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PubMed2026

Two-Year Outcomes of a 211 Care Coordination Trial.

BACKGROUND AND OBJECTIVES: Early screening for developmental concerns enables timely diagnosis and referral, yet many families face barriers accessing services. Prior work showed that early childhood care coordination could improve timely service connection. This study assessed developmental outcomes among children participating in a randomized controlled trial of Information and Referral Federation of Los Angeles County (211LA). METHODS: Participants, aged 21-42 months, were randomized to usual care or the 211LA intervention. Developmental and behavioral measures, including the Parental Evaluation of Developmental Status Developmental Milestones Assessment Level (PEDS-DM-AL) and the Child Behavior Checklist (CBCL), were collected at baseline and 24 months later. The sample included 499 participants, 250 in the 211LA intervention and 249 in usual care. Primary analyses examined changes in PEDS-DM-AL and CBCL scores over the 24-month period by study arm. Post hoc analyses compared family characteristics between intervention and control families who enrolled in services. RESULTS: Developmental and behavioral measures showed some clinically insignificant change over time, but these changes did not differ by condition (expressive/receptive language skills mastered: P > .9; autism, attention, aggression, and externalizing behavior T scores: P > .4). Post hoc analyses identified potentially relevant imbalances between the treatment arms at baseline as well as in the subgroup that enrolled in services, with families assigned to the 211LA intervention being more likely to have a non-US born parent and a parent with limited English proficiency compared with families assigned to usual care. Intervention families enrolled in services also used telehealth more frequently and received a lower duration of services than those receiving usual care. CONCLUSIONS: This study measured the indirect influence of service enrollment through 211LA care coordination on developmental outcomes. Although increased service enrollment through 211LA did not affect developmental outcomes, we hypothesize this may be because of several factors, including overrepresentation of a subset of historically underrepresented families in the 211LA intervention, suboptimal performance of our developmental assessment tool, and complexity of conducting a trial of this magnitude during the COVID-19 pandemic, which may have diminished the ability of this trial to demonstrate developmental benefits despite demonstrated service enrollment gains.

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

Gene-environment interaction between perinatal oxytocin exposure and Pten mutation shapes epigenetic reprogramming of oxytocin signaling and behavior in mice.

Synthetic oxytocin (Pitocin) is the most commonly used pharmacologic agent for induction and augmentation of labor. Beyond its uterotonic effects, oxytocin plays a critical role in neurodevelopment and social behavior. Dysregulated oxytocin signaling has been implicated in autism spectrum disorder (ASD), raising concern that perinatal exposure to exogenous oxytocin may have lasting neurodevelopmental consequences. This study aimed to determine whether offspring harboring a genetic predisposition for ASD are differentially impacted by perinatal oxytocin exposures, with a focus on long-term oxytocin signaling and autism-like behavior. Pregnant mice carrying offspring with heterozygous mutations in phosphatase and tensin homolog deleted on chromosome ten (Pten), a well-established monogenic risk factor for ASD, received continuous oxytocin versus phosphate-buffered saline (PBS) control via micro-osmotic pumps during late gestation. Wild-type (WT) offspring exposed to each treatment served as a secondary control. Adult offspring were assessed for oxytocin receptor (Oxtr) methylation in the frontal cortex and hippocampus, oxytocin expression in the hypothalamus, serum oxytocin levels, and were subject to a battery of social and anxiety-related behavior tests. Perinatal oxytocin exposure produced genotype-dependent effects in offspring. Epigenetic analyses revealed bidirectional remodeling of Oxtr methylation in the frontal cortex and hippocampus, with increased exon 1 methylation in WT mice and decreased methylation in Pten-mutant mice, resulting in significant genotype-treatment interactions. Hypothalamic oxytocin expression increased following treatment regardless of genotype, though baseline levels were higher in Pten-mutant mice. Neither oxytocin treatment nor genotype impacted long-term serum oxytocin levels. Behavioral outcomes were modest but context-specific: repetitive behaviors and cognition performance were unchanged, but oxytocin-treated Pten-mutant mice exhibited increased anxiety-like behavior alongside improved social memory. In contrast, oxytocin-treated WT mice showed reduced social novelty preference. Exploratory analyses suggested potential sex-dependent trends. Our findings support a model in which genetic susceptibility shapes the epigenetic encoding of early-life hormonal signals, thereby recalibrating oxytocin system function and downstream behavioral outcomes. Together, these data highlight the context-dependent effects of perinatal oxytocin exposure and argue against uniformly beneficial or detrimental effects, emphasizing the importance of gene-environment interactions in neurodevelopmental trajectories.

