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PubMed2026

Spinal meningiomas: histopathological grading using a benchmark radiomics model with notes on disease control.

OBJECTIVE: Spinal meningiomas (SMs) are common primary spinal tumors for which surgery is considered the first-line treatment when safe and feasible. The ability to extrapolate the tumor grade from preoperative imaging may significantly inform early patient expectation-setting regarding recurrence. Building on radiomics studies in cranial meningiomas, the authors aimed to construct a benchmark radiomics model to preoperatively identify the histological grade of SMs. METHODS: Institutional surgical records from May 2012 to November 2025 were queried for pathology-confirmed meningiomas below the foramen magnum, with preoperative contrast-enhanced imaging available for segmentation. SMs were classified as low-grade (WHO grade 1) and high-grade (WHO grade 2 tumors and grade 1 tumors with atypia). Tumors were manually segmented, and features were extracted using the PyRadiomics software package. An ensemble model of k-nearest neighbors, random forest, and support vector machine classifiers was trained using nested cross-validation on a subset of 10 features to differentiate tumor grades. Clinical data for the cohort were also extracted, and disease control in an adjunctive clinical series was assessed. RESULTS: Seventy-four patients were included in radiomics analysis, with an area under the receiver operating characteristic curve of 0.879 and a mean F1 score of 0.748. The model's top 5 features were all texture features that differed significantly (p < 0.05) across low- and high-grade SMs. These included measures of tumor textural and contrast-enhancement heterogeneity, with overlap with features reported in radiomics models for histological grading of intracranial meningiomas. Fifty-five patients with a median radiographic follow-up of 22.2 (range 1.9-86.4) months remained for clinical analysis after exclusion of patients with less than 1 month of follow-up and syndromic meningiomas. Four recurrences occurred at a median of 20.8 (range 1.8-41.8) months. High-grade tumor pathology did not significantly impact progression-free survival (p = 0.682, log-rank test; Cox regression high vs low grade hazard ratio [HR] 0.62, 95% CI 0.06-6.11, p = 0.685). Subtotal resection was associated with poorer progression-free survival than gross-total resection (p = 0.004, log-rank test; Cox regression subtotal vs gross-total resection HR 10.62, 95% CI 1.46-77.05, p = 0.019). These findings remain contextualized within a relatively limited follow-up window and small recurrence event count, suggesting a need to characterize the interplay between tumor grade and extent of resection as drivers of local disease control in SMs. CONCLUSIONS: A preoperative radiomics model can stratify high-grade SMs using open-source tools applied to single-institution data.

Humans↗

PubMed2026

Current Diagnostic Pathways for Rheumatoid Arthritis-Associated Interstitial Lung Disease Result in Substantial Underdiagnosis and Excess Mortality: A Multicenter Norwegian Quality Assurance Audit.

OBJECTIVE: Recent guidelines suggest risk-stratified screening for rheumatoid arthritis-associated interstitial lung disease (RA-ILD). However, the diagnostic gap between current routine care and this screening approach remains unquantified. We assessed currently detected RA-ILD in Norway, benchmarking findings against recent screening-based estimates of the true disease burden. METHODS: This 10-year quality assurance audit across six centers covered 43% of the Norwegian population. RA-ILD cases identified via ICD-10 codes were confirmed by manual chart review. Prevalence was calculated relative to a registry-derived total RA background population and benchmarked against a 10% expected target derived from recent prospective studies. Mortality was compared to a 3:1 frequency-matched RA control group using Cox proportional hazards regression. RESULTS: Among 17,305 RA patients, 188 (1.1%) had verified ILD; when benchmarked against an expected 10% prevalence, this indicates an 89% diagnostic gap in routine clinical care. Mean age at ILD detection was 67.5 years. Most cases (93.6%) possessed ≥2 established risk factors for RA-ILD: 93.6% were seropositive, 76.1% had smoking histories, while RA onset age ≥60 and persistently increased inflammatory laboratory markers were present in over half of patients. RA-ILD was associated with significantly increased mortality; 66 (4.1/100 person-years) deaths occurred in the RA-ILD group vs. 120 (2.3/100 person-years) among RA controls (HR 1.77; 95% CI: 1.31-2.39, p<0.001). CONCLUSION: When comparing to prevalence expectations, current routine care may leave a substantial proportion of cases undetected, primarily capturing a high-risk phenotype with excess mortality. Systematic, risk-stratified screening is needed to bridge this diagnostic gap, aiming to enable earlier intervention.

