Clinical chemistry最新文献_第10页

Machine Learning-Based Detection of Bladder Cancer by Urine cfDNA Fragmentation Hotspots that Capture Cancer-Associated Molecular Features 通过捕捉癌症相关分子特征的尿液 cfDNA 片段热点进行基于机器学习的膀胱癌检测

IF 9.3 2区医学

Clinical chemistry Pub Date : 2024-10-21 DOI: 10.1093/clinchem/hvae156

Xiang-Yu Meng, Xiong-Hui Zhou, Shuo Li, Ming-Jun Shi, Xuan-Hao Li, Bo-Yu Yang, Min Liu, Ke-Zhen Yi, Yun-Ze Wang, Hong-Yu Zhang, Jian Song, Fu-Bing Wang, Xing-Huan Wang

{"title":"Machine Learning-Based Detection of Bladder Cancer by Urine cfDNA Fragmentation Hotspots that Capture Cancer-Associated Molecular Features","authors":"Xiang-Yu Meng, Xiong-Hui Zhou, Shuo Li, Ming-Jun Shi, Xuan-Hao Li, Bo-Yu Yang, Min Liu, Ke-Zhen Yi, Yun-Ze Wang, Hong-Yu Zhang, Jian Song, Fu-Bing Wang, Xing-Huan Wang","doi":"10.1093/clinchem/hvae156","DOIUrl":"https://doi.org/10.1093/clinchem/hvae156","url":null,"abstract":"Background cfDNA fragmentomics-based liquid biopsy is a potential option for noninvasive bladder cancer (BLCA) detection that remains an unmet clinical need. Methods We assessed the diagnostic performance of cfDNA hotspot-driven machine-learning models in a cohort of 55 BLCA patients, 51 subjects with benign conditions, and 11 healthy volunteers. We further performed functional bioinformatics analysis for biological understanding and interpretation of the tool’s diagnostic capability. Results Urinary cfDNA hotspots-based machine-learning model enabled effective BLCA detection, achieving high performance (area under curve 0.96) and an 87% sensitivity at 100% specificity. It outperformed models using other cfDNA-derived features. In stage-stratified analysis, the sensitivity at 100% specificity of the urine hotspots-based model was 71% and 92% for early (low-grade Ta and T1) and advanced (high-grade T1 and muscle-invasive) disease, respectively. Biologically, cfDNA hotspots effectively retrieved regulatory elements and were correlated with the cell of origin. Urine cfDNA hotspots specifically captured BLCA-related molecular features, including key functional pathways, chromosome loci associated with BLCA risk as identified in genome-wide association studies, or presenting frequent somatic alterations in BLCA tumors, and the transcription factor regulatory landscape. Conclusions Our findings support the applicability of urine cfDNA fragmentation hotspots for noninvasive BLCA diagnosis, as well as for future translational study regarding its molecular pathology and heterogeneity.","PeriodicalId":10690,"journal":{"name":"Clinical chemistry","volume":"20 1","pages":""},"PeriodicalIF":9.3,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142452175","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Correction to: Ready, Set, Screen: The Role of the Clinical Laboratory in Eliminating Chronic Hepatitis B Infection. 更正为准备，开始，筛查：临床实验室在消除慢性乙型肝炎感染中的作用》。

IF 9.3 2区医学

Clinical chemistry Pub Date : 2024-10-17 DOI: 10.1093/clinchem/hvae169

引用次数: 0

Mass Spectrometry Meets Free Light Chains: A Path toward Greater Diagnostic Precision. 质谱法与自由光链：实现更高精度诊断的途径。

IF 9.3 2区医学

Clinical chemistry Pub Date : 2024-10-17 DOI: 10.1093/clinchem/hvae155

Maria Alice Vieira Willrich

引用次数: 0

Personalized Reproductive Hormone Monitoring in Sweat. 汗液中的个性化生殖激素监测。

IF 7.1 2区医学

Clinical chemistry Pub Date : 2024-10-03 DOI: 10.1093/clinchem/hvae052

Robert D Maynard

引用次数: 0

Laboratory Testing for Celiac Disease: Clinical and Methodological Considerations. 乳糜泻的实验室检测：临床和方法学考虑因素。

