The precise diagnosis and stratification of psychiatric disorders remain formidable challenges in modern medicine, hindered by the absence of objective biomarkers and reliance on subjective clinical criteria. Recent advances in multi-omics technologies, including genomics, transcriptomics, proteomics, metabolomics, and epigenomics, have revolutionized our understanding of complex neuropsychiatric conditions such as schizophrenia, bipolar disorder, major depressive disorder, and autism spectrum disorder. This review critically evaluates the current landscape of multi-omics research in psychiatry, highlighting methodological innovations, integrative strategies, and translational potential for biomarker discovery and clinical implementation. By synthesizing data across diverse molecular layers, multi-omics approaches enable a systems-level view of psychiatric disorders as multifactorial entities shaped by molecular, cellular, environmental, and neurocircuitry interactions. Despite promising advances in diagnostic accuracy and personalized treatment, significant barriers persist, including data heterogeneity, analytical complexity, and the translational gap between molecular signatures and clinical phenotypes. This review systematically explores the contributions of individual omics domains, emerging frameworks for multimodal data integration, the role of systems biology and network-based models, and the impact of large-scale consortia in driving clinical translation.
{"title":"Multi-omics biomarkers in psychiatric disorders diagnosis and stratification","authors":"Seyyed Hossein Khatami , Sanam Anoosheh , Marzieh Khodaparast , Amir Maghsoudloonejad , Ehsan Dadgostar , Amir Asadi , Mahya Kaveh , Malihe Mehdinejad Haghighi","doi":"10.1016/j.cca.2026.120887","DOIUrl":"10.1016/j.cca.2026.120887","url":null,"abstract":"<div><div>The precise diagnosis and stratification of psychiatric disorders remain formidable challenges in modern medicine, hindered by the absence of objective biomarkers and reliance on subjective clinical criteria. Recent advances in multi-omics technologies, including genomics, transcriptomics, proteomics, metabolomics, and epigenomics, have revolutionized our understanding of complex neuropsychiatric conditions such as schizophrenia, bipolar disorder, major depressive disorder, and autism spectrum disorder. This review critically evaluates the current landscape of multi-omics research in psychiatry, highlighting methodological innovations, integrative strategies, and translational potential for biomarker discovery and clinical implementation. By synthesizing data across diverse molecular layers, multi-omics approaches enable a systems-level view of psychiatric disorders as multifactorial entities shaped by molecular, cellular, environmental, and neurocircuitry interactions. Despite promising advances in diagnostic accuracy and personalized treatment, significant barriers persist, including data heterogeneity, analytical complexity, and the translational gap between molecular signatures and clinical phenotypes. This review systematically explores the contributions of individual omics domains, emerging frameworks for multimodal data integration, the role of systems biology and network-based models, and the impact of large-scale consortia in driving clinical translation.</div></div>","PeriodicalId":10205,"journal":{"name":"Clinica Chimica Acta","volume":"585 ","pages":"Article 120887"},"PeriodicalIF":2.9,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146135746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Background and aims: Diabetes mellitus (DM) accelerates the onset and progression of coronary artery disease (CAD), yet metabolomic profiling in individuals with both conditions remains limited. NMR-based metabolomics offers a comprehensive assessment of metabolic alterations and may improve cardiovascular risk stratification. Hence, the objective of this study was to characterize serum metabolic signatures associated with varying CAD severity in diabetic patients and evaluate their relationship with conventional clinical measures.
Methods: Eighty-eight diabetic patients undergoing coronary angiography were categorized into three groups: normal coronaries (DNC), chronic stable angina (DCSA), and myocardial infarction (DMI). Serum was mechanically filtered (3-kDa cutoff) and analyzed using 800 MHz 1H NMR spectroscopy. Spectral data underwent univariate ANOVA, PCA, PLS-DA, and OPLS-DA. Metabolites with VIP > 1.0 were identified. ROC and regression analyses assessed discriminative performance and clinical-metabolic associations.
Results: Multivariate analyses showed clear separation among DNC, DCSA, and DMI. Seventeen metabolites distinguished the groups, with aspartate, methylguanidine, arginine, and creatinine identified as key metabolic signatures. Clinical measures-Troponin I, LDL, LDH, and total cholesterol-also demonstrated strong discriminatory ability. Combined ROC models achieved high sensitivity and specificity. Significant correlations linked myocardial injury and lipid dysregulation with nitrogen- and amino-acid-related metabolites.
Conclusions: Filtered-serum 1H NMR metabolomics reliably differentiates CAD severity in diabetic patients, revealing metabolic signatures associated with oxidative stress, amino-acid disruption, and lipid imbalance. Integrating metabolic and clinical measures offers a promising precision-medicine approach for early detection and risk stratification in DM-related CAD.
