Urokinase-type plasminogen activator receptor (uPAR) is a glycosylphosphatidylinositol (GPI)-anchored cell surface receptor composed of three homologous domains (DI, DII, and DIII), forming a flexible structure that mediates interactions with multiple ligands. Through these interactions, uPAR plays a key role in extracellular matrix (ECM) remodeling, cell invasion, proliferation, and migration, making it a promising therapeutic target in various diseases. In addition to its membrane-bound form, uPAR can be released into circulation following cleavage of its GPI anchor, generating soluble uPAR (suPAR), which is detectable in blood, serum, plasma, urine, and other biological fluids. Proteolytic cleavage within the linker region between domains I and II produces three main isoforms: full-length suPAR (suPARI-III), suPAR domain I (suPARDI), and suPAR domains II-III (suPARDII-III). suPAR has been extensively investigated as a biomarker of systemic chronic inflammation across multiple conditions. During COVID-19 pandemic, suPAR gained attention as a predictor of disease severity and mortality in acute patients, reinforcing its role as a marker of sepsis. Elevated suPAR levels have also been reported in diseases such as systemic sclerosis (SSc), systemic lupus erythematosus (SLE), and various malignancies, supporting its diagnostic and prognostic value. Several analytical methods are currently available for suPAR quantification, including ELISA, turbidimetric immunoassays, and chemiluminescence immunoassays. However, these assays differ in their ability to detect specific isoforms, which may vary in biological and clinical significance. This review summarizes the clinical relevance of suPAR, examines the functional roles of its isoforms, and evaluates current detection methods and their implications for clinical practice.
BACKGROUND:Early detection biomarkers are needed to support the timely detection and monitoring of ulcerative colitis (UC) and its potential association with colorectal cancer (CRC). We used Data-Independent Acquisition (DIA) proteomics on paired colorectal tissue and exudate samples to define the pathway changes and identify serum-accessible markers. METHODS:We employed DIA-based mass spectrometry to profile the proteomes of paired tissue and exudate samples from patients with UC and CRC. Differentially expressed proteins (DEPs) were analyzed using Gene Ontology (GO), KEGG, and Gene Set Enrichment Analysis (GSEA). Four candidate biomarkers, CDA, REG4, LCN2, and TNS1, were validated in serum samples using ELISA and receiver operating characteristic (ROC) curve analysis. RESULTS:DIA proteomic analysis of colorectal tissues and exudates identified >7000 proteins and revealed profound microenvironmental remodeling associated with UC and CRC. Heatmap clustering of the top 50 features showed consistent change patterns across comparisons, supporting the robustness of the differential signatures. DEPs in the UC group were enriched in crucial biological processes (BPs), including oxidative phosphorylation, the TCA cycle, autophagy, lysosomes, and immune dysregulation. The DEPs ih the CRC group were clustered into many vital BPs, such as glycolysis, focal adhesion, ECM remodeling, suppressed antigen presentation, N-glycan biosynthesis and p53 pathway inhibition. Exudate profiling uniquely revealed complement and coagulation activation, consistent with a systemic prothrombotic and immunosuppressive state. Compared with the control group, the AUC values for CDA, REG4, LCN2, and TNS1 were 0.841, 0.878, 0.922, and 0.800 in the UC group, and 0.880, 0.752, 0.887, and 0.718 in the CRC group, respectively. CONCLUSIONS:UC and CRC are primarily driven by a metabolic shift from oxidative phosphorylation to glycolysis, progressive immune silencing, and extracellular matrix remodeling. These findings indicate that CDA, REG4, LCN2, and TNS1 have strong potential as serum biomarkers for the early detection of UC and CRC.
