Electronic Journal of the International Federation of Clinical Chemistry and Laboratory Medicine最新文献

{"title":"Association of Serum Sortilin and Insulin Resistance in Patients with Gestational Diabetes Mellitus.","authors":"Rupa Thakur, Leena Chand, Anjana Vinod, Sowmya Krishnamurthy","doi":"","DOIUrl":"","url":null,"abstract":"<p><strong>Introduction: </strong>Gestational diabetes mellitus is a condition in which glucose intolerance develops during pregnancy. Sortilin is a type I transmembrane protein, that belongs to the VPS10 family of post-Golgi trafficking receptors that is involved in signaling, intracellular sorting, and transport of proteins. Sortilin is required for the storage of glucose and co-expressed with the glucose transporter GLUT4 in differentiated adipocytes. This study aimed to evaluate serum sortilin level in individuals with GDM and to elucidate its relation with insulin resistance.</p><p><strong>Methodology: </strong>This was a case-control study. It involved 80 pregnant women, 40 with GDM (case group) and 40 healthy pregnant women (control group). Fasting blood glucose, HbA1c, insulin, HOMA-IR, and sortilin were analyzed in all the participants. Statistical analysis was performed with SPSS, version 26.0.</p><p><strong>Results: </strong>Age, gestational week, and blood pressure showed no significant difference in both groups. Body mass Index (BMI), fasting blood glucose, glycated hemoglobin (HbA1c), fasting insulin, homeostasis model assessment of insulin resistance (HOMA-IR), and sortilin were significantly higher in the GDM group. Maternal serum sortilin showed a statistically significant positive correlation with BMI, fasting plasma glucose, serum insulin, HOMA-IR, and HbA1c. (r:0.37, p:<0.05; r:0.64, p:<0.001; r:0.38, p:<0.001; r:0.42, p<0.001, r:0.68, p<0.001 respectively). The Receiver Operating Characteristics (ROC) curve indicated sortilin as a potential biomarker for the prediction of cases with an area under a curve of 0.98 (p-value<0.001) and for the cut-off point value of >2.6ng/ml, the sensitivity was 97.5%, specificity was 97.5%, false negative rate was 2.5% and false positive rate was 2.5%. The Youden's index was 0.95, thus indicating the diagnostic accuracy of sortilin.</p><p><strong>Conclusion: </strong>This study aimed to evaluate serum sortilin level in individuals with GDM and to elucidate its relation with insulin resistance. In this study, we observed that serum sortilin concentration was significantly higher in individuals with gestational GDM than in healthy pregnant women. Also, serum sortilin had a statistically significant positive correlation with fasting blood glucose, fasting insulin, HOMA-IR, and glycated hemoglobin. The area under the curve of ROC and the Youden's index shows the impeccable diagnostic accuracy of sortilin.</p>","PeriodicalId":37192,"journal":{"name":"Electronic Journal of the International Federation of Clinical Chemistry and Laboratory Medicine","volume":"36 4","pages":"538-545"},"PeriodicalIF":0.0,"publicationDate":"2025-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12743336/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145850843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cardio-Renal-Metabolic Laboratory Profile: An Integrated Strategy for the Prevention and Management of Chronic Non-Communicable Diseases.","authors":"Luis Figueroa Montes","doi":"","DOIUrl":"","url":null,"abstract":"<p><strong>Objective: </strong>This article emphasizes the need to integrate cardiovascular, renal, and metabolic assessments into routine clinical practice, with the aim of improving the prevention, diagnosis, and management of chronic non-communicable diseases (NCDs).</p><p><strong>Methods: </strong>We conducted a comprehensive review of the current medical literature on cardio-renal-metabolic syndrome, alongside a critical analysis of traditional clinical profiles. Based on these findings, we propose a novel integrated laboratory profile that captures the interconnections between these systems, aiming to enhance diagnostic accuracy and patient management.</p><p><strong>Results: </strong>Significant gaps in current fragmented assessments were identified, leading to the development of a profile that integrates key biomarkers from all three systems for a more comprehensive and accurate evaluation. This profile will be implemented according to the complexity of the care levels and according to the patient's stage of cardio-renalmetabolic syndrome.</p><p><strong>Conclusion: </strong>Implementation of the new integrated cardiorenal-metabolic profile could likely optimize clinical care, reduce healthcare costs, and improve patient outcomes. However, its success will depend on the technical and logistical capabilities of clinical laboratory networks, as well as the stage of the patient's disease and the level of care at which it is implemented.