{"title":"NMR-based metabolic measures of chronic stable angina and myocardial infarction in patients with diabetes mellitus.","authors":"Ashish Gupta, Shiridhar Kashyap, Khushbhu Meena, Deepak Kumar, Amit Gaurav, Ankit Kumar Sahu, Subhash Yadav, Sudeep Kumar, Aditya Kapoor","doi":"10.1016/j.cca.2026.120883","DOIUrl":"https://doi.org/10.1016/j.cca.2026.120883","url":null,"abstract":"<p><strong>Background and aims: </strong>Diabetes mellitus (DM) accelerates the onset and progression of coronary artery disease (CAD), yet metabolomic profiling in individuals with both conditions remains limited. NMR-based metabolomics offers a comprehensive assessment of metabolic alterations and may improve cardiovascular risk stratification. Hence, the objective of this study was to characterize serum metabolic signatures associated with varying CAD severity in diabetic patients and evaluate their relationship with conventional clinical measures.</p><p><strong>Methods: </strong>Eighty-eight diabetic patients undergoing coronary angiography were categorized into three groups: normal coronaries (DNC), chronic stable angina (DCSA), and myocardial infarction (DMI). Serum was mechanically filtered (3-kDa cutoff) and analyzed using 800 MHz <sup>1</sup>H NMR spectroscopy. Spectral data underwent univariate ANOVA, PCA, PLS-DA, and OPLS-DA. Metabolites with VIP > 1.0 were identified. ROC and regression analyses assessed discriminative performance and clinical-metabolic associations.</p><p><strong>Results: </strong>Multivariate analyses showed clear separation among DNC, DCSA, and DMI. Seventeen metabolites distinguished the groups, with aspartate, methylguanidine, arginine, and creatinine identified as key metabolic signatures. Clinical measures-Troponin I, LDL, LDH, and total cholesterol-also demonstrated strong discriminatory ability. Combined ROC models achieved high sensitivity and specificity. Significant correlations linked myocardial injury and lipid dysregulation with nitrogen- and amino-acid-related metabolites.</p><p><strong>Conclusions: </strong>Filtered-serum <sup>1</sup>H NMR metabolomics reliably differentiates CAD severity in diabetic patients, revealing metabolic signatures associated with oxidative stress, amino-acid disruption, and lipid imbalance. Integrating metabolic and clinical measures offers a promising precision-medicine approach for early detection and risk stratification in DM-related CAD.</p>","PeriodicalId":10205,"journal":{"name":"Clinica Chimica Acta","volume":" ","pages":"120883"},"PeriodicalIF":2.9,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146141050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-06DOI: 10.1016/j.cca.2026.120886
Rui Wu, Tianjiao Zhang, Chuanbao Zhang
Diabetes is a chronic metabolic disease, and blood glucose monitoring is crucial for its management. Glycated albumin (GA) is a product of non-enzymatic glycation of glucose with human serum albumin (HSA) in the blood and can reflect the average blood glucose levels over the past 2-3 weeks. It compensates for the limitations of glycated hemoglobin (HbA₁c) in short-term blood glucose monitoring and in special populations such as those with hemoglobin disorders. However, there are various methods for detecting GA, including chemical colorimetry, boronate affinity chromatography, immunoassays, enzymatic methods, and mass spectrometry. Traditional detection methods have been replaced by enzyme-based test kits using fully automated biochemical analyzers due to their lack of traceability and cumbersome operation and enzymatic methods have thus become the most commonly used method for clinical GA detection. However, the lack of standardized reference measurement procedures leads to significant variations in detection results among different enzymatic assay kits, making the standardization of GA detection particularly critical. This review summarizes the early detection methods, clinically common enzymatic assays, and standardized liquid chromatography-tandem mass spectrometry (LC-MS/MS) methods to elaborate on the development of GA detection methods. Further analyzes the current status and existing challenges of GA detection standardization, aiming to improve the consistency of results among different detection methods and promote the advancement of GA detection standardization.