Interstitial lung disease↗

PubMed2026

Reinforcement learning-based dynamic ensemble for missense variant effect prediction and tiered prioritization of VUS.

BACKGROUND: Accurate classification of missense variants remains a challenging task despite major advances in genomics. Numerous computational models have been developed to assist in variant classification, but often require repeated integration and benchmarking efforts. Ensemble methods have been proposed to overcome the limitations of single predictors, but mostly rely on fixed, predefined weights that constrain their ability to capture interactions among predictive signals. METHODS: We present GenixRL, a dynamic ensemble framework that reformulates model fusion as a reinforcement learning optimization problem. GenixRL uses a Q-learning agent to learn a policy that dynamically weights the probabilistic outputs of complementary predictors, including BayesDel (addAF and noAF), ClinPred, and MetaRNN. Replacing static weighting with policy learning allows GenixRL to adaptively identify optimal weightings and substantially improve classification accuracy. RESULTS: In benchmark evaluation against 25 state-of-the-art predictors, GenixRL achieved an AUROC of 0.9644 on an independent ClinVar dataset. On saturation genome editing assays for BRCA1 and BRCA2, GenixRL achieved the best performance and ranked highest on 14 of 17 clinically significant genes in a zero-shot evaluation. Applied to uncertain and conflicting ClinVar variants, GenixRL enabled tiered, evidence-based prioritization of hundreds of thousands of variants as likely pathogenic or pathogenic with high confidence, supported by orthogonal population evidence from gnomAD. CONCLUSION: GenixRL advances pathogenicity prediction for missense variants and provides an adaptive ensemble that sorts variants of uncertain significance into tiered candidates for expert curation and functional validation.

Mutation, Missense↗

PubMed2026

Assessing perinatal depression identifying abilities among maternal and child health workers in rural China using smartphone-based virtual patients: a multi-center cross-sectional study.

OBJECTIVE: To assess rural maternal and child health (MCH) workers' virtual patients (VPs)-assessed performance in identifying perinatal depression (PND) using smartphone-based VPs, and to identify factors associated with this performance in rural Hunan, China. METHODS: A multicentre cross-sectional study was conducted in Hunan Province, China. A standardized questionnaire collected demographic and work-related characteristics of rural MCH workers. Smartphone-based VPs were used to assess PND identification performance in a simulated clinical scenario. An overall score ≥60 was used as a prespecified operational benchmark across consultation, ancillary assessment, diagnosis, management, and health education domains. Data were analyzed using SPSS 26.0. RESULTS: A total of 375 rural MCH workers participated, yielding an effective response rate of 90.4%. Only 25.9% met the prespecified operational benchmark for VP-assessed PND identification performance. The mean accuracy scores for consultation, ancillary assessment, diagnosis, management, and health education were 94%, 48%, 64%, 58%, and 74%, respectively. Complete consultation accuracy was higher among MCH workers from township health centers than among those from county-level MCH hospitals. MCH workers aged 18-39 years showed higher odds of complete diagnostic accuracy for PND than those aged ≥40 years. CONCLUSIONS: Smartphone-based VP assessment was feasible in rural MCH settings and revealed suboptimal PND identification performance. Mobile VPs may help identify frontline performance gaps and inform targeted training, but further validation against real-world clinical performance, or standardized patient encounters is needed before large-scale implementation. These findings may support targeted capacity-building for rural MCH workers and more equitable perinatal mental health care.

Humans↗

PubMed2026

Meta-PseU: A meta-classifier for robust prediction of RNA pseudouridine modification sites from long sequences.