</p>","PeriodicalId":37192,"journal":{"name":"Electronic Journal of the International Federation of Clinical Chemistry and Laboratory Medicine","volume":"36 4","pages":"564-574"},"PeriodicalIF":0.0,"publicationDate":"2025-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12743332/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145850844","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analyzing the Utility of Pooled Sera as Internal Quality Control for Immunoassay Parameters by an EWMA-backed Statistical Mechanism (e-PSQC): A Comparison with Commercially Available Material and Westgard Rules.","authors":"Prakruti Dash, Saurav Nayak, Tanushree Roy","doi":"","DOIUrl":"","url":null,"abstract":"<p><strong>Background: </strong>Internal Quality Control (IQC) is a cornerstone of clinical laboratory operations, ensuring reliability in diagnostic testing. Commercial IQC materials, though effective, pose challenges of high cost, limited availability, and susceptibility to matrix effects. Pooled sera (PS), derived from discarded patient samples, offer a cost-effective alternative. However, the stability and performance of pooled sera as IQC material in immunoassays need robust evaluation, particularly when combined with advanced statistical tools like Exponentially Weighted Moving Average (EWMA).</p><p><strong>Objective: </strong>To evaluate the utility of pooled sera as IQC material for immunoassay parameters using the EWMA statistical approach and compare its performance against commercially available IQC materials.</p><p><strong>Method: </strong>A study was conducted in the clinical biochemistry laboratory at AIIMS Bhubaneswar. Pooled sera were prepared from discarded patient samples, aliquoted, and stored at -5°C. Five immunoassay parameters (Free T3, Free T4, TSH, Cortisol, and Ferritin) were monitored over 60 days using both pooled sera and commercial IQC materials. EWMA charts with a 7-day smoothing window were employed to assess error detection. Performance metrics, including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Concordance Correlation Coefficient (CCC), were analyzed. Statistical significance was set at p<0.05.</p><p><strong>Results: </strong>The EWMA analysis of pooled sera (ePSIQC) closely mirrored commercial IQC performance. Concordance was significant for all parameters except Free T4. The ePSIQC method demonstrated superior early error detection compared to traditional Westgard Multirules. Pooled sera remained stable throughout the study duration, with deviations observed only in a few instances.</p><p><strong>Conclusion: </strong>Pooled sera, combined with EWMA, provides a cost-effective, stable, and reliable alternative to commercial IQC materials for immunoassays. The enhanced error detection capability of EWMA strengthens laboratory quality control, offering a viable solution for resource-limited settings.</p>","PeriodicalId":37192,"journal":{"name":"Electronic Journal of the International Federation of Clinical Chemistry and Laboratory Medicine","volume":"36 4","pages":"484-490"},"PeriodicalIF":0.0,"publicationDate":"2025-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12743331/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145850851","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Metabolic and Cardiac Complications in a Case of Adrenal Carcinoma.","authors":"Lechuang Chen, Jieli Li, Qing H Meng","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":37192,"journal":{"name":"Electronic Journal of the International Federation of Clinical Chemistry and Laboratory Medicine","volume":"36 4","pages":"575-580"},"PeriodicalIF":0.0,"publicationDate":"2025-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12743337/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145850995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Tribulations, Triumphs, and Governance: Shaping the Future of Artificial Intelligence in Healthcare.","authors":"Anna Carobene","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Artificial intelligence (AI) is driving a profound transformation across the healthcare landscape, with the potential to enhance diagnostic accuracy, optimize clinical decision-making, improve resource allocation, and advance personalized medicine. In public health, AI is redefining infectious disease epidemiology by enabling outbreak forecasting, genomic surveillance, and data-driven policy support, even in the presence of incomplete information. Within clinical laboratories, AI plays a pivotal and expanding role. It facilitates automation of complex