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PubMed2026

AI echo INSIGHT study: A prospective blinded randomized trial of artificial intelligence echocardiogram interpretation.

BACKGROUND: Transthoracic echocardiography (TTE) is the most commonly performed cardiac imaging modality with over 30 million studies annually. Demand for timely expert interpretation continues to outpace capacity, creating diagnostic delays and inter-observer variability that impact patient care. Recent research has suggested computer vision artificial intelligence (AI) models can generate accurate preliminary comprehensive TTE reports, however, prospective evaluation is needed to determine whether AI-assisted TTE interpretation can improve clinician efficiency while preserving diagnostic accuracy. METHODS: AI ECHO INSIGHT is a prospective randomized blinded clinical trial conducted at Kaiser Permanente Northern California that will evaluate 1200 historical TTE studies (1000 consecutive unselected studies plus 200 with moderate or greater valvular disease) interpreted using three workflows: (1) AI-generated preliminary report finalized by a blinded cardiologist (AI-assisted); (2) cardiologist-generated preliminary report finalized by a blinded cardiologist (cardiologist-assisted); and (3) sonographer-generated preliminary report finalized by a blinded cardiologist (sonographer-assisted). The primary outcome is the rate of substantial change between preliminary and final reports, comparing the AI-assisted workflow to the pooled cardiologist-assisted and sonographer-assisted workflows. Secondary outcomes include cardiologist interpretation time for report finalization, superiority testing for diagnostic accuracy, and reporting consistency. CONCLUSION: AI ECHO INSIGHT is a prospective randomized blinded clinical trial evaluating the clinical impact of AI-assisted TTE interpretation on diagnostic accuracy, cardiologist efficiency, and reporting consistency in real-world echocardiography workflows. TRIAL REGISTRATION: ClinicalTrials.gov registration number NCT07229300.

Humans↗

PubMed2025

Enhanced fracture detection on radiographs with AI assistance for clinicians: a systematic review and meta-analysis.

BACKGROUND: Emergency radiographic interpretation for fractures is prone to missed or misdiagnoses. Artificial intelligence (AI) is expected to become a powerful tool to assist clinicians in fracture detection. PURPOSE: A systematic review and meta-analysis was performed to assess whether AI improves clinicians' ability to detect fractures on radiographs. MATERIALS AND METHODS: A literature search was conducted in PubMed, Web of Science, and Cochrane Library for studies published between January 1, 2010, and October 10, 2025. A meta-analysis of diagnostic accuracy studies was performed using a Summary Receiver Operating Characteristic (SROC) curve. The quality of included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool. Subgroup analysis and meta-regression were conducted to explore potential sources of heterogeneity. RESULTS: A total of 26 studies were included . The pooled sensitivity of clinicians increased from 77% (95% CI: 72-81) to 87% (95% CI: 83-90) with AI assistance, while the pooled specificity improved from 88% (95% CI: 85-90) to 92% (95% CI: 89-94). The corresponding AUC values were 0.90 (95% CI: 0.87-0.92) before and 0.95 (95% CI: 0.93-0.97) after AI assistance. Eight studies were rated as high risk of bias. Subgroup analysis and meta-regression identified potential sources of heterogeneity, including fracture location, AI model type, high risk of bias, and reference standards. CONCLUSION: AI assistance significantly improves clinicians' diagnostic performance in detecting fractures on radiographs for extremity and trunk fractures.

Humans↗

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

Pilot randomized trial of intermittent theta-burst stimulation versus H-Coil transcranial magnetic stimulation for treatment-resistant depression.

