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PubMed2026

Community-driven advances in computational mass spectrometry: The perspective of EuBIC-MS members.

Advances in data acquisition, artificial intelligence, and integrative bioinformatics are driving the rapid evolution of computational mass spectrometry, and in turn, transforming modern proteomics, metabolomics, and lipidomics. These developments have greatly increased the scale and complexity of mass spectrometry data, underscoring the importance of evolving accurate, transparent, efficient and reproducible data processing workflows. Addressing these challenges requires collaborative innovation that brings together expertise in software engineering, statistics, and biology. The European Bioinformatics Community for Mass Spectrometry (EuBIC-MS), an initiative of the European Proteomics Association (EuPA), fosters a culture of open, community-driven development through its biennial Developers Meetings and Winter Schools. This commentary summarizes the scientific background and outcomes of the EuBIC-MS Developers Meeting 2025, which took place in Novacella, Italy. Three keynote presentations highlighted major frontiers in the field: deep proteome and phosphoproteome profiling, text mining for protein-protein interaction extraction, and scalable proteomics for AI-driven drug discovery. Seven community-selected hackathons addressed emerging challenges such as single-cell proteomics data analysis, FAIR metadata extraction, deep learning frameworks, R-Python interoperability, and DIA validation. Together, these efforts demonstrate the potential for scientific and technical innovation to arise from open collaboration, and highlight how community-driven initiatives can accelerate progress in computational mass spectrometry. SIGNIFICANCE: Modern proteomics increasingly depends on computational advances to translate complex, high-dimensional data into biological knowledge. The EuBIC-MS Developers Meeting 2025 exemplifies how community-driven collaboration can directly accelerate this process by bringing together experts from bioinformatics, statistics, and experimental proteomics to co-develop open, interoperable, and reproducible analytical tools. By fostering shared software frameworks, transparent benchmarking, and collaborative problem solving, the EuBIC-MS community helps ensure that technological innovation translates into reliable biological insights. This collaborative model strengthens the foundation for quantitative, system-level understanding of proteomes and establishes a sustainable path for integrating artificial intelligence and next-generation data acquisition into routine biological discovery. This commentary shows some current highlights in the field of computational mass spectrometry and community-based approaches undertaken during the most recent Developers Meeting to solve these challenges. The approaches discussed and initiated during the meeting - ranging from deep proteome profiling and phosphosite mapping to text mining, single-cell data analysis, and FAIR metadata extraction - address key bottlenecks that currently limit the biological interpretability and comparability of proteomics data.

Mass Spectrometry↗
PubMed2026

Adolescents' Growing Sensitivity to Psychosocial Stressors: Evidence From Two Decades of Health Behaviour in School-aged Children (HBSC) Data in the Nordic Countries.

PURPOSE: We tested a perception-based explanation for rising psychosomatic complaints among adolescents in the Nordic countries. Specifically, we examined whether adolescents have become more sensitive to psychosocial stressors, reflected in stronger associations between stressors and psychosomatic complaints in 2022 than in 2002. METHODS: Data were drawn from the 2002 to 2022 waves of the Health Behaviour in School-aged Children survey among 15-year-olds in five Nordic countries (N = 7,263 in 2002 and 6,739 in 2022). Psychosomatic complaints were examined in relation to stressors in the following three domains: interpersonal relationships, school-related strain, and body- and activity-related factors. Moderated regression analyses tested whether associations between stressors and complaints differed between survey years. Sex and perceived family finances were included as covariates. RESULTS: Four of five psychosocial stressors showed stronger associations with psychosomatic complaints in 2022 than in 2002. Perceived poor family finances, although less prevalent in 2022, were more strongly related to complaints. Girls consistently reported higher levels of psychosomatic complaints, and the association between school pressure and complaints was stronger among girls. DISCUSSION: Although several psychosocial stressors declined in prevalence, their associations with psychosomatic complaints strengthened over time. These findings suggest that rising complaints may reflect changes in how psychosocial stressors are linked to adolescents' psychosomatic symptoms, rather than increases in exposure to those stressors. This shift highlights the importance of considering how adolescents interpret and respond to everyday stress when addressing population trends in mental health.

