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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↗

PubMed2026

Surgical management and outcomes of total colonic aganglionosis in children: A systematic review and meta-analysis.

AIM: Total colonic aganglionosis (TCA) is a rare form of Hirschsprung disease, and there is no consensus regarding its optimal surgical management. This systematic review and meta-analysis aimed to evaluate different surgical approaches and outcomes in children with TCA. METHODS: A systematic search of PubMed/MEDLINE and Embase was performed for studies published between January 2000 and December 2025. The review followed PRISMA guidelines and was prospectively registered in PROSPERO (CRD420251078401). Eligible studies included patients aged ≤18 years with TCA who underwent conventional pull-through procedures (CPT; Duhamel, Soave, Swenson, Rehbein, and Ikeda-Soper) or non-conventional techniques (NCPT; STATE procedure, J-pouch, right- or left-sided colonic patch pull-through, and ileocecal patch). A subgroup analysis comparing Duhamel and ileoanal pull-through procedures (IAPT) was also performed. Outcomes included fecal incontinence, Hirschsprung-associated enterocolitis (HAEC), requirement for additional interventions, postoperative intestinal obstruction, and mortality. Meta-analysis was performed using jamovi software, version 2.3.28, with p < 0.05 considered statistically significant. RESULTS: Seven studies including 134 patients compared CPT (n = 85) with NCPT (n = 49), and ten studies including 274 patients compared Duhamel (n = 143) with IAPT (n = 131). Across both comparisons, pooled odds ratios (ORs) showed no statistically significant differences in fecal incontinence, HAEC, requirement for additional interventions, postoperative intestinal obstruction (Duhamel vs IAPT only), or mortality. For CPT versus NCPT, the pooled ORs were 1.1 for fecal incontinence (95% CI, 0.44-2.73; p = 0.837), 1.1 for HAEC (95% CI, 0.49-2.71; p = 0.743), 4.3 for requirement for additional interventions (95% CI, 0.86-22.1; p = 0.074), and 3.4 for mortality (95% CI, 0.52-21.5; p = 0.198). For Duhamel versus IAPT, the pooled ORs were 1.4 for fecal incontinence (95% CI, 0.60-3.36; p = 0.423), 0.6 for HAEC (95% CI, 0.22-2.06; p = 0.503), 1.8 for requirement for additional interventions (95% CI, 0.62-5.50; p = 0.262), 1.1 for postoperative intestinal obstruction (95% CI, 0.21-6.01; p = 0.875), and 1.03 for mortality (95% CI, 0.25-4.20; p = 0.965). CONCLUSION: No statistically significant differences were identified between CPT and NCPT or between Duhamel and IAPT for the evaluated outcomes in children with TCA. However, the absence of statistically significant differences should not be interpreted as evidence of equivalence, particularly given the small sample sizes, wide confidence intervals, and clinical and methodological heterogeneity of the studies included. The choice of surgical approach should be individualized according to disease extent, patient-specific factors, institutional experience, and surgical expertise. TYPE OF STUDY: Meta-analysis. LEVEL OF EVIDENCE: III.

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

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

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

Stigma, discrimination-related events, and determinants among adult people living with systemic lupus erythematosus (SLE): Systematic review and indicator-level meta-analysis.

BackgroundSystemic lupus erythematosus (SLE) is a complex autoimmune disease with 0.4 million new cases diagnosed annually. With its wide variety of visible and invisible manifestations, people living with SLE report being exposed to stigmatization, which impacts their personal and professional lives. However, the current literature is unclear on whether healthcare management teams assess this concern during follow-up. This study aims to synthesize existing evidence on the prevalence and determinants of stigma among people living with SLE.MethodsThis systematic review and meta-analysis gathered evidence from observational studies identified from three databases on 16 July 2025. Dual independent screening, data extraction, and risk-of-bias assessment (using the Newcastle-Ottawa Scale) were performed. Results were synthesized using descriptive statistics, narrative synthesis, and indicator-level meta-analyses.ResultsWithin the past two decades, 11 studies comprising 2254 people living with SLE reported and measured stigma- and discrimination-related events using various scales. Stigma was found to be prevalent across its three constructs: interpersonal, perceived, and intrapersonal stigma. This review demonstrated that people living with SLE reported a moderate overall burden of stigma (34.71 [95% CI 26.15, 43.27]), with average stigma scores indicating psychological impact. Additionally, nearly one in two persons (46% [95% CI 28-66%]) experienced at least one form of stigma or discrimination, most commonly social isolation and unfair treatment. Mental health associations were correlated with higher stigma burden.ConclusionThis review demonstrates that stigma and discrimination are not just social challenges but also critical determinants of health. With cautious interpretation, pooled evidence reveals a consistent high prevalence of stigma and discrimination, which act as "toxic" stressors, creating a vicious cycle with psychological stress and psychiatric manifestations and disease activity. There is an urgent clinical need to move beyond a mere biological approach to disease assessment and management and to begin screening for the "invisible" burden of invalidation and discrimination.

