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Biomedical subjects

Christopher Wilson

Publications and source records attributed to Christopher Wilson.

2 recordsLinked to original sources

iModMix: integrative module analysis for multi-omics data.

SUMMARY: Integrative Module Analysis for Multi-omics Data (iModMix) is a biology-agnostic framework that enables the discovery of novel associations across any type of quantitative abundance data, including but not limited to transcriptomics, proteomics, and metabolomics. Instead of relying on pathway annotations or prior biological knowledge, iModMix constructs data-driven modules using graphical lasso to estimate sparse networks from omics features. These modules are summarized into eigenfeatures and correlated across datasets for horizontal integration, while preserving the distinct feature sets and interpretability of each omics type. iModMix operates directly on matrices containing expression or abundances for a wide range of features, including but not limited to genes, proteins, and metabolites. Because it does not rely on annotations (e.g., KEGG identifiers), it can seamlessly incorporate both identified and unidentified metabolites, addressing a key limitation of many existing metabolomics tools. iModMix is available as a user-friendly R Shiny application requiring no programming expertise (https://imodmix.moffitt.org), and as a Bioconductor R package for advanced users (https://bioconductor.org/packages/release/bioc/html/iModMix.html). The tool includes several public and in-house datasets to illustrate its utility in identifying novel multi-omics relationships in diverse biological contexts. AVAILABILITY AND IMPLEMENTATION: iModMix is freely available from Bioconductor (https://bioconductor.org/packages/release/bioc/html/iModMix.html), and the example dataset package (iModMixData) is also available from Bioconductor (https://bioconductor.org/packages/release/ data/experiment/html/iModMixData.html). The R package source code and Docker are available from GitHub: https://github.com/biodatalab/iModMix. Shiny application can be accessed at: https://imodmix.moffitt.org.

Multiomics

Investigation of a Mycobacterium fortuitum prosthetic joint infection outbreak at two ambulatory surgery centers in Tennessee.

OBJECTIVE: This study outlines the investigation into an outbreak of Mycobacterium fortuitum infections involving 17 cases undergoing hip or knee surgeries at two ambulatory surgery centers (ASCs) in Tennessee from January 2023 to November 2024. Notably, the outbreak could not be attributed to contaminated water sources, which are typically associated with non-tuberculous mycobacteria (NTM) outbreaks, presenting a unique challenge. METHODS: Outbreak investigation steps included Infection Prevention (IP) assessments, case-control study, environmental sampling, whole genome sequencing, and a healthcare personnel (HCP) exposure questionnaire. RESULTS: IP assessment highlighted several concerns, including no formal facility water management program (WMP), a lack of dedicated IP personnel and certified sterile processing staff, the absence of a formalized system for tracking surgical site infections, and a notable gap in understanding the requirements for reporting diseases. The case-control findings revealed a significant association between the presence of a surgical technologist in the operating room during the procedures and the occurrence of NTM infections, indicated by an odds ratio of 55.77 (95% CI [3.16-985.44]; P = 0.0097). Thirteen clinical isolates collected at one ASC and three additional isolates collected at a second ASC were highly related by whole genome sequencing. CONCLUSION: The study further elucidates valuable insights gained from the outbreak response, including the gaps in surveillance within the ambulatory surgical setting and systematic collection of cultures from environmental sources. It emphasizes the importance of thorough vetting, onboarding, continuing education, and practice monitoring for HCP.

Humans