ArticleScientific reports2024
Deep-learning image analysis for high-throughput screening of opsono-phagocytosis-promoting monoclonal antibodies against Neisseria gonorrhoeae.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed, 5 citations in OpenAlex.
- Human antibodies as emerging drugs for antimicrobial resistance.Trends in immunology · 2026Review
- Discovery and characterization of an anti-Frontiers in microbiology · 2026Article
- A comprehensive update on the application of high-throughput fluorescence imaging for novel drug discovery.Expert opinion on drug discovery · 2025Review
- Antibody-based alternatives to animal testing for toxin detection and antitoxin evaluation.Frontiers in toxicology · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
Antimicrobial resistance (AMR) is nowadays a global health concern as bacterial pathogens are increasingly developing resistance to antibiotics. Monoclonal antibodies (mAbs) represent a powerful tool for addressing AMR thanks to their high specificity for pathogenic bacteria which allows sparing the microbiota, kill bacteria through complement deposition, enhance phagocytosis or inhibit bacterial adhesion to epithelial cells. Here we describe a visual opsono-phagocytosis assay which relies on confocal microscopy to measure the impact of mAbs on phagocytosis of the bacterium Neisseria gonorrhoeae by macrophages. With respect to traditional CFU-based assays, generated images can be automatically analysed by convolutional neural networks. Our results demonstrate that confocal microscopy and deep learning-based analysis allow screening for phagocytosis-promoting mAbs against N. gonorrhoeae, even when mAbs are not purified and are expressed at low concentration. Ultimately, the flexibility of the staining protocol and of the deep-learning approach make the assay suitable for other bacterial species and cell lines where mAb activity needs to be investigated.
Indexed as
Identifiers
What OpenQuestion holds
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.