Search Results (369)

Background: Non-ST segment elevation acute coronary syndrome (NSTE-ACS) is conventionally diagnosed using electrocardiography and serial blood biomarker measurements. We investigated a noninvasive, bloodless, and electrode-free diagnostic strategy using a wrist-worn infrared spectrophotometric biosensor (Infrasensor). Results: In a prospective multicenter study of 595 patients with suspected NSTE-ACS enrolled across 13 sites in two countries, participants were stratified into five analytical cohorts. With 200 multi-ethnic controls and a leave-one-cohort-out external validation, a machine learning model detected high-grade coronary obstruction with an area under the receiver operating characteristic curve (AUC) of 0.87 (95% CI: 0.84–0.90), 90% specificity, and 84% positive predictive value—surpassing standard risk scores. A secondary model predicted freedom from NSTE-ACS and adverse outcomes over 30 days with an AUC of 0.89 (95% CI: 0.87–0.92), 99% sensitivity, and 96% negative predictive value. Conclusions: The Infrasensor demonstrated high diagnostic accuracy for high-grade coronary obstruction and 30-day adverse outcome prediction, surpassing conventional risk scores across a prospective, multi-ethnic, multicenter cohort. These findings support its potential as a rapid, noninvasive point-of-care tool for early NSTE-ACS risk stratification. Full article

PSA testing plays an important role in the diagnostic workup of prostate cancer; despite this, its cancer specificity is a well-recognised limitation. Consequently, more reliable, non-invasive diagnostic tools that are capable of improving specificity while maintaining sensitivity are needed, thereby enabling better risk stratification, personalised patient management, and reduction in unnecessary invasive procedures. In this study, we characterised the small RNA profile—including miRNAs and tsRNAs—in seminal small extracellular vesicles (sEVs) using high-throughput sequencing to expand biomarker discovery for distinguishing benign from malignant prostate conditions in patients with moderately elevated PSA levels, a setting where non-invasive biomarkers are critically needed. Our analysis confirms a small number of differentially represented sncRNA transcripts contained in seminal sEVs between benign and malignant prostate disease, most of which are of low abundance. This result suggests that shared underlying molecular mechanisms are likely to occur between both prostate disease conditions, especially in patients with moderate PSA levels. Subsequent RT-qPCR analysis revealed differences in expression in PCa compared with healthy controls but not when compared with benign prostatic disease. Given the complexity of the underlying pathophysiological process and the heterogeneity in clinical phenotypes, this limitation was addressed through the application of multivariate approaches (including isomiRs or tRFs), which are proposed as fluid-based biomarkers for prostate cancer, collectively offering improved accuracy and enhancing the negative predictive value of PSA used in clinical settings. Full article

Background: Acute cellular rejection (ACR) remains a significant cause of graft dysfunction after liver transplantation and requires timely detection. Liver biopsy, the diagnostic gold standard for ACR, is invasive and unsuitable for frequent monitoring, while liver enzyme tests lack specificity for detecting ACR. Circulating microRNAs (miRNAs), including miR-122 and miR-885, have been previously identified as predictive biomarkers of ACR in liver transplant recipients. HepatoTrack™ is a serum-based miRNA RT-qPCR assay designed for noninvasive assessment of ACR using these biomarkers. This study evaluated the analytical performance and clinical validity of HepatoTrack™ for diagnosing ACR in liver transplant recipients. Methods: HepatoTrack™ uses 100 μL of serum and a one-step RT-qPCR workflow. It quantifies miR-122 and miR-885 normalized to miR-23a, with synthetic cel-miR-39 included as an exogenous control. Analytical validation assessed performance characteristics. Clinical performance was evaluated in a cohort of liver transplant recipients undergoing for-cause liver biopsy and used for model development and validation. Results: Analytical validation demonstrated robust assay performance. The HepatoTrack™ Prediction Score (HPS) algorithm was trained using 47 subjects from the training cohort based on a linear regression model incorporating longitudinal miRNA changes. In the independent testing cohort (n = 37), HPS achieved a sensitivity of 92.9%, specificity of 73.9%, positive predictive value (PPV) of 68.4%, and negative predictive value (NPV) of 94.4% for biopsy-confirmed ACR. HPS achieved an area under the curve (AUC) of 0.831 compared with 0.731 for ALT and 0.660 for AST. Conclusions: HepatoTrack™ support the analytical and clinical validity for detection of biopsy-confirmed acute cellular rejection in liver transplant recipients. The assay provides a noninvasive molecular test to aid in the diagnosis of acute cellular rejection and may complement existing post-transplant diagnostic evaluation. Full article

