Toward Individualized Prediction of Response to Methotrexate in Early Rheumatoid Arthritis: A Pharmacogenomics-Driven Machine Learning Approach.

Objective: To test the ability of machine learning (ML) approaches with clinical and genomic biomarkers to predict methotrexate treatment response in patients with early rheumatoid arthritis (RA).

Methods: Demographic, clinical, and genomic data from 643 patients of European ancestry with early RA (mean age 54 years; 70% female) subdivided into a training (n = 336) and validation cohort (n = 307) were used. The genomic data comprised 160 single-nucleotide polymorphisms (SNPs) previously associated with RA or methotrexate metabolism.

Evaluation of Self-Collected Midturbinate Nasal Swabs and Saliva for Detection of SARS-CoV-2 RNA.

Rapid and accurate diagnostic testing is essential to bring the ongoing COVID-19 pandemic to an end. As the demand for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) testing continues to increase amid supply shortages, many laboratories have investigated the use of sources other than nasopharyngeal (NP) swabs. Saliva and midturbinate (MT) nasal swabs are attractive alternatives, as they allow for self-collection and are well accepted by patients.

Direct Detection from Surveillance Swabs, Blood, and Urine Using a Laboratory-Developed PCR Method.

is an emerging fungal pathogen with cases reported in countries around the world and in 19 states within the United States as of August 2020. The CDC has recommended that hospitals perform active surveillance upon admission for patients with the appropriate risk factors. Currently, active surveillance requires that local hospitals send surveillance swabs to a public health laboratory for analysis.

Multicenter Study Demonstrates Standardization Requirements for Mold Identification by MALDI-TOF MS.

Objectives: Rapid and accurate mold identification is critical for guiding therapy for mold infections. MALDI-TOF MS has been widely adopted for bacterial and yeast identification; however, few clinical laboratories have applied this technology for routine mold identification due to limited database availability and lack of standardized processes. Here, we evaluated the versatility of the NIH Mold Database in a multicenter evaluation.