BACKGROUND AND AIM:Loss of tolerance to GP2, an antimicrobial immune-modulating component of intestinal cells and receptor on microfold cells, is associated with disease severity in Crohn's disease (CD). However, the role of GP2 in inflammatory bowel diseases remains poorly understood. This study aimed to evaluate fecal GP2 levels in patients with ulcerative colitis (UC) and CD and to examine associations with disease activity, response to biologic therapy, and microbial features. METHODS:We conducted a retrospective study of adults with UC, CD, and healthy controls recruited at a tertiary IBD clinic. Fecal GP2 levels and serum anti-GP2 antibodies were measured using ELISA and correlated with disease activity, inflammatory biomarkers (CRP, fecal calprotectin and elastase activity), and microbiome assessed by 16S rRNA amplicon sequencing. RESULTS:The study included 87 patients with CD, 58 with UC, and 31 healthy controls. Fecal GP2 levels were significantly lower in UC, particularly in active UC, compared with CD or controls (P ≤ 0.05). In CD, fecal GP2 levels did not differ significantly from controls across activity strata but correlated with elastase activity. Further, fecal GP2 levels increased following induction therapy among clinical responders and were associated with gut microbial diversity. No correlation was observed between serum anti-GP2 and fecal GP2 levels, or serum anti-GP2 and responsiveness to induction therapy. CONCLUSIONS:Fecal GP2 concentrations are reduced in UC, particularly during active disease, but are preserved in CD. This suggests a disease-specific pattern in UC, potentially reflecting altered microbial interactions or increased luminal protein degradation.
BACKGROUND:Traumatic Brain Injury (TBI) is a prevalent neurological condition with significant public health implications. The heterogeneity in TBI's pathophysiology presents challenges in diagnosis and prognosis, highlighting the need for sensitive and specific biomarkers. Metabolomic studies have emerged as a promising approach to identify such biomarkers. OBJECTIVE:This study aimed to define the serum metabolome in patients with acute TBI and to identify metabolites that could differentiate severe from mild TBI, potentially serving as novel biomarkers for diagnosis and prognosis. METHODS:We conducted a metabolomic analysis on 12 severe TBI patients, 33 mild TBI patients, and 30 healthy controls. Blood samples were collected within 24 h post-injury, and metabolomic profiling was performed using LC-MS/MS and GC-MS. Data preprocessing and statistical analysis were conducted using Progenesis QI software, followed by Random Forest analysis and ROC curve assessments for metabolite selection and validation. RESULTS:The serum metabolome of TBI patients significantly differed from non-TBI patients, with distinct metabolic signatures correlating with injury severity. Methoxyamine was identified as a potential biomarker, showing increased levels in severe TBI compared to mild TBI and healthy controls. In the random forest analysis, methoxyamine was ranked among the top discriminating metabolites between severe and mild TBI, achieving an AUC of 0.8333 in the testing dataset. Network toxicology and molecular docking suggested a potential interaction between methoxyamine and HSP90AA1, a protein associated with TBI severity as well. CONCLUSION:Our findings indicate that specific serum metabolites, particularly methoxyamine, may serve as novel biomarkers for differentiating severe from mild TBI. Further research is warranted to explore their potential as therapeutic targets and their role in TBI pathophysiology.
OBJECTIVES:Neurofibromatosis type 1 (NF1) is a genetic multisystem disorder characterized by café-au-lait macules, neurofibromas, and other clinical features. Patients with NF1 have an increased risk of developing various tumours, including pheochromocytomas and paragangliomas (PPGLs), rare neuroendocrine tumours that secrete catecholamines. The prevalence of PPGL in NF1 patients varies, with some studies reporting rates up to 7.7%. Diagnosis relies on clinical symptoms such as hypertension, headaches, and palpitations, alongside biochemical and imaging studies. CASE PRESENTATION:This report presents a 50-year-old woman with a clinical diagnosis of NF1, based on family history and multiple diagnostic criteria, who presented with episodic abdominal pain and symptoms suggestive of pheochromocytoma. Imaging revealed a left adrenal mass, and biochemical urine analysis showed markedly elevated catecholamine metabolites. After appropriate medical management, she underwent surgical resection of the tumour. Genetic testing identified a pathogenic germline splice-site variant in the NF1 gene, confirming the hereditary nature of her condition. CONCLUSIONS:The case highlights the importance of genetic testing in patients with PPGL, particularly those with NF1, as it influences management, prognosis, and family counseling. Early identification of pathogenic variants allows for tailored surveillance strategies and informs at-risk relatives. Current guidelines recommend routine biochemical screening in NF1 patients for PPGLs, though optimal imaging intervals remain debated. This case underlines the necessity of integrating clinical, biochemical, radiological, and genetic data for comprehensive care in NF1-associated pheochromocytoma.
