Journal of Applied Laboratory Medicine最新文献_第3页

Fluctuating Hyperparathyroidism after Surgery. 手术后波动性甲状旁腺功能亢进。

IF 1.9

Journal of Applied Laboratory Medicine Pub Date : 2025-09-04 DOI: 10.1093/jalm/jfaf112

Benjamin Telford, Nada Fanous, Carmen Gherasim, David M Manthei

引用次数: 0

Atypical Appearance of Lipoprotein X on Agarose Gel Electrophoresis. 脂糖凝胶电泳中脂蛋白X的非典型外观。

IF 1.9

Journal of Applied Laboratory Medicine Pub Date : 2025-09-03 DOI: 10.1093/jalm/jfaf070

Riona Singh-Gansan, Jean Pienaar, Dee Mary Blackhurst, Adrian David Marais

引用次数: 0

Clinical and Molecular Characteristics of a Patient with 11-Beta-Hydroxylase Deficiency. 11- β -羟化酶缺乏症患者的临床和分子特征。

IF 1.9

Journal of Applied Laboratory Medicine Pub Date : 2025-09-03 DOI: 10.1093/jalm/jfaf038

Malgorzata Monika Brzozowska, Jeremy Allen Knott, Laura Buckton, Andrea Rita Horvath, David J Handelsman

引用次数: 0

Commentary on A Pregnant Woman with Severe Gestational Hypertriglyceridemia: Why? 1例重度妊娠期高甘油三酯血症孕妇：为什么？

IF 1.9

Journal of Applied Laboratory Medicine Pub Date : 2025-09-03 DOI: 10.1093/jalm/jfaf089

Kayode A Balogun

引用次数: 0

Clinical Evaluation and Epidemiologic Application of an Adenoviral Species-Typing Model, based on Syndromic PCR Melt-Curve Data. 基于证候PCR熔融曲线数据的腺病毒分型模型的临床评价及流行病学应用。

IF 1.9

Journal of Applied Laboratory Medicine Pub Date : 2025-09-03 DOI: 10.1093/jalm/jfaf094

Jay D Jones, Benjamin W Galvin, Brooklyn A Noble, Varvara Probst, James D Chappell, Andrew J Spieker, Natasha B Halasa, Thomas C Robbins, Jonathan E Schmitz

{"title":"Clinical Evaluation and Epidemiologic Application of an Adenoviral Species-Typing Model, based on Syndromic PCR Melt-Curve Data.","authors":"Jay D Jones, Benjamin W Galvin, Brooklyn A Noble, Varvara Probst, James D Chappell, Andrew J Spieker, Natasha B Halasa, Thomas C Robbins, Jonathan E Schmitz","doi":"10.1093/jalm/jfaf094","DOIUrl":"10.1093/jalm/jfaf094","url":null,"abstract":"Background: Human adenoviruses (HAdV) elicit diverse infections, most notably within the respiratory tract. While HAdV is a target in clinical-use PCR assays to diagnose respiratory infections, techniques are not widely available to determine individual HAdV species within clinical specimens. An initial model was previously developed to predict HAdV species from BIOFIRE® Respiratory Pathogen Panels (RPP), evaluated in silico and through contrived specimens. This model was based on melt-curve data of 5 individual amplification reactions underlying the adenoviral result.Methods: In this study, the initial model is updated to better reflect prior knowledge of HAdV respiratory epidemiology and applied to a dataset of clinical HAdV-detected RPP samples independently subtyped via PCR. Revised model performance was further assessed through application to clinical proficiency testing events in the BIOFIRE Syndromic Trends database (Trends), a near-real-time network of clinical-use BIOFIRE testing results. The revised model was applied to >100 000 HAdV-detected results in Trends from the United States since 2019.Results: Among the independently typed specimens, the revised model accuracy was 95.2% (180/189): 79/82 for HAdV-B, 97/102 for HAdV-C, and 4/5 for HAdV-E. In the Trends dataset, these analyses indicated dynamic epidemiology for HAdV species, including a shift of B-vs-C prevalence at the onset of the COVID-19 pandemic, which has more recently returned to prepandemic ratios, along with low-level prediction of species less typically associated with respiratory infection.Conclusion: In silico modeling of melt-curve data from the BIOFIRE RPP can enhance HAdV species surveillance efforts and define viral epidemiology at local, regional, and national levels.","PeriodicalId":46361,"journal":{"name":"Journal of Applied Laboratory Medicine","volume":" ","pages":"1176-1187"},"PeriodicalIF":1.9,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144733821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Blue-Green Inclusions in Neutrophils: An Early Indicator of Poor Prognosis? 中性粒细胞中的蓝绿色包涵体：预后不良的早期指标？

IF 1.9

Journal of Applied Laboratory Medicine Pub Date : 2025-09-03 DOI: 10.1093/jalm/jfaf057

Felipe De la Fuente, Pau Martínez, Romina Malutan

引用次数: 0

Data-Driven Reference Intervals in Modern Laboratory Medicine: New Web Tools to Refine Vitamin D Status. 数据驱动的参考区间在现代实验室医学：新的网络工具，以完善维生素D状态。

IF 1.9

Journal of Applied Laboratory Medicine Pub Date : 2025-09-03 DOI: 10.1093/jalm/jfaf084

Álex Barragán Muñoz, Aleix B Fabregat-Bolufer

引用次数: 0

SBC-SHAP: Increasing the Accessibility and Interpretability of Machine Learning Algorithms for Sepsis Prediction. SBC-SHAP：提高败血症预测机器学习算法的可访问性和可解释性。