BACKGROUND AND OBJECTIVES: Pseudouridine (Ψ) represents one of the most abundant and conserved RNA modifications. Ψ provides an additional hydrogen-bond donor that enhances RNA structural stability and modulates translation. It participates in diverse biological processes, including RNA-protein interactions, splicing, translational control, and stress responses. Aberrant pseudouridylation is implicated in cancer, neurodegenerative disorders, and autoimmune diseases. Despite its biological importance, experimental identification of Ψ sites remains time-consuming and costly, limiting the feasibility of transcriptome-wide profiling. Computational approaches have therefore become essential complements to experimental techniques. However, state-of-the-art machine-learning and deep-learning predictors often suffer from limited generalizability due to small training datasets. To overcome these issues, we aim at constructing new long-sequence datasets and developing a novel Ψ site predictor. METHODS: New long-sequence datasets were constructed as benchmarks for RNA Ψ-site prediction. The Ψ modification sites in RMBase 3.0 were mapped to the reference genomes across three species of human, mouse, and yeast, and the RNA sequences with a length of 201 were generated by extending the upstream and downstream from the mapped, central sites. To eliminate sequence redundancy, the sequences were clustered using CD-HIT with a 70% sequence identity threshold. We developed Meta-PseU, a logistic regression-based meta-classifier that considered 118 machine learning and deep learning classifiers. The datasets and programs are freely accessible at https://github.com/kuratahiroyuki/MetaPseU. RESULTS: By optimizing model configuration, we proposed the Meta-PseU model stacking 32 machine learning and deep learning classifiers out of 118 classifiers. Meta-PseU substantially improved model generalizability, overcoming a key limitation of existing approaches. It greatly outperformed state-of-the-art predictors and achieved increasing accuracy with increasing sequence length. CONCLUSIONS: Long-sequence datasets were newly constructed as benchmarks for RNA Ψ-site prediction. Meta-PseU offers a new framework for robust Ψ-site identification by using long sequences.

Pseudouridine↗

PubMed2026

Proficiency-based training and evidence-based methodology: a systematic review and meta-analysis.

OBJECTIVE: To assess adherence of self-labelled proficiency-based progression (PBP) studies to evidence-based PBP criteria and examine associations with training outcomes. METHODS: A systematic review and meta-analysis were conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and registered in the International Prospective Register of Systematic Reviews. PubMed, CENTRAL, EMBASE, MEDLINE, and Scopus were searched from inception to 1 March 2023. Prospective English-language studies on healthcare procedural training reporting objective performance outcomes were included; non-prospective, non-quantitative, non-procedural, non-English studies, and reviews were excluded. Pre-specified outcomes included adherence to 18 evidence-based PBP criteria and objective performance metrics (errors, steps, time); secondary outcomes included proficiency benchmark achievement and Likert ratings. Data extraction was performed independently by multiple reviewers. Study quality was assessed using the Medical Education Research Study Quality Instrument and risk of bias by two investigators. Effect sizes were pooled using random-effects models (DerSimonian-Laird), expressed as the ratio of means (ROM) for continuous outcomes and bias-corrected odds ratios for dichotomous outcomes. RESULTS: Of 646 studies identified 175 met inclusion criteria. In the PBP studies (n = 18), 94% fulfilled minimum criteria (use of a proficiency benchmark, its quantitative definition, and requirement for demonstration prior to progression) vs 36% of non-PBP studies (n = 157). If all PBP criteria were included, 83% of PBP studies used these criteria vs only 2% of non-PBP-studies. In quantitative analysis (27 randomised clinical studies, 761 participants), ROM results showed that PBP training reduced the number of performance errors by 58% (P < 0.001) and procedural time by 28% (P = 0.006), increasing number of steps performed by 22% (P = 0.03). When stratified based on number of criteria fulfilled, meta-regression demonstrated that increasing the number of PBP criteria fulfilled was associated with progressive and systematic trainee performance improvement. CONCLUSIONS: The more training methodologies adhere to established PBP criteria, the better training outcome will be.

Humans↗

PubMed2026

Public health, public protest: The role of health burdens and healthcare access in protest mobilisation.

Health and politics are intertwined, yet few studies have examined the association between health and protest. This study examined whether population health burdens were associated with protest incidence and whether healthcare access modified these associations. Analysis was based on an unbalanced 2004-2023 country-year panel, combining protest counts from ACLED with rates for 22 GBD causes. Mixed-effects negative-binomial models estimated incidence-rate ratios (IRRs) with interactions for healthcare access (±1 SD). Two-way fixed-effects Poisson models were estimated as a benchmark to distinguish cross-national associations from within-country dynamics. Health burdens were systematically, but heterogeneously, associated with protest. Rates for several non-communicable burdens were associated with protest, notably musculoskeletal disorders (IRR 1.72, 95% CI 1.37-2.15), neoplasms (1.24, 1.06-1.44), substance-use disorders (1.32, 1.12-1.56) and HIV/AIDS and other STIs (1.24, 1.12-1.38). Higher healthcare access generally attenuated health-protest associations. Fixed-effects models confirmed several associations (e.g. HIV/AIDS, neoplasms) but revealed that others (e.g. maternal/neonatal disorders, enteric infections) were driven primarily by cross-national differences. Population health burdens were associated with cross-national variation in protest mobilisation. Chronic, non-communicable burdens were associated with heightened protest, whereas poverty-linked and early-life burdens were associated with lower mobilisation. Healthcare access was associated with attenuation of these relationships.