OBJECTIVES:High-sensitivity cardiac troponin T (hs-cTnT) testing is central to myocardial injury diagnosis, but non-reproducible ("outlier") results may complicate interpretation. Real-world data on their frequency and implications remain limited. We evaluated non-reproducible hs-cTnT results in our laboratory. METHODS:This retrospective study at Başakşehir Çam and Sakura City Hospital used hs-cTnT measurements from June 2023-December 2025. Non-reproducible results were identified by a critical difference (CD)-based approach applied to repeat testing of original serum and lithium-heparin plasma specimens on a Roche cobas e801 platform (fifth-generation assay). The common change criteria (3C) served as an alternative classifier. Chi-square tests compared outlier rates between serum and plasma and between URL-triggered and delta-triggered subgroups. Directional changes on repeat and additional turnaround time were also assessed. RESULTS:Among 10,815 repeated measurements, non-reproducible results occurred in 3.3% (n = 356; 95% CI 2.97-3.64). They were more frequent in plasma than in serum (5.4% vs. 0.7%, p < 0.001) and most common at initial concentrations of 10-20 ng/L (n = 223), followed by 20-30 ng/L (n = 92). Rates were 2.29% in URL-triggered and 3.87% in delta-triggered repeats (p < 0.001). On repeat, most results decreased (95.8%); increases were uncommon (4.2%). Repeat testing added a median delay of 42 min. The 3C criterion identified 425 results, comprising all CD-flagged cases plus 69 additional. CONCLUSIONS:Non-reproducible hs-cTnT results occur with appreciable frequency and may be influenced by sample matrix and concentration range. Most represent isolated elevations decreasing on repeat, potentially affecting workflow and time-sensitive decisions. Selective repeat testing with careful interpretation may preserve analytical reliability while minimizing delays.
BACKGROUND:Lower respiratory infections (LRIs) cause significant morbidity and mortality in elderly individuals, but the mechanisms driving severe deterioration remain unclear. METHODS:This prospective study enrolled 105 patients aged ≥60 with suspected LRIs between October 2024 and April 2025. Bronchoalveolar lavage fluid (BALF) was analyzed using 16S rRNA sequencing, metagenomics, untargeted metabolomics, and cytokine profiling. Multi-omics data were integrated into a tripartite network, and severity-associated signatures were identified via PLS-DA, logistic regression, and ROC analysis. RESULTS:The cohort included 40 severe (sLRIs) and 65 mild (mLRIs) cases. sLRIs exhibited reduced microbial diversity, shifting from commensal genera to opportunistic pathogens (Klebsiella, Corynebacterium, Elizabethkingia), with Klebsiella pneumoniae as a major bacterial hub. Metabolomics revealed 180 differential metabolites. Phenylalanine and beta-Alanine metabolism emerged as key severity-associated pathways. sLRIs showed accumulation of pro-inflammatory metabolites L-phenylalanine and phenylpyruvic acid. L-3-phenyllactic acid (PLA) served as the central metabolic hub. Cytokine profiling revealed local hyperinflammation (elevated IL-1β, IL-6, IL-8, TNF-α, IFN-γ), with IL-6 as central hubs. Multivariate analysis identified PLA and IL-8 as independently associated with severe status. Combined metabolic-immune signatures achieved high diagnostic accuracy (AUC: 0.858-0.882). CONCLUSIONS:sLRIs in elderly patients are characterized by microbial dysbiosis, opportunistic pathogen enrichment, and remodeled Phenylalanine and beta-Alanine metabolism that correlates with hyperinflammation. BALF PLA and IL-8 represent promising metabolic-immune biomarkers for severity stratification.
