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Results for “multimodal integration”

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PubMed2026

Enterocutaneous Fistula-Associated Sepsis and Mortality: Development and Validation of a Multimodal Artificial Intelligence Prediction Model.

BACKGROUND: Predicting enterocutaneous fistula (ECF)-associated sepsis and mortality poses significant challenges in digital health care due to the disease's complexity and heterogeneous clinical manifestations. Current approaches that rely on single-modal data or traditional scoring systems often fail to capture the intricate immune-inflammatory dynamics and multisystem involvement in patients with ECF. OBJECTIVE: This study aims to develop an artificial intelligence (AI)-driven multimodal fusion model integrating clinical, imaging, and transcriptomic data for early prediction of ECF-associated sepsis and 28-day mortality, addressing the limitations of conventional single-dimensional models. METHODS: This study leveraged publicly available datasets (Medical Information Mart for Intensive Care III [MIMIC-III], electronic Intensive Care Unit [eICU], and The Cancer Genome Atlas) to construct a multimodal framework. Clinical parameters were processed using Extreme Gradient Boosting, abdominal imaging features were extracted via convolutional neural networks, and transcriptomic profiles were analyzed with variational autoencoders. A Transformer-based fusion network was employed for joint prediction and validated through cross-validation and external testing. Key features were identified using Shapley Additive Explanations and Local Interpretable Model-Agnostic Explanations interpretability algorithms, while immune regulatory mechanisms were explored via weighted gene co-expression network analysis. RESULTS: The multimodal model achieved an area under the curve (AUC) of 0.89 for predicting sepsis and 28-day mortality, outperforming unimodal models (clinical-only model, AUC 0.72, and imaging-only model, AUC 0.78). Critical predictors included Sequential Organ Failure Assessment score, lactate levels, intra-abdominal free fluid on imaging, and immunoregulatory genes (programmed death-ligand 1 [PD-L1] and indoleamine 2,3-dioxygenase 1 [IDO1]). Mechanistic analysis revealed distinct immune reprogramming in patients with sepsis, characterized by increased regulatory T cells and M2 macrophages, along with downregulated cluster of differentiation 8+ (CD8+) T cells. CONCLUSIONS: This multimodal AI model offers an innovative digital solution in medical informatics, enabling precise early risk stratification for ECF-associated sepsis. By integrating multisource data and providing interpretable insights into immune-inflammatory pathways, the model enhances health care quality for patients with ECF and paves the way for personalized intervention strategies.

Humans↗

PubMed2024

Protocol to perform integrative analysis of high-dimensional single-cell multimodal data using an interpretable deep learning technique.

The advent of single-cell multi-omics sequencing technology makes it possible for researchers to leverage multiple modalities for individual cells. Here, we present a protocol to perform integrative analysis of high-dimensional single-cell multimodal data using an interpretable deep learning technique called moETM. We describe steps for data preprocessing, multi-omics integration, inclusion of prior pathway knowledge, and cross-omics imputation. As a demonstration, we used the single-cell multi-omics data collected from bone marrow mononuclear cells (GSE194122) as in our original study. For complete details on the use and execution of this protocol, please refer to Zhou et al.1.

Deep Learning↗

PubMed2026

An Integrative Morphological and Genomic Analysis With a Refined Fluorescence In Situ Hybridization (FISH) Threshold and Novel Kinase Fusions in a Large Asian Cohort of Spitzoid Neoplasms.

