Background: Accurate diagnostic strategies to identify SARS-CoV-2 positive individuals rapidly for management of patient care and protection of health care personnel are urgently needed. The predominant diagnostic test is viral RNA detection by RT-PCR from nasopharyngeal swabs specimens, however the results are not promptly obtainable in all patient care locations. Routine laboratory testing, in contrast, is readily available with a turn-around time (TAT) usually within 1-2 hours. Method: We developed a machine learning model incorporating patient demographic features (age, sex, race) with 27 routine laboratory tests to predict an individual’s SARS-CoV-2 infection status. Laboratory testing results obtained within 2 days before the release of SARS-CoV-2 RT-PCR result were used to train a gradient boosting decision tree (GBDT) model from 3,356 SARS-CoV-2 RT-PCR tested patients (1,402 positive and 1,954 negative) evaluated at a metropolitan hospital. Results: The model achieved an area under the receiver operating characteristic curve (AUC) of 0.854 (95% CI: 0.829-0.878). Application of this model to an independent patient dataset from a separate hospital resulted in a comparable AUC (0.838), validating the generalization of its use. Moreover, our model predicted initial SARS-CoV-2 RT-PCR positivity in 66% individuals whose RT-PCR result changed from negative to positive within 2 days. Conclusion: This model employing routine laboratory test results offers opportunities for early and rapid identification of high-risk SARS-CoV-2 infected patients before their RT-PCR results are available. It may play an important role in assisting the identification of SARS-CoV-2 infected patients in areas where RT-PCR testing is not accessible due to financial or supply constraints.

The Chat Generative Pre-trained Transformer (ChatGPT) is a natural language processing tool driven by artificial intelligence and trained on massive amounts of data ( 1, 2). ChatGPT has been gaining significant attention since its release in November 2022 due to its extraordinary ability to generate human-like answers in response to text input in a conversational context. The initial backbone large language model that supports ChatGPT was GPT-3.5. In March 2023, an upgraded version, GPT-4, was released with the promise of improved accuracy. Although there is a widespread interest in the use of ChatGPT, its application in medicine is controversial ( 3, 4). ChatGPT was not extensively trained on biomedical data nor were its responses thoroughly assessed by medical experts. In particular, it is likely that patients and clinicians will turn to ChatGPT for assistance in interpreting laboratory data or understanding how to utilize clinical laboratory services. Here we evaluated the ability of GPT-4 to answer a representative set of questions frequently encountered in the laboratory medicine field, ranging from basic knowledge to complex interpretation of laboratory data in clinical context.

BACKGROUND. Circulating severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA may represent a more reliable indicator of infection than nasal RNA, but quantitative reverse transcription PCR (RT-qPCR) lacks diagnostic sensitivity for blood samples. METHODS. A CRISPR-augmented RT-PCR assay that sensitively detects SARS-CoV-2 RNA was employed to analyze viral RNA kinetics in longitudinal plasma samples from nonhuman primates (NHPs) after virus exposure; to evaluate the utility of blood SARS-CoV-2 RNA detection for coronavirus disease 2019 (COVID-19) diagnosis in adults cases confirmed by nasal/nasopharyngeal swab RT-PCR results; and to identify suspected COVID-19 cases in pediatric and at-risk adult populations with negative nasal swab RT-qPCR results. All blood samples were analyzed by RT-qPCR to allow direct comparisons. RESULTS. CRISPR-augmented RT-PCR consistently detected SARS-CoV-2 RNA in the plasma of experimentally infected NHPs from 1 to 28 days after infection, and these increases preceded and correlated with rectal swab viral RNA increases. In a patient cohort (n = 159), this blood-based assay demonstrated 91.2% diagnostic sensitivity and 99.2% diagnostic specificity versus a comparator RT-qPCR nasal/nasopharyngeal test, whereas RT-qPCR exhibited 44.1% diagnostic sensitivity and 100% specificity for the same blood samples. This CRISPR-augmented RT-PCR assay also accurately identified patients with COVID-19 using one or more negative nasal swab RT-qPCR results. CONCLUSION. Results of this study indicate that sensitive detection of SARS-CoV-2 RNA in blood by CRISPR-augmented RT-PCR permits accurate COVID-19 diagnosis, and can detect COVID-19 cases with transient or negative nasal swab RT-qPCR results, suggesting that this approach could improve COVID-19 diagnosis and the evaluation of SARS-CoV-2 infection clearance, and predict the severity of infection. TRIAL REGISTRATION. ClinicalTrials.gov. NCT04358211. FUNDING. Department of Defense, National Institute of Allergy and Infectious Diseases, National Institute of Child Health and Human Development, and the National Center for Research Resources.