BACKGROUND: Intermittent theta burst stimulation (figure-8-coil iTBS) and H7-coil repetitive transcranial magnetic stimulation (rTMS) are FDA-cleared treatments for major depression; yet their comparative effectiveness in treatment-resistant depression (TRD) has not been evaluated in randomized trials. This pilot randomized trial was designed to obtain preliminary comparative estimates and to explore whether baseline cognitive functioning relates to early remission. METHODS: Twenty-eight adults with TRD were randomized to six weeks of figure-8-coil iTBS delivered to the dorsolateral prefrontal cortex (DLPFC) (n = 15) or H7-coil rTMS delivered to the dorsomedial prefrontal cortex (DMPFC) (n = 13). The primary outcome was change in 17-item Hamilton Depression Rating Scale (HRSD-17) score from baseline to week 6, analyzed with ANCOVA. Additional outcomes included response, remission, and symptom trajectories through week 18. Exploratory analyses examined the association between baseline cognitive functioning, such as executive functions and memory, and remission. RESULTS: Twenty-five participants completed all 30 sessions. Adjusted week-6 HRSD-17 scores did not differ between groups (mean difference -0.40, 95% CI -5.23 to 4.43; p=.865). Response rates were 40.0% for figure-8-coil iTBS and 50.0% for H7-coil rTMS (p>.60), and remission rates were identical across groups (20.0%). Remitters showed higher baseline executive functioning than non-remitters in exploratory analyses, although these associations were not confirmed in adjusted models. CONCLUSION: In this pilot trial, figure-8-coil iTBS and H7-coil rTMS showed symptom improvement, with no clear between-group differences. Exploratory findings suggest a potential signal involving executive functioning that warrants further investigation. These results inform the feasibility and design of larger comparative trials. TRIAL REGISTRATION: ClinicalTrials.gov (NCT05902312).

Adult↗

PubMed2026

Comparing the efficacy of chlorhexidine and povidone-iodine for surgical site disinfection: a systematic review and meta-analysis from randomized controlled trials.

BACKGROUND: Randomized controlled trials report conflicting evidence on the efficacy of different skin disinfectants for preventing surgical site infection (SSI). METHODS: We systematically searched PubMed, Web of Science, Cochrane Library, and Embase for RCTs published up to February 2025 comparing preoperative skin disinfection with povidone-iodine (PVI) versus chlorhexidine (CH). Primary outcomes were overall, superficial, deep, and organ/space SSI rates. Secondary outcomes included hospital stay, readmission, and reoperation. RESULTS: CH was superior to PVI in preventing overall SSI (26 studies, n = 29,356; RR: 0.89; 95% confidence interval [CI]: 0.80 to 0.99). The overall SSI incidence rate in the CH group was 7.1% (1,045/14,677), compared with 7.8% (1,152/14,679) in the PVI group, equating to an 11% reduction in relative risk and a 0.7% reduction in absolute risk. The number needed to treat to prevent one SSI was 143. CH demonstrated superiority over PVI in preventing superficial SSI (13 studies, n = 16,867; RR: 0.77; 95% CI: 0.64 to 0.92), but not for deep SSI (11 studies, n = 15,842; RR: 1.00; 95% CI: 0.77 to 1.29) or organ SSI (9 studies, n = 9,471; RR: 1.17; 95% CI: 0.89 to 1.53). No significant differences were found in hospital stay, readmission, or reoperation rates between the two groups. CONCLUSION: CH demonstrates statistical superiority over PVI in preventing overall and superficial SSI, though the absolute clinical benefit is modest. No significant differences were observed for deep or organ/space SSI, nor for secondary outcomes including hospital length of stay, readmission, or reoperation rates.

Humans↗

PubMed2026

Efficacy and Safety of the Dual Glucagon-Like Peptide-1 and Glucagon Receptor Agonist Mazdutide in Predominantly Chinese Adults With Obesity and/or Type 2 Diabetes: A Systematic Review and Meta-Analysis.