Differentiating atypical Spitz tumors (ASTs) from true Spitz melanomas (SMs) and conventional melanomas with spitzoid features (MSFs) remains a formidable diagnostic challenge. Because current molecular epidemiological data are overwhelmingly derived from Caucasian cohorts, the genomic landscape of Asian populations remains largely unexplored. To elucidate the molecular progression landscape and refine the diagnostic criteria, we performed a comprehensive multimodal analysis-integrating histomorphology, immunohistochemistry, multiprobe fluorescence in situ hybridization (FISH), and targeted RNA/DNA-based next-generation sequencing (NGS)-on a cohort of 140 spitzoid neoplasms. This cohort, comprising 126 ASTs, 8 SMs, and 6 MSFs, represents the largest Asian cohort to date. Malignant phenotype strongly correlated with lesional asymmetry, deep atypical mitoses, a sheet-like growth pattern, diffuse preferentially expressed antigen of melanoma positivity, and significant loss of p16 expression (64.3% in SM/MSF vs 9.5% in ASTs; P < .0001). Building upon the established melanoma FISH criteria, we optimized a prognostic threshold of ≥2 FISH abnormalities specifically tailored for spitzoid neoplasms. We demonstrated that isolated single chromosomal aberrations (particularly MYB loss) are relatively stable events that are frequent in indolent ASTs, whereas our refined ≥2 threshold yielded 100% sensitivity and 92.5% specificity for predicting regional lymph node metastasis/local recurrence. Molecularly, NGS identified mutually exclusive initiating driver alterations (comprising kinase fusions and HRAS mutations) in 89.9% of true Spitz neoplasms, a remarkably high prevalence suggesting a distinct genetic background in Asian populations. We also characterized 5 entirely novel kinase fusions (ZNF24::ROS1, PCBP1::ROS1, NUMA1::RET, CBWD1::ALK, and TPR::NTRK1). Furthermore, NGS definitively segregated true Spitz neoplasms from morphological mimics (MSF), which lacked fusions and were driven by canonical genomic alterations of the conventional melanoma pathway. Integrating these genomic landscapes validated a stepwise progression model. Although isolated kinase fusions drove indolent ASTs, malignant SM invariably harbored concurrent pathogenic secondary alterations, demonstrating a profound reliance on CDKN2A/B, TP53, and CDK4 aberrations. Ultimately, we propose an integrated diagnostic algorithm combining morphological evaluation, the refined FISH threshold, and comprehensive NGS profiling, providing a precise, evidence-based framework for pathway classification and clinical management of spitzoid neoplasms.

fluorescence in situ hybridization↗

PubMed2026

Unifying multimodal single-cell data with a mixture-of-experts β-variational autoencoder framework.

Multimodal single-cell assays profile complementary layers of cell state, but integration is complicated by modality mismatch, sparsity, and uneven cohort coverage. Here, we present Unified Variational Inference (UniVI), a scalable mixture-of-experts β-variational autoencoder that learns a shared latent space while preserving modality-specific structure. UniVI couples modality-specific encoders/decoders with a shared latent prior and a symmetric cross-modal alignment objective, enabling consistent integration of paired measurements without curated feature-link graphs or preannotated reference atlases; optional supervised heads can be added when labels are available. Across paired RNA-protein (CITE-seq) and RNA-chromatin (10x Genomics Multiome, SHARE-seq) data spanning human PBMCs and mouse back skin-a nonhematopoietic tissue with continuous differentiation hierarchies-UniVI produces coherent embeddings, improves label transfer, and enables cross-modal reconstruction and denoising. Extending to trimodal measurements, UniVI maintains robust three-way alignment among RNA, chromatin accessibility, and surface proteins (TEA-seq), and accommodates DNA methylation in a paired scNMT-seq mouse gastrulation proof-of-concept under beta-binomial likelihoods. Performance degrades gracefully under severe cell type imbalance and in the presence of modality-exclusive populations. In an acute myeloid leukemia mosaic design, a paired RNA-protein bridge anchors independent RNA-only and protein+genotype cohorts, revealing genotype-associated neighborhoods that sharpen with mutation-aware fine-tuning. UniVI thus provides a flexible, interpretable framework for multimodal integration across paired, trimodal, and mosaic study designs and supports practical reference-to-query projection in partially observed studies.

Journal Article↗

PubMed1977

A Golgi study on the hypothalamus of amphibia. The neuronal typology.