Animals↗

PubMed2026

Multimodal alignment improves generalizability of genomic biomarker prediction in computational pathology.

Computational pathology models that use digitized histopathology whole-slide images have the potential to become a cost-effective and scalable alternative to molecular assays for the prediction of genomic biomarkers, a key task in precision oncology. However, as new genomic biomarkers are discovered or quantified, large, labeled datasets must be prospectively collected to train new models. To address this challenge, we developed multimodal alignment for biomarker learning and generalization (MARBLE), a multimodal contrastive pretraining strategy that integrates structured biomarker knowledge into representation learning of histopathology images. MARBLE aligns histopathology-derived representations with representations of genomic biomarkers generated by a large language model (LLM) and a protein language model (PLM). This biologically informed alignment enables data-efficient generalization to novel, out-of-distribution biomarkers. Using the MSK-IMPACT cohort of over 40,000 patients across multiple biomarker panel versions, we design experiments grounded in real-world data to demonstrate the value of our proposed approach.

CP: computational biology↗

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

Non-destructive prediction of lead content in oilseed rape leaves by fluorescence hyperspectral technology based on neural network.

Based on fluorescence hyperspectral imaging (FHSI), this study targeted rapid, non-destructive quantification of lead (Pb) content in oilseed rape leaves treated with varying silicon (Si) concentrations, acquiring fluorescence spectra over the 484.43-1001.61 nm wavelength range. To optimize spectral data quality, preprocessing methods (Savitzky-Golay smoothing, first derivative, detrending) were comprehensively compared. Characteristic wavelengths were then selected via interval variable iterative shrinkage, which effectively compressed data dimensionality and reduced computational load. A hybrid SE-CL1DA model, fusing a 1D convolutional neural network, a long short-term memory network and SE attention mechanism was constructed, with Bayesian optimization tuning hyperparameters to boost stability. The BO-SE-CL1DA outperformed both traditional machine learning and insufficiently optimized deep learning model (Rp2=0.9609, RMSE = 0.0377 mg/kg, RPD = 5.1736), thus enabling accurate Pb estimation, supporting Si-regulated heavy metal stress management and facilitating agricultural contamination monitoring.

Plant Leaves↗

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

Heterogeneity in Teriflunomide Treatment Arms: A Systematic Review and Meta‑Regression of Randomised Multiple Sclerosis Trials.

BACKGROUND: Teriflunomide is widely used as an active comparator in Phase 3 randomised trials for relapsing multiple sclerosis (RMS). Temporal changes in disease activity within teriflunomide-treated cohorts have not been systematically examined. OBJECTIVES: To assess temporal trends in relapse and disability outcomes across teriflunomide arms of Phase 3 multiple sclerosis (MS) trials and identify predictors of between-trial heterogeneity. METHODS: We performed a systematic review and meta-analysis of Phase 3 randomised controlled trials including a teriflunomide arm. PubMed, Scopus, and ClinicalTrials.gov were searched up to October 2025. Annualised relapse rate (ARR) and 12- and 24-week confirmed disability worsening (CDW) were extracted together with baseline characteristics. Risk of bias was assessed using the Cochrane Risk of Bias 2 tool. Random-effects meta-analyses, meta-regression, and sensitivity analyses were performed. RESULTS: Twelve teriflunomide cohorts from eight trials involving 4,900 adults with RMS were included. ARR ranged from 0.11 to 0.37 with substantial heterogeneity (I2 = 94%). Trial start year was inversely associated with ARR and explained a large proportion of between-study variability in exploratory meta-regression analyses. Confirmed disability worsening outcomes also showed substantial heterogeneity with a weaker trend toward lower event rates in more recent trials. CONCLUSION: Teriflunomide-treated trial populations have shifted toward lower relapse activity over time, and trial start year was the principal predictor of between-trial heterogeneity in ARR in exploratory analyses. These findings most plausibly reflect evolving recruitment and diagnostic practices rather than changes in drug efficacy. Accounting for these temporal dynamics is essential when interpreting outcomes from RMS trial using teriflunomide as comparator.