AIM: To assess the effects of mazdutide on body weight, HbA1c, metabolic outcomes, and adverse events in adults with overweight/obesity and/or type 2 diabetes (T2D). METHODS: This systematic review and meta-analysis included randomized controlled trials (RCTs) comparing mazdutide with placebo or active comparators in adults with overweight/obesity and/or T2D, identified through PubMed, Scopus, Web of Science, and ClinicalTrials.gov to 20 February 2026. Co-primary outcomes were percent change in body weight and change in HbA1c. Secondary outcomes included other weight-related and metabolic outcomes, as well as safety. Random-effects models were used to generate pooled mean differences (MDs) or risk ratios with 95% confidence intervals, and the certainty of the evidence (COE) was assessed using GRADE. RESULTS: Nine RCTs (N = 2292; most with low risk of bias) were included. In overweight/obesity without diabetes, mazdutide 3, 4, and 6 mg reduced body weight more than placebo (MDs -6.56%, -9.92%, and -11.1%, respectively; very low COE due to substantial heterogeneity and few trials). In T2D, mazdutide 4 and 6 mg reduced body weight and HbA1c versus placebo (moderate COE) and also outperformed dulaglutide for both outcomes. Mazdutide also improved waist circumference, lipids, liver enzymes, and uric acid levels. Gastrointestinal adverse events were more frequent, but serious adverse events and treatment discontinuation rates were comparable with those of the comparators. CONCLUSIONS: Mazdutide was associated with dose-dependent reductions in body weight and HbA1c, with broader metabolic benefits in predominantly Chinese adults with obesity and/or T2D. Longer-term, multi-ethnic studies are needed to confirm durability, generalizability, and cardiovascular safety.

Humans↗

PubMed2026

A systematic approach to standardizing the visual appearance of endometriotic lesions for artificial intelligence recognition.

INTRODUCTION: Numerous studies have shown that the diagnostic performance and reproducibility of visual recognition of endometriosis during laparoscopy are poor. The use of artificial intelligence (AI) seems relevant for exhaustive lesion recognition. Standardization of the visual classification of lesions, in the form of an ontology, is an essential prerequisite to enable medical experts to annotate surgical data consistently and subsequently allow engineers to train and build an artificial intelligence tool for endometriosis recognition. MATERIAL AND METHODS: A systematic search was conducted in the MEDLINE (via PubMed), EMBASE, and the Cochrane Library databases up to May 2022, aiming to identify studies describing the laparoscopic visual appearance of superficial endometriosis, endometriomas, and deep infiltrating endometriosis. The accumulated data in the literature concerning the visual appearance of the different forms of endometriosis were used to create an ontology that could be used for artificial intelligence applications. RESULTS: Out of 932 articles screened, 35 studies were selected based on the inclusion criteria of human subjects with histologically confirmed endometriosis lesions visualized via laparoscopy. The selected studies were reviewed to develop a visual ontology of endometriosis lesions observed via laparoscopy. The lesions were categorized into 4 classes and further subdivided into 11 subclasses: superficial (black, red, white, or subtle), adhesions (dense or filmy), deep (obliteration, retraction, or deformation), and ovarian (endometrioma or chocolate fluid). The positive predictive value (PPV) varied across lesion types: black lesions (PPV 47%-97%), red lesions (PPV 33%-100%), white lesions (PPV 20%-81%), and ovarian endometriosis (PPV 42%-98%). Nonspecific lesions such as adhesions (PPV 16%-50%) and subtle superficial lesions (PPV 0%-67%) presented lower PPVs. Deep endometriosis lesions, often buried within organs, required indirect signs (obliteration, retraction, deformation) for identification. CONCLUSIONS: The visual ontology proposed in this systematic search could facilitate the detection and classification of endometriosis lesions using artificial intelligence. This study highlights the challenges of reaching a consensus on lesion recognition and classification in AI projects due to the diverse visual presentations of endometriosis.

Humans↗

PubMed2026

Do wound protectors reduce contamination in total shoulder arthroplasty? A randomized controlled trial.