Humans↗
PubMed2026

Transvalvular Flow Rate is Associated With Mortality Rate and Lifetime Loss in Aortic Valve Stenosis: A Meta-Analysis of Reconstructed Time-to-Event Data.

Low-flow states are associated with adverse outcomes in aortic stenosis (AS), but the prognostic value of transvalvular flow rate (TFR) has not been consistently established across studies. This study is a systematic review and meta-analysis of reconstructed time-to-event data was performed in accordance with Preferred Reporting Items for Systematic Reviews and Meta-analyses. PubMed/MEDLINE, EMBASE, and Cochrane Library were searched for studies (published by November 14, 2025) comparing low versus normal TFR in AS. Data were collected from Kaplan-Meier curves. The primary endpoint was all-cause mortality. Survival was assessed using pooled Kaplan-Meier curves, Cox regression, flexible parametric survival models, and restricted mean survival time (RMST) analysis. A total of 9 studies including 6,494 patients were analyzed; 2,575 (39.7%) had low TFR. At 8 years of follow-up, estimated survival was 34.1% (95% confidence interval [CI] 24.7% to 47%) in the low-TFR group and 63% (95% CI 58.9% to 67.4%) in the normal-TFR group. Low TFR was associated with higher all-cause mortality (hazard ratio 1.59, 95% CI 1.45 to 1.74, p < 0.001). We observed a progressively greater hazard over time, with the hazard ratio approaching 1.9 by 8 years. At 8 years, RMST in the normal-TFR group was 7.37 years (95% CI 7.21 to 7.53 years) versus 5.07 years (95% CI 4.91 to 5.23 years) in the low-TFR group, representing a lifetime loss of 2.3 years in the low-TFR group (&#x394;RMST -2.30 years, 95% CI -2.53 to -2.07 years, p < 0.001). In patients with AS, low TFR is associated with significantly higher mortality and lifetime loss. These findings support TFR as a clinically meaningful marker for risk stratification in AS.

Aortic Valve Stenosis↗
PubMed2026

The Childhood Cancer and Leukemia International Consortium (CLIC): Expanding global collaboration in pediatric cancer etiology research.

Childhood cancers are rare, but incidence has risen modestly in countries with robust registration, partly reflecting improved diagnosis. In high-income countries, cancer is the leading cause of disease-related death in children. Marked inequities in incidence, survival, and research capacity underscore the need for large-scale collaboration to identify environmental, genetic, and contextual determinants of risk. The Childhood Cancer and Leukemia International Consortium (CLIC) was established in 2007 to study the etiology of childhood leukemia and later expanded in 2019 to include other childhood cancers, principally solid tumors. CLIC pools harmonized, individual-level data from case-control and cohort studies, obtained through interviews, record linkage (insurance claims, registries), or geographic information systems, and integrates germline genomic data where available. Membership has grown from 13 studies in 9 countries to 57 studies in 21 countries; recruitment spans the early 1960s to the present and encompasses approximately 150,000 cases across all tumor types and 300,000 controls with clinical, demographic, and exposure data, centralized via harmonized data dictionaries at the Data Coordination Center, established in 2014 at the International Agency for Research on Cancer, and supported by a secure analysis platform. Pooled analyses across diverse populations have implicated parental age, prenatal vitamin or folic acid use, mode of delivery, fetal growth, selected congenital anomalies, occupational or household exposures (e.g., pesticides), paternal smoking, and markers of early-life immune modulation (e.g., breastfeeding, daycare attendance) in leukemia risk, informing carcinogen evaluation and prevention. The integration of genetic ancestry and germline susceptibility data is clarifying ancestry-related differences in leukemia biology and outcomes, while confirming risk loci with population-specific effects. CLIC is now adding polygenic risk scores and exposomic data to refine etiologic subtyping and identify modifiable pathways, while broadening representation from underserved regions through partnership-building and capacity-strengthening.