workflows, supports diagnostic interpretation, and contributes to analytical performance improvements. Particularly promising is its integration into point-of-care testing, enabling decentralized diagnostics and broader access to timely care, especially in resource-constrained settings. However, these advancements are not without challenges. Concerns regarding algorithmic bias, lack of data representativeness, and risks to privacy and transparency must be carefully addressed. Moreover, the ethical and societal implications of AI are increasingly central. As emphasized by Pope Francis, while AI may accelerate access to knowledge and innovation, it also risks deepening global disparities and promoting a \"throwaway culture\" that undermines human dignity. His appeal for a \"culture of encounter\" rooted in equity, justice, and inclusion aligns with the mission of public health and laboratory medicine. This paper, based on the invited lecture delivered at the Clinical Laboratories Artificial Intelligence Revolution (CLAIR) 2025 conference, explores these themes through a critical lens. International scientific societies such as the IFCC are called to foster equitable implementation of AI by promoting access to training, infrastructure, and governance frameworks thus ensuring that AI contributes meaningfully to global health solidarity and equity.</p>","PeriodicalId":37192,"journal":{"name":"Electronic Journal of the International Federation of Clinical Chemistry and Laboratory Medicine","volume":"36 4","pages":"605-614"},"PeriodicalIF":0.0,"publicationDate":"2025-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12743338/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145851013","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leveraging AI to Enhance Electronic Health Records.","authors":"Sanja Stankovic","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>The artificial intelligence (AI) integration in the medical field benefits both clinicians and patients. One of the most exciting applications of AI in healthcare is its role in electronic health records (EHRs). Leveraging AI to enhance EHR holds incredible potential for streamlining processes and reducing errors, enhancing clinical decision support and improving interoperability, contributing to more accurate, personalized, and effective healthcare, improved patient outcomes and quality of life. This article provides an overview of the role of AI in EHR, illustrates several examples of how AI incorporation into EHRs transforms healthcare delivery, illustrates how AI-powered EHRs impact healthcare stakeholders, and highlights the challenges and barriers of AI adoption in EHR systems.</p>","PeriodicalId":37192,"journal":{"name":"Electronic Journal of the International Federation of Clinical Chemistry and Laboratory Medicine","volume":"36 4","pages":"618-623"},"PeriodicalIF":0.0,"publicationDate":"2025-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12743341/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145850986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing Laboratory Quality: A Comprehensive Sigma Metric Analysis for Diverse Biochemical Parameters.","authors":"Shobha C Ramachandra, Kusuma K Shivashankar, Akila Prashant, Swetha N Kempegowda","doi":"","DOIUrl":"","url":null,"abstract":"<p><strong>Background: </strong>Ensuring quality in the analytical phase of clinical chemistry is paramount for accurate diagnosis and treatment. Sigma metrics offer a quantitative framework to assess and enhance laboratory performance. In this study, we intend to comprehensively assess diverse biochemical parameters using three different QC databases to determine their suitability and design a tailor-made QC plan based on this assessment.</p><p><strong>Methods: </strong>This is a retrospective study, from an NABL-accredited laboratory. The coefficient of variation (CV) % was obtained from the IQC results and the Bias % from Proficiency Testing (PT) results. The Sigma value was calculated using the TEa from three different biological variation databases (EFLM database, Westgard database, CLIA database). QGI was calculated for parameters with a Sigma value <3.</p><p><strong>Results: </strong>Around 28-33 parameters in different instruments showed a Sigma value <3 (poor performance). However, several parameters lack TEa values in the CLIA database, preventing their inclusion in assessments of acceptability.</p><p><strong>Conclusion: </strong>By integrating Sigma calculations with established TEa standards, this study helped in identifying areas needing improvement. This comprehensive assessment ensured the evaluation of the performance of diverse analytes, thereby ensuring higher accuracy and reliability in patient test results.