Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Time-Lapse Deep Learning for Single-Cell Subcellular Structural Phenotypic Antimicrobial Susceptibility Testing.

Analytical chemistry - 9 Dec 2025

Jing Wenwen, Zhang Tianran, Chen Xi, Wang Xiaofei, Fan Ying, Feng Meng, Jin Kai, Sui Guodong, Liu Chenbin, Cheng Xunjia

Abstract excerpt

Antimicrobial resistance (AMR) is a global health concern that complicates the effective treatment of infections, resulting in an increased severity of illness and elevated healthcare costs. Traditional phenotypic antimicrobial susceptibility testing (AST) relies on laborious culturing and interpretation of visible growth, resulting in delays ranging from 24 h to several days. Genotypic assays detect only known...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Anti-Bacterial Agents
Share to Deep Learning

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Escherichia coli

Share to Microbial Sensitivity Tests

Share to Phenotype

Share to Single-Cell Analysis

Share to Time-Lapse Imaging

Source

Identifiers

PMID: 41283773
DOI: 10.1021/acs.analchem.5c04676

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 5 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Time-Lapse Deep Learning for Single-Cell Subcellular Structural Phenotypic Antimicrobial Susceptibility Testing.PMID 41283773Read ↗

Deep Learning and Single Cell Phenotyping for Rapid Antimicrobial Susceptibility TestingSemantic proximity Read ↗

Phenotypic Antimicrobial Susceptibility Testing with Deep Learning Video Microscopy.Semantic proximity Read ↗

Label-free Pathogen Identification with Microscopy Imaging and Deep LearningSemantic proximity Read ↗

Ribosome Phenotypes Enable Rapid Antibiotic Susceptibility Testing in <i>Escherichia coli</i>Semantic proximity Read ↗

Highly sensitive quantitative phase microscopy and deep learning complement whole genome sequencing for rapid detection of infection and antimicrobial resistanceSemantic proximity Read ↗

Combining machine learning with high-content imaging to infer ciprofloxacin susceptibility in clinical isolates of Salmonella TyphimuriumSemantic proximity Read ↗

MICNet: Prediction of antibiotic susceptibility from microscopic images using transfer learningSemantic proximity Read ↗

One-day phenotypic drug susceptibility testing for <i>Mycobacterium tuberculosis</i> variant <i>bovis</i> BCG using single-cell imaging and a deep neural networkSemantic proximity Read ↗

Antibiotic lethality dictates mycobacterial infection outcomesSemantic proximity Read ↗

Rapid Single-Cell Phenotypic Antifungal Susceptibility Testing on a SlipChip Enabled by Deep Learning.Semantic proximity Read ↗

Phenotypic drug susceptibility testing for Mycobacterium tuberculosis variant bovis BCG in 12 hoursSemantic proximity Read ↗

Infection Inspection: Using the power of citizen science to help with image-based prediction of antibiotic resistance in<i>Escherichia coli</i>Semantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.