HYPOTHESIS: Cutibacterium acnes is the most frequent cause of shoulder prosthetic joint infection with skin edges as a source of wound contamination. The primary purpose of this study was to determine if the use of a wound protector device decreases the deep wound bacterial colonization in primary shoulder arthroplasty. The secondary purpose was to assess the effect of device usage on deltopectoral muscle and cephalic vein injury. METHODS: This was a prospective, randomized controlled trial. A total of 100 patients undergoing primary total shoulder arthroplasty were enrolled and randomized into 2 groups: a wound protector group and a control group. Five patients withdrew from the study, leaving 48 patients in the wound protector group and 47 controls. Three deep wound culture swabs were taken after final arthroplasty implantation. The surgeon also graded deltoid, pectoralis major, and cephalic vein injury on a 0-3 scale based on modification to the Tscherne classification of soft tissue injury. The primary outcome of this study was positive culture results for C acnes. Secondary outcomes included total bacterial culture positivity as well as soft tissue injury grades. A subanalysis removing likely contaminant positive cultures (growth >7 days and 1 colony only) was also performed. Comparisons between groups were made using Fisher exact test for categorical outcomes and t tests and Mann-Whitney U tests for continuous variables. RESULTS: The use of a wound protector did not result in any significant differences compared with controls in the rate of positive cultures for C acnes (15% vs. 21%, P = .593) or all bacteria (15% vs. 26%, P = .304). Removing likely contaminant positive cultures did not demonstrate any significant difference in culture positivity (9% vs. 17%, P = .355, for C acnes; 9% vs. 19%, P = .231, for all bacterial species). The wound protector group had better soft tissue injury scores for the deltoid muscle (P < .001) and pectoralis muscle (P < .001). No difference in cephalic vein injury was noted between the 2 groups (P > .05). No difference in surgical time was noted. CONCLUSION: The use of a surgical wound protector device in total shoulder arthroplasty did not significantly decrease bacterial colonization of the deep wound. However, soft tissue damage to the deltoid and pectoralis muscle was less severe in the wound protector group. These findings suggest that this device reduces iatrogenic soft tissue injury.

Humans↗

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

Automated CEAP Classification of Venous Duplex Reports Using Multimodal Artificial Intelligence.

OBJECTIVE: To develop and internally validate a prototype multimodal artificial intelligence system for automated CEAP (Clinical, Etiological, Anatomical and Pathophysiological) classification of venous duplex ultrasound (VDUS) reports, integrating natural language processing of free-text components with computer vision analysis of hand-drawn anatomical diagrams. METHODS: Single centre retrospective observational study using routinely collected clinical data. One thousand consecutive venous duplex ultrasound reports from Cambridge University Hospitals NHS Foundation Trust, UK (July 2024 - May 2025) were labelled according to the CEAP classification, excluding the Etiological component, which could not be reliably determined from duplex reports alone. Transfer learning was applied using ClinicalBERT for text and MobileNetV3 for diagrammatic data. Clinical classes were predicted from request line text. Text- and image-based pathophysiological models were developed for four anatomical territories (Great Saphenous Vein, Small Saphenous Vein, Deep system, Perforators), combined using late fusion with probability averaging. RESULTS: The clinical CEAP model achieved accuracy of 0.91, macro-F1 of 0.82, and macro-AUC of 0.98. Pathophysiological prediction varied, with text models broadly outperforming image models. Fusion yielded heterogeneous benefits, improving SSV performance but reducing Deep system accuracy. The performance of the final pathophysiological CEAP fusion models varied across anatomical territories: accuracy ranged from 0.70-0.92 and macro-AUC from 0.80-0.92. CONCLUSION: This study demonstrates the feasibility of automated CEAP classification from VDUS reports. Despite class imbalance affecting minority class predictions, the strong discriminatory performance validates this multimodal ML model for extracting clinically meaningful information from real-world data. This approach offers potential, pending external validation, to streamline vascular services through automated triage and guideline-compliant decision making.

Artificial intelligence↗

PubMed2026

Challenges and future directions in AI-driven biomaterials for microbiome-associated oral infectious diseases: A systematic review.

