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PubMed2026

Precision medicine in combating antimicrobial resistance: A comprehensive review.

Antimicrobial resistance (AMR) represents one of the most pressing threats to global public health, undermining the effectiveness of modern antimicrobial therapy and challenging decades of medical progress. This comprehensive review examines the transition from broad-spectrum empirical therapy toward precision medicine as an integrated framework for improving antimicrobial use and combating AMR. Precision medicine seeks to tailor treatment decisions by combining pathogen-specific genomic and resistance data with relevant host characteristics to optimize therapy while limiting unnecessary antimicrobial exposure and the selective pressures that drive resistance. The review synthesizes advances reported from 2020, highlighting established and emerging approaches including rapid molecular diagnostics, next-generation sequencing, CRISPR-based detection, machine learning (ML)-assisted decision support, precision dosing, and targeted therapeutics such as bacteriophage therapy, antimicrobial peptides, and bacterial proteolysis-targeting chimeras. Rather than functioning as isolated technologies, these approaches achieve their greatest clinical value when integrated within antimicrobial stewardship programs and a One Health framework that recognizes the interconnected human, animal, and environmental drivers of resistance. Despite considerable progress, important challenges remain, including equitable access to advanced technologies, interpretation of increasingly complex datasets, workforce and infrastructure limitations, and evolving regulatory pathways for novel diagnostics and therapeutics. This review concludes that while precision medicine is not a standalone solution, its successful implementation will depend on coordinated integration of diagnostics, host factors, computational tools, pharmacological optimization, and stewardship strategies to improve patient outcomes while preserving the long-term effectiveness of existing antimicrobials.

Antimicrobial resistance↗

PubMed1996

Students' learning of clinical sonography: use of computer-assisted instruction and practical class.

RATIONALE AND OBJECTIVES: The application of information technology to teaching radiology will profoundly change the way learning is mediated to students. In this project, the integration of veterinary medical students' knowledge of sonography was promoted by a computer-assisted instruction program and a subsequent practical class. METHODS: The computer-assisted instruction program emphasized the physical principles of clinical sonography and contained simulations and user-active experiments. In the practical class, the students used an actual sonographic machine for the first time and made images of a tissue-equivalent phantom. Students' responses to questionnaires were analyzed. RESULTS: On completing the overall project, 96% of the students said that they now understood sonographic concepts very or reasonably well, and 98% had become very or moderately interested in clinical sonography. CONCLUSIONS: The teaching and learning initiatives enhanced an integrated approach to learning, stimulated student interest and curiosity, improved understanding of sonographic principles, and contributed to an increased confidence and skill in using sonographic equipment.

Attitude↗

PubMed2026

Dietary Polyphenol Acteoside-Related Molecular Signatures in Clear Cell Renal Cell Carcinoma: Multi-Omics Profiling and Functional Validation of IMPDH1.

Clear cell renal cell carcinoma (ccRCC) is characterized by substantial metabolic and molecular heterogeneity, but the disease-relevant programs associated with acteoside, a dietary polyphenol, remain poorly understood. We integrated predicted acteoside targets with bulk, single-cell, and spatial transcriptomic data from ccRCC and combined molecular subtyping with cross-cohort machine-learning analysis. Acteoside-related signatures were preferentially enriched in malignant compartments and increased with tumor grade and stage. Consensus clustering identified two molecular subtypes with distinct biological and clinical features. C1 was associated with immune activation, metabolic activity, and more favorable survival, whereas C2 showed greater genomic instability, reduced renal epithelial differentiation, and poorer outcomes. We further benchmarked multiple machine-learning strategies and established a 10-gene prognostic model that retained predictive performance across independent cohorts, with IMPDH1 emerging as the strongest risk-associated feature. Functional experiments confirmed the biological relevance of IMPDH1: its knockdown suppressed ccRCC cell proliferation, DNA synthesis, colony formation, and migration, whereas overexpression produced the opposite effects. Together, these findings indicate that acteoside-related molecular signatures capture clinically relevant heterogeneity in ccRCC and provide a framework for linking dietary-polyphenol-related molecular space with tumor biology. The identification and functional validation of IMPDH1 further highlight its potential importance in ccRCC progression.