Humans↗

PubMed2026

One Year After a Cyberattack: Lessons Learned and Dosimetric Analysis of Contingency Radiotherapy Plans.

PURPOSE: Cyberattacks on health care institutions pose significant risks to patient care, particularly in radiotherapy departments, which are heavily reliant on digital systems. This study examines the impact of a ransomware attack on our hospital and evaluates the effectiveness of the contingency measures implemented to resume radiotherapy treatments. METHODS AND MATERIALS: Following the cyberattack, our radiotherapy department faced a complete shutdown. After an initial estimate considering a shutdown of several weeks, a contingency plan was executed, including manual patient data retrieval and collaboration with a backup hospital. Contingency plans were prepared and delivered within hours, despite a partial lack of information. These plans allowed some patients to restart treatment 3 days after the attack. A dosimetric analysis was performed for the contingency plans, including various pathologies, mainly glioblastoma, head and neck cancers, and lung cancer. We compared the original and contingency plans in terms of dose coverage to the clinical target volume, biological effective dose, and their clinical impact as assessed at the 1‑year follow‑up after the cyberattack. RESULTS: Treatments resumed within 12 days at our hospital. Patients with glioblastoma showed good target coverage because of generous margins, resulting in favorable outcomes. In head and neck cases, the lack of detailed imaging led to significant target volume misses, suggesting that more conservative initial treatments could have been beneficial. Lung cases demonstrated accurate peripheral lesion targeting but faced challenges in central lesions because of the absence of positron emission tomography information. In most cases, the approach of using a contingency plan, even with limited information, led to a higher biological effective dose than would have been achieved if treatment had been stopped until full recovery at our hospital. CONCLUSIONS: The study highlights the critical importance of robust contingency planning in radiotherapy departments, emphasizing the need for backup systems and tailored approaches based on tumor location and available diagnostic information. These lessons emphasize that preparedness for digital disruptions should not focus exclusively on information and technology infrastructure.

Humans↗

PubMed2026

Unveiling the power of TIIC: A prognostic tool for esophageal adenocarcinoma.

BACKGROUND: Esophageal adenocarcinoma (EAC) remains a lethal malignancy with limited prognostic tools for guiding immunotherapy. Tumor-infiltrating immune cells (TIICs) play a critical role in EAC prognosis and treatment response. METHODS: We integrated single-cell RNA sequencing and bulk transcriptome data from TCGA and GEO databases. TIIC-specific RNAs were identified via tissue specificity index calculation combined with machine learning feature selection. Twenty machine learning algorithms were benchmarked to construct an optimal TIIC signature score (TIIC-Score) based on the comprehensive C-index. Immunotherapy response, genomic mutation, and copy number variation were analyzed. Summary-data-based Mendelian randomization (SMR) and two-sample Mendelian randomization (MR) were performed to explore genetic associations. Core prognostic TIIC-related genes were functionally validated in esophageal cancer cell lines through loss-of-function assays. RESULTS: The TIIC-Score demonstrated robust prognostic value for 1-, 2-, and 3-year overall survival across multiple cohorts, outperforming 22 published models. High TIIC-Score was associated with poor survival and increased chromosomal instability. Mutation profiling revealed high frequencies of TP53 (78.2%), TTN (48.7%), and SYNE1 (30.8%). MR analysis identified a significant association between gastro-oesophageal reflux and EAC risk at SNP rs8130507. Functionally, CCNI was upregulated in esophageal cancer cells, and its knockdown suppressed malignant phenotypes while promoting apoptosis, supporting its pro-tumorigenic role. CONCLUSION: The TIIC-Score provides a novel prognostic framework for EAC that effectively stratifies patient risk and may help identify individuals most likely to benefit from immunotherapy.

Esophageal adenocarcinoma↗

PubMed2026

Structured robotic colorectal training in a non-tertiary NHS hospital: a 502-case consecutive cohort implementation study.