</p>","PeriodicalId":37192,"journal":{"name":"Electronic Journal of the International Federation of Clinical Chemistry and Laboratory Medicine","volume":"36 4","pages":"546-555"},"PeriodicalIF":0.0,"publicationDate":"2025-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12743343/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145850840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Implementing Machine Learning in the Clinical Laboratory: Opportunities and Challenges.","authors":"He Sarina Yang","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":37192,"journal":{"name":"Electronic Journal of the International Federation of Clinical Chemistry and Laboratory Medicine","volume":"36 4","pages":"615-617"},"PeriodicalIF":0.0,"publicationDate":"2025-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12743347/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145850904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Biological Reference Values for Newborn Screening Parameters in Accordance to Gestational Age and Birth Weight- A Prospective Study.","authors":"Suprava Patel, Neharani Verma, Seema Shah, Rachita Nanda, Eli Mohapatra","doi":"","DOIUrl":"","url":null,"abstract":"<p><strong>Background: </strong>Disease specific biomarkers are ideal tool to detect the presence of the disorder. Timely detection of disorders can improve the health outcome. The metabolic arrangements in pre-term (PT) and low-birth weight (LBW) newborns differ from those term-born and have normal-birth weight (NBW). Hence, it is crucial to compare the values across study groups and establish a dedicated reference values for each group considering the gestational-age and birth weight.</p><p><strong>Methods: </strong>The prospective study was conducted on the cohort of 2860 newborns who underwent newborn screening (NBS) in dried-bloodspot samples within five days of birth. The study groups included were TERMNBW, TERMLBW, PTNBW and PTLBW.</p><p><strong>Results: </strong>The central tendency measures and the comparison of the NBS parameters across the study groups are presented. Males recorded a higher n17-OHP (p<0.001) median(range) compared to female newborns whereas nIRT (p=0.008) and nMSUD (p<0.001) were higher in female newborns. nTSH values was higher in TERMNBW than the PTLBW group (p=0.03). n17-OHP levels in TERMNBW and TERMLBW groups were lower than PTNBW and PTLBW (<0.001) newborns. nBIOT range of 378.8U and nG6PD range of 17.1U/gHb was highest in TERMNBW. The reference value observed for nTSH, n17-OHP and nIRT were respectively, 9.2mIU/L, 48.6nmol/L, 95.0μg/dL in TERMNBW and 16.9mIU/L, 70.2nmol/L, 76μg/dL in PTLBW. nG6PD reference level were respectively 2.0 and 1.6u/gHb in TERMNBW and PTLBW groups. The nBIOT levels were 52.7U and 48.0U respectively. Reference values were nearly similar for nPKU, nGAL and nMSUD.</p><p><strong>Conclusion: </strong>The study has provided a detailed comparison and reference levels observed in various study groups and sub-groups considering the gestational-age and birth weight of the newborns.</p>","PeriodicalId":37192,"journal":{"name":"Electronic Journal of the International Federation of Clinical Chemistry and Laboratory Medicine","volume":"36 4","pages":"516-537"},"PeriodicalIF":0.0,"publicationDate":"2025-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12743333/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145850853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Present Where AI is Enhancing Laboratory Medicine, A Future Where It Redefines Healthcare.","authors":"Damien Gruson","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Artificial Intelligence (AI) has transitioned from a technological concept to a foundational component of healthcare innovation. In laboratory medicine, it is no longer a question of whether AI will play a role, but rather how it will be responsibly integrated to amplify clinical value, operational efficiency, and equitable patient care. This article explores the current and future impact of AI across diagnostic workflows, clinical decision-making, personalized prevention strategies, and hospital governance. It also highlights the ethical, legal, and sustainability considerations critical to ensuring AI supports a smarter, fairer, and more sustainable healthcare system.</p>","PeriodicalId":37192,"journal":{"name":"Electronic Journal of the International Federation of Clinical Chemistry and Laboratory Medicine","volume":"36 4","pages":"595-598"},"PeriodicalIF":0.0,"publicationDate":"2025-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12743344/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145850858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}