IMPDH1↗

PubMed1993

Automatic knowledge base refinement: learning from examples and deep knowledge in rheumatology.

MESICAR is a second generation expert system which contains very general descriptions of rheumatological disorders in the primary medical care field. With the help of a detailed hierarchical description of the human anatomy the system is able to support diagnostic decisions. The paper describes how machine learning techniques are used to automatically construct more specific disease descriptions for common, frequently occurring cases. The system MESICAR-LEARN implements a learning method which integrates analytical and empirical learning techniques. Cases diagnosed by MESICAR form the training examples, and MESICAR's knowledge base is used as domain theory. The learned concepts are integrated into a hierarchy of disease descriptions. They support efficient and fast reasoning on common cases in addition to the general diagnostic support afforded by MESICAR's deep knowledge.

Algorithms↗

PubMed2026

Biological Parts in Yeast Synthetic Biology: From Regulatory Elements to Predictive Design Platforms.

Yeasts, particularly Saccharomyces cerevisiae, are important eukaryotic chassis for synthetic biology because of their tractable genetics, versatile toolkits, and broad utility in metabolic engineering and functional genomics. Progress in this field has been driven by biological parts that enable programmable control of gene expression and cellular behavior. Early efforts focused mainly on promoters, terminators, and other regulatory elements for tuning individual genes. However, as engineering expanded to multigene pathways, genetic circuits, and dynamic regulatory systems, the limits of part-centric design became clear. Part performance is often shaped by genomic context, chromatin state, host physiology, and interactions with other components, which restricts modularity and predictability. In response, yeast synthetic biology is shifting toward integrated design frameworks combining multilayer regulation, standardized assembly, automated experimentation, and computational modeling. This review provides an integrated perspective on the evolution of biological parts across DNA-, RNA-, and protein-level regulation, connecting these advances with assembly frameworks, biofoundries, and machine learning to trace the trajectory from part-centric engineering toward predictive, system-level design in yeast synthetic biology.

Biofoundry↗

PubMed2026

NMR metabolomics and glycomics for cancer detection in patients with non-specific symptoms: a prospective observational cohort study.

BACKGROUND: Early cancer diagnosis in patients with non-specific symptoms is limited by the lack of discriminatory tests. Within the Oxfordshire Suspected CANcer (SCAN) pathway, exploratory biomarker work showed that serum 1H NMR-based metabolomics can identify cancer with high accuracy. SCAN2 evaluated whether integrating metabolomics with glycomics provides complementary molecular information and improves discrimination in a clinically complex, real-world population. METHODS: Serum from 369 SCAN patients (59 cancers) was analysed using AXINON® System-derived NMR metabolomics and HPLC-MS glycomics. Machine-learning models were trained to predict cancer status, with performance assessed by receiver operating characteristic (ROC) analysis of pooled cross-validated predictions. To place cancer risk in a broader clinical context, a second classifier modelling alternative non-cancer diagnosis was incorporated, and mean predicted probabilities from both models were jointly projected into a two-dimensional space, maintaining strict separation of training and test data. FINDINGS: In the full cohort, integration of glycomics with metabolomics achieved an AUC of 0.814 (95% CI 0.808-0.820). In a refined sub-cohort excluding major comorbidities and selected cancer types (32 cancers, 277 non-cancers), performance improved to an AUC of 0.884 (95% CI 0.879-0.890). Discriminatory features included cancer-associated biantennary fucosylated glycans alongside amino acid metabolites (glutamate, histidine) and lipoprotein-related measures. A classifier distinguishing metastatic from non-metastatic disease (n = 29 vs. 30) achieved an AUC of 0.80. Joint probability analysis in the full cohort preserved cancer-associated signatures across comorbidity burden, with projection-based classification achieving an accuracy of 89.2% (95% CI 85.7-92.6). INTERPRETATION: These findings validate the SCAN1 metabolomic signature in a more clinically complex cohort and indicate that integrating glycomics with metabolomics provides complementary biological information for cancer discrimination. Joint probability analysis provides an interpretable framework for cancer risk stratification within multimorbid diagnostic pathways, supporting the clinical potential of scalable multi-omics blood testing. FUNDING: EPSRC, EU Horizon 2020, Wellcome/MLSTF, Novo Nordisk Foundation.