Robotic-assisted colorectal surgery has expanded rapidly across NHS practice in the UK. Structured unit-wide training pathways are essential for safe technology adoption, yet published outcome data from non-tertiary hospitals remain limited. This study describes the implementation and feasibility of a unit-wide robotic colorectal program at a high-volume non-tertiary hospital, reporting outcomes across 502 consecutive resections performed by eight consultant surgeons and presenting these in the context of nationally published benchmarks. A retrospective cohort study of 502 consecutive robotic colorectal resections performed at York Teaching Hospital between May 2022 and December 2025. Eight consultant surgeons (A-H) participated in a structured four-phase training pathway incorporating simulation training, proctored cases, complexity-based case progression, and formal credentialing. Primary outcomes were 30-day mortality, unplanned return to theatre (RTT), and anastomotic leak (AL). Anastomotic leak was calculated using only patients who underwent anastomosis as the denominator. Procedure-stratified and individual surgeon outcomes with 95% confidence intervals were reported. Risk-adjusted cumulative sum (RA-CUSUM) analysis was performed to evaluate learning curves. Outcomes are presented descriptively alongside nationally published reference data; no formal statistical comparison against national benchmarks was performed. 502 robotic colorectal resections were performed. Mean patient age was 70.0 ± 11.3 years; 58.4% were male. Median ASA grade was III. The indication was malignancy in 89.2% of cases. Length of stay was non-normally distributed and is therefore reported using median and interquartile range in the revised analysis. Key outcomes: - 30-day mortality: 1.0% (5/502; 95% CI 0.4-2.3%) - Unplanned return to theatre (RTT): 5.2% (26/502; 95% CI 3.6-7.5%) - Anastomotic leak (AL): 3.3% (15/450; 95% CI 2.0-5.5%; denominator = patients with anastomosis) - 30-day unplanned readmission: 5.0% (25/502; 95% CI 3.4-7.2%) - Conversion to open surgery: 3.6% (18/502; 95% CI 2.3-5.6%) - Lymph node yield ≥12: 91.3% of cancer resections - R0 resection rate: 95.1% of cancer resections All primary outcomes fell within or below the published reference ranges used for descriptive context. RA-CUSUM trajectories were heterogeneous: no surgeon crossed the predefined upper control limit, but several curves showed later upward movement. Accordingly, the analysis is interpreted as safety surveillance rather than evidence of uniform performance improvement. RA-CUSUM monitoring showed that no surgeon crossed the predefined upper control limit; however, heterogeneous trajectories precluded a claim of uniform performance improvement.

Humans↗

PubMed2026

Testing How Mindfulness Skills Change for Novice Meditators Using Headspace: Examining Trait Mindfulness and Perceived Stress as Moderators.

Mindfulness-based interventions are found to effectively reduce stress and improve mental health outcomes. Yet, it is not always clear how the mindfulness skills of attention and acceptance develop throughout the intervention. This knowledge gap is especially pertinent for novice meditators learning these skills for the first time, including whether some individuals are more prone to learning them. Using a randomized waitlist-controlled trial, we tested the effect of the app Headspace on changes in attention and acceptance over 8 weeks among participants new to mindfulness meditation. Further, we tested the moderating effects of trait mindfulness and perceived stress. Non-faculty university employees were randomized to a Headspace or waitlist control condition. Trait mindfulness and perceived stress were measured at baseline. Ecological momentary assessment survey data for attention and acceptance were collected five times a day in 4-day bursts at baseline and 2, 5, and 8 weeks post-randomisation. Attention and acceptance were significantly higher at Week 8 compared to baseline for the Headspace group, but not the control group. For the Headspace group, both skills showed significant change by Week 2. Trait mindfulness moderated this effect with those who were lower in trait mindfulness displaying greater increases in attention, but not acceptance. Perceived stress also moderated this effect with those who were lower in perceived stress displaying greater increases in attention and acceptance. Our discussion draws attention to implications for matching intervention content to individual needs to ensure participants reporting different levels of characteristics benefit from mindfulness training.

Humans↗

PubMed2026

A Multi-omics Regulated Cell Death Framework Defines Immune Phenotypes and Guides Precision Therapy in Colorectal Cancer.

