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ProtoECGNet: Case-Based Interpretable Deep Learning for Multi-Label ECG Classification with Contrastive Learning.

Deep learning-based electrocardiogram (ECG) classification has shown impressive performance but clinical adoption has been slowed by the lack of transparent and faithful explanations. Post hoc methods such as saliency maps may fail to reflect a model's true decision process. Prototype-based reasoning offers a more transparent alternative by grounding decisions in similarity to learned representati...

Clinical Validation and Post-Implementation Performance Monitoring of a Neural Network-Assisted Approach for Detecting Chronic Lymphocytic Leukemia Minimal Residual Disease by Flow Cytometry.

: Flow cytometric detection of minimal residual disease (MRD) in chronic lymphocytic leukemia (CLL) is complex, time-consuming, and subject to inter-operator variability. Deep neural networks (DNNs) offer potential for standardization and efficiency improvement, but require rigorous validation and monitoring for safe clinical implementation. : We evaluated a DNN-assisted human-in-the-loop approach...

Advanced traffic conflict analysis for safety evaluation at roundabouts under mixed traffic using extreme value theory.

Roundabout safety evaluation in non-lane-based, heterogeneous traffic conditions in low-middle-income countries brings challenges due to unavailable/u...

Can large language models detect drug-drug interactions leading to adverse drug reactions?

BACKGROUND: Drug-drug interactions (DDI) are an important cause of adverse drug reactions (ADRs). Could large language models (LLMs) serve as valuable...

The Lack of Neurofeedback Training Regulation Guidance and Process Evaluation May be a Source of Controversy in Post-Traumatic Stress Disorder-Neurofeedback Research: A Systematic Review and Statistical Analysis.

Neurofeedback (NF) based on brain-computer interface (BCI) is an important direction in adjunctive interventions for post-traumatic stress disorder (...

Heat to hypoxia cross-adaptation: Effects of 6-week post-exercise hot-water immersion on exercise performance in acute hypoxia.

Cross-adaptation occurs when exposure to one environmental stressor (e.g., heat) induces protective responses to another (e.g., hypoxia). Although pos...

Comparison of Machine Learning Algorithms and Bayesian Estimation in Predicting Tacrolimus Concentration in Tunisian Kidney Transplant Patients During the Early Post-Transplant Period.

BACKGROUND AND OBJECTIVE: Model-informed precision dosing (MIPD), based on a Bayesian approach and machine learning (ML) algorithms, is a suitable app...

Event driven neural network on a mixed signal neuromorphic processor for EEG based epileptic seizure detection.

Long-term monitoring of biomedical signals is essential for the modern clinical management of neurological conditions such as epilepsy. However, devel...

CLEAR-Shock: Contrastive LEARning for Shock.

Shock is a life-threatening condition characterized by generalized circulatory failure, which can have devastating consequences if not promptly treate...

A robotic-assisted intervention (ROBERT®) to enhance muscle strength in the hip flexor muscles following spinal cord injury: A pilot within-person randomized controlled trial.

OBJECTIVE: To determine whether a robotic-assisted intervention increases hip flexor muscle strength in people with spinal cord injury (SCI) and marke...

Referral patterns, influencing factors, and satisfaction related to referrals of patients with rheumatic diseases to other healthcare professionals: an online survey of rheumatologists.

Managing rheumatic diseases requires teamwork, but referral patterns and challenges remain poorly understood. This study explored rheumatologists' per...

Machine learning models for acute kidney injury prediction and management: a scoping review of externally validated studies.

Despite advancements in medical care, acute kidney injury (AKI) remains a major contributor to adverse patient outcomes and presents a significant cha...

Leveraging AI to explore structural contexts of post-translational modifications in drug binding.

Post-translational modifications (PTMs) play a crucial role in allowing cells to expand the functionality of their proteins and adaptively regulate th...

Development of an activity-based ratiometric electrochemical substrate for measuring circulating dipeptidyl peptidase-IV/CD26 in whole blood samples.

Dipeptidyl peptidase-IV (DPP-IV) is a circulating blood biomarker that diagnose pancreatic and thyroid cancers, as well as type 2 diabetes. Although c...

Efficacy of eprinomectin 5 mg/mL topical solution administered twice pour on at 1 mg per kg body weight against sarcoptic mange (Sarcoptes scabiei) in sheep.

Sarcoptic mange of sheep is a skin disease which compromises the health and welfare of the animals, impacts their productivity and is zoonotic. In Eur...

Improved integrated framework for flooded crop damage and recovery assessment: A multi-source earth observation and participatory mapping in Hadejia, Nigeria.

Flooding has increasingly significant adverse effects on global food security, and there is a lack of a framework to effectively integrate remote sens...

Using machine learning involving diagnoses and medications as a risk prediction tool for post-acute sequelae of COVID-19 (PASC) in primary care.

BACKGROUND: The aim of our study was to determine whether the application of machine learning could predict PASC by using diagnoses from primary care ...

All-Electrical Control of Spin Synapses for Neuromorphic Computing: Bridging Multi-State Memory with Quantization for Efficient Neural Networks.

The development of energy-efficient, brain-inspired neuromorphic computing demands advanced memory devices capable of mimicking synaptic behavior to a...

Prediction of acute and chronic kidney diseases during the post-covid-19 pandemic with machine learning models: utilizing national electronic health records in the US.

BACKGROUND: COVID-19 has been linked to acute kidney injury (AKI) and chronic kidney disease (CKD), but machine learning (ML) models predicting these ...

Bayesian-optimized recursive machine learning for predicting human-induced changes in suspended sediment transport.

The suspended sediment load (SSL) of a river is a key indicator of water resource management, river morphology, and ecosystem health. This study analy...