{"title":"Glycated albumin: detection methods and standardization.","authors":"Rui Wu, Tianjiao Zhang, Chuanbao Zhang","doi":"10.1016/j.cca.2026.120886","DOIUrl":"https://doi.org/10.1016/j.cca.2026.120886","url":null,"abstract":"<p><p>Diabetes is a chronic metabolic disease, and blood glucose monitoring is crucial for its management. Glycated albumin (GA) is a product of non-enzymatic glycation of glucose with human serum albumin (HSA) in the blood and can reflect the average blood glucose levels over the past 2-3 weeks. It compensates for the limitations of glycated hemoglobin (HbA₁c) in short-term blood glucose monitoring and in special populations such as those with hemoglobin disorders. However, there are various methods for detecting GA, including chemical colorimetry, boronate affinity chromatography, immunoassays, enzymatic methods, and mass spectrometry. Traditional detection methods have been replaced by enzyme-based test kits using fully automated biochemical analyzers due to their lack of traceability and cumbersome operation and enzymatic methods have thus become the most commonly used method for clinical GA detection. However, the lack of standardized reference measurement procedures leads to significant variations in detection results among different enzymatic assay kits, making the standardization of GA detection particularly critical. This review summarizes the early detection methods, clinically common enzymatic assays, and standardized liquid chromatography-tandem mass spectrometry (LC-MS/MS) methods to elaborate on the development of GA detection methods. Further analyzes the current status and existing challenges of GA detection standardization, aiming to improve the consistency of results among different detection methods and promote the advancement of GA detection standardization.</p>","PeriodicalId":10205,"journal":{"name":"Clinica Chimica Acta","volume":" ","pages":"120886"},"PeriodicalIF":2.9,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146140846","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Autoimmune diseases comprise a broad spectrum of disorders in which both the innate and adaptive branches of the immune system malfunction, mistakenly attacking the body's own tissues and organs. This breakdown in self-tolerance is marked by the generation of autoantibodies that drive tissue damage. Identifying individuals who are likely to develop more aggressive disease at an early stage is crucial, as it enables earlier therapeutic intervention and improves long-term clinical outcomes. Yet, traditional diagnostic tools (such as autoantibody levels, inflammatory mediators, and acute-phase biomarkers) often fall short in reliably forecasting disease trajectory or predicting response to therapy due to their limited sensitivity and specificity. These shortcomings have intensified interest in more advanced molecular markers, particularly those uncovered through genomic, transcriptomic, and epigenetic analyses. Among these novel biomarker candidates, non-coding RNAs have emerged as especially promising regulators and indicators of autoimmune pathology. Their potential highlights the importance of directing future research toward large-scale, multicenter, longitudinal studies that combine multi-omics methodologies with machine-learning–driven analyses to develop reliable and clinically meaningful prognostic signatures. Ultimately, the identification of diverse biomarkers and innovative analytical methods will play a pivotal role in enhancing the diagnosis, prognosis, and treatment of autoimmune diseases. This review synthesizes current evidence on the prognostic value of ncRNAs, emphasizing their ability to predict future clinical outcomes such as disease severity, risk of organ-specific damage (e.g., lupus nephritis, rheumatoid joint erosion), likelihood of disease flare, and therapeutic response. We detail the mechanisms by which specific ncRNAs influence key pathogenic processes and explore their clinical application as stable, accessible biomarkers in liquid biopsies.
{"title":"Non-coding RNAs as prognostic biomarkers in autoimmune disease","authors":"Maryam Rahnama , Siamak Rezaeiani , Navid Ghasemzadeh , Milad Ahmadaghdami , Arezoo Mesri , Mortaza Taheri-Anganeh , Ebrahim Mazloomi","doi":"10.1016/j.cca.2026.120884","DOIUrl":"10.1016/j.cca.2026.120884","url":null,"abstract":"<div><div>Autoimmune diseases comprise a broad spectrum of disorders in which both the innate and adaptive branches of the immune system malfunction, mistakenly attacking the body's own tissues and organs. This breakdown in self-tolerance is marked by the generation of autoantibodies that drive tissue damage. Identifying individuals who are likely to develop more aggressive disease at an early stage is crucial, as it enables earlier therapeutic intervention and improves long-term clinical outcomes. Yet, traditional diagnostic tools (such as autoantibody levels, inflammatory mediators, and acute-phase biomarkers) often fall short in reliably forecasting disease trajectory or predicting response to therapy due to their limited sensitivity and specificity. These shortcomings have intensified interest in more advanced molecular markers, particularly those uncovered through genomic, transcriptomic, and epigenetic analyses. Among these novel biomarker candidates, non-coding RNAs have emerged as especially promising regulators and indicators of autoimmune pathology. Their potential highlights the importance of directing future research toward large-scale, multicenter, longitudinal studies that combine multi-omics methodologies with machine-learning–driven analyses to develop reliable and clinically meaningful prognostic signatures. Ultimately, the identification of diverse biomarkers and innovative analytical methods will play a pivotal role in enhancing the diagnosis, prognosis, and treatment of autoimmune diseases. This review synthesizes current evidence on the prognostic value of ncRNAs, emphasizing their ability to predict future clinical outcomes such as disease severity, risk of organ-specific damage (e.g., lupus nephritis, rheumatoid joint erosion), likelihood of disease flare, and therapeutic response. We detail the mechanisms by which specific ncRNAs influence key pathogenic processes and explore their clinical application as stable, accessible biomarkers in liquid biopsies.</div></div>","PeriodicalId":10205,"journal":{"name":"Clinica Chimica Acta","volume":"585 ","pages":"Article 120884"},"PeriodicalIF":2.9,"publicationDate":"2026-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146135688","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-05DOI: 10.1016/j.cca.2026.120880
Shreya Garge, Gayatri Gaikwad, Vasanti Suvarna
Objectives: Phosphatases are pivotal in regulating phosphorylation homeostasis by catalyzing biomolecular dephosphorylation, thereby modulating signaling pathways, metabolic networks, and cellular functions. Dysregulation of phosphatase activity is implicated in diverse pathologies, including hepatobiliary dysfunction, metabolic bone disorders, prostate cancer, and lysosomal storage syndromes. This review aims to critically evaluate optical biosensing strategies for phosphatase detection, with emphasis on isoform-specific diagnostics and clinical applicability.