The neuronal typology in the hypothalamus of the frog and the crested newt was studied by the Golgi technique. In the newt, piriform, multipolar or cerebrospinal fluid (CSF)-contacting neurons of relatively primitive type, according to the classification of Ramón-Moliner, are encountered in the preoptic area. Moreover, magnocellular neurons are impregnated. In the frog the preoptic area shows a more varied typology. The posterior hypothalami of the frog and the newt exhibit mainly bipolar CSF-contacting and piriform neurons. These latter are generally "tufted", but some bipolar of multipolar cells are encountered, especially in the frog. The simple anatomical organization of the amphibian hypothalamus corresponds well with the pattern of "generalized" integrative area where multimodal sensory inputs converge--including visceral information from cerebrospinal fluid by means of hypothalamic CSF-contacting sensors--to regulate the neuroendocrine outflow.

Animals↗

PubMed2026

Impact of early nurse-led implementation of an intensive care unit diary following major trauma on quality of life: The QUALITRAU randomized controlled trial.

BACKGROUND: Survivors of major trauma often experience long-term impairments in health-related quality of life (HRQoL) and post-traumatic stress disorder (PTSD). Intensive care unit (ICU) diaries have been proposed to reduce psychological sequelae, but evidence remains conflicting and not specific to trauma patients. OBJECTIVE: To assess whether, in patients with major trauma, a nurse-led ICU diary implemented within the first 48 h after trauma improves HRQoL at 1 year vs. usual care. METHODS: The QUALITRAU randomized controlled trial was conducted in three ICUs of a French tertiary hospital. Adult patients with major trauma (Injury Severity Score > 15) were randomized within 48 h of admission to receive either an ICU diary combined with usual care or usual care alone. The primary outcome was HRQoL at 12 months, assessed with the 4 domains of the WHOQOL-BREF questionnaire. Secondary outcomes included PTSD severity measured with the Impact of Event Scale (IES). Analyses were performed on an intention-to-treat basis. RESULTS: Between November 2014 and November 2016, 208 patients were randomized (101 intervention, 107 control), with primary outcome available for 121 (53 intervention, 68 control). Median age was 35 years [IQR 25-51], 81% were men, and 63% had severe traumatic brain injury. At 12 months, there were no differences between intervention and control groups in the WHOQOL-BREF domains (physical: 5.7 [IQR 4.6-11.4] vs 9.1 [IQR 4.6-13.1],P = 0.16; psychological: 8.0 [IQR 6.7-13.3] vs 11.3 [IQR 6.7-13.3],P = 0.08; social: 5.3 [IQR 4.0-14.7] vs 12.0 [IQR 4.0-14.7],P = 0.10; environment: 8.0 [IQR 5.5-14.5] vs 12.0 [IQR 5.5-15.5], P = 0.05). IES scores were also not different. CONCLUSIONS: Early implementation of nurse-led ICU diaries was not associated with improved long-term HRQoL or reduced PTSD symptoms in patients with major trauma. IMPLICATION FOR CLINICAL PRACTICE: These findings suggest that ICU diaries may need to be integrated into broader, multimodal rehabilitation strategies and may depend on factors such as timing, content, or patient characteristics.

Humans↗

PubMed2026

Efforts towards a precision medicine approach in juvenile idiopathic arthritis.

Juvenile idiopathic arthritis (JIA) is the commonest group of childhood arthritides. Despite the availability of advanced therapeutics, many children and young people (CYP) with JIA experience disease flares, and in some, chronic joint damage. Tailoring treatment based on unique biological profiles would benefit CYP with JIA given their variable clinical presentation and disease course. To date, biomarkers to predict treatment response are lacking. With advances in single cell technologies, we are now able to profile the genes and proteins of target tissues at unprecedented resolution to define the biological basis of disease and guide novel treatment approaches. The complex analyses and combination of biological and clinical outcome data from large datasets across disease phenotypes have become possible with the development of computational and machine learning methods. Here, we summarize the strategies to integrate data through multimodal based approaches to maximize precision medicine and research priorities for CYP with JIA.

Humans↗

PubMed2026

Research agenda to advance anhedonia assessment, understanding and treatment: an ECNP-GALENOS expert meeting report.

