Biology Methods and Protocols

BackgroundPostoperative delirium is a common complication in surgical patients, and is associated with a multitude of negative outcomes, including mortality, dementia, and increased healthcare costs. Therefore, a better understanding of what factors contribute to postoperative delirium, especially those that can be easily obtained, is important. MethodsWe conducted a retrospective cohort study using patients from the Medical Information Mart for Intensive Care (MIMIC)-IV database. Adult patient...

Digital breast tomosynthesis (DBT) is a powerful imaging modality that allows for improved lesion visibility, characterization, and localization compared to conventional two-dimensional digital mammography. DBT has been increasingly adopted in screening and diagnostic settings globally, particularly for women with dense breast tissue where tissue overlap presents a significant diagnostic challenge. Here we describe DBT-2026, a real world imaging dataset with 558 DBT exams from 558 patients with ...

ObjectiveThis study investigates whether incorporating physiological coupling concepts into neural network design can support stable and interpretable feature learning for histopathological image classification under limited data conditions. MethodsA physiologically inspired architecture, termed CardioPulmoNet, is introduced to model interacting feature streams analogous to pulmonary ventilation and cardiac perfusion. Local and global tissue features are integrated through bidirectional multi-h...

Cutaneous squamous cell carcinoma (cSCC) poses significant clinical challenges due to its rising incidence and potential for metastasis. Histopathologic risk stratification is further limited by substantial inter-observer variability. Unsupervised AI approaches based on content-based image retrieval offer scalable and interpretable decision support for diagnostic pathology. The objective of this study was to evaluate the use of image retrieval within histopathology atlases to stratify cSCC tumo...

BackgroundPropofol dosing guidelines recommend age-based reductions because hypnotic sensitivity increases in older adults. Most real-world evaluations of induction practice, however, have relied on total weight-normalized dose (mg/kg) rather than estimating cerebral exposure using pharmacokinetic models. Because age-related pharmacokinetic changes alter the relationship between administered dose and peak effect-site concentration (Ce,max), mg/kg surrogates may misrepresent true age-dependent ex...

Early and reliable discrimination between malignant and benign breast tumors is essential for clinical decision-making and for reducing unnecessary invasive procedures. This study presents a lightweight and reproducible machine-learning pipeline that integrates standard feature normalization with logistic regression to classify breast tumors using the Breast Cancer Wisconsin (Diagnostic) dataset (WDBC), which contains 569 samples described by 30 quantitative features derived from digitized fine-...

BackgroundAutomated systems, including large language models, are increasingly used to support data extraction in diagnostic systematic reviews. However, their reliability, safety, and repeatability under realistic extraction conditions remain insufficiently characterized. ObjectiveTo benchmark the end-to-end reliability of automated systems for extracting diagnostic accuracy data from published uro-oncologic studies, with a focus on correctness, abstention behavior in non-derivable scenarios, ...

Oral potentially malignant disorders (OPMD) are mucosal diseases with an increased risk of progression to cancer, although not all cases develop cancer in patients lifetimes. Although epithelial dysplasia (ED) grading is the current approach for assessing the risk of malignant transformation (MT), cancer risk prediction is limited by its subjective interpretation and inaccuracy. To address these challenges, this study developed COCOH (Comprehensive Oral Cancer predictor in OPMD using Histopathol...

BackgroundHuge neutrophilic infiltrates within lesional and perilesional tissue in hidradenitis suppurativa (HS) give rise to the hypothesis that neutrophil extracellular trap (NET) formation may further drive systemic immune activation in HS. As intrinsic constituents of NETs, nucleosomes-particularly circulating nucleosome containing Histone H3.1 (H3.1-nucleosomes)-serve as reliable indicators of NETosis in the blood. ObjectivesTo investigate whether plasma H3.1-nucleosomes, fluctuate with HS...

ObjectivesMultiple myeloma (MM) is the second most common hematologic malignancy in the U.S.; however, the etiology is poorly understood. We investigated social determinants of health (SDoH) associated with MM incidence and survival among low-income Black and White participants in the Southern Community Cohort Study (SCCS). MethodsThe SCCS enrolled participants aged 40-79 years from 12 Southeastern states. We examined associations between SDoH (residential racial segregation, neighborhood depri...