BACKGROUND:Atherosclerosis (AS) is still the major cause of cerebrovascular atherosclerotic stenosis (CAS). Sialic acid (SA) has garnered significant attention in atherosclerosis research. Some clinical studies demonstrated the potential of SA as a predictive marker for cardiovascular diseases. However, no clinical studies have yet explored the relationship between SA and CAS. METHODS:From January 2017 to October 2023, 5806 patients aged over 18 years were retrospectively evaluated. Spearman correlation analysis examined the relationship between serum SA levels and clinical characteristics of CAS patients. Univariate and multivariate logistic regression analyses assessed the association between SA and stenosis. The Restricted Cubic Spline analysis method was used to reveal the influence of SA on CAS. The Receiver Operating Characteristic Curve was employed to describe the diagnostic efficacy of SA as a potential biomarker for predicting CAS. RESULTS:Our study identified a positive correlation between SA levels and CAS severity. Additionally, SA levels demonstrated a dose-response relationship with both the degree of stenosis and the number of stenotic vessels. Furthermore, a statistically significant difference in SA levels was observed between patients with symptomatic and asymptomatic CAS. The receiver operating characteristic (ROC) model showed an AUC of 0.713 (95% CI: 0.700-0.726) for SA in predicting CAS. CONCLUSION:Serum SA levels demonstrated a dose-response relationship with both the degree of stenosis and the number of stenotic vessels. These findings suggest that SA may serve as a potential biomarker for identifying CAS.
Accurate laboratory assessment of circulating lipids underpins cardiovascular risk stratification, yet clinical interpretation depends not only on the assays but on the formula chosen to estimate low-density lipoprotein cholesterol (LDL-C). This review integrates the 2019-2025 evidence on laboratory methods for triglycerides (TG), total cholesterol (TC), and high-density lipoprotein cholesterol (HDLC), and on the formulas estimating LDL-C, VLDL-C, and non-HDL cholesterol, to determine how these should be measured, reported, and harmonized in Brazil, where lipid thresholds are adapted from international consensus. A PRISMA 2020 systematic search (PROSPERO CRD420251241064) of PubMed/MEDLINE, Scopus, SciELO, LILACS, Web of Science, and Embase retrieved 57,915 records; after removing 38,210 duplicates, 19,705 titles/abstracts were screened, 312 full texts assessed, and 25 sources included. Enzymatic colorimetric assays remain standard for TG, TC, and HDLC. For LDL-C, Martin/Hopkins classifies more accurately than Friedewald (89.6% vs 83.2% correct categorization in 5,051,467 patients), particularly at high TG and low LDL-C, while Sampson/NIH and modified Sampson/NIH extend reliable estimation into hypertriglyceridemia and very low LDL-C; direct measurement is reserved for TG beyond the validated range. Although the review centers on the Friedewald, Martin/Hopkins, and Sampson/NIH families that dominate guideline practice, other published equations exist and are addressed in context. In Brazil, atherogenic-lipid thresholds are risk-based decision limits rather than reference intervals; national surveys describe lipid distributions but were not designed to establish them. Analytical standardization through traceability programs, multicenter validation of formulas, and-where the distribution-based construct applies (HDLC, pediatrics)-nationally derived reference intervals are priorities for equitable cardiovascular risk assessment in Brazil.
BACKGROUND:Anal squamous cell carcinoma (ASCC) is a rare gastrointestinal cancer linked to high-risk human papillomavirus (HPV) infection in approximately 90% of cases. Current circulating tumor DNA (ctDNA) approaches in ASCC primarily rely on pathogenic HPV-based biomarkers; however, these methods do not detect all HPV strains and are not applicable to HPV-negative tumors. To address this limitation, we developed a ddPCR assay targeting hypermethylated genomic CpG biomarkers, allowing ctDNA detection independent of tumor HPV status. METHODS:An anal cancer methylation-specific multiplex droplet digital PCR (AnMM-ddPCR) assay was developed to target five previously described CpG biomarkers hypermethylated in ASCC: ASCL1, LHX8, WDR17, ZIC1, and ZNF582, and the ALB reference gene. Patient samples and samples from non-cancer controls were analyzed using the BioRad QX600 ddPCR multiplexing platform. RESULTS:CpG-methylated biomarker levels were significantly higher in ASCC tissue compared with normal tissue and whole blood. The AnMM-ddPCR assay successfully detected all five ctDNA markers in plasma from ASCC patients, achieving an AUC of 0.72 (95% CI: 0.55-0.89; P = 0.018) in baseline plasma samples from ASCC patients with T1 or T2 tumors ≤4 cm, versus 0.90 (95% CI: 0.76-1.00; P = 0.0005) in plasma from patients with T2 tumors >4 cm or T3 tumors. At a specificity of 91.30%, sensitivity increased with stage from 52.94% (95% CI: 30.96-73.83) to 88.89% (95% CI: 56.50-99.43). CONCLUSION:The AnMM-ddPCR assay enables HPV-independent ctDNA detection in plasma samples from anal cancer patients and supports its evaluation in future liquid biopsy applications.
