We present predictive models for comprehensive systems analysis of Clostridioides difficile, the etiology of pseudomembranous colitis. By leveraging 151 published transcriptomes, we generated an EGRIN model that organizes 90% of C. difficile genes into a transcriptional regulatory network of 297 co-regulated modules, implicating genes in sporulation, carbohydrate transport, and metabolism. By advancing a metabolic model through addition and curation of metabolic reactions including nutrient uptake, we discovered 14 amino acids, diverse carbohydrates, and 10 metabolic genes as essential for C. difficile growth in the intestinal environment. Finally, we developed a PRIME model to uncover how EGRIN-inferred combinatorial gene regulation by transcription factors, such as CcpA and CodY, modulates essential metabolic processes to enable C. difficile growth relative to commensal colonization. The C. difficile interactive web portal provides access to these model resources to support collaborative systems-level studies of context-specific virulence mechanisms in C. difficile.
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http://dx.doi.org/10.1016/j.chom.2021.09.008 | DOI Listing |
J Transl Med
December 2024
Tongji Medical College, Maternal and Child Health Hospital of Hubei Province, Huazhong University of Science and Technology, Wuhan, Hubei Province, 430070, China.
Background: As a prevalent and deadly malignant tumor, the treatment outcomes for late-stage patients with cervical squamous cell carcinoma (CSCC) are often suboptimal. Previous studies have shown that tumor progression is closely related with tumor metabolism and microenvironment reshaping, with disruptions in energy metabolism playing a critical role in this process. To delve deeper into the understanding of CSCC development, our research focused on analyzing the tumor microenvironment and metabolic characteristics across different regions of tumor tissue.
View Article and Find Full Text PDFBMC Med Inform Decis Mak
December 2024
Department of Health Informatics, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia.
Background: Digital health has emerged as a promising solution for enhancing health system in the recent years, showing significant potential in improving service outcomes, particularly in low and middle-income countries where accessing essential health service is challenging. This review aimed to determine the effectiveness of short message services on focused antenatal care, skilled birth attendance, and postnatal care improvement in low and middle-income countries.
Method: Electronic databases such as PubMed, EMBASE, Scopus, Cochrane, and Google and Google Scholar were searched.
BMC Ophthalmol
December 2024
Department of Pharmacology & Therapeutics, College of Medicine & Health Sciences, Arabian Gulf University, Manama, Kingdom of Bahrain.
Background: Prostaglandin analogs are first-line treatments for open-angle glaucoma due to their proven efficacy in reducing intraocular pressure. Despite their topical administration, systemic adverse drug Events (ADEs) have been reported. This study investigates the systemic ADEs associated with topical prostaglandin analogs using the United States Food and Drug Administration (USFDA) Adverse Drug Event Reporting System (AERS) database.
View Article and Find Full Text PDFBMC Oral Health
December 2024
Center of Excellence on Oral Microbiology and Immunology, Department of Microbiology, Faculty of Dentistry, Chulalongkorn University, Henri Dunant Rd, Bangkok, 10330, Thailand.
Background: Microorganisms in dental unit water (DUW) play a significant role in dental bioaerosols. If the methods used to decontaminate DUW also help improve air quality in dental clinics is worth exploring. In this study, we aim to identify the source of bacteria in dental bioaerosols and investigate the impact of waterline disinfectants on the quantity and composition of bacteria in DUW and bioaerosols.
View Article and Find Full Text PDFBiomech Model Mechanobiol
December 2024
Department of Mechanical Engineering, Michigan State University, East Lansing, MI, USA.
This study presents a novel methodology for high-resolution 3D bladder modeling during filling, developed by leveraging improved imaging and computational techniques. Using murine bladder filling data, the methodology generates accurate 3D geometries across time, enabling in-depth mechanical analysis. Comparison with a traditional spherical model revealed similar stress trends, but the 3D model permitted nuanced quantifications, such as localized surface curvature and stress analysis.
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