Longitudinal studies are needed to evaluate the SARS-CoV-2 mRNA vaccine antibody response under real-world conditions. This longitudinal study investigated the quantity and quality of SARS-CoV-2 antibody response in 846 specimens from 350 patients, comparing BNT162b2-vaccinated individuals (19 previously diagnosed with COVID-19, termed RecoVax; and 49 never diagnosed, termed NaiveVax) with 122 hospitalized unvaccinated (HospNoVax) and 160 outpatient unvaccinated (OutPtNoVax) COVID-19 patients. NaiveVax experienced delay in generating SARS-CoV-2 total antibodies (TAb) and surrogate neutralizing antibodies (SNAb) after the first vaccine dose (D1) but rapid increase in antibody levels after the second dose (D2). However, these never reached RecoVax’s robust levels. In fact, NaiveVax TAb and SNAb levels decreased 4 weeks after D2. For the most part, RecoVax TAb persisted, after reaching maximal levels 2 weeks after D2, but SNAb decreased significantly about 6 months after D1. Although NaiveVax avidity lagged behind that of RecoVax for most of the follow-up periods, NaiveVax did reach similar avidity by about 6 months after D1. These data suggest that 1 vaccine dose elicits maximal antibody response in RecoVax and may be sufficient. Also, despite decreasing levels in TAb and SNAb over time, long-term avidity may be a measure worth evaluating and possibly correlating to vaccine efficacy.

Objectives: Despite growing interest in artificial intelligence (AI) and machine learning (ML), many laboratory professionals lack experience with developing in-house AI systems or implementing those supplied by external providers. The IFCC Committee on AI in Laboratory Medicine (C-AILM) conducted a survey to collect the status of AI/ML applications, challenges, and expert perspectives on key technical considerations. Methods: An 20-item survey was distributed to laboratory professionals experienced in AI. It covered application status (in-house or provider-supplied, with or without regulatory approval); essential information to request from AI system providers; validation or verification practices; monitoring strategies; and perceived implementation challenges. Results: Fifty complete responses from global experts were received. AI implementation in clinical laboratories was limited and heterogeneous. Most respondents agreed that AI systems provided externally, regardless of regulatory approval status, require local verification. Key information needed from providers included performance metrics from original and external datasets, and demographics of the training/test populations. For both approved and non-approved models, high-priority verification studies were local performance analysis, confirmation of intended-use alignment, and verification of privacy and security safeguards. Top monitoring strategies were regular accuracy checks and comparison against human decision-making. Leading challenges were insufficient IT infrastructure and lack of practical implementation guidelines. Conclusions: Although many challenges remain, clinical laboratories demonstrate strong enthusiasm for AI, particularly with the growing prevalence of commercial AI products. The timely expert insights from our survey and C-AILM recommendations for both AI system providers and clinical laboratories on essential information, verification requirements, and monitoring strategies will inform standardized guideline development.

Anti-Müllerian hormone (AMH) is produced by growing ovarian follicles and provides a diagnostic measure of reproductive reserve in women; however, the impact of AMH on folliculogenesis is poorly understood. We cotransplanted human ovarian cortex with control or AMH-expressing endothelial cells in immunocompromised mice and recovered antral follicles for purification and downstream single-cell RNA sequencing of granulosa and theca/stroma cell fractions. A total of 38 antral follicles were observed (19 control and 19 AMH) at long-term intervals (>10 weeks). In the context of exogenous AMH, follicles exhibited a decreased ratio of primordial to growing follicles and antral follicles of increased diameter. Transcriptomic analysis and immunolabeling revealed a marked increase in factors typically noted at more advanced stages of follicle maturation, with granulosa and theca/stroma cells also displaying molecular hallmarks of luteinization. These results suggest that superphysiologic AMH alone may contribute to ovulatory dysfunction by accelerating maturation and/or luteinization of antral-stage follicles.