Search Results (6,320)

We previously developed a four-marker panel for the diagnosis of colorectal cancer (CRC) and adenoma. This study aimed to identify novel bacterial markers to improve adenoma detection using metagenomics and qPCR. Candidate markers were identified from metagenomic data (n = 492) using ANCOM-BC2 and Spearman’s rank correlation analysis and were subsequently validated in an independent cohort (n = 426). Diagnostic performance was assessed both individually and in combination with our previously identified markers and FIT. Metagenomic analysis identified 21 candidate markers that increased along the normal–adenoma–carcinoma axis. Two top candidates, Cloacibacillus porcorum (Cp) and Intestinimonas butyriciproducens, were validated via qPCR and showed significant correlations with metagenomic abundances (both p < 0.0001). ROC analysis demonstrated that Cp levels significantly distinguished CRC and adenoma from controls, whereas I. butyriciproducens distinguished only CRC. The prevalence of Cp was significantly higher in adenoma and CRC than in controls (all p < 0.05). Multivariate analysis confirmed that Cp was independently associated with CRC and adenoma diagnoses. Adding Cp to the four-marker panel improved diagnostic sensitivity from 44.8% to 58.7% for adenoma and from 85.7% to 88.6% for CRC (specificity = 85%). When further combined with FIT, Cp improved sensitivity from 47.6% to 64.3% for adenoma and from 95.2% to 96.2% for CRC (specificity = 84.6%). C. porcorum is a novel bacterial marker that may aid in the non-invasive diagnosis of colorectal adenoma. Full article

Background/Objectives: Nutritional rickets is a childhood bone disorder leading to skeletal deformities and life-long disabilities. Early-stage diagnosis remains challenging due to the limited availability of non-invasive tools. This study explores metabolic variation associated with the active disease stages and with etiological factors, such as nutritional deficiencies and biochemical alterations. Methods: Untargeted ¹H NMR spectroscopy-based metabolomics were performed on urine and plasma samples collected from Bangladeshi children with radiologically active rickets (AR; n = 24; aged 2.98 ± 1.19 years), inactive rickets (IR; n = 36; aged 3.39 ± 1.87 years), and healthy matched controls (n = 58; aged 3.58 ± 1.59 years). This analysis also integrated corresponding clinical biochemistry and dietary intake data previously collected from the cohort. Results: Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA) identified the 24 h urinary excretion of 13 metabolites to vary with AR, including those previously associated with bone metabolism such as β-aminoisobutyrate, N-methylnicotinamide, taurine and hypoxanthine. Biochemically, AR was strongly characterized by increased plasma alkaline phosphatase and decreased iFGF23. The multi-block integration of metabolomic, biochemical, and nutritional data achieved an 18.6% classification error rate. Children with IR exhibited metabolic profiles similar to healthy controls, aligning with their clinical resolution. Conclusions: Active nutritional rickets presents a distinct metabolic profile, highlighting novel biologically relevant metabolites. These exploratory signals provide insights into the physiological impact of the disease and warrant further targeted investigation to assess their potential for informing early non-invasive detection and preventive interventions. In the long term, such tools are vital to prevent irreversible skeletal damage and to help mitigate lifelong physical disability and the resulting social vulnerability for affected children. Full article

