A harmonized resource of integrated prostate cancer clinical, -omic, and signature features.

Genomic and transcriptomic data have been generated across a wide range of prostate cancer (PCa) study cohorts. These data can be used to better characterize the molecular features associated with clinical outcomes and to test hypotheses across multiple, independent patient cohorts. In addition, derived features, such as estimates of cell composition, risk scores, and androgen receptor (AR) scores, can be used to develop novel hypotheses leveraging existing multi-omic datasets.

curatedPCaData: Integration of clinical, genomic, and signature features in a curated and harmonized prostate cancer data resource.

Genomic and transcriptomic data have been generated across a wide range of prostate cancer (PCa) study cohorts. These data can be used to better characterize the molecular features associated with clinical outcomes and to test hypotheses across multiple, independent patient cohorts. In addition, derived features, such as estimates of cell composition, risk scores, and androgen receptor (AR) scores, can be used to develop novel hypotheses leveraging existing multi-omic datasets.

Pharmacokinetic Analysis and In Vitro Synergy Evaluation of Cefiderocol, Sulbactam, and Tigecycline in an Extensively Drug-Resistant Pneumonia Patient Receiving Continuous Venovenous Hemodiafiltration.

Antimicrobial treatments for extensively drug-resistant (XDR-AB) infections have proven lackluster, while dosing challenges in patients receiving continuous renal replacement therapy continue. We describe a patient receiving cefiderocol, ampicillin/sulbactam, and tigecycline for XDR-AB while undergoing continuous venovenous hemodiafiltration. The clinical course, cefiderocol and sulbactam pharmacokinetics, and synergy assessments are described.

Evaluation of tools for identifying large copy number variations from ultra-low-coverage whole-genome sequencing data.

Background: Detection of copy number variations (CNVs) from high-throughput next-generation whole-genome sequencing (WGS) data has become a widely used research method during the recent years. However, only a little is known about the applicability of the developed algorithms to ultra-low-coverage (0.0005-0.