ArticleFrontiers in microbiology2024
Bioinformatic analysis reveals the association between bacterial morphology and antibiotic resistance using light microscopy with deep learning.
Article in Frontiers in microbiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
6 citing papers in PubMed.
- Single-cell morphological responses as predictors of ciprofloxacin resistance inMicrobiology spectrum · 2026Article
- Emerging threats and zoonotic implications of antibiotic-resistant Staphylococcus spp. in domesticated cats: a review.Antonie van Leeuwenhoek · 2026Review
- Deep learning-based high-information-content graph representation of early stage bacterial biofilms.NPJ biofilms and microbiomes · 2026Article
- Application of few-shot learning and transfer learning based on YOLOv6 in the recognition of bacteria in sputum M-ROSE.Frontiers in cellular and infection microbiology · 2026Article
- Development of a Mass-Producible Microfluidic Device for Single and Bulk Mycobacteria Investigations.Biosensors · 2025Article
- Detecting antibiotic resistance: classical, molecular, advanced bioengineering, and AI-enhanced approaches.Frontiers in microbiology · 2025Review
Corrections and comments
- Erratum issued
- Erratum issued
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Although it is well known that the morphology of Gram-negative rods changes on exposure to antibiotics, the morphology of antibiotic-resistant bacteria in the absence of antibiotics has not been widely investigated. Here, we studied the morphologies of 10 antibiotic-resistant strains of
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.