Clinical chemistry最新文献

Background: The increasing prevalence of xylazine in the illicit drug supply is a growing concern for major health consequences in individuals who use fentanyl mixed with xylazine, but limited data are available regarding the pharmacokinetics of xylazine in humans.

Methods: Xylazine was quantified in serial remnant plasmas collected from 28 patients starting at the initial patient encounter and continuing for up to 52 h from presentation, using LC-MS/MS to calculate the terminal half-life for xylazine. Xylazine metabolites were identified by product ion scanning, and multiple reaction monitoring was used to estimate the relative abundance of xylazine metabolites in 74 collected plasma samples.

Results: The median terminal half-life for xylazine was calculated to be 12.0 h (range: 5.9-20.8). Oxo-xylazine and sulfone-xylazine metabolites were detected in all plasma specimens that contained xylazine.

Conclusions: The half-life of xylazine in humans is longer than previously observed in animal studies, which furthers the current understanding of the expected duration of effects in individuals who use fentanyl mixed with xylazine and the window of detection. Both oxo-xylazine and sulfone-xylazine appear to circulate in plasma for as long as xylazine.

Background: Early detection of the cell type changes underlying several genitourinary tract diseases largely remains an unmet clinical need, where existing assays, if available, lack the cellular resolution afforded by an invasive biopsy. While messenger RNA in urine could reflect the dynamic signal that facilitates early detection, current measurements primarily detect single genes and thus do not reflect the entire transcriptome and the underlying contributions of cell type-specific RNA.

Methods: We isolated and sequenced the cell-free RNA (cfRNA) and sediment RNA from human urine samples (n = 6 healthy controls and n = 12 kidney stone patients) and measured the urine metabolome. We analyzed the resulting urine transcriptomes by deconvolving the noninvasively measurable cell type contributions and comparing to plasma cfRNA and the measured urine metabolome.

Results: Urine transcriptome cell type deconvolution primarily yielded relative fractional contributions from genitourinary tract cell types in addition to cell types from high-turnover solid tissues beyond the genitourinary tract. Comparison to plasma cfRNA yielded enrichment of metabolic pathways and a distinct cell type spectrum. Integration of urine transcriptomic and metabolomic measurements yielded enrichment for metabolic pathways involved in amino acid metabolism and overlapped with metabolic subsystems associated with proximal tubule function.

Conclusions: Noninvasive whole transcriptome measurements of human urine cfRNA and sediment RNA reflects signal from hard-to-biopsy tissues exhibiting low representation in blood plasma cfRNA liquid biopsy at cell type resolution and are enriched in signal from metabolic pathways measurable in the urine metabolome.

Background: The reference change value (RCV) is calculated by combining the within-subject biological variation (CVI) and local analytical variation (CVA). These calculations do not account for the variation seen in preanalytical conditions in routine practice or CVI in patients presenting for treatment. As a result, the RCVs may not reflect routine practice or align with clinicians' experiences. We propose a novel RCV approach based on routine patient data that is potentially more clinically relevant.

Methods: This study used the refineR algorithm to determine RCVs using serial patient data extracted from a local Laboratory Information System (LIS). The model was applied to biomarkers with a range of result ratio distributions varying from normal to log-normal. Results were compared against conventional formula-based RCVs using CVI estimates from a state-of-the-art biological variation study. Monte Carlo simulations were also used to validate the LIS data approach.

Results: The RCVs estimated from LIS data were: 11-deoxycortisol (men): -70%/+196%, 17-hydroxyprogesterone (men): -49%/+100%, albumin: -10%/+11%, androstenedione (men): -47%/+96%, cortisol (men): -54%/+51%, cortisone (men): -32%/+51%, creatinine: -16%/+14%, phosphate (women): -23%/+29%, phosphate (men): -27%/+29%, testosterone (men): -38%/+60%. The formula-based RCV estimates showed similar but slightly lower results, and the Monte Carlo simulations confirmed the applicability of the new approach.

Conclusions: RCVs may be estimated from patient results without prior assumptions about the shape of the ratios between serial results. Laboratories can determine RCVs based on local practice and population.

Background: Tandem repeats (TRs) are abundant in the human genome and associated with repeat expansion disorders. Our study aimed to develop a tandem repeat panel utilizing targeted long-read sequencing to evaluate known TRs associated with these disorders and assess its clinical utility.

Methods: We developed a targeted long-read sequencing panel for 70 TR loci, termed dynamic mutation third-generation sequencing (dmTGS), using the PacBio Sequel II platform. We tested 108 samples with suspected repeat expansion disorders and compared the results with conventional molecular methods.

Results: For 108 samples, dmTGS achieved an average of 8000 high-fidelity reads per sample, with a mean read length of 4.7 kb and read quality of 99.9%. dmTGS outperformed repeat-primed-PCR and fluorescence amplicon length analysis-PCR in distinguishing expanded from normal alleles and accurately quantifying repeat counts. The method demonstrated high concordance with confirmatory methods (rlinear = 0.991, P < 0.01), and detected mosaicism with sensitivities of 1% for FMR1 CGG premutation and 5% for full mutations. dmTGS successfully identified interruptive motifs in genes that conventional methods had missed. For variable number TRs in the PLIN4 gene, dmTGS identified precise repeat counts and sequence motifs. Screening 57 patients with suspected genetic muscular diseases, dmTGS confirmed repeat expansions in genes such as GIPC1, NOTCH2NLC, NUTM2B-AS1/LOC642361, and DMPK. Additionally, dmTGS detected CCG interruptions in CTG repeats in 8 myotonic dystrophy type 1 patients with detailed characterization.

Conclusions: dmTGS accurately detects repeat sizes and interruption motifs associated with repeat expansion disorders and demonstrates superior performance compared to conventional molecular methods.