Anhedonia, broadly defined as a reduced ability to experience interest or pleasure, represents an important transdiagnostic neuropsychiatric symptom dimension which may benefit from targeted diagnostics and treatments. Different lines of research have proposed that it comprises multiple facets, including deficits in anticipatory ('wanting') and consummatory ('liking') reward processing as well as reward learning and affects different aspects of life (eg, social, physical, cognitive). Certain facets-more specifically anticipation, motivation and reward learning-likely involve blunted phasic dopaminergic signalling. However, recent meta-analytical evidence of human depression studies indicates that prodopaminergic antidepressants produce relatively small improvements in anhedonia symptoms and suggest that mechanisms beyond dopamine likely contribute to anhedonia. This stimulated an expert meeting to review the literature and define priorities for future research in anhedonia. A central key priority is developing a translational biologically-informed nomenclature and consensus that solves the current mismatch between constructs, paradigms and measures, and mechanisms, which separates discrete reward-related processes such as effort allocation, reward learning and anticipatory interest versus consummatory pleasure. Clinical research priorities are improved multimodal measurement tools, integrating neurobiological frameworks (eg, neuroimaging, electrophysiology and liquid biomarkers capturing dopaminergic, glutamatergic, opioid and immunometabolic pathways) and transdiagnostic studies across neuropsychiatric disorders and developmental stages. Innovative trial designs that explicitly target anhedonic phenotypes as a primary outcome and test mechanism-based interventions are also needed. Translational research recommendations include back-translation strategies that begin with patient-relevant phenotypes followed by the development of comparable human and animal tasks that target reward-related processes, such as effort allocation, reward learning and anticipatory interest versus consummatory pleasure, improve cross-species behavioural paradigms and enhance methodological rigour and reproducibility. Collectively, these recommendations will help refine the conceptualisation of anhedonia and advance its role within precision psychiatry as a mechanistically grounded target across multiple disorders.

Humans↗

PubMed2026

Impact of PerioperAtive LidocAine Infusions on Enhanced Recovery After Noncardiac Surgery (IMPALA-ERAS) in an inpatient setting: rationale, design and protocol for a sequential, repeated crossover trial.

INTRODUCTION: Multimodal analgesic strategies designed to minimise perioperative opioid exposure are fundamental components of enhanced recovery after surgery (ERAS) pathways. Despite widespread implementation of ERAS protocols, the optimal analgesic regimen remains undefined, as the individual contributions of specific agents to overall analgesic efficacy and opioid-sparing effects are not fully elucidated. Intravenous lidocaine, a widely utilised local anaesthetic, possesses both analgesic and anti-inflammatory properties and has been associated with improved gastrointestinal recovery. This study seeks to pragmatically evaluate the impact of incorporating perioperative intravenous lidocaine infusion into established ERAS pathways on postoperative functional recovery. METHODS AND ANALYSIS: The Impact of PerioperAtive LidocAine Infusions (IMPALA) on ERAS trial is a single-centre, pragmatic, cluster-randomised, double-blinded, placebo-controlled study. A total of 2290 patients undergoing elective colorectal surgery, emergency general surgery, urology, ventral hernia repair, surgical oncology or spine surgery will be randomly assigned to receive either intraoperative and postoperative intravenous lidocaine infusions (administered for up to 48 hours) or placebo as part of a standardised multimodal analgesic regimen integrated into established ERAS pathways. The primary outcome is case mix index-adjusted resource length of stay, defined as the time interval from surgical initiation to hospital discharge adjusted for case mix index. The primary outcome is total inpatient opioid consumption within the first 72 hours, reported in oral morphine milligram equivalents. Secondary outcomes include various in-hospital clinical endpoints derived from the electronic health record. ETHICS AND DISSEMINATION: This protocol and accompanying statistical analysis plan outline the study design, primary and secondary endpoints and analytic methodology. The IMPALA-ERAS trial has received ethical approval from the Vanderbilt University Institutional Review Board (IRB: 250617). The findings will be disseminated via peer-reviewed publications and presentations at national conferences. Results from this trial are expected to inform evidence-based practices regarding perioperative lidocaine infusion and its potential contributions to enhanced postoperative recovery in surgical patients. TRIAL REGISTRATION NUMBER: NCT07224711.