Humans↗

PubMed2026

PGS-GS: a framework integrating polygenic scores and genomic selection in animal breeding.

Genomic prediction has become a central paradigm in biology, enabling quantitative inference of genetic contributions to complex traits across humans, animals, and plants. Although genomic research in human genetics and animal breeding shares a highly homologous methodological foundation, significant barriers persist in their analytical paradigms and application scenarios. This study aims to promote cross-disciplinary integration by introducing human-derived polygenic scores (PGS) algorithms into animal genomic selection (GS) and proposing a PGS-GS framework with a preliminary weighting-based implementation. We systematically benchmarked the predictive performance and computational efficiency of 20 algorithms, including classical linear models, machine learning, PGS, and PGS-GS using both array and whole-genome sequencing (WGS) data across four major agricultural species: beef cattle, sheep, pigs, and chickens. Our results demonstrate that PGS and PGS-GS algorithms achieve predictive accuracy competitive with genomic best linear unbiased prediction (GBLUP) while offering markedly higher computational efficiency. Moreover, incorporating PGS-derived prior information into weighted linear and non-linear models outperformed conventional weighted GBLUP. The results provide empirical evidence to inform algorithm selection and highlight the potential of integrating human-derived PGS methodologies into animal genomic prediction frameworks.

Animals↗

PubMed2026

Enhanced identification of key bacterial motility genes via a cross-species genomic hybrid feature machine learning approach.

Efficient and accurate identification of functional genes is critical to biological research, yet traditional single-species approaches are often limited by low efficiency. Previously, we established a novel method for identifying key genes using cross-species protein domain features and machine learning. However, the high multiplicity of gene members associated with specific domains creates a substantial workload for subsequent experimental validation. To address this, this study proposes an enhanced approach that integrates EggNOG-based protein sequence annotation with domain analysis. Unannotated sequences are subsequently analyzed for protein domains, generating a comprehensive "direct gene annotation plus domain" hybrid feature matrix. While the hybrid matrix model yielded comparable predictive accuracy, it significantly enhanced feature resolution: the top 50 predicted features were all known motility-related genes or domains. Furthermore, among the top 100 ranked features, 58 are confirmed to be directly related to motility based on experimental evidence. Although strict genus-level control still yielded 51 confirmed features, excessive taxonomic restriction drastically reduces the number of training genomes, which may paradoxically impair identification efficiency. These results demonstrate that the new method effectively reduces the subsequent experimental workload and enables high-throughput identification of functional genes in a single analysis. With accuracy and efficiency far exceeding those of existing single-species identification methods, it provides a highly efficient solution for mining key genes underlying other complex bacterial phenotypes.

Machine Learning↗

PubMed2026

The Role of Artificial Intelligence for Intimate Partner Violence Prevention: A Systematic Review.

INTRODUCTION: Intimate partner violence (IPV), encompassing physical, sexual, emotional and economic abuse, remains a pervasive global health concern. Traditional prevention efforts face obstacles such as underreporting, delayed detection and limited personalised support. Emerging artificial intelligence (AI) approaches offer new opportunities to enhance IPV prevention. AIM: This systematic review maps and synthesises evidence on AI-driven tools in IPV prevention based on studies published between 2004 and 2024. METHODS: Following PRISMA 2020 guidelines and PROSPERO registration, we searched PubMed, Embase, CINAHL, PsycINFO, IEEE Xplore and Web of Science. Eligible studies explicitly evaluated AI technologies targeting IPV prediction, screening, intervention or support delivery. Study quality was appraised using the Mixed Methods Appraisal Tool (MMAT). RESULTS: Of 1304 records initially identified, 41 studies met eligibility criteria. AI applications ranged from machine learning (ML) for risk prediction and natural language processing (NLP) for IPV detection in clinical and social media data, to image analysis for forensic evaluation and chatbot-based support. Predictive modelling demonstrated strong discriminative performance, while NLP-based screening detected IPV with notable sensitivity. Chatbots showed feasibility and user acceptability, but evidence of their direct impact on reducing IPV incidence was limited, with one randomised controlled trial showing a modest reduction. Key challenges identified included algorithmic bias, data privacy risks and barriers to integration across health and social care systems. DISCUSSION: AI-informed interventions show promise for improving IPV detection, risk assessment, and scalable support, but questions remain about long-term effectiveness, ethical fairness, transparency and equitable implementation. Future interdisciplinary research should address these concerns to responsibly deploy AI in IPV prevention. RELEVANCE TO CLINICAL PRACTICE: The findings highlight the importance of trauma-informed, culturally responsive care and provider training in AI applications. Nurse-led innovation and policy advocacy will be crucial for safe, equitable integration of AI in IPV prevention.