IF 7.1 2区医学

Clinical chemistry Pub Date : 2024-10-03 DOI: 10.1093/clinchem/hvae098

Steffen Husby, Rok Seon Choung, Cæcilie Crawley, Søren T Lillevang, Joseph A Murray

{"title":"Laboratory Testing for Celiac Disease: Clinical and Methodological Considerations.","authors":"Steffen Husby, Rok Seon Choung, Cæcilie Crawley, Søren T Lillevang, Joseph A Murray","doi":"10.1093/clinchem/hvae098","DOIUrl":"10.1093/clinchem/hvae098","url":null,"abstract":"Background: Celiac disease (CeD) has an estimated prevalence of 1%-3%. The classical clinical presentation is malabsorption, but now patients may present with more subtle symptoms such as constipation, osteoporosis, or iron deficiency anemia. Children may also present with poor growth.CeD has a strong genetic component, and high-risk groups include first-degree relatives with CeD, patients with co-existing autoimmune diseases, and patients with chromosomal aberrations.Content: Diagnostic tests for CeD include duodenal histology, serology, and genetic testing. Duodenal histology has traditionally been the gold standard of diagnosis. However, serological tests, especially IgA tissue transglutaminase antibodies (TTG-IgA), are widely used and diagnostic algorithms are based primarily on TTG-IgA as a starting point. Human leukocyte antigen typing may also be incorporated to determine genetic risk for CeD. Guidelines for children endorse biopsy avoidance provided high levels of TTG-IgA, with diagnostic accuracy being comparable to duodenal biopsy. Confirmation may be achieved by identifying IgA endomysial antibodies in a separate blood sample. Subjects with low positive TTG-IgA levels and subjects with IgA deficiency need a biopsy to establish a diagnosis of CeD. The clinical follow-up of CeD usually includes a repeat TTG-IgA examination. In adults, healing may be delayed or incomplete, and a rare consequence of refractory celiac disease is transformation to enteric T-cell lymphoma.Summary: Laboratory testing, in particular TTG-IgA, plays a central role in the diagnosis and has an accuracy comparable to histology. Diagnostic algorithms utilizing laboratory testing are critical for the development of novel strategies to improve diagnosis.","PeriodicalId":10690,"journal":{"name":"Clinical chemistry","volume":" ","pages":"1208-1219"},"PeriodicalIF":7.1,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141888707","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Machine Learning-Based Sample Misidentification Error Detection in Clinical Laboratory Tests: A Retrospective Multicenter Study. 基于机器学习的临床实验室检验样本识别错误检测：一项回顾性多中心研究

IF 7.1 2区医学

Clinical chemistry Pub Date : 2024-10-03 DOI: 10.1093/clinchem/hvae114

Hyeon Seok Seok, Shinae Yu, Kyung-Hwa Shin, Woochang Lee, Sail Chun, Sollip Kim, Hangsik Shin