Humans↗

PubMed2026

Multimodal artificial intelligence and machine learning in oncology: from data integration to precision cancer care.

Cancer remains a major global health burden, with approximately 20 million new cases and 9.7 million cancer-related deaths reported globally in 2022. While advances in radiological imaging, molecular profiling, and clinical data have enhanced the interpretation of disease progression, the availability of multiple such modalities still does not meet the needs of a large patient population. This narrative review focuses on the role of multimodal artificial intelligence and machine learning in bridging the gap in interpreting heterogeneous modalities to improve risk prediction, prognostic assessment, and treatment decision-making in precision oncology. Multimodal frameworks such as Pathomic Fusion illustrate how complementary histopathological and genomic information can be integrated for cancer diagnosis and prognostic modeling. Multimodal models have demonstrated potential in virtual biopsy, cancer screening, prognostic prediction, radiotherapy planning, intraoperative guidance, and clinical-trial design using digital twins and synthetic control arms. The major limitations of incorporating multimodal artificial intelligence and machine learning in oncology include data heterogeneity, demographic or institutional biases, and reproducibility challenges that hinder translation. Accordingly, appropriate data-governance strategies, fairness audits, and privacy-preserving approaches such as federated learning should be considered where appropriate. Future progress will depend on the development of standardized benchmarking datasets, robust external validation, seamless integration with electronic health records and picture archiving and communication systems, and the implementation of explainable, secure, and clinically validated multimodal artificial intelligence frameworks that support precision oncology in routine clinical practice.

deep learning↗

PubMed2026

Multimodal risk assessment for oral potentially malignant disorders: Integrating patient-centered and specimen-derived data.

BACKGROUND: Oral potentially malignant disorders exhibit heterogeneous malignant transformation risk that clinical approaches fail to adequately predict. Histopathologic dysplasia grading, the reference standard of risk assessment, is associated with poor interobserver reliability and limited prognostic discrimination. It is necessary to define other potential patient- and tissue-associated risk modifiers to improve patient-specific disease prediction. TYPES OF STUDIES REVIEWED: PubMed was queried for patient- and specimen-derived factors as they relate to oral cancer and oral potentially malignant disorders, with preference for systematic review and meta-analysis articles published within the past 5 years. When not available, guidelines from the American Cancer Society, National Cancer Institute, or other national organizations or the most recent best articles were referenced to support the data presented. RESULTS: Within patient-associated factors, validated measures of tobacco and alcohol exposure, clinical lesion characteristics, systemic health factors including metabolic syndrome components, comorbidity risk, and dental health indexes were found. Within specimen-derived data, tissue-based analyses encompassing histopathology and advanced molecular profiling (genomic, epigenomic, transcriptomic, spatial approaches), blood-based germline and somatic mutation analysis, and saliva-based microbiome characterization and inflammatory biomarker assessment were addressed. PRACTICAL IMPLICATIONS: Malignant transformation reflects intersecting patient and specimen risk pathways that affect each patient differently; no single modality captures this complexity. Realizing precision prognostication in oral precancer will require coordinated expansion and standardization of data collection across research groups. This review is intended to guide covariate selection for prospective study design, improve reproducibility, and ultimately enable the development of validated multimodal risk prediction tools for clinical deployment.

Humans↗

PubMed2026

AI-Supported, Integrative Prediction of Postoperative Delirium: Protocol for the CONFUSED Study.