BACKGROUND AND AIM:Accurate assessment of ionized calcium (Ca++) is critical in clinical settings but remains technically and logistically challenging in many healthcare facilities. This study aimed to evaluate the performance of machine learning (ML) models in predicting Ca++ levels measured by blood gas analysis, using routinely available biochemical parameters-total calcium (TotCa), total protein, and albumin-and to compare them with values obtained through direct measurement and established correction formulas. MATERIALS AND METHODS:A retrospective analysis was conducted on 84,410 patients aged 20-70 years (43,863 men, 40,547 women). Whole-blood Ca++, serum TotCa, albumin, and total protein levels were retrieved from hospital records. Three ML algorithms-Support Vector Machine (SVM), Random Forest (RF), and Gradient Boosting (GB)-were trained and validated using 5-fold cross-validation. Their performance was benchmarked against three conventional correction formulas: Hanna, Zeisler, and Butler. RESULTS:Among the conventional formulas, the Hanna method showed the highest mean absolute error (MAE = 0.3626), while Zeisler (MAE = 0.0719) and Butler (MAE = 0.0988) performed more closely to measured Ca++. The ML models outperformed all formula-based methods, with GB (R2 = 0.6742), SVM (R2 = 0.6732), and RF (R2 = 0.6730) achieving the highest explained variance. In contrast, Butler and Zeisler yielded R2 values of 0.2684 and 0.4879, respectively. CONCLUSIONS:ML models demonstrate superior predictive accuracy for Ca++ compared with conventional correction formulas when using routine biochemical parameters. These findings support the potential integration of ML-based tools into clinical decision support systems. Future research should address model interpretability, pH incorporation, and prospective external validation.
Background Clinical laboratory results guide the vast majority of medical management pathways and Occurrence Management ensures diagnostic safety across the total testing process (TTP). However, execution in resource-limited settings (RLS) is severely hindered by infrastructural constraints like grid instability, workforce shortages, unreliable paper-based data systems, and punitive institutional cultures that suppress incident reporting and error capture. Objectives This review evaluates unique system-level and organizational barriers to error management in low-resource laboratories and synthesizes a scalable, phased operational framework to optimize continuous quality improvement and patient safety. Methods A comprehensive literature search was conducted across PubMed/MEDLINE, Scopus, Web of Science, Google Scholar, and AJOL for publications from January 2010 to April 2026. Guided by the TTP framework integrated with the Plan-Do-Check-Act (PDCA) cycle, a PRISMA-informed screening isolated 67 eligible records for thematic synthesis and framework development. Main Text Laboratory errors are highly asymmetric, with up to 68.2% concentrated in the pre-analytical phase. Primary failure points stem from human-system interface lapses, manual transcription workflows, and cold-chain failures during power outages. To bridge the gap with international quality standards (ISO 15189:2022), this paper establishes a phased, six-stage occurrence management roadmap scaled for varying tiers of healthcare delivery. Practical, low-cost interventions include implementing non-punitive “just culture” reporting policies, using cost-effective in-house pooled patient sera for quality control, deploying offline-capable open-source laboratory information systems, and forming interdisciplinary clinical-laboratory committees. To facilitate bench deployment, the framework is supported by open-access templates designed to guide standardized reporting, structured root cause analysis (Five Whys/Ishikawa checklists), corrective actions, ledger tracking, and automated Process Sigma performance indicator dashboard monitoring. Conclusion Strengthening error tracking in RLS is fully viable through targeted operational changes without extensive capital investment. Shifting from an individual blame orientation to system-centric learning, paired with stepwise accreditation mentorship models (SLMTA/SLIPTA), significantly reduces diagnostic defects and ensures health system sustainability.