Humans↗

PubMed2026

Measuring economic efficiency in adult intensive care units: A systematic review of methods, metrics, and evidence.

OBJECTIVES: Intensive care units (ICUs) consume substantial hospital resources, yet "efficiency" is inconsistently defined and measured. This study systematically reviewed how economic efficiency has been conceptualised and quantified in adult ICUs and appraised the quality of evidence. METHODS: Following PRISMA 2020 and a PROSPERO-registered protocol (CRD420251107866), we searched MEDLINE, Embase, CINAHL, Cochrane Library and Web of Science (2000-August 2025), plus global grey sources. Eligible studies explicitly defined efficiency and reported an efficiency metric/model linking ICU inputs (e.g., staff, beds/capacity, time, consumables, or costs) to outputs/outcomes (e.g., throughput/discharges, length of stay/resource use, risk-adjusted mortality). Dual independent screening and extraction were performed. Study quality was appraised using MMAT, and findings were synthesised narratively (SWiM), given heterogeneity. RESULTS: 39 studies (2001-2025) from 17 countries were included, all from high-income or upper-middle-income settings. Four methodological families were identified: (1) frontier modelling (predominantly DEA; occasional SFA/RFDH), (2) benchmarking indicators (risk-adjusted mortality and LOS/resource-use ratios; "efficiency matrix" quadrant classification), (3) cost-outcome evaluations, and (4) operational/process metrics. Across families, variation in decision-making units, input/output selection, and risk adjustment limited comparability; long-term and patient-reported outcomes were absent, and equity considerations were uncommon. CONCLUSIONS: ICU efficiency research is feasible but fragmented and often methodologically limited. Standardised definitions, validated risk adjustment, uncertainty quantification, and inclusion of patient-centred and equity-relevant outcomes are needed before efficiency metrics can reliably inform value-based decision making.

Intensive Care Units↗

PubMed2026

Applications of quantum AI in brain disorder diagnosis: A systematic review.

BACKGROUND AND OBJECTIVE: Brain disorder diagnosis and prediction remain challenging because neuroimaging, electrophysiological, behavioral, and multimodal data are high-dimensional, noisy, heterogeneous, and limited by small clinical cohorts. This systematic review synthesised applications of quantum artificial intelligence (QAI) for brain disorder diagnosis, prediction, detection, and monitoring. METHODS: Following PRISMA guidelines, studies published from 2016 to 13 January 2026 were retrieved from Scopus, Web of Science, and IEEE Xplore. After screening, 36 studies met the eligibility criteria and were qualitatively analysed according to disorder category, data modality, QAI method, implementation setting, validation strategy, and performance. RESULTS: At the broader disease-group level, neurodegenerative disorders were the most frequently investigated, followed by mental health and psychiatric disorders. At the individual level, Parkinson's disease and schizophrenia were the leading applications, followed by depression, anxiety, Alzheimer's disease, and stress-related tasks. MRI-based modalities were the most frequently used data source, followed by multimodal data and EEG. Methodologically, primary QAI approaches were dominated by quantum neural and QDL architectures, followed by quantum-inspired optimization or feature-selection methods and quantum-kernel/conventional QML classifiers. Qiskit/IBM Quantum and PennyLane were the most frequently reported quantum software frameworks. However, most studies relied on simulators, classical quantum-inspired implementations, or unclear implementation settings, with limited real-hardware evaluation. CONCLUSIONS: QAI shows emerging potential for brain disorder analysis, particularly through hybrid quantum-classical learning, quantum neural architectures, quantum-kernel methods, and quantum-inspired optimization. Nevertheless, current evidence remains preliminary and requires larger datasets, subject-level and external validation, fair classical benchmarking, noise-resilient circuits, real quantum hardware evaluation, explainability, and clinical validation.