Humans↗
PubMed2026

Predicting training outcomes for developmental dyslexia from EEG data.

Developmental dyslexia (DD) is characterised by lower-than-average reading abilities and is diagnosed in approximately 10% of individuals. The societal barriers may limit professional fulfilment and psychological wellbeing of individuals with DD, calling for the development of effective interventions to counteract them. As DD is associated with challenges in both phonological and visuo-attentional domains, different longitudinal training approaches were developed to strengthen them. However, they require a considerable amount of personal, social and economic resources and the outcomes may vary depending on individual differences in behavioural and neurophysiological functionality. Hence, predicting training outcomes might help in developing personalised treatment protocols and optimising the use of resources. In the present work we applied machine learning to resting-state EEG to predict longitudinal training outcomes in adults with DD enrolled in a randomized clinical trial. In particular, one group received a visuo-attentional training combined with transcranial alternating current stimulation (tACS), another group received visuo-attentional training with sham/placebo stimulation, and the third group received a phonological training with sham/placebo stimulation. The improvement in text reading speed was associated with spectral power in low-beta and individual frequencies in the alpha (IAF) and beta (IBF) bands, while the improvement in pseudoword reading was associated with IBF. The findings highlight the potential of capturing neural markers of treatment responsiveness in DD. Future studies should focus on the generalisability of predictive models to real-world settings, while investigating whether specific EEG markers predict responsiveness to distinct remediation protocols, thus supporting the development of personalised interventions.

Humans↗
PubMed2026

Identifying biomarkers of accelerated ageing in cancer patients from routine clinical data.

INTRODUCTION: Cancer and ageing have a bidirectional relationship: age is the strongest risk factor for cancer, and cancer and treatments can accelerate ageing. Therefore, biological age can differ from chronological age; biomarkers are needed to stratify interventions to minimise accelerated ageing. METHODS: PhenoAge was calculated from routine blood test results of patients attending a Geriatric Oncology clinic. PhenoAgeAccel was the residual from a regression of PhenoAge against age. RESULTS: Data were available for 173 patients (62% male). Mean PhenoAge was higher than age (84.3 (12.6) vs 76.2 (7.24), p&#x202f;<&#x202f;0.001), though the two were correlated (r&#x202f;=&#x202f;0.579, p&#x202f;<&#x202f;0.001). Unlike age, PhenoAge and PhenoAgeAccel were associated with one-year mortality (PhenoAge OR=1.083, 95% CI: 1.038-1.136; PhenoAgeAccel OR=1.096, 95% CI: 1.047-1.155). PhenoAge correlated with Clinical Frailty Score and Timed Up and Go (CFS: Rs=0.31, p&#x202f;<&#x202f;0.001; TUG: Rs=0.25, p&#x202f;<&#x202f;0.005); there were no correlations with age. PhenoAgeAccel correlated with the number of CGA interventions made (Rs=0.17, p&#x202f;<&#x202f;0.05), unlike age and PhenoAge. Patients with diabetes mellitus had a higher PhenoAgeAccel compared to those without (3.40 vs -1.71, p&#x202f;=&#x202f;0.002). In patients receiving systemic anti-cancer treatment, patients with PhenoAgeAccel calculated pre-treatment had less age acceleration than those with PhenoAgeAccel calculated post-treatment, both overall (2.18 vs -2.87; p&#x202f;=&#x202f;0.048) and in matched samples (n&#x202f;=&#x202f;21, 7.76 vs -2.87, p&#x202f;<&#x202f;0.001). CONCLUSIONS: PhenoAgeAccel is a greater predictor of risk than chronological age in older people with cancer. This makes it a promising biomarker to stratify patients for holistic geriatric assessment, dose reductions, or future geroprotective measures which could be integrated within electronic healthcare record systems.

Humans↗
PubMed2026

Novelty seeking and rapid symptom improvement across active and sham accelerated iTBS conditions: A pooled individual-patient data analysis.