Artificial intelligence, particularly machine learning and deep learning, is a rapidly evolving field that is increasingly permeating all areas of modern society, including public healthcare. In the present work, we provide a comprehensive introduction to the application of machine learning in routine clinical practice and biomedical research, including biomarker discovery. The most widely used machine learning models are introduced together with their key characteristics, strengths, and limitations. Furthermore, the fundamental principles governing the interpretation of model outputs and communication with regulatory authorities are discussed and illustrated using both real-world and synthetic datasets through modeling in the Python programming environment. Particular emphasis is placed on maintaining data quality and ensuring robust validation procedures in the context of dynamic, continuously updated AI systems intended for diagnostic software registered as medical devices (SaMDs). The importance of data governance, external validation, interpretability, and clinical oversight is highlighted as a prerequisite for safe and effective implementation of AI technologies in healthcare - throughout both the development and commercialization phases. Finally, current trends in medical artificial intelligence are reviewed, together with their potential benefits and associated risks for patients. The presented overview aims to facilitate a deeper understanding of the opportunities and challenges associated with the integration of AI-driven solutions into modern healthcare systems.
Human exposure to micro- and nanoplastics (MNPs) is increasingly relevant to clinical toxicology, but the field is not yet ready for routine patient-level testing. This narrative review evaluates MNPs as emerging clinical analytes from the perspective of diagnostic laboratory medicine. The central question is how laboratories can measure, interpret and act on toxicological information in human specimens without overstating immature evidence. Current studies have reported MNP-related signals in blood, urine, placenta, breast milk, lung tissue, vascular plaques and other tissues, yet comparisons are constrained by inconsistent definitions, heterogeneous matrices, variable sample preparation, incomplete contamination control, method-dependent reporting units and limited outcome-linked data. Particle-based methods such as micro-Fourier-transform infrared and Raman spectroscopy preserve size and morphology information but have practical detection limits and throughput constraints. Mass-based approaches such as pyrolysis-gas chromatography/mass spectrometry quantify polymer mass but can lose particle-level information and may be vulnerable to matrix interferences. Clinical laboratories should therefore treat MNP measurement as a high-complexity analytical problem requiring matrix-matched validation, procedural and field blanks, uncertainty estimates, orthogonal confirmation for consequential claims, and conservative interpretive comments. At present, MNP testing is best suited to research biomonitoring, occupational and public-health surveillance, exposure-source investigations and translational cohorts linking particle measurements to validated effect biomarkers. The review proposes reporting tiers, readiness levels and a laboratory roadmap to convert uncertain exposure signals into reproducible, interpretable and clinically responsible toxicological information.
BACKGROUND:Distinguishing idiopathic central precocious puberty (ICPP) from premature thelarche (PT) remains a clinical challenge and often necessitates GnRH stimulation testing. Kisspeptin-10 (Kp-10), Neurokinin B (NKB), and Neuropeptide Y (NPY) are key regulators of GnRH secretion and may serve as surrogate biomarkers. We evaluated whether these neuropeptides, alone or in combination with basal gonadotropins, could provide clinically useful discrimination of ICPP and PT. METHODS:In this prospective study, Indian girls aged 6-9 years were enrolled as controls (n = 40), ICPP (n = 33), and PT (n = 23). Anthropometry, basal LH, FSH, estradiol, pelvic ultrasonography, and plasma Kp-10, NKB, and NPY levels were assessed. Diagnostic performance was evaluated using receiver operating characteristic (ROC) analysis. Logistic regression models assessed incremental predictive value: Model 1 included the Kp-10/basal LH ratio; Model 2 added bone age advancement (BA-CA); and Model 3 further included basal estradiol. Clinical utility was examined using decision curve analysis (DCA). RESULTS:Anthropometric parameters did not differ between ICPP and PT. Kp-10 and NKB levels were higher in both early-puberty groups than controls, but neither marker alone discriminated ICPP from PT. The composite Kp-10/basal LH ratio showed superior performance, with an ROC-derived cut-off <4.07 ng/mIU (sensitivity 72.7%, specificity 87.0%, accuracy 78%). All three models showed similar discrimination (AUC 0.78-0.79), with no meaningful improvement after adding BA-CA or estradiol. DCA demonstrated a higher net benefit for the Kp-10/basal LH ratio compared with treat-all or treat-none strategies across clinically relevant thresholds. CONCLUSION:The Kp-10/basal LH ratio provides robust discrimination and meaningful clinical utility in differentiating ICPP from PT and may reduce reliance on GnRH stimulation testing.