BACKGROUND: Postoperative delirium (POD) is a frequent and serious complication in older surgical patients, characterized by acute cognitive dysfunction and fluctuating levels of consciousness. POD is associated with prolonged hospitalization, long-term cognitive decline, reduced quality of life, and increased mortality. Despite its clinical relevance, the underlying pathophysiological mechanisms remain poorly understood, and reliable biomarkers for early prediction and prevention are lacking. OBJECTIVE: The CONFUSED study aims to identify molecular and clinical predictors of POD by integrating clinical data with proteomic, transcriptomic, and epigenetic analyses. The primary objective is to develop predictive models for POD using multimodal data. Secondary objectives include the identification of delirium-associated genes, proteins, and epigenetic signatures, as well as the exploration of patient subgroups at increased risk for POD. METHODS: CONFUSED is a prospective observational cohort study conducted at a German university hospital. Adult patients undergoing major surgery under general anesthesia will be enrolled until 100 cases of POD have been observed, which is expected to require a total sample size of approximately 200 to 300 patients. Blood samples are collected at 4 predefined time points: before premedication, immediately after surgery, and on postoperative days 2 and 5. Samples undergo comprehensive proteomic profiling, transcriptomic analysis using RNA microarrays, DNA methylation analysis, and genotyping of selected polymorphisms. Clinical data, including demographics, comorbidities, perioperative variables, medications, and delirium assessments using the Confusion Assessment Method (CAM) and CAM for the intensive care unit, are systematically recorded. Statistical analyses include univariate and multivariate methods, as well as machine learning approaches such as random forests and support vector machines, to identify relevant biomarkers and develop predictive models. The study protocol follows STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) and TRIPOD (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis) guidelines and was approved by the responsible ethics committees. RESULTS: The study was registered in the German Clinical Trials Register (DRKS00033854) on March 18, 2024. Recruitment started in January 2024 and is ongoing at the time of manuscript submission. As of now, 135 patients have been enrolled. Sample collection and laboratory analyses are ongoing. Data analysis began in January 2026, with first results anticipated in July 2026. Final data lock is anticipated after the completion of recruitment. CONCLUSIONS: By integrating multimodal molecular data with clinical parameters and applying advanced machine learning techniques, the CONFUSED study aims to improve the prediction and understanding of POD. The results are expected to support the development of personalized preventive strategies and contribute to improved perioperative care for patients at risk of POD.

Humans↗

PubMed2026

The role of circulating tumor DNA (ctDNA) to detect minimal residual disease in locally advanced gastroesophageal carcinoma: the BUTTERFLY study.

BACKGROUND: Despite advances in perioperative and neoadjuvant strategies, patients with locally advanced gastroesophageal cancers remain at high risk of recurrence after curative intent treatment. No validated biomarkers are available to detect minimal residual disease (MRD) or to guide post-operative risk-adapted management. Circulating tumor DNA (ctDNA) has emerged as a noninvasive tool for disease monitoring; single-parameter or tumor-informed assays, however, may lack sensitivity in low-tumor burden settings. Multimodal, tumor-agnostic approaches may overcome these limitations. METHODS: The BUTTERFLY study is a prospective, multicenter observational study enrolling patients with stage II-III gastric, gastroesophageal junction, or esophageal cancer treated with perioperative chemotherapy or neoadjuvant chemoradiotherapy followed by surgery. It evaluates the diagnostic performance and prognostic value of an academic, tumor-agnostic, multimodal ctDNA assay for MRD detection and prognostic stratification. Serial plasma samples are collected from baseline through post-operative follow-up and at relapse. Cell-free DNA is analyzed using the Agnostic Liquid Biopsy Multimodal Advancement (ALMA) platform, integrating tumor fraction estimation, somatic copy number alterations, fragmentomic features, single-nucleotide variants, and whole-genome methylation profiling. Multimodal features are combined with clinical variables using machine learning-based models to enhance MRD detection and relapse risk stratification. The primary endpoint includes sensitivity and specificity of ALMA-defined ctDNA/MRD status at the 4-8 weeks after surgery landmark, whereas secondary endpoints assess diagnostic performance at other time points and associations between ctDNA status and dynamics with disease-free survival, overall survival, treatment response, and lead time to recurrence. FUTURE PERSPECTIVES: If validated, this tumor-agnostic, multimodal ctDNA approach may enable earlier molecular relapse detection and support personalized post-operative management strategies.

circulating tumor DNA (ctDNA)↗

PubMed2026

Spectral-Proteomic Integration Analysis (SPIA) Deciphers Molecular Trajectories of Breast Cancer and Enables Multitarget Therapeutic Assessment.