Lung cancer remains the leading cause of cancer-related mortality worldwide in both males and females. Despite recent advances in precision-targeted therapeutics, mortality rates remain high, largely due to delayed diagnoses when curative interventions are no longer feasible. Recent studies from our group demonstrated that the LINE-1 mRNA and associated miRNA cargo of plasma exosomes can be used as sensitive and specific diagnostic and prognostic biomarkers of non-small-cell lung cancer (NSCLC). Because exosomes from various cancer types can be detected in urine, we extended our investigation to examine these analytes in urine exosomes from NSCLC patients. LINE-1 ORF1 and ORF2 mRNA levels, along with miR-21-5p, miR-126-3p, miR-210-3p, miR-221-3p, Let-7b-5p, miR-146a-5p, miR-222-3p, miR-9-5p, and miR-1277-5p, were higher in urine exosomes from NSCLC patients compared to healthy controls. The cargo of urine-derived exosomes often mirrored that of plasma exosomes and correlated with several clinicopathologic characteristics. The strong predictive performance of urine exosomal RNAs distinguishing NSCLC patients from controls suggests these measurements may serve as a complementary and readily accessible source for noninvasive assessment of patients with NSCLC. Full article

Perioperative myocardial ischemia and myocardial injury after noncardiac surgery (MINS) remain prevalent contributors to postoperative morbidity and mortality. Recent advances, including high-sensitivity biomarkers and updated 2024 American Heart Association/American College of Cardiology (AHA/ACC) guidelines, have modified the clinical approach to preoperative ischemic evaluation. This review intends to synthesize contemporary evidence and provide a framework for perioperative ischemic workup. A narrative review of the current literature and major society guidelines was conducted, focusing on perioperative risk stratification, functional capacity assessment, biomarker utilization, noninvasive and invasive diagnostic modalities, and perioperative medical optimization strategies. Contemporary perioperative evaluation favors a stepwise, risk-based approach that uses clinical risk indices, functional capacity, and selective diagnostic testing. Biomarkers such as natriuretic peptides and cardiac troponins enhance risk prediction and enable the detection of MINS, which is strongly associated with increased mortality. Evidence does not support routine preoperative stress testing or prophylactic coronary revascularization in stable patients. Guideline-directed medical therapy, including sustained statin use and attentive management of antiplatelet and beta-blocker therapy, remains central to risk mitigation. Modern perioperative ischemic workup prioritizes individualized, evidence-based evaluation over routine testing. Integration of biomarkers, structured risk assessment, and multidisciplinary management may improve outcomes, though additional research is needed to define optimal strategies for detecting and treating MINS. Full article