Background Sepsis remains a leading cause of intensive care unit (ICU) mortality, necessitating the identification of accurate biomarkers for diagnosis and prognosis. Presepsin (sCD14-ST), generated through monocyte/macrophage phagocytosis of bacterial components, offers infection-specific signals. This study compared the diagnostic and prognostic performance of with of procalcitonin (PCT), C-reactive protein (CRP), interleukin-6 (IL-6), and interleukin-8 (IL-8) within the same patient cohort. Patients and methods This prospective multicenter cohort study enrolled 184 participants across two Algerian tertiary ICUs: 60 healthy controls, 30 patients with pulmonary infection, 46 with sepsis, and 48 with septic shock (Sepsis-3 criteria). All biomarkers were measured using a single blood sample collected within six hours of admission. Diagnostic accuracy was assessed using ROC curve analysis and DeLong's test, and independent predictors of 30-day mortality were identified using binary logistic regression. Results All biomarkers differed significantly between the groups (p < 0.001). Presepsin achieved the highest diagnostic accuracy for septic shock versus healthy controls (AUC = 0.880) and was the best discriminator between sepsis and pulmonary infection (AUC = 0.941) and between septic shock and pulmonary infection (AUC = 0.959), significantly outperforming all the comparators by DeLong's test. The 30-day mortality rate was 51.1%. Non-survivors had markedly higher presepsin levels than survivors (8315.8 vs. 1453.5 pg/mL; p < 0.001). Presepsin yielded the best prognostic AUC for 30-day mortality (AUC = 0.821; cut-off: 2721.52 pg/mL), whereas PCT, CRP, IL-8, and WBC showed near-chance discrimination. In multivariable regression analysis, presepsin was the only independent predictor of mortality. Conclusion Presepsin outperformed all evaluated biomarkers for sepsis diagnosis and 30-day mortality prediction, emerging as the sole independent prognostic marker after adjustment for confounding factors. These findings support its integration into ICU sepsis panels and provide the first validated presepsin mortality cut-off in a critically ill North African population.
Dried blood spot (DBS) sampling represents a minimally invasive, cost-effective alternative to venous blood collection, and advantageous for resource-limited diagnostic settings. The reliability of RNA-based molecular assays using DBS depends on RNA stability under various storage conditions. This study evaluated RNA stability in DBS samples stored at varying temperatures and time points, with and without RNAlater pretreatment of Whatman 903™ protein saver cards to determine their suitability for quantitative PCR (qPCR)-based applications. Whole blood was applied onto standard and RNAlater-pretreated (10 μL; 10 spots/card) Whatman 903™ cards and stored at room temperature (RT), 4 °C, and - 20 °C for 1, 3, and 7 days. Total RNA was extracted, quantified, and assessed for purity prior to cDNA synthesis. ABL1 gene amplification was performed using SYBR green real-time PCR. Analytical agreement between matrices for RNA quantity was assessed using Bland-Altman plot, and cycle threshold (Ct) values were compared. RNA yield and purity varied across storage conditions. The highest RNA yield was observed in RNAlater-pretreated DBS cards stored at RT for 7 days (36.3 ± 0.665 ng/μL), with acceptable purity (A260/280 range: 1.7-2.0). On day 7, whole blood samples stored at 4 °C demonstrated reduced RNA yield compared to treated and untreated DBS cards irrespective of the storage temperature. Consistent ABL1 amplification confirmed preserved RNA integrity in both untreated and treated DBS samples (CV <1%), with improved stability in pretreated cards compared to whole blood (CV < 3%). RNAlater pretreatment enhances RNA stability in DBS, supporting its application for reliable qPCR-based molecular diagnostics in routine and field laboratory settings, especially when concordance is established between venous and capillary sampling.
