Biology subjects

Pitout, J.

Publications and source records attributed to Pitout, J..

3 recordsLinked to original sources

bioRxiv2025

Imputing missing minimum inhibitory concentration (MIC) values for Pseudomonas aeruginosa strains with a Denoising AutoEncoder

Pseudomonas aeruginosa is a problematic pathogen with complex antibiotic resistance patterns. In clinical practice, minimum inhibitory concentration (MIC) tests typically focus on a limited subset of antibiotics, hindering a comprehensive assessment of a strains resistance profile. Here, we introduce MICFiller, a Denoising AutoEncoder (DAE) model designed to impute missing MIC values for 14 antibiotics in Pseudomonas aeruginosa within a specific dilution range by leveraging known MIC measurements for other antibiotics in the same strain. We evaluated the performance of DAE against two other commonly used methods: Multiple Imputation by Chained Equations (MICE) and simple median imputation. The DAE achieved the highest balanced 1-tier accuracy for most antibiotics, with performance closely matching that of MICE. MICFiller is freely accessible through a user-friendly web interface at http://iorgalab.org:4567/micfiller, offering clinicians a more complete view of a strains antibiotic resistance profile.

microbiology↗

bioRxiv2025

You Only Look Once (YOLO) Based Machine Learning Algorithm for Real-Time Detection of Loop-Mediated Isothermal Amplification (LAMP) Diagnostics

Loop-mediated isothermal amplification (LAMP) is a widely used rapid and affordable molecular DNA amplification method with minimal resource requirements. However, visual interpretation of results is subjective and prone to errors, leading to potential false-positive and negative results. To address this limitation, a machine-learning approach is proposed for automated LAMP classification based on digital images. The approach utilizes You Only Look Once (YOLOv8), a fast and robust object detection algorithm to locate and classify tubes within LAMP images, enabling automated categorization as positive or negative. The trained model achieved a high overall accuracy of 95.5% in classifying LAMP images into positive or negative. Additionally, the approach had a 98.0% precision and 92.7% recall for positive cases and 93.4% precision and 98.2% recall for negative cases, demonstrating its potential for real-time LAMP diagnosis and enhanced assay performance. This project demonstrated the platforms suitability for real-time testing, offering an easy operation and rapid results.

microbiology↗

bioRxiv2017

Genomic epidemiology of global Klebsiella pneumoniae carbapenemase (KPC)-producing E. coli

The dissemination of carbapenem resistance in Escherichia coli has major implications for the management of common human infections. blaKPC, encoding a transmissible carbapenemase (KPC), has historically largely been associated with Klebsiella pneumoniae, a predominant plasmid (pKpQIL), and a specific transposable element (Tn4401, ~10kb). Here we characterize the genetic features of the emergence of blaKPC in global E. coli, 2008-2013, using both long-and short-read whole genome sequencing.\n\nAmongst 43/45 successfully sequenced blaKPC-E. coli strains, we identified high strain (n=21 sequence types, 18% of annotated genes in the core genome); plasmid ([≥]9 replicon types); and blaKPC-associated, mobile genetic element (MGE) diversity (50% not within complete Tn4401 elements). We also found evidence of interspecies, regional and international plasmid spread. In several cases blaKPC was found on high copy number, small Col-like plasmids, previously associated with horizontal transmission of resistance genes in the absence of antimicrobial selection pressures.\n\nE. coli is a common human pathogen, but also a commensal in a multiple environmental and animal reservoirs, and easily transmissible. The association of blaKPC with a range of MGEs previously linked to the successful spread of widely endemic resistance mechanisms (e.g. blaTEM, blaCTX-M) suggests that it is likely to become similarly prevalent.

microbiology↗