Background: Early and objective identification of cognitive decline in aging populations remains a clinical challenge. Pupillary light reflex (PLR) and eye movement parameters represent non-invasive, quantitative biomarkers of autonomic and central nervous system integrity, yet their diagnostic utility for cognitive impairment in community-dwelling older adults, particularly in those with mild or borderline impairment (predominantly GDS-Stage 2), remains underexplored. Methods: This cross-sectional study analyzed 383 community-dwelling older adults (mean age 69.78 ± 6.29 years; 68.7% female). Ten PLR parameters (n = 202 with complete PLR measurements) and ten eye movement parameters were measured. Associations with cognitive decline (deterioration grade, GDS 2–4) were evaluated using Spearman correlation analysis and multivariate linear regression (adjusted for age, sex, BMI, and hypertension). Stratified analyses and ordinal logistic regression sensitivity analysis were performed to assess robustness. FDR correction (Benjamini–Hochberg) was applied for multiple comparisons. Predictive modeling was conducted using ElasticNet regression with 5-fold cross-validation. Results: After FDR correction, resting pupil diameter (ρ = −0.47, q < 0.001), constriction amplitude (ρ = −0.40, q < 0.001), mean constriction velocity (ρ = −0.36, q < 0.001), mean dilation velocity (ρ = −0.36, q < 0.001), and all eye movement velocity parameters (ρ = −0.22 to −0.41, q < 0.001) demonstrated significant negative correlations with cognitive decline. Multivariate regression confirmed resting pupil diameter (β = −0.286, q < 0.001) and constriction amplitude (β = −0.223, q < 0.001) as independent predictors. Sensitivity analysis using ordinal logistic regression yielded consistent results. Predictive modeling yielded modest performance for the primary outcome (PLR-only cross-validated R² = 0.184), whereas models using eye movement features alone or in combination with PLR features performed near chance (R² ≤ 0.04) or showed instability, indicating that these parameters are not yet suitable as standalone diagnostic tools. Exploratory analyses of depression and anxiety were limited by floor effects (≥89% zero scores). Conclusions: PLR and eye movement parameters show significant negative associations with cognitive decline in older adults, particularly in a sample skewed toward mild impairment (predominantly GDS-Stage 2). These findings provide preliminary, hypothesis-generating signals that warrant validation in clinical samples with broader cognitive impairment distributions, and these parameters should not yet be considered standalone diagnostic biomarkers. Full article

Background: Urothelial carcinoma requires frequent surveillance because of its high recurrence rate, particularly in patients with non-muscle-invasive disease. Although cystoscopy remains the standard method for diagnosis and follow-up, it is invasive, costly, and associated with patient discomfort. Urinary biomarkers have emerged as potential tools for improving surveillance and reducing unnecessary cystoscopies. Methods: We performed a narrative review of studies published between 2017 and 2026 evaluating urinary biomarkers in urothelial carcinoma. Particular attention was given to assay mechanisms, diagnostic performance, clinical applications, and integration into surveillance techniques. Results: The most extensively studied biomarkers were Xpert Bladder Cancer Monitor, Bladder EpiCheck, ADXBLADDER, and Cxbladder. Most molecular assays demonstrated higher sensitivity than urinary cytology, particularly for the detection of high-grade recurrence. Reported negative predictive values frequently exceeded 95%, suggesting potential utility in identifying patients at low risk of clinically significant recurrence. Xpert Bladder Cancer Monitor and Bladder EpiCheck were supported by the largest body of surveillance evidence, whereas Cxbladder and mutation-enhanced platforms showed promise for risk stratification and individualized follow-up. Evidence supports the use of urinary biomarkers as adjuncts to cystoscopy rather than replacements. Conclusions: Modern urinary biomarkers provide clinically useful information during the surveillance of urothelial carcinoma, especially for excluding high-grade recurrence and assisting the interpretation of equivocal findings. Future biomarker-guided surveillance strategies may reduce the burden of cystoscopy while maintaining oncological safety. Further studies are required to improve specificity and sensitivity in order to fully integrate these biomarkers into diagnostic and follow-up protocols. Full article