INTRODUCTION: Major depressive disorder (MDD) is highly prevalent and often treatment-resistant. Accelerated intermittent theta burst stimulation (aiTBS) is a promising intervention for treatment-resistant depression (TRD), though outcomes vary. Personality traits have been examined in relation to rTMS outcomes, yet their role in aiTBS remains underexplored. This pooled individual-patient-data analysis of two randomized, sham-controlled trials examined associations between baseline Temperament and Character Inventory (TCI) traits and one-week symptom change, and whether they differed by condition. METHODS: The left dorsolateral prefrontal cortex was targeted for 20 sessions over 4&#xa0;days. Personality was assessed with the TCI, depression severity with the 17-item Hamilton Depression Rating Scale (HDRS-17). TCI-symptom-change associations were examined with a robust linear mixed-effects model, adjusting for age, gender, repeated measurements, and study membership. RESULTS: 104 participants were included (M/F 45/59; mean age 40.9&#xa0;&#xb1;&#xa0;12.7; active/sham 50/54). The model yielded a Time &#xd7; Novelty Seeking interaction (&#x3b2;&#xa0;=&#xa0;-1.70, p&#xa0;=&#xa0;0.021): higher baseline Novelty Seeking was associated with faster symptom reduction, without a between-arm difference. However, the interaction did not survive Holm correction across 14 trait-interaction tests (adjusted p&#xa0;=&#xa0;0.294) and is therefore exploratory. No other interaction reached the uncorrected threshold. CONCLUSIONS: Higher baseline Novelty Seeking showed a nominal association with faster symptom reduction, without a difference between active and sham conditions. Because it did not survive multiplicity correction and was not reproduced in within-arm analyses, it is preliminary and may reflect contextual or nonspecific processes. Independent replication is required before temperament assessment can be clinically informative.

Humans↗
PubMed2026

Mortality of Individuals With PRNP Variants Associated With Prion Disease in the United States, 1998-2024.

BACKGROUND AND OBJECTIVES: To characterize the survival of individuals with pathogenic PRNP variants-including to estimate annual hazards, to judge the accuracy of previously reported survival data, and to evaluate the utility of public record searches in determining vital status. METHODS: In this single-center cohort study, we gathered data on individuals who received positive antemortem PRNP genetic tests at the US National Prion Disease Pathology Surveillance Center (NPDPSC), including both diagnostic tests in symptomatic individuals, and predictive tests in asymptomatic individuals. Genetic test and autopsy results were queried from the NPDPSC database, and public record searches were conducted using online tools. RESULTS: Four hundred four individuals received positive genetic test results. Of 206 cases symptomatic at the time of genetic testing, 188 are likely now deceased based on typical disease duration for their genetic variants. Combined autopsy and public record searches in combination confirmed 174 of these deaths, for an estimated 92.6% sensitivity. We evaluated the age-dependent penetrance of the reportedly highly penetrance variants D178N and E200K and the reportedly low-penetrance variant V210I. Among 99 initially asymptomatic individuals with the pathogenic E200K variant, more than 936 person-years of follow-up, 18 deaths were observed, significantly fewer than 27.4 expected according to life tables based on retrospective data. The age-dependent penetrance of E200K calculated from these longitudinal data was significantly lower than that from retrospective data, with 69% penetrance by age 80 and a median age at death of 75. For the pathogenic D178N variant, the median age at death was 57, which was numerically later, but not significantly different from, that seen in retrospective data. For V210I, just 2 deaths occurred, both after age 90, consistent with minimal penetrance. DISCUSSION: Our data support high penetrance of PRNP D178N and E200K variants and low penetrance of V210I. For E200K, the age at onset distribution appears to be shifted slightly later, and lifetime risk slightly lower, than previously reported. Autopsy data and public death records in combination were sensitive and concordant for determining long-term outcomes, but additional prospective data should be gathered to support future preventive trials.