Methods: A comprehensive analysis was conducted on emerging optical biosensing platforms, including nanomaterial-assisted colorimetric assays, ratiometric fluorescence sensors, localized surface plasmon resonance (LSPR), and surface-enhanced Raman spectroscopy (SERS). These modalities were assessed against key clinical criteria such as sensitivity, isoform specificity, multiplexing capability, and regulatory feasibility.
Results: Optical biosensors demonstrate significant advancements over conventional p-nitrophenyl phosphate (pNPP)-based assays, offering enhanced sensitivity, substrate stability, and isoform discrimination. Specific applications include detection of prostatic acid phosphatase (PAP) and tartrate-resistant acid phosphatase (TRAP) in oncology, lysosomal acid phosphatase in neurodegenerative conditions, and alkaline phosphatase in bone and liver pathologies. These platforms show promise for integration into theragnostic systems and digital health infrastructures.
Conclusions: Optical biosensing technologies represent a transformative approach to phosphatase detection, enabling real-time monitoring and predictive analytics in precision diagnostics. Their integration into clinical workflows could facilitate early disease detection, personalized treatment strategies, and improved patient outcomes.
{"title":"Recent advances in phosphatases as new biomarker in personalized medicine.","authors":"Shreya Garge, Gayatri Gaikwad, Vasanti Suvarna","doi":"10.1016/j.cca.2026.120880","DOIUrl":"https://doi.org/10.1016/j.cca.2026.120880","url":null,"abstract":"<p><strong>Objectives: </strong>Phosphatases are pivotal in regulating phosphorylation homeostasis by catalyzing biomolecular dephosphorylation, thereby modulating signaling pathways, metabolic networks, and cellular functions. Dysregulation of phosphatase activity is implicated in diverse pathologies, including hepatobiliary dysfunction, metabolic bone disorders, prostate cancer, and lysosomal storage syndromes. This review aims to critically evaluate optical biosensing strategies for phosphatase detection, with emphasis on isoform-specific diagnostics and clinical applicability.</p><p><strong>Methods: </strong>A comprehensive analysis was conducted on emerging optical biosensing platforms, including nanomaterial-assisted colorimetric assays, ratiometric fluorescence sensors, localized surface plasmon resonance (LSPR), and surface-enhanced Raman spectroscopy (SERS). These modalities were assessed against key clinical criteria such as sensitivity, isoform specificity, multiplexing capability, and regulatory feasibility.</p><p><strong>Results: </strong>Optical biosensors demonstrate significant advancements over conventional p-nitrophenyl phosphate (pNPP)-based assays, offering enhanced sensitivity, substrate stability, and isoform discrimination. Specific applications include detection of prostatic acid phosphatase (PAP) and tartrate-resistant acid phosphatase (TRAP) in oncology, lysosomal acid phosphatase in neurodegenerative conditions, and alkaline phosphatase in bone and liver pathologies. These platforms show promise for integration into theragnostic systems and digital health infrastructures.</p><p><strong>Conclusions: </strong>Optical biosensing technologies represent a transformative approach to phosphatase detection, enabling real-time monitoring and predictive analytics in precision diagnostics. Their integration into clinical workflows could facilitate early disease detection, personalized treatment strategies, and improved patient outcomes.</p>","PeriodicalId":10205,"journal":{"name":"Clinica Chimica Acta","volume":" ","pages":"120880"},"PeriodicalIF":2.9,"publicationDate":"2026-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146137205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Background: Idiopathic membranous nephropathy (IMN) is a major cause of nephrotic syndrome and end-stage renal disease, but the gold-standard diagnostic method is invasive. This study aims to develop a non-invasive diagnostic model for IMN, focus on the diagnostic value of anti-phospholipase A2 receptor antibody (anti-PLA2R-Ab).
Patients and methods: In this single-center retrospective study,we included 9524 patients with chronic kidney disease patients who received renal biopsies, extracted 139 clinicopathological data from their records, and divided them into two groups based on pathological results.Renal biopsy cases were collected to form an independent external validation cohort.Seven machine learning methods were used to develop and verify models, and anti-PLA2R-Ab data were used to optimize and evaluate these models. Seventy percent of the patients were used for training, and the other 30% for verification. The area under the receiver operating characteristic curve, F1-score, accuracy, and confusion matrix were used to evaluate the diagnostic performance of the models.