{"title":"Machine Learning-Based Sample Misidentification Error Detection in Clinical Laboratory Tests: A Retrospective Multicenter Study.","authors":"Hyeon Seok Seok, Shinae Yu, Kyung-Hwa Shin, Woochang Lee, Sail Chun, Sollip Kim, Hangsik Shin","doi":"10.1093/clinchem/hvae114","DOIUrl":"10.1093/clinchem/hvae114","url":null,"abstract":"Background: In clinical laboratories, the precision and sensitivity of autoverification technologies are crucial for ensuring reliable diagnostics. Conventional methods have limited sensitivity and applicability, making error detection challenging and reducing laboratory efficiency. This study introduces a machine learning (ML)-based autoverification technology to enhance tumor marker test error detection.Methods: The effectiveness of various ML models was evaluated by analyzing a large data set of 397 751 for model training and internal validation and 215 339 for external validation. Sample misidentification was simulated by random shuffling error-free test results with a 1% error rate to achieve a real-world approximation. The ML models were developed with Bayesian optimization for tuning. Model validation was performed internally at the primary institution and externally at other institutions, comparing the ML models' performance with conventional delta check methods.Results: Deep neural networks and extreme gradient boosting achieved an area under the receiver operating characteristic curve of 0.834 to 0.903, outperforming that of conventional methods (0.705 to 0.816). External validation by 3 independent laboratories showed that the balanced accuracy of the ML model ranged from 0.760 to 0.836, outperforming the balanced accuracy of 0.670 to 0.773 of the conventional models.Conclusions: This study addresses limitations regarding the sensitivity of current delta check methods for detection of sample misidentification errors and provides versatile models that mitigate the operational challenges faced by smaller laboratories. Our findings offer a pathway toward more efficient and reliable clinical laboratory testing.","PeriodicalId":10690,"journal":{"name":"Clinical chemistry","volume":" ","pages":"1256-1267"},"PeriodicalIF":7.1,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142035438","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Reference Intervals Revisited: A Novel Model for Population-Based Reference Intervals, Using a Small Sample Size and Biological Variation Data. 参考区间再探讨：使用小样本量和生物变异数据的基于人群的参考区间新模型。

IF 7.1 2区医学

Clinical chemistry Pub Date : 2024-10-03 DOI: 10.1093/clinchem/hvae109

Abdurrahman Coşkun, Sverre Sandberg, Ibrahim Unsal, Deniz I Topcu, Aasne K Aarsand

{"title":"Reference Intervals Revisited: A Novel Model for Population-Based Reference Intervals, Using a Small Sample Size and Biological Variation Data.","authors":"Abdurrahman Coşkun, Sverre Sandberg, Ibrahim Unsal, Deniz I Topcu, Aasne K Aarsand","doi":"10.1093/clinchem/hvae109","DOIUrl":"10.1093/clinchem/hvae109","url":null,"abstract":"Background: Conventional population-based reference intervals (popRIs) are established on the ranking of single measurement results from at least 120 reference individuals. In this study, we aimed to explore a new model for popRIs, utilizing biological variation (BV) data to define the reference interval (RI) limits and compared BV-based popRI from different sample sizes with previously published conventional popRIs from the same population.Methods: The model is based on defining the population set point (PSP) from single-measurement results of a group of reference individuals and using the total variation around the PSP, derived from the combination of BV and analytical variation, to define the RI limits. Using data from 143 reference individuals for 48 clinical chemistry and hematology measurands, BV-based popRIs were calculated for different sample sizes (n = 16, n = 30, and n = 120) and considered acceptable if they covered 90% of the population. In addition, simulation studies were performed to estimate the minimum number of required reference individuals.Results: The median ratio of the BV-based to conventional RI ranges was 0.98. The BV-based popRIs calculated from the different samples were similar, and most met the coverage criterion. For 25 measurands ≤16 reference individuals and for 23 measurands >16 reference individuals were required to estimate the PSP.Conclusions: The BV-based popRI model delivered robust RIs for most of the included measurands. This new model requires a smaller group of reference individuals than the conventional popRI model and can be implemented if reliable BV data are available.","PeriodicalId":10690,"journal":{"name":"Clinical chemistry","volume":" ","pages":"1279-1290"},"PeriodicalIF":7.1,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142055140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ADLM 2024 Annual Scientific Meeting Abstracts: Bold Advancements in Laboratory Medicine. ADLM 2024 年度科学会议摘要：实验室医学的大胆创新。

IF 7.1 2区医学

Clinical chemistry Pub Date : 2024-10-03 DOI: 10.1093/clinchem/hvae127

Christina C Pierre, Joshua Hayden, Mark A Marzinke

引用次数: 0

An Interference That Makes You Blue? 让你忧郁的干扰？

IF 7.1 2区医学

Clinical chemistry Pub Date : 2024-10-03 DOI: 10.1093/clinchem/hvae099

Ruth Melka, Christopher W Farnsworth, Yanchun Lin

引用次数: 0

Correction to: High Lead Levels in 2 Independent and Authenticated Locks of Beethoven's Hair. 更正：贝多芬两束经鉴定的独立头发中的高铅含量。