Chronic kidney disease (CKD) is a progressive and irreversible disorder affecting over 850 million individuals globally and is associated with significant morbidity, mortality, and healthcare burden. Conventional diagnostic approaches rely on intermittent laboratory measurements, including serum creatinine, estimated glomerular filtration rate (eGFR), and urinary albumin, which provide limited temporal resolution and fail to capture dynamic physiological changes. Recent advances in wearable biosensing technologies offer new opportunities for continuous, non-invasive monitoring of biochemical and physiological markers relevant to renal function. This review provides a comprehensive analysis of wearable biosensors for CKD monitoring, focusing on sensing mechanisms (electrochemical, optical, and field-effect transistor), biofluid interfaces (sweat, interstitial fluid, and saliva), and materials engineering strategies enabling flexible, high-performance devices. Emphasis is placed on biofluid transport dynamics, analytical performance across sampling matrices, and system-level integration with wireless communication and digital health platforms. Key challenges limiting clinical translation, including biofouling, enzymatic instability, and variability in biofluid composition, are examined—alongside emerging solutions such as antifouling interfaces, synthetic recognition elements, and multimodal sensing architectures. Finally, regulatory pathways and the role of artificial intelligence in digital nephrology are discussed. This review highlights the potential of wearable biosensors to transform CKD management through continuous monitoring, early detection, and personalized therapeutic intervention. Full article

Background/Objectives: Histologic mucosal healing is an increasingly recognized therapeutic target in inflammatory bowel disease (IBD), yet reliable non-invasive correlates remain limited. This study aimed to evaluate circulating cytokine patterns as detectability-based immune signals across the spectrum of histologic disease activity. Methods: In this prospective cross-sectional study, 59 patients with IBD and 36 healthy controls were enrolled. Serum interleukin-10 (IL-10) and interleukin-23 (IL-23) were quantified by ELISA. Histologic activity was graded using the Geboes score. Associations were assessed using non-parametric methods and multivariable logistic regression with Firth penalization. Results: IL-10 demonstrated apparent separation across histologic states, primarily driven by reduced detectability in active inflammation, and was inversely associated with histologic severity. IL-10 remained associated with histologic status, although estimates should be interpreted cautiously. Detectable IL-23 was confined to moderate-to-severe inflammation and did not show graded discrimination, with interpretation limited by the small number of detectable observations. Conclusions: IL-10 and IL-23 exhibit complementary patterns, reflecting detectability-based regulatory signaling and a severity-dependent inflammatory threshold, respectively, without evidence of independent clinical utility for IL-23 in the present dataset. These findings are exploratory and require validation in larger prospective cohorts. Full article

Early diagnosis of AMI is crucial for improving patient outcomes, yet current clinical tools often lack the requisite sensitivity and specificity for reliable early detection. As neutrophils are the first innate immune responders mobilized following infarction, we employed an integrated multi-omics and machine learning approach to identify neutrophil-driven molecular signatures with diagnostic potential. By analyzing multiple peripheral blood transcriptomic datasets, we conducted differential expression and immune infiltration analyses, followed by machine learning-based feature selection to pinpoint key genes linked to neutrophil activity. Integration of these findings with single-cell transcriptomic data further clarified the neutrophil-specific expression patterns of candidate genes during AMI progression. Using a joint diagnostic model, we identified MCEMP1, NFE2, and AQP9 as the most informative predictors, with MCEMP1 emerging as the primary contributor. Experimental validation in a murine model of myocardial infarction (MI) confirmed rapid upregulation of MCEMP1 after injury, closely mirroring the kinetics of neutrophil infiltration. Collectively, these findings delineate a neutrophil-associated molecular profile of early AMI and highlight MCEMP1 as a promising noninvasive biomarker and a potential therapeutic target for modulating neutrophil-driven myocardial injury. Full article

This article presents an intelligent differential capacitive bioelectronic sensing system that provides an experimental foundation for future AI-assisted reliable microfluidic reagent delivery in automated pathology. The proposed platform integrates a slot-type microfluidic chamber, a differential slot-line capacitive sensor, embedded readout and signal-conditioning electronics, and a supervisory state assessment concept within a unified architecture. Its purpose is to support stable microliter-scale reagent exchange together with non-invasive process observability in automated staining workflows. The experimental study included flow calibration, analysis of feed direction and chamber tilt angle, preliminary vibration-assisted bubble mobilization, and evaluation of the sensing subsystem. The results showed that reliable operation is achieved only within a practically admissible regime in which fluidic stability and sensing informativeness overlap. In the investigated setup, upper-feed delivery and low chamber tilt angles provided the most favorable filling conditions, while the differential capacitive subsystem enabled stable detection of liquid-state changes in narrow microtubes. The reported results establish a foundation for future AI-assisted transport-state recognition and adaptive monitoring in automated pathology platforms. Full article