Colorectal cancer (CRC) is molecularly and immunologically heterogeneous, contributing to variable treatment response. Because regulated cell death (RCD) intersects with tumor metabolism, immune regulation, and therapeutic susceptibility, we built an RCD-centered framework for CRC stratification. Multi-cohort transcriptomic data were used to infer RCD subtypes with non-negative matrix factorization (NMF) and non-negative least squares (NNLS). Genomic, bulk RNA-seq, single-cell RNA-seq, and spatial transcriptomic datasets were integrated to characterize subtype-associated biology. Machine-learning models were developed for immunotherapy response and survival-risk estimation. Candidate compounds were screened by GDSC2-based drug-sensitivity modeling and molecular docking, and FSTL3 was functionally assessed in vitro. The framework separated CRC samples into two RCD-related phenotypes resembling immune-hot and immune-cold states. RCD1 showed immune activation and higher mutational burden, whereas RCD2 showed immune-suppressed features, intratumoral heterogeneity, and aggressive biology. RCD-associated signatures showed potential for predicting immunotherapy response and survival risk. Dasatinib was prioritized for immune-cold, high-risk tumors, with preliminary evidence supporting its activity in CRC cells, while functional assays suggested a role for FSTL3 in growth, invasion, epithelial-mesenchymal transition, and apoptosis regulation. These findings suggest that RCD-based multi-omics analysis may refine CRC stratification and help generate therapeutic hypotheses.

Colorectal cancer↗

PubMed2026

Impact of Baseline Polyneuropathy Severity on Eplontersen Efficacy in the NEURO-TTRansform Clinical Trial.

BACKGROUND AND AIMS: In the NEURO-TTRansform clinical trial (NCT04136184), eplontersen improved neuropathy impairment and quality of life (QoL) through Week 66 versus the NEURO-TTR historical placebo in patients with hereditary transthyretin amyloidosis with polyneuropathy (ATTRv-PN). This analysis assessed the impact of baseline ATTRv-PN severity on eplontersen response in patients from NEURO-TTRansform. METHODS: This post hoc analysis grouped patients into tertiles by baseline Neuropathy Impairment Score (NIS): T1 (least baseline impairment: 3.5 to < 27.5; n = 67), T2 (27.5 to < 55.0; n = 67), and T3 (55.0 to < 127.8; n = 66). Outcomes assessed were neuropathy impairment (modified NIS+7 [mNIS+7] and NIS, Neuropathy Symptom and Change, Polyneuropathy Disability), QoL (Norfolk QoL-Diabetic Neuropathy), physical functioning (36-Item Short-Form Health Survey Physical Component Summary), nutritional status (modified body mass index), and serum transthyretin levels; these were compared with NEURO-TTR historical placebo. Autonomic dysfunction (Composite Autonomic Symptom Score-31), disability (Rasch-built Overall Disability Scale), and walking speed (10-Meter Walk Test) were also assessed. Final assessments were carried out following 65/66 or 81/85 weeks of treatment. RESULTS: Mean mNIS+7 composite scores were maintained over 85 weeks with eplontersen (changes from baseline of -4.5 [T1], -1.3 [T2], and -2.6 [T3] points). Other disease parameter scores were similarly maintained or improved with eplontersen. Patients receiving placebo experienced disease worsening across outcomes. T1 mean disease scores were typically better than in T2 and T3. INTERPRETATION: Consistent, sustained benefits of eplontersen were observed regardless of baseline ATTRv-PN severity. These findings strengthen the importance of early treatment initiation for patients with ATTRv-PN across the disease spectrum.

Humans↗

PubMed2026

Functional neuroimaging subtypes of obsessive-compulsive disorder: A systematic review and meta-analysis.

Obsessive-compulsive disorder (OCD) exhibits substantial clinical heterogeneity that may reflect underlying neurobiological diversity. Neuroimaging-based subtyping may advance precision psychiatry by identifying biologically distinct subgroups with differential treatment responses. This study systematically synthesized evidence from functional neuroimaging subtyping studies in OCD to identify reproducible neurobiological subtypes, characterize their clinical profiles, and establish a consensus-based classification framework. We reviewed 40 original studies employing machine learning, clustering, normative modeling, or classification approaches, encompassing approximately 8,150 patients. Consensus clustering identified three reproducible neurobiological subtypes. The Limbic-Hyperactive subtype, comprising approximately 40% of patients, exhibited amygdala and insula hyperconnectivity, elevated anxiety levels, predominant contamination and washing symptoms, and favorable response to cognitive-behavioral therapy. The Fronto-Striatal-Hypoconnected subtype, comprising approximately 35% of patients, demonstrated reduced orbitofrontal-striatal connectivity, cognitive inflexibility, predominant checking and ordering symptoms, and a favorable response to selective serotonin reuptake inhibitors. The Global-Disrupted subtype, comprising approximately 25% of patients, exhibited widespread connectivity disruption, greater symptom severity, and poor treatment response. Support vector machine classification achieved 81.5% accuracy for subtype assignment, though classification of OCD versus healthy controls showed limited generalizability in multisite settings (AUC 0.567-0.673). These findings support a neuroimaging-based framework for personalized treatment selection but require prospective validation.

