Objective: To develop, externally validate, and test a series of computer algorithms to accurately predict antibiotic susceptibility test (AST) results at the time of clinical diagnosis, up to 3 days before standard urine culture results become available, with the goal of improving antibiotic stewardship and patient outcomes.
Patients And Methods: Machine learning algorithms were developed and trained to predict susceptibility or resistance using over 4.7 million discrete AST classifications from urine cultures in a cohort of adult patients from outpatient and inpatient settings from 2012 to 2022.
BACKGROUNDProstate cancer (PC) is driven by aberrant signaling of the androgen receptor (AR) or its ligands, and androgen deprivation therapies (ADTs) are a cornerstone of treatment. ADT responsiveness may be associated with germline changes in genes that regulate androgen production, uptake, and conversion (APUC).METHODSWe analyzed whole-exome sequencing (WES) and whole-transcriptome sequencing (WTS) data from prostate tissues (SU2C/PCF, TCGA, GETx).
View Article and Find Full Text PDFImportance: Frontal fibrosing alopecia (FFA) is an increasingly prevalent form of follicular lichen planus, causing irreversible hair loss predominantly in postmenopausal individuals. An earlier genome-wide meta-analysis of female FFA identified risk loci in genes implicated in self-antigen presentation and T-cell homeostasis, including HLA-B*07:02, ST3GAL1, and SEMA4B. However, CYP1B1, which is important for hormone metabolism, was also implicated with the substitution of serine for asparagine at position 453 (c.
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