Humans↗

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

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

Leveraging traveller genomics for LMIC diarrhoeal disease management.

Diarrhoeal pathogens impose a substantial global health burden, disproportionately affecting low- and middle-income countries (LMICs). However, in these settings, health-seeking behaviours, suboptimal microbiological capacity, and challenges in establishing genomics capacity constrain effective surveillance, including surveillance of antimicrobial resistance (AMR). In contrast, high-income countries routinely generate and share large volumes of diarrhoeal pathogen genomes through established systems, with a significant proportion originating from travellers returning from LMICs. These data reveal strong geographical structuring of lineages and clinically relevant AMR patterns, demonstrating untapped potential to support improvements in geographically granulated surveillance to support antimicrobial treatment recommendations. In this opinion article, we outline the potential to integrate traveller-derived microbial genomic data into LMIC public health decision-making and highlight the scientific, ethical, practical, and governance considerations for implementation.

antimicrobial resistance↗

PubMed2026

Facilitators and Barriers to Volunteers' Involvement in Palliative Care: A Qualitative Meta-Synthesis.

OBJECTIVE: This study aims to systematically synthesize qualitative evidence on facilitators and barriers to volunteer involvement in palliative care services, providing insights to inform strategies for strengthening volunteer support systems. METHODS: PubMed, Web of Science, Embase, Cochrane Library, Medline, EBSCO, ProQuest, China National Knowledge Infrastructure, Wanfang, VIP, and Sinomed were searched from inception to December 2025 to identify qualitative studies examining factors influencing volunteer participation in palliative care. Methodological quality was assessed using the Joanna Briggs Institute Critical Appraisal Checklist for Qualitative Research. Data were analyzed using Thomas and Harden's thematic synthesis approach and managed using NVivo 12.0 software, following the Enhancing Transparency in Reporting the Synthesis of Qualitative Research (ENTREQ) guidelines. RESULTS: Thirty-one studies involving 1042 participants were included, yielding 68 findings. Facilitators included intrinsic motivation and meaning-making at the individual level; supportive relationships and teamwork at the interpersonal level; structured support and professional recognition at the organizational level; social recognition and resource integration at the community level; and institutional safeguards and governmental incentives at the policy level. Barriers included emotional burden and limited competencies at the individual level; relationship conflicts and insufficient collaboration at the interpersonal level; management deficiencies at the organizational level; community resource imbalances at the community level; and inadequate regulations and incentives at the policy level. CONCLUSION: Volunteer participation in palliative care is influenced by multiple interacting factors. Strengthening training and support systems, enhancing team collaboration, and improving institutional frameworks may help sustain volunteer engagement and improve the quality of palliative care services.

Palliative Care↗

PubMed2026

Leading with Innovation: Maternal Health Transformation in New York City Health + Hospitals.

New York City's (NYC) maternal health crisis drew close attention in the late 2010s, driven by alarming data: Approximately 30 women died annually during childbirth in NYC, Black non-Hispanic women were 12 times more likely to die than white women, and more than 3,000 women experienced life-threatening birth complications each year. In response, NYC committed $12.8 million in July 2018 to reduce maternal mortality and eliminate racial disparities.NYC Health + Hospitals (H+H)-the nation's largest public health system, serving 1.1 million patients annually with roughly 15,000 births per year-became the primary vehicle for this initiative. With 80 percent of the system's deliveries covered by Medicaid and a patient population that is 51.2 percent Hispanic and 27.1 percent Black, H+H is uniquely positioned to lead the fight against maternal health inequity.Three flagship programs anchor H+H's response to the city's maternal mortality rate. The OB Simulation Program, launched in 2012 and expanded in 2018, was the first in the nation to use mannequins of color to train thousands of providers in obstetric emergencies. The Maternal Home Program, piloted at H+H's Kings County Hospital in 2019 and scaled system-wide by 2021, has served more than 10,341 patients, generating more than 33,000 referrals for social, behavioral health, and community resources. The Cardio-Obstetrics Program located at Kings County Hospital targets cardiovascular disease-the leading cause of maternal death among Black women-through screening, education, and community outreach. These programs are a health equity imperative, made more urgent by impending federal Medicaid cuts resulting from the H.R.1 One Big Beautiful Bill Act (passed on July 4, 2025).

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

Are there any common effects in preclinical models of micro- and nanoplastic (MNP) exposure? A systematic review.