Humans↗

PubMed2026

Artificial intelligence for anticancer drug discovery from natural products of macroalgae and sponges: A systematic review.

Marine natural products (MNPs) from macroalgae and marine sponges have inspired clinically important anticancer agents, including the cytarabine pharmacophore and the eribulin scaffold, while cyanobacterial dolastatin chemistry supplies the auristatin payloads of several marine-inspired antibody-drug conjugates (ADCs) such as brentuximab vedotin. Artificial intelligence (AI) methods, encompassing both classical machine learning (ML) with hand-engineered features and modern deep learning (DL) with many-layered neural networks, are increasingly supporting key decisions in natural-product anticancer drug discovery, including bioactivity prediction, target identification, absorption, distribution, metabolism, excretion and toxicity (ADMET) filtering, generative analogue design, and the selection of preclinical candidates. DL architectures relevant to this field include graph neural networks, transformer-based molecular generators, diffusion models for protein-ligand docking, and convolutional networks for mass spectrometry, while classical ML contributes interpretable fingerprint-based bioactivity models and molecular networking for dereplication. This review follows a systematic literature review methodology to organize the landscape of AI methods now applied to MNP anticancer discovery, distinguishing ML and DL approaches where relevant, situating them within the chemical context of macroalgal and sponge-derived oncology leads, and critically examining published case studies, including validation level (computational, in vitro, in vivo, clinical). The principal bottleneck for medical translation has shifted partly from algorithmic capability toward data infrastructure and experimental validation. Sparse, heterogeneous, and taxonomically biased bioactivity records limit what current models can learn and reduce the reliability of AI-prioritized candidates entering the preclinical pipeline. A roadmap is proposed that prioritizes open MNP-specific benchmarks, symbiont-aware modeling, and active learning loops with synthesizability and ADMET constraints. These AI workflows may accelerate the prioritization of marine-derived anticancer leads and support earlier, more evidence-based translational decisions in oncology drug development.

Biological Products↗

PubMed2026

Test-retest reliability of spatiotemporal, kinematic, and kinetic measures in marker-based 3D gait analysis: A systematic review.

BACKGROUND: Marker-based 3D gait analysis (3DGA) is widely used to quantify impairments and evaluate treatment effects. For longitudinal clinical interpretation, clinicians and researchers need reference values for inter-session measurement error. For this purpose, this systematic review synthesized Standard Error of Measurement (SEM) values for spatiotemporal, kinematic, and kinetic (moments) outcomes obtained from marker-based 3DGA studies. METHODS: PubMed and Scopus were searched (final search: 11 December 2025). Studies reporting inter-session test-retest SEM and/or MDC for steady-state overground or treadmill walking using marker-based motion capture were included. Two authors screened records and appraised methodological/reporting quality using a custom tool informed by COSMIN, GRRAS, and biomechanics-specific items. Due to heterogeneity, results were synthesized descriptively using study-level median SEM values, stratified by joint, plane, population (healthy, pathological, single subgroups), and walking condition. Minimal Detectable Change (MDC) values were computed for all available data. RESULTS: Thirty-four studies (762 participants, 44.2% females) were included, with substantially more evidence for overground than treadmill walking. Overground spatiotemporal outcomes showed low errors (walking speed SEM of 0.06 m/s; timing typically ≤0.03 s; spatial parameters generally ≤0.03 m). For joint kinematics during overground walking, median SEMs were 2.4° (sagittal), 1.9° (frontal), and 3.3° (transverse). The corresponding joint-kinetic SEMs were approximately 0.06, 0.04, and 0.03 Nm/kg, respectively. Treadmill data followed similar patterns. SIGNIFICANCE: Marker-based 3DGA allows for accurate assessment of spatiotemporal, kinematic, and kinetic gait features. We provided detailed SEM/MDC lookup tables to support clinical decision-making. Results further offer a benchmark for validating emerging gait assessment technologies (e.g., markerless systems) against realistic limits of marker-based 3DGA.

Humans↗

PubMed2026

Ventriculostomy-Related Infections by Country-Income Level: A Systematic Review and Bayesian Hierarchical Meta-analysis.