Raman spectroscopy and mass spectrometry-based proteomics offer deeply complementary yet largely disconnected views of cancer biology: the former provides a label-free, real-time biochemical phenotype, while the latter delivers a quantitative inventory of specific protein effectors. Bridging this gap remains a fundamental challenge in analytical biomedicine. Here, we introduce Spectral-Proteomic Integration Analysis (SPIA)─a novel, data-driven integrative framework that systematically links Raman spectroscopic phenotypes with quantitative proteomic profiles through machine learning and statistical correlation. Using a DMBA-induced rat breast cancer model with and without Toremifene (TOR) intervention, SPIA dynamically maps tumor microenvironment remodeling, capturing progressive collagen deposition and lipid metabolic reprogramming. An SVM classifier trained on Raman spectra achieves exceptional diagnostic accuracy (AUC ≥ 99.0%) and successfully predicts TOR therapeutic response. Proteomic analysis identifies 1,350 differentially expressed proteins, with convergent machine learning feature selection (LASSO, Random Forest, XGBoost) pinpointing core regulators including Luc7l2, Nucb1, Cbx3, and Csnk2a1. Crucially, Spearman correlation analysis between key Raman bands and core DEPs reveals strong, statistically robust associations (median ρ ∼ 0.75 in the 1533-1669 cm-1 region), empirically validating SPIA's core integrative logic. Leveraging this multimodal map, we elucidate a multitarget mechanism for TOR involving concurrent suppression of collagen deposition and correction of aberrant lipid metabolism. SPIA establishes a powerful, generalizable paradigm for integrating phenotypic and molecular data, with broad implications for biomarker discovery, drug mechanism elucidation, and precision oncology.

Animals↗

PubMed1979

The effect of delayed feedback on infant learning reexamined.

The study reexamines the effect of delayed reinforcement upon contingency behavior in 6- to 8-month-old infants and attempts to account for the temporal discrepancy between span of integration and contingency memory. A modified delayed-reinforcement scheduling procedure enabled a previous methodological criticism to be discounted. The findings confirmed that whereas infants revealed reliable acquisition under immediate reinforcement, a 3-sec delay (whether reset or nonreset) precluded response acquisition, as did 6-sec and 10-sec delay of reinforcement. The findings are interpreted in terms of an informational-load hypothesis which relates short-term memory to the integration and/or segregation of multimodal input.

Conditioning, Operant↗

PubMed2026

mmContext: an open framework for multimodal contrastive learning of omics and text data.

SUMMARY: Multimodal approaches are increasingly leveraged for integrating omics data with textual biological knowledge. Yet there is still no accessible, standardized framework that enables systematic comparison of omics representations with different text encoders within a unified workflow. We present mmContext, a lightweight and extensible multimodal embedding framework built on top of the open-source Sentence Transformers library. The software allows researchers to train or apply models that jointly embed omics and text data using any numeric representation stored in an AnnData.obsm layer and any text encoder available in Hugging Face. mmContext supports integration of diverse biological text sources and provides pipelines for training, evaluation, and data preparation. We train and evaluate models for a RNA-Seq and text integration task, and demonstrate their utility through zero-shot classification of cell types and diseases across four independent datasets. By releasing all models, datasets, and tutorials openly, mmContext enables reproducible and accessible multimodal learning for omics-text integration. AVAILABILITY AND IMPLEMENTATION: Pretrained checkpoints and full source code for our custom MMContextEncoder are available on Hugging Face huggingface.co/jo-mengr. The Python package github.com/mengerj/mmcontext provides the model implementation and training and evaluation scripts for custom training. The releases for the publication can be accessed via zenodo: adata_hf_datasets: doi.org/10.5281/zenodo.19185217 and mmContext: doi.org/10.5281/zenodo.19185493.