Oral biofilm-induced antimicrobial resistance is the core pathogenic mechanism of microbiome-associated oral infectious diseases (dental caries, periodontitis, peri-implantitis, and endodontic infection). Traditional therapies and biomaterials are limited by poor biofilm penetration, drug resistance induction, single functionality, and inadequate adaptation to dynamic oral microenvironmental changes (e.g., pH fluctuations, salivary rinsing, masticatory stimulation). Artificial intelligence (AI) has transformed the field by integrating materials science, microbiology, and stomatology data. Via machine learning, deep learning, and multi-physics simulation, AI optimizes biomaterial physicochemical properties, decodes microenvironmental signals, constructs precise sensing-response loops, and supports the full chain of material design, performance prediction, and action simulation, advancing treatment from empirical intervention to precision regulation. This systematic review retrieved literature from PubMed, Embase, and Web of Science (January 2016-January 2026) using keywords across three dimensions: AI, biomaterials, and oral microbiome. Following inclusion/exclusion criteria, 99 articles were included. It elaborates on five core mechanisms of AI-driven oral biomaterials (precise oral microbiome analysis, targeted material design/optimization, performance prediction/simulation, targeted delivery/intervention, effect evaluation/dynamic regulation), analyzes their applications in microbiome-targeted biomaterial research and development (R&D) and clinical practice for the four major oral infectious diseases, addresses technical bottlenecks (insufficient targeting specificity and precision of biomaterials, poor stability and durability in complex oral microenvironments, inadequate biofilm disruption capacity, and clinical translation obstacles), and proposes future directions (multimodal design to enhance targeting specificity, structural and component optimization to improve stability/durability, development of multi-mechanism synergistic biofilm disruption strategies, strengthening translational research for clinical application, and deep integration of AI in the full chain of biomaterial R&D). This work provides comprehensive theoretical and practical support for the R&D, optimization, and clinical translation of AI-driven microbiome-targeted oral biomaterials.

Humans↗

PubMed2026

Effect of knee and hip joint positions on passive stiffness of the rectus femoris and vastus lateralis in healthy individuals.

Passive muscle stiffness is a key determinant of musculoskeletal function and is influenced by structural components such as titin, connective tissue, and fascia. However, the effects of joint position, muscle depth, and sex on quadriceps passive stiffness remain unclear. To investigate the passive stiffness of the rectus femoris (RF) and vastus lateralis (VL) under different joint configurations, muscle depths, and between sexes using shear wave elastography (SWE). Thirty-six healthy young adults (18 men and 18 women) participated in this randomized crossover study. Passive stiffness was assessed in four positions of knee flexion: supine with 60° (SUP60), supine with 20° (SUP20), sitting with 60° (SIT60), and sitting with 20° (SIT20). SWE measurements (m/s) were obtained from 30 regions of interest (ROIs) per muscle, categorized into superficial, intermediate, and deep levels. Data were analyzed using Generalized Estimating Equations (GEE). A significant effect of position was observed, with higher stiffness values in the SUP60 condition for both RF and VL (p < 0.001). Superficial regions consistently exhibited greater stiffness compared to intermediate and deep regions across all positions (p < 0.001). Additionally, men demonstrated significantly higher stiffness values than women (p < 0.001). Significant interactions were found between position and muscle, as well as position and depth. Quadriceps passive stiffness is influenced by joint position, muscle depth, and sex. The SUP60 position elicits the highest stiffness, while superficial muscle regions are consistently stiffer. These findings highlight the non-uniform mechanical behavior of the quadriceps and may have implications for clinical assessment, rehabilitation, and exercise prescription. Clinical trial registration: This study was registered at Clinicaltrials.gov in June 06th, 2023. Register number NCT05905406. Link to access https//clinicaltrials.gov/study/NCT05905406.

Humans↗

PubMed2026

The efficacy of non-invasive brain stimulation interventions in obsessive-compulsive disorder management: A network meta-analysis of randomized controlled trials.