Results: We analyzed 8840 patients and 10 indicators, excluding anti-PLA2R-Ab, to develop and validate diagnostic models, and then analyzed 2457 patients and 6 indicators, including anti-PLA2R-Ab, to develop and validate optimized models. With or without anti-PLA2R-Ab, the CatBoost model provided more accurate diagnosis of IMN (internal vs. external verification AUC:0.921 vs.0.901 and 0.950 vs.0.904, respectively) than anti-PLA2R-Ab alone (AUC: 0.867).
Conclusion: The CatBoost model was an accurate and non-invasive method that provided better diagnosis of IMN than anti-PLA2R-Ab in Chinese patients. This model is especially when anti-PLA2R-Ab testing and kidney biopsy are difficult or impossible.
{"title":"Development of machine learning-driven non-invasive diagnostic models for idiopathic membranous nephropathy in Chinese patients.","authors":"Qian Wang, Xiaolong Wang, Shibin Su, Weiguang Zhang, Quan Hong, Qiang Lyu, Shuwei Duan, Ying Zheng, Guichun Tang, Pu Chen, Jiaona Liu, Chengliang Yin, Jinlong Shi, Guangyan Cai, Xiangmei Chen, Zheyi Dong","doi":"10.1016/j.cca.2026.120882","DOIUrl":"10.1016/j.cca.2026.120882","url":null,"abstract":"<p><strong>Background: </strong>Idiopathic membranous nephropathy (IMN) is a major cause of nephrotic syndrome and end-stage renal disease, but the gold-standard diagnostic method is invasive. This study aims to develop a non-invasive diagnostic model for IMN, focus on the diagnostic value of anti-phospholipase A2 receptor antibody (anti-PLA2R-Ab).</p><p><strong>Patients and methods: </strong>In this single-center retrospective study,we included 9524 patients with chronic kidney disease patients who received renal biopsies, extracted 139 clinicopathological data from their records, and divided them into two groups based on pathological results.Renal biopsy cases were collected to form an independent external validation cohort.Seven machine learning methods were used to develop and verify models, and anti-PLA2R-Ab data were used to optimize and evaluate these models. Seventy percent of the patients were used for training, and the other 30% for verification. The area under the receiver operating characteristic curve, F1-score, accuracy, and confusion matrix were used to evaluate the diagnostic performance of the models.</p><p><strong>Results: </strong>We analyzed 8840 patients and 10 indicators, excluding anti-PLA2R-Ab, to develop and validate diagnostic models, and then analyzed 2457 patients and 6 indicators, including anti-PLA2R-Ab, to develop and validate optimized models. With or without anti-PLA2R-Ab, the CatBoost model provided more accurate diagnosis of IMN (internal vs. external verification AUC:0.921 vs.0.901 and 0.950 vs.0.904, respectively) than anti-PLA2R-Ab alone (AUC: 0.867).</p><p><strong>Conclusion: </strong>The CatBoost model was an accurate and non-invasive method that provided better diagnosis of IMN than anti-PLA2R-Ab in Chinese patients. This model is especially when anti-PLA2R-Ab testing and kidney biopsy are difficult or impossible.</p>","PeriodicalId":10205,"journal":{"name":"Clinica Chimica Acta","volume":" ","pages":"120882"},"PeriodicalIF":2.9,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146118200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-01DOI: 10.1016/j.cca.2026.120879
Qamar Abuhassan , Mutaz Jamal Al-khreisat , R. Roopashree , Maitreyee Panda , T. Sudhakar , Vipasha Sharma , Ashish Singh Chauhan , Guzal Klebleeva
Melanoma remains one of the most aggressive forms of skin cancer, with an increasing incidence and limited therapeutic success in advanced stages. Early detection and precise prognostic assessment are critical for improving patient outcomes, yet conventional biomarkers often lack sensitivity and specificity. In recent years, noncoding RNAs (ncRNAs), including microRNAs, long noncoding RNAs, and circular RNAs, have emerged as pivotal regulators of melanoma biology. These molecules modulate key pathways involved in tumor initiation, progression, metastasis, and therapeutic resistance while also demonstrating remarkable stability in biofluids, making them attractive candidates for minimally invasive diagnostics. This narrative review synthesizes current evidence on the role of ncRNAs as emerging biomarkers in melanoma, highlighting their potential utility in early detection, prognostic stratification, and prediction of therapeutic response. Furthermore, we discuss methodological advances in ncRNA profiling, translational challenges, and future directions for integrating ncRNA-based assays into clinical practice. By consolidating mechanistic insights and clinical relevance, this review underscores the promise of ncRNAs as transformative tools in precision oncology for melanoma management.