Computational Biology↗

PubMed2026

CRISPR-Enabled functional genomics in hPSCs-derived neural models for autism spectrum disorder.

Autism Spectrum Disorder (ASD) is a genetically heterogeneous neurodevelopmental condition in which hundreds of individually rare risk variants converge on a small number of shared biological pathways, including synaptic scaffolding, chromatin remodeling, excitation-inhibition balance, and cellular energy metabolism. Translating this genetic heterogeneity into mechanistic insight requires experimental systems capable of interrogating individual gene functions in human-relevant neural contexts at scale. CRISPR-enabled functional genomics in human pluripotent stem cell (hPSC)-derived neural models, spanning neural progenitors, cortical and inhibitory neurons, astrocytes, microglia, and brain organoids, provides precisely this capability. By integrating pooled perturbation screens with multimodal readouts including single-cell and spatial transcriptomics, chromatin accessibility profiling, proximity labeling proteomics, multi-electrode array electrophysiology, and metabolic flux analysis, these platforms enable systematic, causal mapping of ASD gene function at system resolution. Early applications have already revealed convergent mechanisms: BAF complex disruption expands the ventral progenitor pool and biases its fate toward oligodendrocyte and interneuron lineages; ADNP loss impairs microglial synaptic pruning through altered endocytic trafficking; and mTOR pathway dysregulation in PTEN- and TSC2-perturbed models links genetic risk directly to metabolic and mitochondrial dysfunction. Computational frameworks including MIMOSCA and SCEPTRE enable causal network reconstruction and pseudotime inference from these datasets, moving the field from gene lists toward pathway-level models of ASD pathobiology. Translational applications leverage isogenic iPSC panels and variant-level base and prime editing to stratify ASD variants by functional impact, informing gene therapy design for haploinsufficient targets such as CHD8 and SCN2A via AAV or antisense oligonucleotide delivery. Remaining challenges, including model developmental immaturity, batch variability, and the difficulty of modeling polygenic risk, are addressed by a roadmap integrating spatial perturbomics, AI-driven causal inference, and population-scale standardized biobanks. This review synthesizes the current state of CRISPR-based functional genomics in human stem cell neural models as a coherent experimental framework for converting ASD genetic associations into mechanistic understanding and therapeutic opportunity.

Humans↗

PubMed2025

Foundation model based multimodal transformer framework for survival analysis in HER2 stratified breast cancer.

Objective. To improve survival prediction for HER2-positive breast cancer by integrating histopathological, molecular, and clinical data using a multimodal transformer framework.Approach. We propose a multimodal transformer framework for breast cancer survival prediction using HER2 stratified (SurvMBC), a foundation model-enhanced architecture that fuses three data modalities: whole-slide images, clinical narratives, and molecular features. Tumor microenvironment features are extracted using a pathology language and image pre-training (PLIP), clinical narratives are processed with BioBERT, and miRNA expression plus DNA methylation data are embedded using Gen2Vec. These representations are integrated through a cross-modal transformer with attention mechanisms for survival prediction.Main results. The model was evaluated on 1,095 HER2-positive breast cancer patients from The Cancer Genome Atlas. SurvMBC achieved a concordance index (C-index) of 0.857 (95% CI: 0.834, 0.880), a low integrated Brier score, and a strong inverse negative binomial log-likelihood. Risk stratification based on model outputs significantly separated high- and low-risk groups (log-rankp< 0.01) and showed strong associations with tumor stage, grade, and hormone receptor status (allp< 0.05).Significance. SurvMBC demonstrates the effectiveness of multimodal fusion in addressing tumor heterogeneity and improving prognostic accuracy. The attention-based integration enables context-aware learning of survival-relevant features across modalities, supporting individualized risk stratification and risk-adaptive treatment planning for HER2 stratified breast cancer patients.

Breast Neoplasms↗