Early detection of Alzheimer’s disease (AD) and mild cognitive impairment (MCI) remains critically important, yet conventional neuroimaging and biomarker-based approaches are costly, invasive, and poorly scalable for population screening. Speech offers a non-invasive, cost-effective alternative cognitive biomarker, but existing systems rarely integrate its multiple linguistic dimensions. We present Multi-Spec MCI-Net, a multimodal framework for HC/MCI classification that jointly models three complementary speech representations: token-level semantics via dVAE and BERT operating on Mel spectrograms; temporal prosodic dynamics via a 1D-CNN with attention; and discourse-level semantic coherence via a graph convolutional network. A gated fusion mechanism adaptively weights these modalities, yielding clinically interpretable predictions tailored to individual phenotypic profiles. Evaluated on the Chinese NCMMSC2021_AD challenge dataset and the DementiaBank Mandarin subset, the model achieves 89.29% accuracy and 0.9584 ROC AUC on NCMMSC2021_AD, with 92.31% MCI recall—critical for minimizing false negatives in screening contexts. Evaluation on the combined NCMMSC2021_AD and DementiaBank Mandarin dataset attains 77.46% accuracy and 0.8280 AUC, demonstrating robustness across spontaneous dialog and picture description tasks. Ablation studies confirm that multimodal fusion outperforms the semantic-only baseline by 5.16 percentage points, with each branch contributing non-redundant diagnostic information. These results establish an effective, interpretable approach for scalable, speech-based early MCI screening. Full article

Background/Objectives: Non-small cell lung cancer (NSCLC) remains associated with high mortality, frequent late-stage diagnosis, biological heterogeneity, and recurrence after treatment. Although molecular and immunohistochemical biomarkers have transformed treatment selection, there remains a need for accessible, repeatable, and clinically practical circulating biomarkers that may support prognosis and post-treatment monitoring. This review discusses prolactin (PRL) as a candidate supplementary biomarker in NSCLC, with particular emphasis on its biological rationale, potential prognostic relevance, and possible role in personalized risk stratification after systemic therapy. Methods: This narrative review summarizes current evidence on established biomarkers in NSCLC, the physiology and regulation of PRL, PRL/PRLR signaling in cancer biology, mechanisms of PRL dysregulation in lung cancer, and available clinical observations concerning PRL alterations in NSCLC. Particular attention is given to the distinction between prognostic and predictive biomarkers, longitudinal monitoring, pituitary involvement, immune checkpoint inhibitor-related endocrine effects, and biological, pharmacological, and analytical confounders affecting PRL interpretation. Results: Current evidence suggests that PRL may be biologically relevant in NSCLC through its involvement in pathways related to cell proliferation, survival, angiogenesis, invasion, epithelial–mesenchymal transition, immune modulation, and possible therapy resistance. Clinical observations indicate that altered PRL levels may occur in advanced disease, pituitary involvement, systemic inflammation, stress, or during anticancer and supportive treatment. However, PRL lacks cancer specificity and is influenced by multiple confounders, including circadian rhythm, stress, endocrine disorders, macroprolactin, cachexia, medications, and assay variability. Available clinical data remain limited and are largely derived from small studies or case-based evidence. Conclusions: PRL should not currently be considered a standalone diagnostic, predictive, or treatment-selective biomarker in NSCLC. Its most realistic potential role is as a supplementary circulating marker within multimarker prognostic and monitoring models. Prospective validation with standardized sampling, assay procedures, and confounder adjustment is required before clinical implementation. Full article

Circulating tumor DNA (ctDNA) has emerged as a transformative tool in cancer diagnostics, enabling the non-invasive detection of tumor-derived DNA fragments released into the bloodstream through cellular lysis or active secretion. ctDNA measurement has demonstrated its clinical usefulness, including early cancer detection, identification of resistance mechanisms, and screening of asymptomatic individuals. In addition to prognosis, ctDNA analysis is increasingly used to guide adaptive treatment strategies by detecting minimal residual disease and tracking tumor evolution in real time. Recent advances in artificial intelligence are poised to further enhance the clinical impact of ctDNA, transforming it from a passive monitoring biomarker into a dynamic molecular sensor integrated into predictive clinical decision models. However, broad implementation of ctDNA-based assays in routine practice requires rigorous prospective validation, cross-platform standardization, and regulatory approval to unlock its full potential in precision oncology. Full article