Non-invasive brain stimulation (NIBS) has been widely used as an alternative treatment for obsessive compulsive disorder (OCD). However, the most effective NIBS parameters are unclear. To compare the efficacy of NIBS in OCD. We conducted a systematic review and network meta-analyses (NMA) to combine direct and indirect comparisons of NIBS.Systematic searches were conducted in Cochrane CENTRAL, EMBASE, PubMed, and Web of Science from inception to June 20, 2025. Forty-two randomized sham-controlled trials (n = 1456) were included. All statistical analyses were conducted with R statistical software. Bayesian NMAs mainly using the BUGSnet package and gemtc package. Five NIBS protocols produced statistically significant reductions in Yale-Brown Obsessive Compulsive Scale (Y-BOCS) scores compared with sham stimulation: high-frequency rTMS over the FzFCz (Hf-rTMS-FzFCz; MD -11.77, 95% CrI -20.62 to -3.09), low-frequency rTMS over F3F4 (Lf-rTMS-F3F4; MD -9.93, 95% CrI -18.07 to -1.65), low-frequency rTMS over FCz (Lf-rTMS-FCz; MD -3.25, 95% CrI -6.06 to -0.40), high-frequency deep TMS over FzFC (Hf-dTMS-FzFC; MD -6.48, 95% CrI -12.32 to -0.50), and 2 mA anodal tDCS over F3 with cathodal over Fp2 (MD -9.34, 95% CrI -16.01 to -3.03).For secondary outcomes, high-frequency deep rTMS over FzFCz produced the largest reduction both in depressive symptoms (SMD -1.24, 95% CrI -1.92 to -0.55) and anxiety scores (SMD -1.88, 95% CrI -2.62 to -1.11), but had no effect on Clinical Global Impression-Severity (CGI-S) scores.Specific NIBS protocols are safe and effective adjunctive treatments for OCD, with promising yet inconclusive improvements in comorbid depressive symptoms. Further high-quality, head-to-head trials are needed.

Humans↗

PubMed2026

Transcriptomics reveals species-specific adaptive strategies to calorie restriction in two Argopecten scallops with distinct lifespans.

Calorie restriction (CR) is a well-established non-genetic intervention for lifespan extension in multiple model organisms. Seasonal food shortage in cold and temperate seas may mimic CR, inducing in bivalves a response similar to that in vertebrates and thereby prolonging life expectancy. However, the relationship and the mechanism underlying the food availability and lifespan in bivalves remain largely unexplored. Two closely related scallop species the short-lived warm-water Argopecten irradians (lifespan <2 years) and the longer-lived cold-water Argopecten purpuratus (7-10 years) provide an ideal comparative system to investigate species-specific adaptive strategies. In this study, we subjected both species to CR for 30 and 56 days and performed comparative transcriptomic profiling, weighted gene co-expression network analysis (WGCNA), and physiological assays to elucidate their distinct molecular responses. Transcriptomic analysis revealed that A. purpuratus exhibited substantially more DEGs than A. irradians at both time points under CR, with both species showing downregulation of metabolic pathways but to different extents. A. irradians mounted an early nutrient-sensing response at 30 days (IGF1R, PIK3R3, INSR suppression), indicating acute sensitivity to limitation; by contrast, A. purpuratus displayed delayed FoxO activation at 56 days, along with its downstream effectors NFKBIA, CREB3L4, and SMAD4, suggesting a gradual adaptive program may link to its extended lifespan. WGCNA identified three negatively correlated modules in each species, with coral2 being the most prominent in A. irradians and darkolivegreen in A. purpuratus. The former was dominated by ciliary motility genes, whereas the latter featured coordinated repression of oxidative phosphorylation. Additionally, both species exhibited conserved suppression of mTOR/S6K growth signaling and activation of cellular maintenance programs. Collectively, these findings expand the understanding of CR-mediated longevity regulation in bivalves and provide candidate gene resources for future functional studies and breeding programs.

Pectinidae↗

PubMed2026

Phenotypic and transcriptomic characterization of biallelic RNU2-2 developmental and epileptic encephalopathy.