Artificial Intelligence↗

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

AI-driven CRISPR screening: optimizing gene editing through automation and intelligent decision support.

BACKGROUND: CRISPR-based genetic screening has become a central methodology in functional genomics, enabling systematic interrogation of gene function, genetic interactions and context-dependent vulnerabilities at scale. However, the rapid expansion of screening modalities-including multi-condition designs, combinatorial perturbations, in vivo applications and single-cell readouts-has exposed fundamental limitations of heuristic-driven experimental design and post hoc statistical analysis. MAIN BODY: This Review synthesizes how artificial intelligence is reshaping CRISPR screening by introducing predictive, adaptive and system-level intelligence across the experimental lifecycle. We organize recent advances into two tightly coupled modules. First, machine learning and deep learning (ML/DL) methods optimize experimental design by learning context-dependent perturbation behavior, anticipating confounding effects and enabling iterative, information-efficient screening strategies. Second, large language model-agent (LLM-agent) systems complement these advances by externalizing scientific reasoning, integrating biological knowledge at scale and coordinating analysis and decision-making in human-in-the-loop workflows. CONCLUSIONS: Together, ML/DL and LLM-agent approaches reframe CRISPR screening from a static analytical pipeline into an intelligent experimental system, with important implications for robustness, scalability and biological discovery.

Artificial Intelligence↗

PubMed2026

Sequence optimization targeting mRNA stability enhances monoclonal antibody titers in CHO cells.

This study presents a DNA sequence optimization approach that integrates mRNA stability as a tunable design parameter to enhance monoclonal antibody expression in Chinese hamster ovary (CHO) cells. A comprehensive combinatorial library of synonymous coding-sequence variants of an IgG1 light chain was integrated as single copies at a defined genomic locus in CHO cells with identical regulatory elements. Steady-state mRNA abundance, quantified by deep sequencing of gDNA and mRNA, served as a proxy for mRNA stability. These data were used to train a machine learning model that predicts mRNA abundance from coding sequence using embeddings from a pre-trained nucleotide transformer. This abundance predictor, together with established translational metrics, was incorporated into a genetic algorithm for multi-objective codon optimization. As proof-of-concept, we optimized sequences encoding Trastuzumab to either maximize or minimize the abundance criterion and obtained benchmark sequences from two commercial providers. Using targeted integration, we generated CHO cell lines and measured protein titer and cell-specific productivity. Sequences optimized for high abundance significantly increased intracellular mRNA levels (+41%), protein titer (+59%), and cell-specific productivity (+85%) relative to low-abundance designs, while viable cell densities remained comparable. Compared to commercial benchmarks, high-abundance sequences achieved significantly higher titer (+70%) and cell-specific productivity (+98%). These findings establish mRNA stability as a practical and complementary design parameter for codon optimization in monoclonal antibody production, with potential applicability to other proteins and expression systems.

CHO↗

PubMed2026

Uncovering hub genes and key pathways responsive to drought stress in rice via meta-analysis of transcriptomic data.