{"title":"Non-coding RNAs as emerging biomarkers in melanoma","authors":"Qamar Abuhassan , Mutaz Jamal Al-khreisat , R. Roopashree , Maitreyee Panda , T. Sudhakar , Vipasha Sharma , Ashish Singh Chauhan , Guzal Klebleeva","doi":"10.1016/j.cca.2026.120879","DOIUrl":"10.1016/j.cca.2026.120879","url":null,"abstract":"<div><div>Melanoma remains one of the most aggressive forms of skin cancer, with an increasing incidence and limited therapeutic success in advanced stages. Early detection and precise prognostic assessment are critical for improving patient outcomes, yet conventional biomarkers often lack sensitivity and specificity. In recent years, noncoding RNAs (ncRNAs), including microRNAs, long noncoding RNAs, and circular RNAs, have emerged as pivotal regulators of melanoma biology. These molecules modulate key pathways involved in tumor initiation, progression, metastasis, and therapeutic resistance while also demonstrating remarkable stability in biofluids, making them attractive candidates for minimally invasive diagnostics. This narrative review synthesizes current evidence on the role of ncRNAs as emerging biomarkers in melanoma, highlighting their potential utility in early detection, prognostic stratification, and prediction of therapeutic response. Furthermore, we discuss methodological advances in ncRNA profiling, translational challenges, and future directions for integrating ncRNA-based assays into clinical practice. By consolidating mechanistic insights and clinical relevance, this review underscores the promise of ncRNAs as transformative tools in precision oncology for melanoma management.</div></div>","PeriodicalId":10205,"journal":{"name":"Clinica Chimica Acta","volume":"585 ","pages":"Article 120879"},"PeriodicalIF":2.9,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146112461","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-31DOI: 10.1016/j.cca.2026.120881
Jonas Schmidt, Sarina Weiß, Frithjof Blessing, Josef Blessing, Peter Schierack, Stefan Rödiger, Rico Hiemann, Dirk Roggenbuck
Objectives: Detection of antinuclear antibody (ANA) via indirect immunofluorescence (IIF) on HEp-2 cells is a screening test for the serological diagnosis of systemic autoimmune rheumatic diseases. Automated interpretation of ANA classification by novel artificial intelligence (AI)-aided pattern recognition was compared with expert reading under routine conditions.
Methods: Consecutive serum samples of 2671 individuals referred to a routine laboratory were analysed for ANA titers and patterns using the automated interpretation system akironNeo. AI-based ANA detection was compared with independent classification by two experienced immunologists according to the international consensus on ANA patterns (ICAP) competence level.
Results: Overall, a good agreement (κ > 0.60) between the different evaluators both for positive/negative classification of ANA fluorescence images as well as for the pattern classification of positive samples with a titer ≥ 1:320 was observed. Positive/negative differentiation at different cut-offs revealed κ values from 0.584 to 0.760 whereas corresponding pattern recognition for interphase, metaphase and cytoplasmic patterns demonstrated κ values from 0.560 to 0.736 for samples scored as positive by all three evaluators.
Conclusions: The AI-based software showed a similar performance compared to human observers. AI-aided ANA image analysis can facilitate the diagnostic workflow of ANA IIF assays and reduce subjectivity during image classification.
{"title":"Comparison of manual with artificial intelligence-aided interpretation of ANA HEp-2 IIF assay patterns in a clinical diagnostics lab.","authors":"Jonas Schmidt, Sarina Weiß, Frithjof Blessing, Josef Blessing, Peter Schierack, Stefan Rödiger, Rico Hiemann, Dirk Roggenbuck","doi":"10.1016/j.cca.2026.120881","DOIUrl":"10.1016/j.cca.2026.120881","url":null,"abstract":"<p><strong>Objectives: </strong>Detection of antinuclear antibody (ANA) via indirect immunofluorescence (IIF) on HEp-2 cells is a screening test for the serological diagnosis of systemic autoimmune rheumatic diseases. Automated interpretation of ANA classification by novel artificial intelligence (AI)-aided pattern recognition was compared with expert reading under routine conditions.</p><p><strong>Methods: </strong>Consecutive serum samples of 2671 individuals referred to a routine laboratory were analysed for ANA titers and patterns using the automated interpretation system akironNeo. AI-based ANA detection was compared with independent classification by two experienced immunologists according to the international consensus on ANA patterns (ICAP) competence level.</p><p><strong>Results: </strong>Overall, a good agreement (κ > 0.60) between the different evaluators both for positive/negative classification of ANA fluorescence images as well as for the pattern classification of positive samples with a titer ≥ 1:320 was observed. Positive/negative differentiation at different cut-offs revealed κ values from 0.584 to 0.760 whereas corresponding pattern recognition for interphase, metaphase and cytoplasmic patterns demonstrated κ values from 0.560 to 0.736 for samples scored as positive by all three evaluators.</p><p><strong>Conclusions: </strong>The AI-based software showed a similar performance compared to human observers. AI-aided ANA image analysis can facilitate the diagnostic workflow of ANA IIF assays and reduce subjectivity during image classification.</p>","PeriodicalId":10205,"journal":{"name":"Clinica Chimica Acta","volume":" ","pages":"120881"},"PeriodicalIF":2.9,"publicationDate":"2026-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146104394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-31DOI: 10.1016/j.cca.2026.120876
Arthur M Chiwaya, Shima M Abdulgader, Patricia Manjate, James Sserubiri, Bayanda Mdoda, Loide N N Shipingana, Dinis Nguenha, Derrick Semugenze, Marc Bañuls, Vinzeigh N Leukes, Adam Penn-Nicholson, Achilles Katamba, Moses Joloba, Willy Ssengooba, Alberto L García-Basteiro, Frank Cobelens, Grant Theron
Background: C-reactive protein (CRP) is recommended to screen people living with HIV (PLWH) for tuberculosis (TB). LumiraDx is a portable platform that uses fingerprick blood. How CRP compares in fingerprick blood and serum is unknown.
