Journal of Applied Laboratory Medicine最新文献

Background: Inborn errors of metabolism comprise a set of more than 2000 known disorders which can result in significant morbidity and may be rapidly fatal. Diagnosing these disorders at birth and treating immediately, however, may often result in a normal to near-normal life for the affected infant. Thus, newborn screening (NBS) has saved or improved the lives of countless individuals since its inception in the 1960s.

Content: This review covers NBS, from its early beginnings up to the current day practice. We follow the evolution of NBS, as well as describe the need and how disorders are added to NBS programs, the testing and how its performance is monitored, and the follow-up to the testing. We also briefly touch on NBS outside the United States.

Summary: Newborn screening in the United States is a major public health success story and it continues to grow and evolve to cover more disorders and utilize new technological advances.

Background: Although age negatively correlates with vaccine-induced immune responses, whether the vaccine-induced neutralizing effect against variants of concern (VOCs) substantially differs across age remains relatively poorly explored. In addition, the utility of commercial binding assays developed with the wild-type SARS-CoV-2 for predicting the neutralizing effect against VOCs should be revalidated.

Methods: We analyzed 151 triple-vaccinated SARS-CoV-2-naïve individuals boosted with BNT162b2 (Pfizer-BioNTech). The study population was divided into young adults (age < 30), middle-aged adults (30 ≤ age < 60), and older adults (age ≥ 60). The plaque reduction neutralization test (PRNT) titers against Delta (B.1.617.2) and Omicron (B.1.1.529) variants were compared across age. Antibody titers measured with commercial binding assays were compared with PRNT titers.

Results: Age-related decline in neutralizing titers was observed for both Delta and Omicron variants. Neutralizing titers for Omicron were lower than those against Delta in all ages. The multiple linear regression model demonstrated that duration from third dose to sample collection and vaccine types were also significant factors affecting vaccine-induced immunity along with age. The correlation between commercial binding assays and PRNT was acceptable for all age groups with the Delta variant, but relatively poor for middle-aged and older adults with the Omicron variant due to low titers.

Conclusions: This study provides insights into the age-related dynamics of vaccine-induced immunity against SARS-CoV-2 VOCs, corroborating the need for age-specific vaccination strategies in the endemic era where new variants continue to evolve. Moreover, commercial binding assays should be used cautiously when estimating neutralizing titers against VOCs, particularly Omicron.

Background: The COVID-19 pandemic emphasized an urgent need for devices used in the self-collection of biospecimens in an evolving patient care system. The mailing of biospecimen self-collection kits to patients, with samples returned via mail, provides a more convenient testing regimen, but could also impart patient sampling variabilities. User compliance with device directions is central to downstream testing of collected biospecimens and clear instructions are central to this goal.

Methods: Here, we performed an evaluation of 10 oral DNA collection devices involving either swab or saliva self-collection and analyzed ease of use and comfort level with a device, as well as DNA recovery quantity/quality and sample stability.

Results: We show that while these DNA quality/quantity metrics are comparable between devices, users prefer direct saliva collection over swab-based devices.

Conclusions: This information is useful in guiding future experiments including their use in human RNA, microbial, or viral sample collection/recovery and their use in clinical testing.

Background: During the COVID-19 pandemic, many molecular diagnostic laboratories performed high-throughput SARS-CoV-2 testing often with implementation of automated workflows. In parallel, vaccination campaigns resulted increasingly in specimens from fully vaccinated patients, with resultant clinical inquiries regarding positive results in this patient population. This prompted a quality improvement initiative to investigate the semi-automated testing workflow for false-positive results. The troubleshooting workflow is described and procedural improvements are outlined that serve as a resource for other molecular diagnostic laboratories that need to overcome testing anomalies in a semi-automated environment.

Methods: This workflow utilized the MagMax-96 Viral RNA kit and the CDC 2019-nCoV RT-qPCR Panel on the Agilent Bravo Liquid-Handler (Bravo). Screening of the environment, personnel, and the mechanical performance of instrumentation using low Ct checkerboard challenges was executed to identify sources of cross-contamination. Evaluation of the assay and reporting design was conducted.

Results: Specimen contamination was observed during the viral extraction process on the Bravo. Changes to the program reduced plate contamination by 50% and importantly revealed consistent hallmarks of contaminated samples. We adjusted the reporting algorithm using these indicators of false positives. False positives that were identified made up 0.11% of the 45 000+ tests conducted over the following 8 months.

Conclusions: These adjustments provided confident and quality results while maintaining turnaround time for patients and pandemic-related public health initiatives. This corrected false-positive rate is concordant with previously published studies from diagnostic laboratories utilizing automated systems and may be considered a laboratory performance standard for this type of testing.