Humans↗

PubMed2026

Hierarchical modeling of tumor subtypes in cell lines using large-scale genomic datasets.

Cancer cell lines (CLs) are widely used to study tumor biology and drug response, yet their translational relevance is often limited by inaccurate subtype annotations. Existing CL-tumor matching approaches are frequently constrained by flat classification schemes, weak subtype definitions, and the exclusion of normal tissue references, leading to potential confounding of tumor-specific and tissue-of-origin signals. To address these limitations, a hierarchical classification (HC) framework is presented in which CLs are aligned with patient tumors across biological resolutions, from organ to molecular subtype. Gene expression profiles from 802 CLs, 5,612 tumors from The Cancer Genome Atlas (TCGA) , and 8,939 non-cancerous tissues were integrated to separate oncogenic signals from tissue-specific signals. Node-specific features were selected using maximum relevance minimum redundancy, and balanced accuracies of 89% in cross-validation and 75%, and 80% on external datasets were achieved. Through the framework, 43 CLs were reassigned, and clinically relevant underrepresented subtypes were identified.

cancer cell lines↗

PubMed2026

Multisensory stimulation for promoting development and preventing morbidity in preterm infants.

RATIONALE: Multisensory stimulation is a structured, developmentally appropriate intervention that provides simultaneous or sequential stimulation of two or more senses (e.g. tactile, auditory, visual, or vestibular) in a controlled and non-stressful manner, with the aim of supporting early neurodevelopment in preterm infants. It has the potential to enhance physiological regulation in preterm infants by stabilizing key functions, such as respiratory patterns, heart rate, and oxygen saturation; reducing the need for respiratory support; and improving feeding performance and sleep regulation. Targeted multisensory interventions have also been associated with improved neurodevelopmental outcomes, including enhanced psychomotor development and visual function. OBJECTIVES: To assess the benefits and harms of multisensory stimulation compared to any single sensory intervention or standard care on major neurodevelopmental disability, mortality, and growth in preterm infants. SEARCH METHODS: We searched CENTRAL, MEDLINE, Embase, Emcare, CINAHL, Epistemonikos, two trial registries, and conference abstracts up to 28 November 2025. We checked reference lists of included trials, and systematic reviews on sensory interventions. ELIGIBILITY CRITERIA: We included 18 randomized controlled trials (RCTs) comparing multisensory stimulation in preterm infants with no intervention (placebo or standard care), and one RCT comparing multisensory stimulation with single-sense stimulation (tactile stimulation). OUTCOMES: Our critical outcomes were major neurodevelopmental disability at 18 to 24 months: cerebral palsy (CP), developmental delay, intellectual impairment, blindness, sensorineural deafness; death during initial hospitalization; and total weight gain (grams), assessed at discharge. When comparing multisensory stimulation with single-sense intervention, we also included weight gain during the intervention, an outcome added during the post-hoc analysis. Important outcomes were duration of hospital stay, of NICU stay, and of respiratory support; and time until full oral feeding. RISK OF BIAS: We used the Cochrane tool, RoB 2. SYNTHESIS METHODS: We conducted meta-analyses using fixed-effect models to calculate risk ratios (RR) for dichotomous data, and mean differences (MDs) for continuous data, each with its 95% confidence intervals (CIs). We assessed statistical heterogeneity by calculating the I2 statistic when we included more than two trials in a meta-analysis. We evaluated the certainty of evidence using GRADE. INCLUDED STUDIES: We included 19 trials (1554 newborn infants): 18 studies compared multisensory stimulation with standard care; one compared multisensory stimulation with single-sensory stimulation (tactile). In 10 studies, the primary aim was to assess the neurobehavioral outcomes of multisensory stimulation on preterm neo-nates. The other nine studies aimed to assess the impact of multisensory stimulation on weight gain during the intervention, weight gain until hospital discharge, length of neonatal intensive care unit (NICU) stay, length of hospital stay, time until full oral feeding, length of respiratory support, or a combination. In the abstract we report results for the critical outcomes only. We identified 13 ongoing studies. Four studies are awaiting assessment. SYNTHESIS OF RESULTS: Multisensory stimulation compared to standard care No studies reported on these major neurodevelopmental disabilities, assessed at 18 to 24 months' corrected age (CA): developmental delay, intellectual impairment, blindness, or sensorineural deafness. One study reported on rates of CP at 12 months of age. The evidence is very uncertain about the effect of multisensory