OBJECTIVE: A significant proportion of individuals with suspected genetic developmental and epileptic encephalopathies (DEEs) remain unsolved following whole genome sequencing (WGS). Here we describe biallelic RNU2-2 variants causing a recently reported, severe, recessive DEE. METHODS: We screened individuals who have received WGS analyses at the Genomic Medicine Centre Karolinska for Rare Diseases for biallelic RNU2-2 variants. Deep phenotyping was performed through reviewing entire medical histories and phenotypic traits were transcribed to their corresponding Human Phenotype Ontology (HPO) term. HPO terms were used to generate pairwise phenotypic similarity scores and assess for significantly shared phenotype enrichment in the RNU2-2 sub-cohort. RNA sequencing analyses were performed in fibroblast and blood tissues to compare splicing events between RNU2-2 individuals and two independent control groups. RESULTS: We identified 14 individuals from nine families with 12 ultra-rare biallelic RNU2-2 variants clustering in the conserved 5' domains. Genotype data from 13 of 14 individuals has been reported previously as part of a larger cohort. All individuals presented with a highly concordant, severe DEE, characterized by severe to profound intellectual disability, inability to walk or communicate, hyperkinesia, and refractory seizures. Infantile spasms and tonic seizures were the predominant seizure types and a Lennox-Gastaut syndrome-like phenotype was common. These individuals had a significantly similar phenotypic signature when compared with 703 individuals with complex pediatric epilepsies (two-sided Monte Carlo permutation test, p = .005). RNA sequencing analyses showed aberrant splicing, with the most pronounced effects in fibroblast tissues in mutually exclusive exon and alternate 3' splice-site events, which were not detectable in blood. SIGNIFICANCE: We present deep phenotyping data and transcriptomic analyses that provide support for rare, 5' clustering biallelic RNU2-2 variants causing this novel, severe DEE. We propose an RNA sequencing methodology on fibroblast tissue for future validation of RNU2-2 variants.

autosomal recessive disease↗

PubMed2026

Single-organ proteomics in Drosophila melanogaster larva.

The combination of genetic accessibility, organ complexity, evolutionary conservation, and cost-efficiency makes Drosophila melanogaster (Dm) a well-known model system for biomedical and fundamental biological research. Proteomic analysis of single organs enables the identification and quantification of proteins expressed in specific organs. This will help to uncover specific biological functions and unique protein profiles that are not detectable in whole-organism analyses. In this study we have isolated single organs form Dm larvae, and we have performed a deep proteomics mapping by following a minimal manipulation preparation procedure. The combined dataset across all organs comprised 9132 identified proteins. As anticipated, principal component analysis (PCA) revealed clear separation between the proteomes of most organs, confirming distinct protein profiles. These findings demonstrate the applicability of the sample preparation strategy for high-resolution proteomic characterization of individual organs in Drosophila. Given the extensive genetic tools available for this model organism, our approach has the potential to open new avenues for proteomic studies in Drosophila melanogaster and any other biological systems where the sample amount is limiting. SIGNIFICANCE STATEMENT: Drosophila melanogaster is a well-known model system for biomedical and fundamental biological research that serves as a valuable in vivo model organism due to its high degree of evolutionary conservation with higher vertebrates, tractable genetics, and logistical efficiency. However, the proteome of Drosophila at single organ level has been elusive to date, due to several factors like low sensitivity of previous generation mass spectrometers and sample preparation procedures, difficult isolation of some organs. In this study we have applied a compilation of advanced methods including minimal sample manipulation together with simple, straightforward and efficient protein extraction and digestion methods. Obtained peptides were minimally handled to be analyzed by applying specific and sensitive nLC methods coupled on-line to state-of-the-art MS/MS system. Altogether, the applied strategy allowed us to get the first single organ study to date for this animal. These datasets represent a significative resource for future genomic, transcriptomic and proteomic studies in Drosophila, as multi-omic integration requires deep proteomics to translate data into functional biochemistry, and serves as a critical bridge and an indispensable standalone resource across the genomic, transcriptomic, and proteomic landscapes.

Animals↗

PubMed2026

Paramedian supracerebellar transtentorial approach for Yaşargil T2 tentorial incisura meningiomas.