AbstractAccurate health information is ineffective if patients cannot understand it. Large Language Model (LLM) health research values veridical precision; however, linguistic accessibility remains an under-examined component of output quality and usability. This study investigated two sources of variability in readability classification: differences across LLM systems and across readability metrics. The analysis tested 1,120 data points from seven systems in English and Portuguese, comparing ba...

Digitizing large histopathology archives requires processing millions of scanned whole slide images that must be validated rapidly. Automated organ-of-origin classification can accelerate quality control and enable early detection of mislabeled specimens. We developed a deep learning model that classifies the organ of origin from H&E-stained slides using a single low-resolution thumbnail per slide in under one second. For training, we used thumbnails from 16,624 slides from the TCGA and CPTAC ar...

Image classification on digital pathology images relies heavily on convolutional neural networks (CNNs), yet the behavior of alternative neural computing paragigms in this domain remains insufficiently characterized. Spiking neural networks (SNNs), which process information through event-driven spike-based dynamics, have recently become trainable at scale but have not been evaluated under standardized colorectal pathology benchmarks. This study presents the first controlled comparison of SNNs an...

BackgroundElectronic Health Records(EHR) are very crucial for Clinical Decision Support Systems and for proper care to be delivered to ICU heart failure patients, there is often missing data due to monitoring device errors thus the need for robust imputation methodologies. ObjectiveTo compare and evaluate three different methodologies for imputing missing data for heart failure patients from the MIMIC-III database: Denoising Autoencoder (DAE), Self-Attention Imputation for Time Series (SAITS), ...

BackgroundPreoperative cardiovascular (CV) risk stratification is essential in non-cardiac surgery, but conventional testing is frequently overused, increasing costs without improving outcomes. Artificial intelligence (AI)-enabled electrocardiography (ECG) may enhance perioperative risk assessment by identifying patients at very low risk for adverse events. ObjectiveThis study aimed to evaluate whether AI-ECG-based risk stratification could help maintain safety and decrease potentially avoidabl...

BackgroundDelivering timely, high-quality feedback on resident scholarly projects is labour-intensive, especially in large programmes. We developed an AI-assisted evaluation system, powered by the open-weight LLaMA-3.1 large-language model (LLM), to generate formative feedback on Family Medicine residents scholarly projects and compared its performance with expert human evaluators. MethodsWe evaluated whether the AI-generated feedback achieves comparable quality to expert feedback. The tool ing...

This study challenges the assumption that undiagnosed cognitive impairment (CI) is driven primarily by patient-level barriers like poor awareness. In a population-weighted cohort of 1,856 older Singaporeans, CI prevalence was 24.7% (95%CI 18.8-31.8); yet the undiagnosed rate was high (81.4%, 95%CI 65.6-90.9), especially for mild CI (97.9%, 95%CI 94.1-99.3). This diagnostic gap persisted despite high symptom awareness (81.3%, 95%CI 63.6-91.5) and help-seeking intent (63.3%, 95%CI 47.5-76.7), with...

BackgroundQuality measurement in intensive care emphasizes task completion--whether assessments were documented and protocols followed. Electronic health record (EHR) systems capture these signals in real time, yet current metrics cannot distinguish task completion from cognitive clinical engagement. A prior analysis demonstrated that omission of orientation assessment predicted a 4.29-fold increase in hospital mortality among low-acuity ICU patients [1]. Whether combining this marker with routi...

Stroke is a major global health burden and a frequent severe complication among patients with coronary heart disease. Early identification of individuals at high risk is essential for prevention; however, conventional clinical models often fail to capture the complex interactions underlying stroke risk in this population. This study developed an integrated machine learning framework to predict stroke risk using a large real-world dataset. Multiple algorithms were evaluated, including logistic re...

Cox proportional hazard regressions are frequently employed to develop prognostic models for time-to-event data, considering both patient-specific and disease-specific characteristics. In high-dimensional clinical modeling, these biological features can exhibit high collinearity due to inter-feature relationships, potentially causing instability and numerical issues during estimation without regularization. For rare diseases such as acute myeloid leukemia (AML), the sparsity and scarcity of data...