ArticleDiscover imaging2025
Deep learning imaging analysis to identify bacterial metabolic states associated with carcinogen production.
Article in Discover imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Investigating the application of IoT mobile app and healthcare services for diabetic elderly: A systematic review.PloS one · 2025Pooled it
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- Engineering the green algae Chlamydomonas incerta for recombinant protein production.PloS one · 2025Article
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- Evaluation of an evidence-based practice continuing education course for Canadian Naturopathic Doctors.PloS one · 2025Article
- Enhancing gesture recognition with multiscale feature extraction and spatial attention.PloS one · 2025Article
- Introducing "Validated entheses-Based reconstruction of activity 2.0" (VERA 2.0): Semi-automated 3D analysis of bone surface changes.PloS one · 2025Article
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- Diazinon residues levels in farm-gate Brassica oleracea var. acephala of Kimira-Oluch smallholder farm improvement project, Kenya.PloS one · 2025Article
- Advancing malware imagery classification with explainable deep learning: A state-of-the-art approach using SHAP, LIME and Grad-CAM.PloS one · 2025Article
- Integrating temporal convolutional networks with metaheuristic optimization for accurate software defect prediction.PloS one · 2025Article
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.
Funding
Abstract
Background: Colorectal cancer (CRC) is a globally prevalent cancer. Emerging research implicates the gut microbiome in CRC pathogenesis. Bacteria such as Methods: Light microscopy images of anaerobically cultured Results: For whole-image analysis, DIBaS-enhanced ResNet models achieved the best performance in distinguishing Conclusions: This study demonstrates feasibility of image-based deep learning models in identifying health-relevant gut bacterial metabolic states. Supplementary Information: The online version contains supplementary material available at 10.1007/s44352-025-00006-1.
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.