudicator on the primary end point. Methods: The benchmark comprises 3139 objective items from educational resources, 3175 research article–derived items from peer-reviewed articles published between 2015 and 2025, and 67 multimodal clinical case studies graded against a standardized A-F rubric with 6 prespecified critical-error types that cap scores at D or F. Eight models were evaluated on the educational objective items: 4 frontier multimodal models (Gemini 2.5 Pro, Grok 4, OpenAI O3, and Claude Sonnet 4 Thinking) were evaluated on the research article–derived items, and on 804 case study evaluations. Adjudication used Gemini 2.5 Pro (objective and research-derived items) and Claude Opus 4.5 (case studies). The case study adjudicator was independently audited against PhD-level audiologist consensus on blinded subsamples, supplemented by a post-stratified human-calibrated sensitivity analysis. Results: A striking task-type dissociation emerged on case studies: clinical recommendations (Q3) achieved a mean score of 89.74 (SD 13.92, 95% CI 88.07‐91.41), a 98.1% (263/268) pass rate, and no dangerous recommendations; audiometric numerical interpretation (Q1) achieved a mean score of 67.89 (SD 18.47, 95% CI 65.68‐70.10), with a 35.4% (95/268) critical-error rate; and differential diagnosis (Q2) achieved a mean score of 67.79 (SD 15.33, 95% CI 65.95‐69.63). Question type, not model selection, dominated performance (eta-squared_H=0.333 vs 0.001; rank biserial ≥0.679). Interreviewer reliability between audiologists was high (quadratic-weighted κ of 0.78 and 0.85 across the 80-item and 50-item audits, respectively). When 2 audiologists regraded all 80 model Q1 responses with the diagnostic images available, the adjudicator’s per-item Q1 labels diverged from human judgment (κ=0.05; overflagging; sensitivity: 19/26, 73%; positive predictive value: 19/53, 36%), yet its reweighted Q1 critical-error rate (36.2%) was broadly consistent with the image-grounded human estimates (28%‐34%), suggesting no systematic inflation of the headline rate. The principal Q3>{Q1, Q2} ranking was preserved under post-stratified human calibration. Web-style multiple-choice items showed ceiling effects (>95% accuracy); short-answer prompts remained challenging (best 30%). Conclusions: Current frontier LLMs show strong recommendation generation but substantial limitations in audiometric numerical interpretation that are shared across models and that an automated adjudicator partially miscalibrated at the per-item level. AUDIOLOGYBENCH characterizes capability boundaries rather than certifying clinical readiness. Deployment of LLM-assisted audiology workflows requires structured human verification of all numerical findings and awareness of fabrication and severity misclassification failure modes documented here.

Background: Patients with breast cancer during postoperative chemotherapy intervals commonly experience psychological distress and reduced resilience while recovering at home. Digital mindfulness interventions may provide accessible psychological support during this vulnerable period; however, evidence regarding tailored interventions for postoperative patients with breast cancer during chemotherapy intervals remains limited. Objective: This study aimed to examine the effectiveness of a digital intervention on psychological resilience in postoperative patients with breast cancer during chemotherapy intervals. Methods: A quasi-experimental study with repeated measures was conducted from October 2021 to June 2022. A total of 80 eligible participants were recruited from the Department of Breast Surgery at a tertiary hospital in Zhejiang Province, China, and 71 completed the study. The control group received routine discharge instructions and nursing follow-ups, whereas the intervention group additionally received an 8-week digital psychological resilience intervention. Outcomes were assessed at baseline (T0), 3 months post intervention (T1), and 6 months post intervention (T2). The measures included the Connor-Davidson Resilience Scale (CD-RISC), Hospital Anxiety and Depression Scale (HADS), Social Support Rating Scale (SSRS), Breast Cancer Survivor Self-Efficacy Scale (BCSSS), and Functional Assessment of Cancer Therapy-Breast (FACT-B). Independent-samples tests, chi-square tests, and repeated-measures ANOVA were performed using SPSS (version 26.0; IBM Corp). Results: No statistically significant baseline differences were observed between the two groups in the outcome measures. At T1, the intervention group had higher CD-RISC scores than the control group (mean 67.58, SD 11.41 vs mean 62.09, SD 10.18; =.036) and higher BCSSS scores (mean 42.36, SD 3.59 vs mean 39.23, SD 4.90; =.003). However, these between-group differences were no longer statistically significant at T2 (>.05). Significant time effects and group×time interaction effects were observed for both psychological resilience and self-efficacy (.05), although both scales showed significant time effects (