Drought stress presents a formidable threat to global rice cultivation, triggering complex molecular responses that impact plant growth and productivity. To decipher the underlying gene expression dynamics, we performed a comprehensive meta-analysis of transcriptomic datasets derived from drought-tolerant rice genotypes. Via microarray data from three independent studies, we identified a set of consistently expressed differentially expressed genes (DEGs) under drought conditions. Integration of functional annotation tools, including GO and KEGG pathway enrichment, revealed key biological processes and signaling cascades involved in stress mitigation, such as ABA signaling, protein folding, and photosynthesis suppression. Protein-protein interaction (PPI) network construction, followed by hub gene identification via maximal clique centrality (MCC), highlighted pivotal regulators including LEA proteins, dehydrins, HSP70, and several transcription factors. Machine learning approaches further prioritize potential biomarkers, with Random Forest models achieving high classification accuracy and pinpointing key predictive genes. Chromosomal localization analysis provided spatial insights into the distribution of these hub genes, whose expression patterns were further compared against qRT-PCR data from previously published studies. This integrative approach identifies candidate genomic markers and mechanistic insights that may support future breeding strategies for drought-tolerant rice, pending experimental validation.

Cytoscape↗

PubMed2026

Machine Learning in Hyperlipidaemia Research: Screening and Experimental Insights into Lipid Metabolism Modulators.

Hyperlipidemia, characterized by elevated blood lipid levels, represents a major global health concern due to its strong association with cardiovascular disease, diabetes, and metabolic syndrome. While current therapies - such as statins, fibrates, bile acid sequestrants, and PCSK9 inhibitors - are effective in controlling hyperlipidemia, they are often associated with adverse effects, potential drug resistance, and suboptimal efficacy in certain patient populations. All of the above underscore the urgent need for safer and more effective therapeutic alternatives. Among the major molecular targets involved in the regulation of lipid metabolism are HMG-CoA reductase, PCSK9, peroxisome proliferator-activated receptors (PPARs), cholesteryl ester transfer protein (CETP), and nuclear receptors, including the liver X receptor (LXR) and farnesoid X receptor (FXR), which are also targets for future antihyperlipidemic drug development. Recent advancements in artificial intelligence (AI) and machine learning (ML) have significantly transformed and accelerated drug discovery by enabling the processing of vast amounts of genomic, proteomic, and chemical data. Furthermore, ML tools such as quantitative structure-activity relationship (QSAR) modelling, deep learning, random forest, and support vector machines (SVM) have proven predictive and effective in identifying novel lipid metabolism modulators, thereby enhancing the efficacy and accuracy of virtual screening. Meanwhile, molecular docking has become an integral part of structure-based drug design (SBDD), and software such as AutoDock, Glide, and GOLD have proven effective in generating accurate ligand-target docking models. Molecular docking, together with ML-based approaches, enables the identification of potent and selective drug candidates. Overall, the combination of ML and molecular docking offers an efficient and accurate platform for antihyperlipidemic drug discovery, helping to overcome the limitations of currently available therapeutic strategies.

HMG-CoA reductase↗

PubMed2026

Profiler: an open web platform for multi-omics analysis.

MOTIVATION: High-throughput multi-omics technologies produce increasingly large and heterogeneous datasets that are difficult to analyze without advanced computational expertise. Existing bioinformatics tools are often fragmented or limited to specific omics types, hindering reproducibility and accessibility. There is a critical need for an integrated, user-friendly, and scalable platform capable of supporting multi-omics analyses across different data modalities. RESULTS: We present Profiler, an open-source, modular platform that unifies data import, quality control, preprocessing, statistical testing, machine and deep learning, biomarker discovery, pathway and drug-target enrichment, and survival modeling within a single reproducible environment. Built in Python with Streamlit, Profiler is available as both a web-based platform deployed on high-performance computing and a desktop version for local execution, enabling flexible usage across computational infrastructures. Profiler supports diverse omics modalities, including proteomics, transcriptomics, lipidomics, and electroencephalogram data. Through applications to glioblastoma proteomic, pancancer, and multi-omics datasets, Profiler reproduced known molecular subtypes, revealed potential therapeutic targets, and generated fully traceable analysis reports within minutes. By integrating advanced analytics behind an intuitive interface, Profiler democratizes multi-omics analysis and provides a robust, scalable foundation for systems biology and precision medicine research. AVAILABILITY AND IMPLEMENTATION: Profiler is open-source and freely available via its web platform (https://prism-profiler.univ-lille.fr) and GitHub (web version: https://github.com/yanisZirem/Profiler_v1_requests_datatests, desktop version: https://github.com/yanisZirem/prism-profiler), and archived on Zenodo (DOI: https://doi.org/10.5281/zenodo.17478158).