The brain tumors possess different causative factors and properties, making their diagnosis and treatment difficult. Growth of these cancers usually leads to compression of the adjacent nerves and obstruction of the flow of cerebrospinal fluid, thus leading to increase in intracranial pressure. This affects the working of brain in many ways; thus, the difficulty involved in its treatment. With the improvements in technology in neuroimaging, including Diffusion Tensor Imaging (DTI), Positron Emission Tomography (PET), and multiparametric Magnetic Resonance Imaging (mpMRI), the diagnosis process has become easy. The effectiveness of any form of therapy in such patients depends primarily on their prognosis. While it is a common practice that physicians determine the prognosis of the disease by considering the age of the patient, histological grade of the tumor, and resection status, now this method has become more comprehensive by adding molecular signature and genetic analyses to the list of criteria. Next-generation sequencing (NGS) allows a reliable molecular classification. It increases the level of risk stratification, facilitating the application of therapies tailored to individual patients. Thus, molecular oncology has greatly changed our views on brain tumors' pathology and prognosis while neoadjuvant treatments aim at increasing the survival rate. On the other hand, radiogenomics is a field of study that combines non-invasive imaging phenotypes and genomic information in order to find unique molecular signatures of tumors without collecting samples from tumors. Molecular biomarkers are absolutely essential in the diagnosis of cancer, treatment monitoring, and recurrence of cancer. Advances in liquid biopsy technology, particularly the methods for circulating tumor DNA (ctDNA) and Extracellular Vesicle (EV) based analysis, have enabled the possibility of non-invasive monitoring of the progression of the tumors over time. This review highlights key studies and important scientific works about imaging technologies, biomarkers, and prognostic factors of malignant brain tumors.
Polycystic ovary syndrome (PCOS) is a highly prevalent and phenotypically diverse endocrine disorder in which insulin resistance (IR) serves as a central molecular driver of major metabolic and reproductive complications, including infertility, obesity, and increased long-term cardiovascular risk. This review examines the complex pathophysiology of PCOS, emphasizing how chronic systemic low-grade inflammation, gut microbiome dysbiosis, and oxidative stress interact to worsen hyperinsulinemia and hyperandrogenemia. It also highlights the clinical value of emerging diagnostic and prognostic biomarkers to improve risk stratification and patient management. Systemic biomarkers, such as pro-inflammatory cytokines, circulating endotoxemia arising from increased intestinal permeability, and epigenetic regulators including miR-146a, may provide prognostic insight into the trajectory of metabolic deterioration. In parallel, endometrial biomarkers, including the glucose transporter GLUT4, implantation-associated genes such as HOXA10, and inflammatory mediators like TNF-α, can support evaluation of impaired uterine receptivity, prediction of assisted reproductive technology (ART) outcomes, and stratification of miscarriage risk. By mapping key interactions within the gut-immune-metabolic axis and detailing localized endometrial dysfunction, this review proposes a framework for integrating targeted biomarker profiling into clinical practice to enable personalized, biomarker-informed interventions aimed at restoring fertility and metabolic health in patients with PCOS.
Traditional serum tumor markers, such as carcinoembryonic antigen (CEA), carbohydrate antigen 19-9 (CA 19-9), cancer antigen 125 (CA 125), α-fetoprotein (AFP), and prostate-specific antigen (PSA), exhibit limited diagnostic sensitivity and specificity during the early stages of disease and in malignancies with overlapping clinical features. This limitation has led to the investigation of complement pathway proteins as potential clinical chemistry analytes. Measurements of circulating complement C1q C chain (C1QC) are frequently derived from total C1q or C1q-binding activity assays, rather than those specific to C1QC. These measurements are linked to the activation of the classical complement pathway, M2-like polarization of tumor-associated macrophages, and immunosuppressive metabolic reprogramming. However, many of the available data are based on transcriptomic analysis of tissues, single-cell analysis, bioinformatic datasets, or small retrospective cohorts without directly measuring the C1QC protein in the circulation. This narrative review critically evaluates C1QC within the clinical chemistry framework, covering its gene structure, proteoform heterogeneity, and post-translational modifications. This study also evaluated the analytical performance metrics of immunoassays, targeted liquid chromatography-tandem mass spectrometry (LC-MS/MS), aptamer-based electrochemical biosensors, and surface plasmon resonance platforms. It critically appraises the reported diagnostic and prognostic associations, including the proposed incremental contributions of multimarker panels. Additionally, external quality assessment, prospective multicenter validation, assay harmonization, and consideration of interference from inflammatory, infectious, or immune-mediated conditions are required for this approach. Compliance with the In Vitro Diagnostic Medical Devices Regulation (IVDR) and United States Food and Drug Administration (FDA) regulatory pathways is also necessary.