IF 1.9

Journal of Applied Laboratory Medicine Pub Date : 2025-09-03 DOI: 10.1093/jalm/jfaf091

Daniel Walke, Daniel Steinbach, Thorsten Kaiser, Alexander Schönhuth, Gunter Saake, David Broneske, Robert Heyer

{"title":"SBC-SHAP: Increasing the Accessibility and Interpretability of Machine Learning Algorithms for Sepsis Prediction.","authors":"Daniel Walke, Daniel Steinbach, Thorsten Kaiser, Alexander Schönhuth, Gunter Saake, David Broneske, Robert Heyer","doi":"10.1093/jalm/jfaf091","DOIUrl":"10.1093/jalm/jfaf091","url":null,"abstract":"Background: Sepsis is a life-threatening condition that is one of the major causes of death worldwide. Early detection of sepsis is required for fast initialization of an appropriate therapy. Complete blood count data containing information about white blood cells, platelets, hemoglobin, red blood cells, and mean corpuscular volume could serve as early indicators. However, previous approaches are limited by their interpretability (i.e., investigating the influence of feature values on individual predictions) and accessibility (i.e., easy accessibility for clinicians without programming expertise).Methods: We developed a graph-based approach for training machine learning (ML) algorithms to incorporate time-series information for prediction based on complete blood count data. Additionally, we investigated the effect of integrating different ratios to a healthy reference measurement to improve the performance of the previously published ML model. Finally, we developed a web application based on our approaches to increase accessibility.Results: While it was irrelevant how exactly the ratio was formed, our approach increased the sensitivity at 80% specificity across all ML models from up to 78.2% to up to 82.9% on an internal dataset (i.e., same tertiary care center) and from 65.4% to 73.4% on an external dataset (i.e., independent tertiary care center) for prospective time-series analysis. Additionally, we propose SBC-SHAP (https://mdoa-tools.bi.denbi.de/sbc-shap), a web application that visualizes the sepsis risks and individual interpretations of several ML classifiers.Conclusions: We are confident that this tool will increase the interpretability and accessibility of ML models for predicting sepsis based on complete blood count data. SBC-SHAP is open-sourced on https://github.com/danielwalke/sbc_app.","PeriodicalId":46361,"journal":{"name":"Journal of Applied Laboratory Medicine","volume":" ","pages":"1226-1240"},"PeriodicalIF":1.9,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144609919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Commentary on An Unusual Presentation of Falsely Elevated Free T4 Levels in a Young Woman. 一位年轻女性游离T4水平异常升高的评论。

IF 1.9

Journal of Applied Laboratory Medicine Pub Date : 2025-09-03 DOI: 10.1093/jalm/jfaf081

Sharon Markham Geaghan

引用次数: 0

Discrepant Risk Assessment between Low-Density Lipoprotein Cholesterol and Low-Density Lipoprotein Size. 低密度脂蛋白胆固醇与低密度脂蛋白大小差异风险评估。

IF 1.9

Journal of Applied Laboratory Medicine Pub Date : 2025-09-03 DOI: 10.1093/jalm/jfaf090

Nicholas E Larkey, Leslie J Donato, Allan S Jaffe, Vlad C Vasile, Jeffrey W Meeusen

{"title":"Discrepant Risk Assessment between Low-Density Lipoprotein Cholesterol and Low-Density Lipoprotein Size.","authors":"Nicholas E Larkey, Leslie J Donato, Allan S Jaffe, Vlad C Vasile, Jeffrey W Meeusen","doi":"10.1093/jalm/jfaf090","DOIUrl":"10.1093/jalm/jfaf090","url":null,"abstract":"Background: Low-density lipoprotein cholesterol (LDL-C) is directly associated with coronary artery disease (CAD) risk. Subfractionation of LDL enables differentiation between large-buoyant LDL (>20.5 nm) and small-dense LDL (≤20.5 nm). Small-dense LDL reportedly increases CAD risk, as do LDL-C and LDL particle (LDL-P) concentrations. Nuclear magnetic resonance spectroscopy (NMR) reports LDL-C, LDL-P, and LDL size (LDL-s). We investigated associations between these outputs and their agreement on CAD risk information.Methods: Associations between LDL-P, LDL-C, and LDL-s measured by NMR were evaluated in serum from clinically ordered samples (n = 26 710), and a subset of patients with CAD evaluation from coronary angiography (n = 356). Correlations were determined using Spearman ρ, and clinical classifications were compared using the following thresholds for increased risk: LDL-C > 160 mg/dL, LDL-P > 1600 nmol/L, and LDL-S < 20.5 nm.Results: The large laboratory NMR data set showed LDL-C was highly correlated with LDL-P (ρ = 0.87), and moderately with LDL-s (ρ = 0.51). Correlation between LDL-P and LDL-s was weakest (ρ = 0.21). When comparing pairwise high- and low-risk laboratory values, concordant values were observed in 99.8%, 43%, and 25% of cases for LDL-P/LDL-C, LDL-s/LDL-P, and LDL-C/LDL-s, respectively. In patients with angiography and NMR results, CAD was diagnosed in 40% (6/15) of patients with concordant high-risk LDL-C and small-dense LDL-s in the smaller patient cohort, but only 19% (6/31) of CAD-positive patients with high-risk LDL-C had small-dense LDL-s.Conclusions: LDL-s and LDL-C are frequently discordant at established LDL-C risk cutoffs. CAD diagnosis was found in similar numbers regardless of LDL-s phenotype.","PeriodicalId":46361,"journal":{"name":"Journal of Applied Laboratory Medicine","volume":" ","pages":"1168-1175"},"PeriodicalIF":1.9,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144561533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0