Journal Article↗
PubMed2026

Menopause in the All of Us Research Program: a descriptive summary of electronic health record and survey response across sociodemographic characteristics.

OBJECTIVES: Menopause is a significant physiological transition with implications for health outcomes (eg, cardiometabolic disease), yet gaps remain in understanding this transition, including how menopause timing and type influence health outcomes. Large-scale cohort studies in midlife (age=40-60) females, including the All of Us Research Program (AoURP), provide opportunities to study menopause across diverse populations and data modalities. We characterized menopause-related data in AoURP, focusing on age distributions and concordance between electronic health record (EHR) diagnosis codes and survey responses. METHODS: We analyzed menopause-related surveys, EHR diagnostic codes, and genomic data among ~396,000 AoURP female participants. We summarized menopause-related variables across data sources, evaluated overlap between survey, EHR, and genomic data sets, and described age distributions overall and across sociodemographic characteristics. RESULTS: Among ~396,000 females, survey responses captured ~193,000 menopause observations, nearly seven times more than EHR diagnoses (~28,000), suggesting under-ascertainment in EHR data. Nearly all females (~99%) with an EHR menopause diagnosis reported menopause in the survey. Approximately 22,000 participants had overlapping menopause-related EHR, survey, and genomic data. Survey age patterns matched expectations, with participants predominantly <40 years reporting premenopausal status and those >60 years reporting postmenopausal status. A small subset with age >70 years (N&#x2248;1,700; 4%) reported no menopause, suggesting response or recall bias. EHR menopause codes were concentrated after age 45 years, with a notable spike at age 65. Modest differences in survey-based menopause age distributions were observed across sociodemographic characteristics (eg, race and ancestry). CONCLUSIONS: These findings inform sampling strategies, power calculations, phenotype definition, and study design for menopause research using AoURP data.

Age↗
PubMed2026

Operationalizing Local Ecological Knowledge for Aquatic Biodiversity Conservation: A Systematic Review and Management Framework.

Effective conservation and management of aquatic biodiversity is severely constrained by the absence of long-term ecological data in small-scale, tropical, and data-poor fisheries, where roughly one-quarter to one-third of freshwater fish species and 37.5% of elasmobranchs are threatened with extinction once Data Deficient species are accounted for. Conventional monitoring and stock-assessment tools are often financially and technically inaccessible in these systems, leaving managers without the evidence needed to prioritize conservation action or implement precautionary governance. Local Ecological Knowledge (LEK) is a largely underutilized resource for natural resource management that can provide temporal depth, spatial resolution, and species-specific ecological insights unavailable from scientific records. We conducted a systematic review and bibliometric synthesis of 60 peer-reviewed studies (1997-2025) applying LEK to assess fish conservation status, examining how, where, and through what methods this knowledge has been used. Our analysis identifies four complementary pathways through which LEK informs conservation management: reconstructing multi-decadal population changes, documenting spatial contraction and habitat loss, detecting extreme rarity and local extirpation, and characterizing intrinsic sensitivity to exploitation based on life-history traits. Despite growing methodological rigor, freshwater systems and African fisheries remain critically underrepresented, and formal integration of LEK into fisheries governance and biodiversity assessment remains the exception rather than the rule. We propose a practical three-stage framework to operationalize LEK within existing management and conservation systems. Recognizing fishing communities as legitimate co-producers of ecological knowledge is both scientifically necessary and an equity imperative for achieving global biodiversity commitments under the Kunming-Montreal Global Biodiversity Framework.

Biodiversity↗
PubMed2026

Artificial Intelligence Technologies in Nursing Clinical Decision-Making: An Umbrella Review.