OBJECTIVE: Tentorial incisura meningiomas, particularly those arising from the middle incisural region (Yaşargil T2), are surgically challenging because of their deep location and compression of critical neurovascular structures. These lesions typically have supratentorial or infratentorial extension but can also extend across both compartments. Although approach selection is often guided by the dominant compartment of tumor extension, for lesions with supratentorial-dominant extension, the optimal approach remains controversial, and a standardized strategy has not been established. Authors of this study evaluated the feasibility and outcomes of the paramedian supracerebellar transtentorial (PST) approach for Yaşargil T2 tentorial incisura meningiomas with a supratentorial-dominant or combined extension. METHODS: The authors retrospectively reviewed data from consecutive patients with radiographic and intraoperative findings consistent with a Yaşargil T2 tentorial incisura lesion treated via the PST approach from September 2005 through December 2025. Patients were placed in a semisitting position whenever feasible and in a semilateral position when semisitting was contraindicated. Collected data included demographics, tumor extension patterns, extent of resection on postoperative MRI, neurological outcomes, histopathology, rate of recurrence, and follow-up. RESULTS: Six patients, 1 male and 5 female, with an overall mean age of 43 years, underwent resection via the PST approach. Four lesions had predominantly supratentorial extension, and 2 had combined supra- and infratentorial growth, with no cases of isolated infratentorial extension. Five patients had been placed in the semisitting position and 1 in a semilateral position because of a cardiac contraindication to the semisitting position. No new permanent neurological deficits were observed. Postoperative MRI showed Simpson grade I resection in all 6 patients. The mean follow-up was 8.6 years. Histopathological analysis revealed 3 WHO grade 1 meningiomas, 1 WHO grade 2 meningioma (clear cell), and 2 solitary fibrous tumors (meningioma mimics). These diagnoses were evaluated according to the 2021 WHO classification. CONCLUSIONS: In this consecutive series, the PST approach was a viable single-corridor strategy for Yaşargil T2 tentorial incisura lesions, including supratentorial-dominant tumors, achieving Simpson grade I resection with no permanent neurological deficits. By providing early devascularization at the tentorial attachment and a gravity-assisted, retractorless working corridor with favorable deep venous visualization, the PST approach challenges compartment dominance as the primary determinant of approach selection.

Humans↗

PubMed2026

Using Organoids to Unlock the Potential of Human Torpor for Spaceflight.

PURPOSE OF REVIEW: This paper reviews the current understanding of the potential for humans to enter a state of torpor/hibernation, and discusses the possibility of inducing torpor in astronauts for long-duration space travel, including some of the physiological, technological, and ethical considerations associated with its implementation. By exploring means to induce torpor in various human organoid systems, we hope such research can provides insights to comprehensive solutions to overcome some of the major hurdles that limit the potential for human to enter a state of torpor during long-duration deep-space missions, and contribute to the ongoing efforts to make such missions more feasible and safer for astronauts. RECENT FINDINGS: On future deep space missions such as NASA's planned missions to the Moon, Mars, and near-Earth asteroids, astronauts will be continuously exposed to environments that are radically different from those on Earth, each presenting multiple logistical and physiological challenges. Beyond the well-documented physiological effects of microgravity, space travelers will encounter a complex radiation environment that may contribute to significant short- and long-term adverse effects on human physiology and increase the risk of cancer and other diseases. Besides these physical challenges, life support systems must also be designed to mitigate psychological impacts of long-term isolation and confinement - all of which collectively pose formidable engineering problems. Hibernation/torpor is a state of prolonged inactivity and metabolic depression used by a wide variety of mammals to survive periods of cold temperatures and food scarcity, including some primates and perhaps even an extinct early line of hominins that lived nearly half a million years ago. Since modern humans share common ancestry with these hominins and hibernating primates, it is likely the human genome encodes the necessary genetic information to hibernate, or at least enter the similar, more transient state of torpor. The reduced body activity, lowered metabolism, and decreased energy requirements that characterize torpor suggest that developing means of inducing such a state in astronauts could address these challenges, including providing a degree of radioprotection. SUMMARY: This review explores the potential application of human torpor as a countermeasure to address the many challenges posed by long-duration spaceflight beyond low-Earth orbit (LEO), discusses various natural hibernating model systems for studying means of inducing a torpor-like state in humans, and highlights the vast potential of using human organoids to test and validate mechanisms that govern induction and maintenance of torpor to identify the means to one day safely induce this state in astronauts to provide additional protection from the myriad stressors of spaceflight.

Astronaut Health↗