Our objective was to perform a systematic review and meta-analysis of published literature on ventriculostomy-related infection (VRI) and evaluate temporal and global trends. We conducted a systematic review and Bayesian hierarchical random-effects meta-analysis of VRI rates in adults, stratified by country-income level (high-income countries [HIC]; low- or middle-income countries [LMIC]), study design, sample size, enrollment period, VRI intervention, and VRI definition. We identified 159 articles published between 1989 and 2025 that included 523,704 patients with 7293 VRIs. The pooled VRI rate was 8.64% [95% CI: 7.44-9.97], with moderate heterogeneity and good model fit. The leave-one-out sensitivity analysis showed a mean absolute change of 0.06% and a maximum change of 0.2%, indicating robust analysis. Five of the 33 represented countries had VRI rates below the global pooled rate of 8.64%. Four were HICs: Singapore (VRI rate 3.3% [0.8-7]), the United States (VRI rate 4.6% [3.4-5.9]), Germany (VRI rate 6.1% [1.1-18.9]), Norway (8.3% [0.3-68.4]), with 1 LMIC: China (8.5% [5.4-12.4]). VRI was significantly higher in studies using definitions beyond CSF culture alone for VRI (+3.16% [0.11- 6.52]) and in those from Europe (+7.29% [4.62-10.10]) and the Western Pacific (+4.09% [1.55-6.98]). No other subgroup demonstrated significant differences. This Bayesian meta-analysis provides global estimates and factors associated with VRI. Standardization of VRI definitions is critical for future benchmarking of VRI rates.

Humans↗

PubMed2026

Incidence of Cirrhosis in Fibrotic Metabolic Dysfunction-Associated Steatohepatitis: A Meta-Analysis of Placebo Arms from Randomized Clinical Trials.

BACKGROUNDS AND AIMS: Metabolic dysfunction-associated steatohepatitis (MASH) with stage F2-F3 fibrosis represents the main target population for emerging pharmacotherapies. However, data on short-term progression to cirrhosis (F4) in this group remain limited. We aimed to evaluate the incidence of cirrhosis in placebo-treated patients with fibrotic MASH in randomized controlled trials (RCTs). METHODS: In this single-arm meta-analysis, we systematically searched PubMed and Cochrane Library from inception to December 13, 2024, for pharmacological Phase ≥ 2 RCTs reporting cirrhosis events (detected in liver biopsy or clinical signs) among patients with fibrotic MASH receiving placebo. Incidence rates were pooled using generalized linear mixed models with Clopper-Pearson confidence intervals (CIs). RESULTS: We identified a total of 11 RCTs, including 586 patients with fibrotic MASH. Total follow-up was 657.23 person-years (PYs), with 83 cirrhosis events reported. The pooled incidence rate was 13.09 per 100 PYs (95% CI 7.81 to 21.12, I2 = 75.6%, τ2 = 0.682). In subgroup analysis, the incidence of cirrhosis was 3.40 per 100 PYs in MASH F2 (95% CI 1.10 to 10.02, I2 = 0%, τ2 = 0) and 17.90 per 100 PYs (95% CI 10.63 to 28.55, I2 = 70.2%, τ2 = 0.561) in MASH F3, with significant differences between stages (p = 0.006). Sensitivity analyses showed consistent estimates. Most RCTs were judged to have a low risk of bias. CONCLUSIONS: This study provides stage-specific data on cirrhosis incidence in fibrotic MASH, highlighting the high short-term risk associated with MASH F3 in trial settings. These data may inform benchmarks to guide event expectations, enrichment strategies, sample size assumptions, and the interpretation of future MASH clinical trials.

Humans↗

PubMed2026

Evaluation of pilocarpine effects on sweat proteome.