AIM: To describe contemporary peer-reviewed literature on artificial intelligence in nurses' clinical decision-making. METHODS: An umbrella review of literature reviews. DATA SOURCES: Four major databases were searched for reviews published between 2019 and 2024. RESULTS: Sixteen literature reviews reported on 965 nursing artificial intelligence primary studies. The studies focused on technology development and emerging performance evaluations, whilst real-world testing or implementation in nursing clinical settings was rare. Rigorous comparative analyses were lacking. While artificial intelligence demonstrates promise in decision-making, challenges such as a lack of controlled studies, algorithmic bias, limited reproducibility and insufficient clinical trials hinder its practical impact. Ethical concerns, transparency and patient data privacy issues pose barriers to AI integration in nursing practice. Ethical and legal guidelines for patient privacy are needed and should be taught along with AI literacy training for nurses. CONCLUSIONS: Artificial intelligence has the potential to enhance clinical nursing decision-making, although evidence is limited by too few examples of nurse participation during development. Underutilisation in administrative nursing functions hinders implementation. Nurses should assume a central role in the design and development of AI applications to ensure that these technologies address the realities of nursing practice. With such improvements, artificial intelligence can transform nursing practice, improve nurses' clinical decision-making and ultimately enhance consumer healthcare outcomes. PATIENT OR PUBLIC INVOLVEMENT: No Patient or Public Involvement. REPORTING METHOD: While there is no reporting checklist for umbrella reviews, the PRISMA guide for systematic reviews was followed.

Artificial Intelligence↗
PubMed2026

Making waves: toward systems-level interpretation of hormonal and endogenous biomarkers in wastewater-based epidemiology.

Wastewater-based epidemiology (WBE) has proven invaluable for population health monitoring, most notably during the COVID-19 pandemic. Yet current WBE largely relies on exogenous markers such as drugs, pathogens, and their metabolites, limiting surveillance to what communities are exposed to. We argue for expanding WBE towards endogenous biomarkers, particularly hormones, which provide insights into physiological stress, metabolic function, and endocrine activity. Hormone-based WBE offers new opportunities to capture population-level biological responses to societal and environmental stressors, disasters, and chronic disease burdens at the community scale. This perspective outlines a systems-level framework for integrating hormonal signals in wastewater with clinical data, behavioral indicators, environmental factors, and digital markers to support more robust and context-aware public health surveillance. We highlight key technical considerations, interpretive challenges, and opportunities for translational pilot studies. By moving beyond exposure tracking toward more integrated interpretation of biological responses, hormone-informed WBE may contribute to more resilient, inclusive, and actionable public health infrastructure.

Humans↗
PubMed2026

Pedagogical Efficacy of LLM-Generated Synthetic Data Versus Real-World Clinical Records: A Randomized Controlled Non-Inferiority Trial.

BACKGROUND: Expert-reviewed clinical cases generated by large language models (LLMs) may supplement case resources in medical education, but their short-term educational performance relative to real-case-derived teaching materials remains uncertain. We compared immediate post-training test performance after teaching with the two types of case materials and assessed non-inferiority against a prespecified margin. METHODS: We conducted a prospective, parallel-group, randomized non-inferiority trial. Through the Wenjuanxing online platform, participants were randomized 1:1 to learn with either real-case-derived teaching cases compiled by clinicians and reviewed by experts or AI-generated clinical cases produced by Gemini 3.0 Pro from fully de-identified matched real cases and reviewed by three senior general surgery specialists with full-professor rank. The primary outcome was the total score on an independent 10-item immediate post-training test (0-10 points), with a prespecified non-inferiority margin of -0.5 points. Secondary outcomes included the training-phase performance score, learning efficiency index, single-item mental effort rating, case realism, and case-source judgment. RESULTS: A total of 403 participants were randomized, of whom 386 were included in the modified intention-to-treat analysis: 192 in the real-case group and 194 in the AI-generated case group. The mean post-training test score was 4.95 (SD, 3.35) in the real-case group and 4.61 (SD, 3.35) in the AI-generated case group. The mean difference (AI-generated minus real-case group) was -0.335 points (95% CI, -1.006 to 0.337). Because the lower bound of the confidence interval was below the prespecified non-inferiority margin of -0.5 points, non-inferiority was not demonstrated (one-sided P = 0.314). No significant between-group differences were observed in the training-phase performance score, learning efficiency index, or single-item mental effort rating. AI-generated cases received lower realism ratings for Level 3 cases. The proportion of participants with at least one high-confidence completely incorrect response was 1.6% in the real-case group and 2.1% in the AI-generated case group. CONCLUSIONS: In this short-term, text-based online case-learning setting, no statistically significant between-group difference was observed in immediate post-training test performance; however, non-inferiority of AI-generated clinical cases relative to real-case-derived teaching materials was not demonstrated.