Methods: CRP was measured in 1034 consecutively recruited contacts of people with TB using fresh fingerprick blood (LumiraDx at point-of-care) and stored (-80 °C) serum [LumiraDx and cobas C-Reactive Protein (Latex) High Sensitive (CRPHS) in laboratories]. Agreement was assessed using Lin's concordance correlation coefficient (CCC), Passing-Bablok (PB) regression, and Bland-Altman (BA) plots. Sensitivity and specificity for TB were evaluated in 156 contacts with microbiological reference standard information, namely culture, Xpert MTB/RIF Ultra, or both.
Results: Strong agreement [CCC = 0.85, PB slope -0.27 (95% confidence interval -0.82, 0.2), BA mean difference 1 (-1,3)] was observed between LumiraDx on fingerprick blood and serum. Similar agreement occurred for serum CRPHS vs. LumiraDx on serum [0.79; 1.1 (-1.1, 2.3); 11 (9, 14)] or fingerprick blood [0.75; 1.3 (-0.6, 2.5); 10 (8, 13)]. Areas under the receiver operating characteristic curves (AUROCs) were 0.747 (0.595, 0.899) for fingerprick LumiraDx, 0.761 (0.628, 0.893) for serum LumiraDx and 0.775 (0.636, 0.914) for serum CRPHS. At >5 mg/L, all tests showed identical sensitivity [77% (70, 83)]. Specificities were 60% (53, 68), 64% (57, 72) and 50% (43, 58), respectively. Serum storage duration did not affect performance.
Conclusions: LumiraDx CRP readouts on fingerprick blood and serum correlate closely. Stored serum can be used for LumiraDx CRP measurement. High sensitivity methods increase the proportion of people who screen false-positive.
{"title":"Bioequivalence of C-reactive protein in fingerprick blood and serum measured using the point-of-care LumiraDx test for tuberculosis diagnosis in exposed contacts.","authors":"Arthur M Chiwaya, Shima M Abdulgader, Patricia Manjate, James Sserubiri, Bayanda Mdoda, Loide N N Shipingana, Dinis Nguenha, Derrick Semugenze, Marc Bañuls, Vinzeigh N Leukes, Adam Penn-Nicholson, Achilles Katamba, Moses Joloba, Willy Ssengooba, Alberto L García-Basteiro, Frank Cobelens, Grant Theron","doi":"10.1016/j.cca.2026.120876","DOIUrl":"10.1016/j.cca.2026.120876","url":null,"abstract":"<p><strong>Background: </strong>C-reactive protein (CRP) is recommended to screen people living with HIV (PLWH) for tuberculosis (TB). LumiraDx is a portable platform that uses fingerprick blood. How CRP compares in fingerprick blood and serum is unknown.</p><p><strong>Methods: </strong>CRP was measured in 1034 consecutively recruited contacts of people with TB using fresh fingerprick blood (LumiraDx at point-of-care) and stored (-80 °C) serum [LumiraDx and cobas C-Reactive Protein (Latex) High Sensitive (CRPHS) in laboratories]. Agreement was assessed using Lin's concordance correlation coefficient (CCC), Passing-Bablok (PB) regression, and Bland-Altman (BA) plots. Sensitivity and specificity for TB were evaluated in 156 contacts with microbiological reference standard information, namely culture, Xpert MTB/RIF Ultra, or both.</p><p><strong>Results: </strong>Strong agreement [CCC = 0.85, PB slope -0.27 (95% confidence interval -0.82, 0.2), BA mean difference 1 (-1,3)] was observed between LumiraDx on fingerprick blood and serum. Similar agreement occurred for serum CRPHS vs. LumiraDx on serum [0.79; 1.1 (-1.1, 2.3); 11 (9, 14)] or fingerprick blood [0.75; 1.3 (-0.6, 2.5); 10 (8, 13)]. Areas under the receiver operating characteristic curves (AUROCs) were 0.747 (0.595, 0.899) for fingerprick LumiraDx, 0.761 (0.628, 0.893) for serum LumiraDx and 0.775 (0.636, 0.914) for serum CRPHS. At >5 mg/L, all tests showed identical sensitivity [77% (70, 83)]. Specificities were 60% (53, 68), 64% (57, 72) and 50% (43, 58), respectively. Serum storage duration did not affect performance.</p><p><strong>Conclusions: </strong>LumiraDx CRP readouts on fingerprick blood and serum correlate closely. Stored serum can be used for LumiraDx CRP measurement. High sensitivity methods increase the proportion of people who screen false-positive.</p>","PeriodicalId":10205,"journal":{"name":"Clinica Chimica Acta","volume":" ","pages":"120876"},"PeriodicalIF":2.9,"publicationDate":"2026-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146104397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-30DOI: 10.1016/j.cca.2026.120878
Tahir S. Pillay , Barbara S. van Deventer , Siphokazi Gwiliza , Evette L. Subramoney , Chantal van Niekerk