Software↗

PubMed2026

Assessing Metal Ion Assignment Accuracy in Protein Data Bank Models via Elemental Spectroscopy.

Accurate representation of metal ions in macromolecular structures is critical for chemical interpretation, computational modeling, and machine-learning methods that rely on Protein Data Bank (PDB) entries. However, the elemental identity of metals modeled in crystallographic structures is often inferred indirectly and rarely validated experimentally. Here, we combine Particle Induced X-ray Emission (PIXE) and X-ray Fluorescence Spectroscopy (XRFS) to determine the elemental composition of protein samples used to generate 70 deposited metalloprotein crystal structures. By analyzing the original protein material employed for crystallization, but before the addition of crystallization buffer solutions, we assess whether the modeled metal ions in deposited structures are consistent with experimentally detectable elemental content. We find that in a majority of cases, the metals modeled in the corresponding PDB entries are inconsistent with the metals present in the protein samples before crystallization, or that additional metals are present but not represented in the structural models. Spectroscopic results were integrated with automated crystallographic validation metrics, including real-space Z-difference (RSZD) analysis and systematic rerefinement, to evaluate atomic-number mismatch at metal sites. PIXE and XRFS show strong agreement for dominant elemental signals and provide complementary, scalable approaches for identifying suspect metal assignments. This work does not address physiological or functional metalation but instead highlights a widespread data integrity issue in deposited macromolecular structures, PDB-wide. These results establish an experimentally corroborated link between elemental identity and crystallographic validation metrics, enabling the large-scale detection of chemically inconsistent annotations in structural databases used for computational modeling and machine learning.

Databases, Protein↗

PubMed2026

Discovery and validation of a multi-protein panel for predicting non-fatal major adverse cardiovascular events in diabetic kidney disease.

OBJECTIVE: To identify plasma protein biomarkers associated with incident non-fatal major adverse cardiovascular events (MACE) in diabetic kidney disease (DKD) patients. RESEARCH DESIGN AND METHODS: We analyzed 317 DKD patients from the UK Biobank. Plasma proteomics and clinical data (demographics, metabolism, renal function) were integrated. In an exploratory discovery phase, three sequential Cox regression models (crude, socio-demographic-adjusted, socio-demographic-metabolic adjusted) screened non-fatal MACE-associated proteins. To prevent information leakage, the cohort was then randomly split into training (70%) and testing (30%) sets; machine-learning feature selection, hyperparameter optimization, and final model development were performed exclusively within the training set. The associated proteins were input into the four-step machine-learning pipeline (LASSO-Cox, random survival forest, Boruta, XGBoost-Cox). Predictive performance was validated using Kaplan-Meier survival analyses, longitudinal trajectory modeling, and ROC benchmarking. An interactive web application was deployed for clinical implementation. RESULTS: Of 1,463 plasma proteins, 561 were associated with non-fatal MACE across Cox models, with 14 overlapping proteins. Nine core proteins (ANG, IL1R1, CXCL14, ESAM, PTGDS, HAVCR1, FGFR2, IGSF8, CCL3) were validated: ANG showed the strongest non-fatal MACE association (HR = 3.88, 95%CI 2.33-6.48, p<0.001), and all high-expression groups had elevated non-fatal MACE risk. GO/KEGG enrichment highlighted inflammatory-immune pathways like positive regulation of MAPK cascade, Cytokine-cytokine receptor interaction and PI3K-Akt signaling pathway as key mechanisms. The model integrating proteins, demographic factors, and clinical variables achieved the highest predictive performance across non-fatal MACE (AUC = 0.768), myocardial infarction (MI) (0.808), and stroke (0.816) outcomes, with superior stability in cross-validation. CoxBoost + Elastic Net framework was selected as the optimal framework via benchmarking of 101 algorithms. The model demonstrated favorable calibration in high-risk patients and yielded positive net clinical benefit across decision thresholds of 5% to 45%. The web tool (https://jiangli2941.github.io/MACE-prediction-v2/) enables input of 28 variables, outputs non-fatal MACE risk status, risk probability, and highlights abnormal indicators. CONCLUSION: Plasma proteomics combined with machine learning identifies robust non-fatal MACE predictors in DKD.

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↗