Humans↗
PubMed2026

The application of artificial intelligence in healthcare practice: A mapping review of systematic reviews.

Artificial intelligence (AI) is rapidly transforming healthcare practice, with growing evidence supporting its use in diagnosis, prognosis, treatment planning, and operational decision-making. The proliferation of systematic reviews in recent years underscores the need for an updated synthesis of the literature to inform research, policy, and practice. We searched PubMed, Web of Science, Scopus, IEEE Xplore, and CINAHL for systematic reviews and meta-analyses published between 2019 and February 2026. Eligible reviews focused on AI applications in healthcare practice, were peer-reviewed, and written in English. A total of 368 reviews met the inclusion criteria. Publication volume increased steadily, peaking in 2025. AI research was concentrated in high-density domains, such as radiology, oncology, and critical care. Across reviews, diagnostic imaging, electronic health record (EHR) data, and biomarkers/laboratory results accounted for 68% of training data sources, though newer data types, such as wearable device and sensor data, emerged from 2022 onward. Diagnosis, prognosis, and treatment comprised over 80% of AI applications, with novel uses emerging in recent years, such as AI-assisted clinical documentation (e.g., ambient documentation tools) and patient education. Ethical concerns were reported in 78.5% of reviews, with privacy, model accuracy, data and algorithmic bias, and explainability as recurrent themes. The proportion of reviews reporting ethical concerns increased from 2021 to 2025. AI applications in healthcare are expanding in scope, diversifying in data sources, and evolving toward novel clinical and operational uses. The human-centered AI or augmented intelligence paradigm, integrating computational precision with clinical expertise, holds significant promise but will require parallel advances in governance, regulatory frameworks, and ethical oversight to ensure safe adoption.

Artificial Intelligence↗
PubMed2026

Temporal Trends and Spatial Variation in Preterm Prelabour Rupture of Membranes: A Population-Based Study.

OBJECTIVE: To describe the temporal trends in Preterm prelabour rupture of membranes (PPROM) in metropolitan France and the geographical distribution at the administrative division level. DESIGN: Exploratory population-based study using administrative data of the French National Health Data System. SETTING: Metropolitan France, 2015 to 2023. POPULATION: Pregnancy with a diagnosis of PROM before 37 SA. METHODS: Annual crude incidence of PPROM was calculated by dividing the number of pregnancies with PPROM diagnosis by the number of live births recorded during the same period. Annual trend was estimated by a binomial negative mixed model. Smoothed standardised incidence ratios were estimated based on a BYM2 model, which accounts for spatial variability between departments. MAIN OUTCOME: PPROM cases, defined as pregnancies with first hospitalizations with a diagnosis of PROM before 37&#x2009;weeks. RESULTS: Over the study period, we included 150&#x2009;615 PPROM cases representing 16&#x2009;735 (&#xb1;596) per year. Incidence of PPROM cases showed an ascending trend over time (incidence rate ratio 1.023 per year; 95% CI: 1.017-1.030) with an annual crude incidence ranging from 2.2% in 2015 to 2.7% in 2023. A decrease in the incidence was observed in 2020 relative to other years (incidence rate ratio 0.903, 95% CI: 0.887-0.920). A map of smoothed SIRs of PPROM cases at the French administrative division level revealed geographical inequalities. CONCLUSIONS: This first population-based study describing PPROM cases in metropolitan France paves the way for further studies to explore environmental hypotheses. Identifying temporal and geographical disparities in PPROM incidence is relevant to public health policy and practice as such disparities argue for the development of targeted prevention strategies in high-risk areas.

