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All Diagnostics & Imaging Drug Discovery Mental Health Hospitals & Care

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Dynamic F1-score-based voting strategies for multi-class classification: an adaptive ensemble approach for non-linear and imbalanced datasets

Classification is a core machine learning task, and ensemble voting methods are widely used to improve predictive accuracy in domains such as medical diagnosis, where class imbalance and non-linear decision boundaries are common. Conventional strategies: Majority Voting (MV), Weighted Voting (WV), and Soft Voting (SV) rely on static or classifier-level weighting schemes that fail to capture per-class differences in classifier reliability. Three dynamic, class-specific voting strategies are introduced: Highest Cl…

Scientific Reports · Health

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AI-powered medicinal chemistry and translational drug development

Medicinal chemistry sits at the center of modern drug discovery, yet translating molecular designs into approved medicines remains slow, expensive, and prone to high attrition across the pipeline from target identification to clinical validation. Artificial intelligence (AI) is beginning to reshape this landscape by enabling large-scale integration, interpretation, and generation of chemical, biological, and clinical data for hypothesis generation, chemical space exploration, and iterative cycles of model-guided…

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The Importance of Artificial Intelligence in Nursing: A Fundamentals of Care Perspective

Aim To analyze the integration of Artificial Intelligence in nursing through the lens of the Fundamentals of Care framework. Design A discursive paper. Methods This discursive paper synthesizes current literature and theoretical perspectives to examine the Relationship, Integration and Context dimensions of the Fundamentals of Care framework in the era of Artificial Intelligence. Results Artificial Intelligence offers substantial benefits in optimizing workflow (Context) and clinical precision (Integration) thro…

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Assessing the Utility of Social Determinants of Health Data in Suicide Prediction Models

Objective This study seeks to explore the utility of social determinants of health (SDoH) variables in suicide prediction models. We aim to assess the impact of individual- and geographic-level SDoH factors on improving the performance of suicide prediction models and the identification of individuals at high risk for suicide. Methods A retrospective sample of 1214 deaths by suicide and 815,544 living patients was identified in the Maryland Suicide Data Warehouse (MSDW) and linked to census tract data through ge…

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Tissue-Agnostic Cellular Morphometric Biomarkers for Risk-Adapted Management Across Gastrointestinal Precancerous Lesions and Cancers

While precision oncology increasingly adopts tissue-agnostic paradigms, current strategies remain heavily reliant on molecular alterations, with limited relevance to early-stage cancers and precancerous lesion management. Here we present an unsupervised and interpretable artificial intelligence framework that defines tissue-agnostic cellular morphometric biomarkers (CMBs) capturing conserved tumor microenvironment (TME) architectures associated with cancer progression across gastrointestinal (GI) organs. Discove…

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Enhanced classification and identification of bacterial and viral microorganisms by integration of MALDI-TOF mass spectrometry with artificial intelligence

The accurate and rapid identification of bacterial pathogens is essential in clinical setups and medical biodefense. Matrix-Assisted Laser Desorption Ionization Time-Of-Flight (MALDI-TOF) mass spectrometry has emerged as a powerful tool for fast and reliable microbial identification. This study assesses the performance of eight Machine Learning (ML) and two Deep Learning (DL) models trained using 5-fold cross validation in classifying microorganisms in a series of experiments based on MALDI-TOF mass spectra (n =…

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Overcoming the opaque side of AI in healthcare: a lifecycle based approach

Introduction Transparency has emerged as a foundational condition for trustworthy Artificial Intelligence (AI) in healthcare. Despite its centrality, practical approaches to systematically operationalize transparency across the entire lifecycle of AI-enabled medical devices remain fragmented and insufficiently structured. This work addresses this gap by proposing a lifecycle-oriented operational approach to guide the consistent implementation and evaluation of transparency in AI-based medical technologies. Areas…

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Pathogenic Genetic Variants, Comorbid Autism and Adaptive Developmental Quotient as Independent Predictors of Intellectual Disability in Children With Global Developmental Delay: An Interpretable Machine Learning Model With Calibrated Risk Estimation

Background Global developmental delay (GDD) frequently precedes intellectual disability (ID), but no validated multivariable prognostic tool exists to support individualised counselling during the initial diagnostic work-up. Existing risk indicators are typically considered in isolation, and their joint contribution within an interpretable predictive framework remains uncertain. Methods We retrospectively analysed 2453 children diagnosed with GDD between January 2014 and December 2023 at a provincial tertiary ch…