Micro- and nanoplastics (MNPs) are emerging contaminants detected in food sources and the marine food chain, raising concerns about human health. Although no causal relationship has been established between MNP exposure and specific diseases, growing evidence suggests adverse developmental, behavioral, cognitive and biochemical effects. This systematic review synthesized evidence from common preclinical neurotoxicology models, including C. elegans, D. rerio, D. melanogaster, in vitro systems and rodents, to identify convergent developmental, behavioral and biochemical outcomes. The protocol was preregistered in OSF, followed PRISMA-P guidelines, applied PICOS criteria, and assessed methodological quality using the European Commission's ToxRTool. Overall, 185 studies were included. Consistent findings showed impaired survival and disrupted development across all models. Behavioral alterations affecting anxiety, memory, learning, sociability and locomotor activity were also consistently reported. In addition, numerous studies identified disruptions in the serotonergic (5-HT) system, including changes in neurotransmitter levels, transporters and metabolic enzymes. Despite methodological heterogeneity, these findings indicate that MNP exposure produces reproducible neurodevelopmental and neurochemical alterations across experimental models. Future studies should improve methodological harmonization, strengthen cross-model comparability and identify robust biomarkers and key mechanisms underlying MNP-induced neurotoxicity, facilitating translation to human health risk assessment frameworks.

Animals↗

PubMed2026

Methylation profiling in CNS tumor diagnostics: a single-centre real-world experience from Central Europe.

Genome-wide DNA methylation profiling has transformed neuro-oncology by providing an objective, machine learning-based taxonomy that mitigates interobserver variability and refines the histo-molecular criteria of the current WHO classification. We evaluate the real-world diagnostic performance and clinical utility of this modality in a prospective, consecutively accrued three-year cohort of 291 central nervous system (CNS) tumors across a mixed adult-pediatric population. Successful profiling was completed in 95.9% of cases. Using the Epignostix classifier, a high-confidence diagnostic match (calibrated score [CS] ≥ 0.84) was achieved in 70.3% of analyzable samples, while 26.5% returned lower-confidence scores (≥ 0.3 to < 0.84) and only 3.2% remained completely unclassifiable (CS < 0.3). When integrated into a comprehensive diagnostic framework, methylation profiling provided clinically useful results in 81.1% of cases, establishing diagnoses in 70 cases submitted for molecular subclassification and resolving diagnostic uncertainty or prompting major revisions in 149 histologically challenging tumors. Within truly ambiguous lesions, integration of methylome data dictated tumor grade modifications in 38.8% of cases (upgrading in 29.4% and downgrading in 9.4%), shifting patient risk stratification. Crucially, over half (52.7%) of the lower-confidence cases yielded meaningful clinical integration when supported by histomorphology and ancillary genetic or immunohistochemical markers, demonstrating that rigid score cutoffs should not dictate assay failure. Discrepant or misleading classifications occurred in 1.9%. Updating bioinformatic pipelines from version 11b4 to 12.8 rescued multiple ambiguous entries, increasing overall clinical utility to 84.1%. These findings demonstrate that integrating computational epigenomics with classical neuropathology enhances diagnostic precision, while highlighting the ongoing need for careful clinical-pathological correlation.

Central nervous system tumors↗

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

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

Artificial Intelligence for Diagnosis, Risk Stratification, and Prognosis of Neuroblastoma - A Systematic Review and Meta-Analysis.

PURPOSE: To synthesizes evidence on artificial intelligence (AI) performance in neuroblastoma (NB) diagnosis, risk stratification, prognosis, and genomic characterization. MATERIALS AND METHODS: A systematic review and meta-analysis was conducted following PRISMA 2020 guidelines (PROSPERO: CRD42024539475) across five databases. Meta-analyses used random-effects models with logit-transformed Area Under the Curve (AUCs) and cluster-robust standard errors. AI models were classified as Machine Learning Models (MLM) or Hybrid Nomograms (HN) based on their construction methodology. RESULTS: Of 3,742 articles identified, 53 were included. MLMs demonstrated higher point estimates than radiologists in differential diagnosis (AUC: 0.87 vs. 0.83), though this difference was not statistically significant and carried substantial uncertainty. HNs achieved stronger performance in risk stratification (AUC: 0.87). AI-derived nomograms (AUC: 0.9) and gene signatures (AUC: 0.8) outperformed conventional prognostic markers descriptively. Chemotherapy response prediction remained below clinical utility thresholds across all model types. Only 33.9% of models reported calibration and 24.5% underwent external validation. CONCLUSIONS: AI demonstrates proof-of-concept across multiple NB clinical domains. However, clinical adoption remains premature given persistent gaps in external validation, calibration, dataset size, and pediatric-specific model development. Future studies should test these models prospectively in multicenter pediatric cohorts, ideally through COG or SIOPEN, using shared definitions for diagnosis, risk group, treatment response, and survival outcomes.

Humans↗