stimulation on CP (RR 0.67, 95% CI 0.28 to 1.58; I² not applicable; 1 study, 18 participants; very low-certainty evidence). The evidence suggests that multisensory stimulation may result in little to no difference in death during initial hospitalization (RR 0.97, 95% CI 0.54 to 1.73; I² not applicable; 1 study, 395 participants; low-certainty evidence). Multisensory stimulation may increase total weight gain prior to discharge (MD 72.67, 95% CI 68.23 to 77.12; I² = 0%; 3 studies, 474 participants; low-certainty evidence). Multisensory stimulation compared to single-sense (tactile) stimulation No studies reported on major neurodevelopmental disability, assessed at 18 to 24 months' CA, or death during initial hospitalization. The evidence is very uncertain about the effect of multisensory stimulation compared to tactile stimulation on weight gain during the intervention (MD -175.00, 95% CI -376.60 to 26.60; I² not applicable; 1 study, 20 participants; very low-certainty evidence). The certainty of the evidence was low to very low across outcomes, primarily due to risk of bias, imprecision from small sample sizes and wide CIs, and in some cases, inconsistency. The evidence base was also limited by the lack of reporting of relevant outcomes and reliance on surrogate outcomes or shorter follow-up periods. AUTHORS' CONCLUSIONS: The available evidence on multisensory stimulation in preterm infants is limited and of low to very low certainty. No included studies reported on major neurodevelopmental disabilities at 18 to 24 months' CA, which represented a critical outcome for this review. Evidence regarding the effect of multisensory stimulation on CP is very uncertain, as it is based on a single small study reporting a surrogate outcome at 12 months. Multisensory stimulation may result in little to no difference in mortality during the initial hospitalization. It may increase total weight gain prior to discharge. However, the clinical significance of this finding is uncertain, particularly given the low certainty of the evidence and the multifactorial nature of growth in preterm infants. The evidence is very uncertain about the effect of multisensory stimulation compared to single-sense (tactile) stimulation on weight gain during the intervention. The only included study did not report major neurodevelopmental disabilities at 18 to 24 months' CA, mortality during the initial hospitalization, or total weight gain prior to discharge, which represented the critical outcomes for this review. Overall, the current evidence does not allow firm conclusions about the effectiveness of multisensory stimulation in promoting development or preventing morbidity in preterm infants. Future studies on multisensory stimulation should use more rigorous designs, larger samples, and report interventions using the template for intervention description and replication (TIDieR) checklist to ensure transparency. They should also report essential outcomes, such as neonatal death, major neurodevelopmental disabilities, length of hospital and NICU stay, time to full oral feeding, duration of respiratory support, and weight gain, to better assess the long‑term effects of multisensory stimulation in preterm infants. FUNDING: This Cochrane review had no dedicated funding. REGISTRATION: Protocol available via DOI: 10.1002/14651858.CD016073.

Humans↗

PubMed2026

Efficacy of a self-guided online resilience intervention for improving mental health among university students: A randomized controlled trial.

Epidemiological data indicates that university students are an at-risk population for the development of mental disorders. Online interventions have been proposed as promising tools for reducing barriers to treatment and establishing easily accessible health-care services promoting mental health and resilience. This study investigated the efficacy of a Learning Management System (LMS)-based self-guided online resilience intervention. 216 university students took part in a randomized controlled trial with an intervention and a waitlist control group and three measurement points (pre, post and follow-up). We conducted per-protocol (PP) and intention-to-treat (ITT) analyses with mental distress as primary outcome, and self-reported resilience and resilience factors as secondary outcomes. Further, attitudes towards online interventions, adherence, satisfaction and possible negative effects were explored. Satisfaction with the intervention was high and PP analyses (n = 150) revealed significant improvements in mental distress and self-compassion at post-test and acceptance at follow-up. No favourable effects were found for self-reported resilience and resilience factors. Adherence was low and ITT analyses revealed no significant effects. Overall, the study provides preliminary evidence for the LMS-based self-guided online intervention as a potentially valuable tool for university mental health services under optimal adherence conditions. Further research into determinants of adherence is needed to improve intervention reach.

Humans↗