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Pathogenic Genetic Variants, Comorbid Autism and Adaptive Developmental Quotient as Independent Predictors of Intellectual Disability in Children With Global Developmental Delay: An Interpretable Machine Learning Model With Calibrated Risk Estimation

From accuracy to service: deciding what artificial intelligence outputs may do in veterinary diagnostic laboratories

Artificial intelligence (AI) tools are entering veterinary diagnostic laboratory service, but reported model accuracy does not determine what the laboratory staff should allow an output to do. This Commentary defines service entry as the point at which an AI output is allowed to influence case triage, interpretation, a draft report, or result release. Before that point, the laboratory staff should first decide whether the submitted specimen can support the question being asked. They should then document 7 decisi…

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Dynamic liquid with shape-shifting induced photocurrent variation in a CuBi<sub>2</sub>O<sub>4</sub> film for antimicrobial biocide recognition

Antimicrobial biocides play a crucial role in infection control. Although traditional detection methods are accurate, they are cumbersome to operate, require trained personnel, and provide only basic recognition without intelligent analysis. Therefore, given the critical role of biocide type and concentration in effective disinfection, there is an urgent need for portable and intelligent monitoring technologies. Here, we present an optical sensor based on an ITO/CuBi 2 O 4 /LaNiO 3 heterojunction. The device fea…

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Dynamic liquid with shape-shifting induced photocurrent variation in a CuBi<sub>2</sub>O<sub>4</sub> film for antimicrobial biocide recognition

A Risk Prediction Model for Normal-Tension Glaucoma Progression Integrating Genetic Markers and Corneal Biomechanics

Objective Normal-tension glaucoma (NTG) is characterized by progressive optic nerve damage despite intraocular pressure remaining consistently within the normal range. Predicting disease progression in patients with confirmed NTG remains challenging. This study aimed to develop and validate an interpretable machine learning model integrating genetic risk scores and corneal biomechanical parameters to predict progression risk in patients with NTG, identify independent predictors, and quantify the contribution of…

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An Umbrella Review of Artificial Intelligence Applications in Mental Health Care

Background Artificial intelligence (AI) is increasingly used in mental health care to address rising demand, workforce shortages and access barriers; however, evidence remains scattered across multiple systematic reviews, limiting synthesis and practical application. Objective To synthesise evidence on AI applications in mental health care, including trends, uses, benefits, challenges and risk-mitigation strategies, guided by an ethics of care framework. Methods This umbrella review of systematic reviews followe…

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Targeting GLS and LPIN2 in renal fibroblasts: potential therapeutic targets for kidney stone disease identified by integrated multi-omics analysis

Kidney stones (KS) are a common urological condition, the aetiology of which remains incompletely understood. This study aimed to investigate the key cell types involved in the formation of kidney stones and the molecular mechanisms associated with calcium metabolism. Single-cell and bulk RNA-seq datasets related to kidney stones were downloaded from the GEO database. Single-cell analysis was performed to explore the heterogeneity of kidney stones and identify differentially expressed genes (DEGs). Candidate gen…

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Targeting GLS and LPIN2 in renal fibroblasts: potential therapeutic targets for kidney stone disease identified by integrated multi-omics analysis

The Lymph Node Ratio as a Predictive Biomarker for Individualized Benefit from Adjuvant Chemotherapy in Gastric Cancer: A Retrospective Cohort and Causal Machine Learning Study

Background Optimizing adjuvant chemotherapy (AC) for gastric cancer (GC) remains challenging due to patient heterogeneity. While the lymph node ratio (LNR) is a known prognostic factor, its role in predicting individualized AC benefit remains underexplored. This study aimed to leverage causal machine learning to explore LNR's role for personalized treatment. Methods We conducted a retrospective cohort study of 2,748 patients undergoing radical gastrectomy (2007-2017, re-staged by AJCC 8th edition). While the ful…

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Integrative single-cell and machine-learning analysis identifies LGALS1 as a macrophage-associated diagnostic and prognostic biomarker in hepatocellular carcinoma

Background Hepatocellular carcinoma (HCC) is characterized by marked heterogeneity and an immunosuppressive microenvironment in which tumor-associated macrophages contribute to disease progression. This study aimed to identify macrophage-associated biomarkers with diagnostic, prognostic, and translational relevance in HCC. Methods Single-cell RNA-sequencing datasets were integrated with bulk transcriptomic and clinical data from TCGA-LIHC, GEO, and ICGC cohorts. Macrophage markers were intersected with tumor-ass…

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