AIMC Topic: Humans

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No longer stuck in the past: new advances in artificial intelligence and molecular assays for parasitology screening and diagnosis.

Current opinion in infectious diseases
PURPOSE OF REVIEW: Emerging technologies are revolutionizing parasitology diagnostics and challenging traditional methods reliant on microscopic analysis or serological confirmation, which are known for their limitations in sensitivity and specificit...

Perspectives on current approaches to virtual screening in drug discovery.

Expert opinion on drug discovery
INTRODUCTION: For the past two decades, virtual screening (VS) has been an efficient hit finding approach for drug discovery. Today, billions of commercially accessible compounds are routinely screened, and many successful examples of VS have been re...

Machine learning of Raman spectra predicts drug release from polysaccharide coatings for targeted colonic delivery.

Journal of controlled release : official journal of the Controlled Release Society
Colonic drug delivery offers numerous pharmaceutical opportunities, including direct access to local therapeutic targets and drug bioavailability benefits arising from the colonic epithelium's reduced abundance of cytochrome P450 enzymes and particul...

A Machine Learning Predictive Model for Ureteroscopy Lasertripsy Outcomes in a Pediatric Population-Results from a Large Endourology Tertiary Center.

Journal of endourology
We aimed to develop machine learning (ML) algorithms for the automated prediction of postoperative ureteroscopy outcomes for pediatric kidney stones based on preoperative characteristics. Data from pediatric patients who underwent ureteroscopy for ...

High-Throughput Deep Learning Detection of Mitral Regurgitation.

Circulation
BACKGROUND: Diagnosis of mitral regurgitation (MR) requires careful evaluation by echocardiography with Doppler imaging. This study presents the development and validation of a fully automated deep learning pipeline for identifying apical 4-chamber v...

Actionable Predictions of Human Pharmacokinetics at the Drug Design Stage.

Molecular pharmaceutics
We present a novel computational approach for predicting human pharmacokinetics (PK) that addresses the challenges of early stage drug design. Our study introduces and describes a large-scale data set of 11 clinical PK end points, encompassing over 2...

Prediction of neurologic outcome after out-of-hospital cardiac arrest: An interpretable approach with machine learning.

Resuscitation
UNLABELLED: Out-of-hospital cardiac arrest (OHCA) is a critical condition with low survival rates. In patients with a return of spontaneous circulation, brain injury is a leading cause of death. In this study, we propose an interpretable machine lear...

Positive-Unlabelled learning for identifying new candidate Dietary Restriction-related genes among ageing-related genes.

Computers in biology and medicine
Dietary Restriction (DR) is one of the most popular anti-ageing interventions; recently, Machine Learning (ML) has been explored to identify potential DR-related genes among ageing-related genes, aiming to minimize costly wet lab experiments needed t...

Utilizing machine learning to integrate single-cell and bulk RNA sequencing data for constructing and validating a novel cell adhesion molecules related prognostic model in gastric cancer.

Computers in biology and medicine
BACKGROUND: Cell adhesion molecules (CAMs) play a vital role in cell-cell interactions, immune response modulation, and tumor cell migration. However, the unique role of CAMs in gastric cancer (GC) remains largely unexplored.

Discovery of potential antidiabetic peptides using deep learning.

Computers in biology and medicine
Antidiabetic peptides (ADPs), peptides with potential antidiabetic activity, hold significant importance in the treatment and control of diabetes. Despite their therapeutic potential, the discovery and prediction of ADPs remain challenging due to lim...