French national health data system↗
PubMed2026

Cancer statistics for Asian American, Native Hawaiian, and Pacific Islander people, 2026.

BACKGROUND: Cancer statistics for Asian American and Native Hawaiian and Pacific Islander (NHPI) people are usually aggregated, masking substantial variation within this heterogeneous population. Herein, the American Cancer Society reports cancer incidence and survival for 8 Asian American and 3 NHPI ethnic groups. METHODS: The authors used population-based cancer registry data from the National Cancer Institute's Surveillance, Epidemiology, and End Results program, for Asian American and NHPI ethnic groups from 2000 through 2022. RESULTS: During 2018-2022, overall cancer incidence ranged from 218.3 per 100,000 Kampuchean people to 474.5 per 100,000 Native Hawaiian people, which was 1.5 times higher than the rate for the aggregated Asian American and NHPI population (307.3 per 100,000). High incidence among Native Hawaiian people is largely driven by the highest rates of female breast, colorectal, and prostate cancers, whereas infection-related cancers were highest among Asian American ethnic groups. For example, liver and stomach cancer incidence is highest among Vietnamese (22.2 per 100,000) and Korean people (17.8 per 100,000), respectively, both of which were nearly twice that in Native Hawaiian people (12.9 and 9.6 per 100,000, respectively). Native Hawaiian and Samoan women are twice and 3 times as likely, respectively, to be diagnosed with uterine corpus cancer as aggregated Asian American and NHPI women or White women. Five-year relative survival ranges from 42% in Laotians to 74% in Asian Indians/Pakistanis, with largest differences for colorectal (43% in Laotians to 72% in Asian Indians/Pakistanis) and prostate (63% in Kampucheans to 97% in Japanese) cancers. CONCLUSIONS: Wide variation in cancer risk within the Asian American and NHPI population highlights the critical need for disaggregated data to effectively target cancer prevention and control interventions.

Adolescent↗
PubMed2026

Discovery and characterization of multifunctional bioactive peptides from Alaska Pollock (Gadus chalcogrammus) milt: hybrid in silico, in vitro, and proteomic approaches.

The growing demand for multifunctional bioactive peptides has sparked interest in underutilized marine by-products as sustainable bioresources. This study explored Alaska Pollock (Gadus chalcogrammus) milt protein as a novel source of peptides with anti-inflammatory, anti-hypertensive, and anti-diabetic effects. Protein composition was analyzed via LC-MS, followed by in silico digestion and bioactivity prediction. Molecular docking identified peptides targeting DPP-IV, &#x3b1;-glucosidase, ACE, GLP-1 receptor, COX-2, MuRF1, and the 20S proteasome. Among the candidates, a promising peptide (CLPPH) was synthesized and validated in vitro, demonstrating inhibitory effects on nitric oxide production, DPP-IV, ACE, and &#x3b1;-glucosidase. These results highlight CLPPH's potential as a multifunctional bioactive peptide and support the valorization of Alaska Pollock milt as a sustainable source for functional foods and nutraceutical applications.

Animals↗
PubMed2026

Family-Wise Error Rate Control in Clinical Trials With Overlapping Populations.

We consider clinical trials with multiple, overlapping patient populations that test multiple treatment policies specifically tailored to these populations. Such designs may lead to multiplicity issues, as false statements will affect several populations. For type I error control, often the family-wise error rate (FWER) is controlled, which is the probability to reject at least one true null hypothesis. If the joint distribution of the test statistics is known, the FWER level can be exhausted by determining critical values or adjusted-levels. The adjustment is typically done under the common ANOVA assumptions. However, the performed tests are then only valid under the rather strong assumption of homogeneous null effects, that is, when the null hypothesis applies to all subpopulations and their intersections. We show that under cancelling null effects, when heterogeneous effects cancel out in some or all subpopulations, this procedure does not provide FWER control. We also suggest different alternatives and compare them in terms of FWER control and their power.

Humans↗