Clinical laboratories face stringent privacy constraints, limited datasets for rare conditions, and rising demands to validate AI algorithms and workflows safely. Synthetic data—artificially generated data that preserve the statistical characteristics of real clinical data without exposing patient identities—has emerged as a powerful tool to address these challenges. This review provides a comprehensive overview of synthetic data in the context of laboratory medicine. We begin by defining synthetic data and describing the main generation methods, from rule-based simulations to modern generative models (including generative adversarial networks, variational autoencoders, and diffusion models) with examples of their use in healthcare. We then delve into key applications in the clinical laboratory: quality control and method validation, education and training, machine learning development, test utilization and workflow simulation, and external quality assessment. Advantages of synthetic data—such as enhanced privacy, scalability, flexibility in simulating rare events, and cost-effectiveness—are discussed with illustrative case studies. We also examine challenges and limitations, including concerns about data fidelity, bias amplification, risks of model overfitting or re-identification attacks, and the cautious stance of regulators that still require real patient data for approvals. Finally, we outline future directions for synthetic data in laboratory medicine, from hybrid real–synthetic datasets and privacy-enhancing techniques to evolving regulatory frameworks and the potential to democratize data access globally. While synthetic data cannot entirely replace real clinical data—especially for regulatory validation—it can significantly augment what laboratories can design, test, and achieve, provided it is used with careful validation and ethical safeguards.
{"title":"Synthetic data in the clinical laboratory: methods, applications, and future prospects","authors":"Tahir S. Pillay , Barbara S. van Deventer , Siphokazi Gwiliza , Evette L. Subramoney , Chantal van Niekerk","doi":"10.1016/j.cca.2026.120878","DOIUrl":"10.1016/j.cca.2026.120878","url":null,"abstract":"<div><div>Clinical laboratories face stringent privacy constraints, limited datasets for rare conditions, and rising demands to validate AI algorithms and workflows safely. Synthetic data—artificially generated data that preserve the statistical characteristics of real clinical data without exposing patient identities—has emerged as a powerful tool to address these challenges. This review provides a comprehensive overview of synthetic data in the context of laboratory medicine. We begin by defining synthetic data and describing the main generation methods, from rule-based simulations to modern generative models (including generative adversarial networks, variational autoencoders, and diffusion models) with examples of their use in healthcare. We then delve into key applications in the clinical laboratory: quality control and method validation, education and training, machine learning development, test utilization and workflow simulation, and external quality assessment. Advantages of synthetic data—such as enhanced privacy, scalability, flexibility in simulating rare events, and cost-effectiveness—are discussed with illustrative case studies. We also examine challenges and limitations, including concerns about data fidelity, bias amplification, risks of model overfitting or re-identification attacks, and the cautious stance of regulators that still require real patient data for approvals. Finally, we outline future directions for synthetic data in laboratory medicine, from hybrid real–synthetic datasets and privacy-enhancing techniques to evolving regulatory frameworks and the potential to democratize data access globally. While synthetic data cannot entirely replace real clinical data—especially for regulatory validation—it can significantly augment what laboratories can design, test, and achieve, provided it is used with careful validation and ethical safeguards.</div></div>","PeriodicalId":10205,"journal":{"name":"Clinica Chimica Acta","volume":"585 ","pages":"Article 120878"},"PeriodicalIF":2.9,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146099677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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