Bladder cancer is a common malignancy with high recurrence rates and significant morbidity, necessitating accurate diagnostic and prognostic tools. Although cystoscopy and transurethral resection of bladder tumor (TURBT) remain reference standards, these approaches are invasive and limited by sampling errors and understaging. Consequently, there is growing interest in non-invasive biomarkers, including urine-based assays, blood-based markers, and imaging-derived parameters. Among these biomarkers, multiparametric magnetic resonance imaging (mpMRI), particularly with the Vesical Imaging Reporting and Data System (VI-RADS), has emerged as a robust non-invasive imaging biomarker for local staging and risk stratification. Recent evidence suggests that mpMRI plays a role in predicting treatment response and recurrence, particularly in the context of neoadjuvant therapy. This review provides a comprehensive overview of current non-invasive diagnostic and prognostic biomarkers in bladder cancer, with a particular emphasis on imaging biomarkers. We discuss their clinical utility, limitations, and future integration into multimodal decision-making frameworks. Full article

Background/Objectives: Spontaneous intracranial hypotension (SIH) is caused by spinal cerebrospinal fluid (CSF) leakage and is typically diagnosed by clinical presentation and characteristic MRI signs; however, objective tools for monitoring physiological changes and treatment response remain limited. Cine phase-contrast MRI (PC-MRI) enables noninvasive quantification of aqueductal CSF dynamics, yet reliable analysis is challenging since the cerebral aqueduct is extremely small and susceptible to low contrast, partial volume effects, and ROI-dependent measurement variability—particularly in SIH where CSF pulsatility is often reduced. Methods: We propose an end-to-end automated framework that integrates (1) a cascade localization–segmentation strategy, consisting of Tiny YOLOv4 detection followed by MultiResUNet segmentation on a YOLOv4-derived cropped ROI; (2) physiology-informed pulsatility-based segmentation (PUBS) to refine anatomical masks into functional flow ROIs; and (3) one-dimensional convolutional neural networks (1D-CNNs) to extract exploratory waveform morphology features from 32-phase cardiac-cycle velocity waveforms. The study includes 39 participants, yielding 59 cine PC-MRI examinations: 11 controls, 28 Pre-treatment SIH scans and 20 Post-treatment Recovery scans. Results: The cascade model significantly improves segmentation robustness compared with a full-image baseline, achieving higher Dice scores and markedly lower boundary errors across cohorts (e.g., Pre-treatment SIH HD95: 1.66 ± 0.74 px vs. 15.37 ± 44.98 px). PUBS refinement reduces quantification deviation from expert manual references in SIH (mean relative error: 7.4% to 5.6%) and improves diagnostic performance for multiple hemodynamic parameters (e.g., downward mean flow AUC: 0.747 to 0.792). For waveform morphology analysis, the end-to-end 1D-CNN classifier was evaluated using repeated-seed participant-level grouped LOOCV. The repeated-seed ensemble prediction showed modest out-of-sample discrimination between Normal controls and Pre-treatment SIH scans, with an AUC of 0.646, a bootstrap 95% confidence interval of 0.455–0.826, and a permutation-test p-value of 0.072. Separately, exploratory analysis of the final baseline-trained 1D-CNN latent space showed marked, apparent Normal-versus-SIH separability and an intermediate recovery distribution in PCA space, suggesting that aqueductal waveform morphology may encode SIH-related physiological information. Conclusions: These findings suggest that SIH-related information may be reflected not only in flow magnitude but also in aqueductal CSF waveform morphology. However, the modest and statistically non-significant out-of-sample performance of the end-to-end 1D-CNN classifier indicates that morphology-based AI features should currently be regarded as exploratory biomarker candidates rather than validated stand-alone diagnostic tools. Larger independent cohorts are required to confirm their reproducibility, physiological meaning, and clinical utility. Full article