Background: The clinically significant Factor V Leiden (FVL) point mutation (1691 G/A) causes replacement of Arg with Gln (glutamine), preventing activated protein C from inactivating Factor V leading to a lengthened clotting process. Individuals with the Factor V Leiden mutations have an increased risk for venous thrombosis. The aim of this study is to compare an unlabeled probe high-resolution melting analysis (HRMA) assay for Factor V Leiden mutation to a TaqMan hydrolysis assay (fluorogenic 5' nuclease PCR hydrolysis assay). HRMA is a post-PCR, homogenous, closed-tube system for the detection of sequence variants. Post-PCR, the amplicons are heated gradually until the melting temperature is reached and the fluorescent dye unbinds from the amplicon and exhibits low fluorescence. A melt-curve analysis is generated that is characteristic of a particular sequence variant. Therefore, HRMA allows for comparison of one base changes in genetic sequences based on their differences in melting rate.

Methods: Blood samples were collected in EDTA tubes and DNA extracted using the Roche MagNaPure. Reactions of both HRMA and TaqMan were carried out on 3 controls (1691 G/G, 1691 G/A, and 1691 G/G and G/A) and 20 samples.

Results: The genotypes for 3 reference controls purchased from Coriell (F5 1691 G/G, FVL 1691 G/A, and Heterozygote 1691 G/G and G/A) were confirmed by both the HRMA and TaqMan FVL assays. All 20 samples were confirmed to be F5 1691 G/G by both HRMA and TaqMan assays.

Conclusions: Comparing the results of the unlabeled probe HRMA FVL assay with a real-time TaqMan probe end point genotyping assay resulted in 100% sensitivity and 100% specificity for both assays.

Background: Standardizing cerebrospinal fluid (CSF) laboratory protocols will improve the reliability and availability of clinical biomarker testing required for prescription of novel Alzheimer disease (AD) therapies. This study evaluated several preanalytical handling and storage factors common to β-amyloid1-42 (Aβ1-42), β-amyloid1-40 (Aβ1-40), and phosphorylated tau (pTau181) concentrations including storage at different temperatures, extended cap contact, various mixing methods, and multiple freeze-thaw cycles.

Methods: Aβ1-42, Aβ1-40, and pTau181 concentrations were measured using LUMIPULSE G1200 automated assays. Samples were collected in polypropylene tubes of various volumes. Sample cap-contact was evaluated by storing samples in upright and inverted positions at either 4°C for 1 week or -80°C for 1 month. To assess mixing methods, samples were freeze-thawed and mixed by inversion, vortex, horizontal roller, or unmixed prior to assay sampling. The impact of successive freeze-thaw cycles was assessed through freezing, thawing, and analyzing CSF samples.

Results: Short-term storage at 4°C did not affect Aβ1-42, Aβ1-40, or pTau181 measurements in any tube type. Tube cap contact affected Aβ1-42 in 2.5 mL tubes and pTau181 levels in 10 mL tubes. No difference was observed between mixing methods. After 4 freeze-thaw cycles, Aβ1-42 significantly decreased but Aβ1-40 remained unchanged. Utilizing the Aβ1-42/Aβ1-40 ratio, Aβ1-42 values normalized, maintaining ratio values within ±5% of baseline measurements.

Conclusions: Storage of CSF at 4°C for 1 week or -80°C for 1 month did not significantly affect Aβ1-42, Aβ1-40, pTau181, or associated ratio measurements. Tube cap-contact impacted pTau181 and pTau181/Aβ1-42 values in larger tubes. Mixing methods are equivalent. The Aβ1-42/Aβ1-40 ratio compensates for freeze-thaw variability up to 4 cycles.

Background: Esophageal cancer (EC) remains a global health challenge, often diagnosed at advanced stages, leading to high mortality rates. Current diagnostic tools for EC are limited in their efficacy. This study aims to harness the potential of microRNAs (miRNAs) as novel, noninvasive diagnostic biomarkers for EC. Our objective was to determine the diagnostic accuracy of miRNAs, particularly in distinguishing miRNAs associated with EC from control miRNAs.

Methods: We applied machine learning (ML) techniques in WEKA (Waikato Environment for Knowledge Analysis) and TensorFlow Keras to a dataset of miRNA sequences and gene targets, assessing the predictive power of several classifiers: naïve Bayes, multilayer perceptron, Hoeffding tree, random forest, and random tree. The data were further subjected to InfoGain feature selection to identify the most informative miRNA sequence and gene target descriptors. The ML models' abilities to distinguish between miRNA implicated in EC and control group miRNA was then tested.

Results: Of the tested WEKA classifiers, the top 3 performing ones were random forest, Hoeffding tree, and naïve Bayes. The TensorFlow Keras neural network model was subsequently trained and tested, the model's predictive power was further validated using an independent dataset. The TensorFlow Keras gave an accuracy 0.91. The WEKA best algorithm (naïve Bayes) model yielded an accuracy of 0.94.

Conclusions: The results demonstrate the potential of ML-based miRNA classifiers in diagnosing EC. However, further studies are necessary to validate these findings and explore the full clinical potential of this approach.