BACKGROUND: Sweat is increasingly recognized as a valuable, non-invasive biofluid for biomarker discovery, yet its composition depends on the stimulation method. This study aimed to determine how pharmacological induction with pilocarpine compares to physiologically induced sweat through exercise in shaping the sweat proteome. RESULTS: We analyzed thermoregulatory sweat from exercise, pilocarpine-induced sweat, and combined pilocarpine plus exercise sweat. Total protein concentrations were similar across conditions, but pilocarpine markedly increased proteomic diversity, with combined pilocarpine plus exercise sweat showing the highest number of identifications. The core sweat proteome remained stable, while pilocarpine selectively enriched low-abundance proteins involved in vesicular trafficking, cytoskeletal remodelling, and metabolism. Proteins linked to the canonical M3-Gq-PLC-Ca2+ pathway, including AQP5, CALML5, and CLIC1, were consistently enriched, confirming cholinergic activation. Pilocarpine-induced sweat also contained plasma-derived and immune-related proteins, reflecting enhanced secretion and reduced ductal reabsorption. CONCLUSIONS: Exercise yields a physiologically relevant but less complex proteome, pilocarpine-induced sweat produces a pharmacologically enriched yet biased profile, and combined pilocarpine plus exercise sweat maximizes protein detection at the expense of interpretability. These findings highlight the critical impact of stimulation paradigm on sweat proteomics and provide a reference framework for biomarker research. SIGNIFICANCE: This study employed LC-MS/MS to systematically characterize eccrine sweat and delineate how stimulation paradigms-exercise, pilocarpine, and their combination-shape its proteomic landscape. By demonstrating that pharmacological induction profoundly alters protein diversity and composition compared to physiologically induced sweat, these findings establish a critical benchmark for sweat-based biomarker research and highlight the need for paradigm-aware sampling strategies in clinical and translational contexts. Nonetheless, several methodological constraints warrant consideration: the limited sample size (five individuals per group), the exclusive inclusion of women under combined oral contraceptive treatment (21 active pills followed by 7 pill-free days), which restricts extrapolation to naturally cycling women, and the focus on healthy young adults (18-25 years), limiting generalizability to older or clinically heterogeneous populations. Despite these limitations, this work provides a foundational framework for optimizing sweat collection protocols and advancing precision approaches in non-invasive diagnostics.

Pilocarpine↗

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

Prostate morcellation devices: a systematic review and meta-analysis of clinical outcomes and efficiency.

OBJECTIVE: To systematically evaluate the efficiency and safety of different prostate morcellators used during prostate enucleation for benign prostatic hyperplasia (BPH). METHODS: A systematic review was performed according to the Preferred Reporting Items for Systematic Review and Meta-analyses guidelines. PubMed/Medline, Scopus, and Web of Science were searched for studies published between October 2012 and May 2025. Studies reporting clinical outcomes of morcellators used during prostate enucleation were included. Primary outcome was morcellation efficiency (g/min). Secondary outcomes included intraoperative complications, device malfunction, and cost when available. Risk of bias (RoB) was assessed using the Risk Of Bias In Non-randomised Studies of Interventions (ROBINS-I), RoB 2 tool, and European Association of Urology case-series criteria. Random-effects meta-analyses and multilevel meta-regression adjusted for prostate volume were conducted. RESULTS: A total of 22 studies comprising 5980 patients were included, most undergoing holmium laser enucleation of the prostate. The most frequently evaluated devices were the Piranha™ (Richard Wolf GmbH, Knittlingen, Germany) and Lumenis VersaCut™ (Lumenis Ltd., Yokneam, Israel; 12 and 13 studies, respectively), followed by DrillCut™ (Karl Storz SE & Co. KG, Tuttlingen, Germany), MultiCut™ (Asclepion Laser Technologies GmbH, Jena, Germany), and Cyber Blade™ (Quanta System SpA, Milan, Italy) systems. Unadjusted weighted mean morcellation efficiency was 7.80 g/min (95% confidence interval [CI] 6.26-9.34 g/min) for Piranha, 4.70 g/min (95% CI 3.61-5.79 g/min) for VersaCut, 5.90 g/min (95% CI 3.16-8.64 g/min) for DrillCut, and 9.59 g/min (95% CI 4.78-14.39 g/min) for MultiCut. In volume-adjusted meta-regression using Piranha as reference, VersaCut remained significantly less efficient (-2.75 g/min; 95% CI -4.58 to -0.92 g/min; P = 0.003), with an even greater difference in prostates >70 mL (-4.34 g/min). Bladder mucosal injury was the most frequently reported complication; however, relatively rare across all devices. Meta-analysis demonstrated a significantly higher risk with VersaCut compared to Piranha (risk ratio 3.22, 95% CI 1.81-5.71), with low heterogeneity. CONCLUSION: This systematic review provides a contemporary clinical benchmark of the efficiency and safety of currently available prostate morcellators. Oscillating blade systems, particularly the Piranha morcellator, demonstrated higher morcellation efficiency and a more favourable safety profile than reciprocating blade devices, with the largest performance differences observed in patients with larger prostates.

Humans↗