MicroRNAs are small noncoding RNA molecules that regulate gene expression at the post-transcriptional level and play a key role in cancer development, progression, and response to therapy. Their relative stability in biological fluids and disease-associated expression patterns have positioned microRNAs as promising candidates for non-invasive cancer biomarkers. Liquid biopsy enables the detection of circulating and fluid-derived microRNAs in a range of biological materials, including blood, urine, saliva, stool, pancreatic cyst fluid, and bile, offering a minimally invasive complement to tissue-based diagnostics. This approach is particularly relevant in pancreatic ductal adenocarcinoma, a malignancy with high mortality driven largely by late diagnosis, aggressive disease course, and limited opportunities for curative treatment. This review summarizes current evidence on microRNA-based liquid biopsy approaches in this cancer, with a focus on diagnostic, prognostic, and predictive relevance. Serum and plasma remain the most extensively studied sources, while urine-based microRNA profiling has shown relatively consistent diagnostic performance across available studies, including in early-stage disease. Pancreatic cyst fluid and bile offer more lesion-proximal molecular information but are limited to selected clinical scenarios because of invasive sampling requirements. In contrast, salivary microRNA signatures show greater variability and lower reproducibility across studies. Overall, liquid biopsy based on microRNA analysis shows promise as a complementary tool for pancreatic ductal adenocarcinoma detection and risk stratification. However, substantial methodological heterogeneity and limited cross-study reproducibility currently limit clinical translation, underscoring the need for standardized workflows and prospective validation of clinically relevant microRNA panels. Full article

Colorectal cancer (CRC) remains one of the leading causes of cancer-related mortality worldwide, highlighting the critical need for non-invasive, accurate, and interpretable diagnostic tools. Metabolomic profiling of fecal microbial extracellular vesicles (EVs) offers a promising yet underexplored avenue for biomarker discovery when integrated with explainable machine learning (ML) frameworks. This study aimed to identify stool-derived microbial EV metabolite biomarkers that discriminate CRC patients from healthy controls and to develop interpretable ML classifiers for non-invasive CRC detection. Metabolomic profiles of fecal microbial EVs from 76 age- and sex-comparable participants (36 CRC, 40 controls) were obtained using LC/QTOFMS and GC/TOFMS. Three ML classifiers (TPOT, LightGBM, XGBoost) were trained and evaluated through 100-repeat stratified hold-out and nested 5-fold cross-validation, with SHAP and LIME applied for global and local interpretability. Fourteen metabolites were significantly dysregulated between the CRC and control groups (adjusted p < 0.05), with 13 upregulated and one (aminoisobutyric acid) downregulated. Furoic acid exhibited perfect diagnostic discrimination, followed by palmitic acid and tyramine. Nested cross-validation demonstrated robust performance: TPOT achieved AUC = 0.997 ± 0.005, sensitivity = 0.973 ± 0.022, and MCC = 0.957 ± 0.033. Hold-out validation corroborated these findings (AUC = 0.998 ± 0.008). SHAP analysis identified furoic acid, palmitic acid, and tyramine as the dominant predictive features, while aminoisobutyric acid exhibited a distinctive protective pattern. LIME analysis corroborated these findings at the individual prediction level. The identified fecal EV-derived metabolite panel—particularly furoic acid, palmitic acid, and tyramine—shows strong potential to predict CRC in a non-invasive, interpretable manner; however, given the modest sample size, these findings should be considered hypothesis-generating and require validation in larger, prospective, multi-center cohorts before clinical translation. Full article

Azoospermia represents the most severe manifestation of male infertility and is classified into obstructive azoospermia (OA) and non-obstructive azoospermia (NOA). NOA patients experience a lack of sperm due to testicular dysfunction, posing significant challenges in clinical diagnosis and treatment. Recent advancements in molecular biology and high-throughput technologies have led to the discovery and validation of numerous biomarkers, including proteins, non-coding RNAs, genetic polymorphisms, and imaging indicators, which have greatly enhanced the understanding of the pathophysiological mechanisms of azoospermia and facilitated non-invasive diagnostic approaches. This review aims to systematically summarize the pathogenesis of azoospermia and critically evaluate the latest advancements in diagnostic and prognostic biomarkers, including small RNAs, proteomic profiles, genetic markers, and imaging features. The overarching goal is to synthesize this knowledge toward the development of integrated, biomarker-guided strategies for precise diagnosis, prognosis prediction, and improved clinical management of azoospermia, particularly NOA. Full article