Background/Objectives: Active surveillance (AS) is recommended for men with low-risk prostate cancer to minimize overtreatment while monitoring for disease progression. However, current surveillance strategies rely heavily on repeat biopsies, which are invasive and associated with morbidity. MyProstateScore 2.0—Active Surveillance (MPS2-AS) is a urine-based biomarker test developed to predict progression to Grade Group ≥ 2 (GG ≥ 2) and Grade Group ≥ 3 (GG ≥ 3) prostate cancers in men on AS. The objective of this study was to analytically validate the reproducibility and robustness of MPS2-AS analyte detection and risk score calculation across key laboratory variables. Methods: Analytical precision was evaluated using pooled urine specimens processed using the MPS2-AS laboratory workflow. Eight pooled urine samples were tested in a within-laboratory design across five days, with two runs per day, and two replicates per run. Additional reproducibility studies assessed variability across three QuantStudio™ 12K Flex Real-Time PCR Systems and three OpenArray™ chip lots. Ten RNA biomarkers were quantified by RT-PCR and used to calculate the MPS2-AS GG1-2 and GG1-3 risk scores. Variance components were estimated using hierarchical ANOVA. Results: The MPS2-AS analyte measurements demonstrated high precision across within-laboratory testing, with standard deviations ranging from 0.00 to 0.60 and coefficients of variation (%CV) from 0.00 to 4.01%. The reproducibility across qPCR instruments and OpenArray chip lots showed similar robustness, with analyte %CVs of ≤4.57% and ≤4.10%, respectively. These stable analyte measurements translated to reproducible model outputs, with %CV ≤ 10.69% for the GG1-2 risk score and ≤7.20% for the GG1-3 risk score across all tested conditions. No systematic bias was observed between runs, days, instruments, or reagent lots. Conclusions: MPS2-AS demonstrates strong analytical precision and reproducibility for quantifying urinary biomarkers and generating GG1-2 and GG1-3 risk scores. These results support the reliability of MPS2-AS for clinical laboratory implementation and its use as a non-invasive tool to inform biopsy decisions in men with Grade Group 1 prostate cancer undergoing active surveillance. Full article

This work presents a noninvasive imaging method to locate veins using a tuned microwave loop resonator. It offers a low-cost, fast, and effective solution to the challenges in venipuncture. The sensor features a loop resonator with a 5.2 mm radius, incorporating a self-tuning mechanism, and operates at 2.408 GHz with a reflection coefficient of −48.77 dB. It generates localized high-intensity electric fields that penetrate tissues to sufficient depths, enabling the detection of veins based on shifts in resonant frequencies that are induced by the varied dielectric properties of blood vessels. Two-dimensional raster scan simulations of the cephalic and median cubital veins yielded a ∼25 MHz downward resonant-frequency shift between vein and non-vein positions, with the median cubital vein still detectable at depths up to 6 mm. To quantify generalization to real tissues, a decision tree classifier trained on 63 simulation samples and evaluated on 335 in vivo measurements achieved 82.09% classification accuracy (sensitivity 81.25%, specificity 83.02%), demonstrating that the simulation-derived frequency contrast transfers reliably to experimental data despite inter-subject tissue variability. Extensive tests conducted demonstrate the sensor’s effectiveness, producing consistent and distinguishable frequency shifts when the sensor moves on the skin across veins. This technology holds significant promise for improving venipuncture accuracy, minimizing complications, and enhancing patient comfort. Full article

Background: Gallbladder carcinoma (GBC) represents one of the most aggressive malignancies of the hepatobiliary system, evolving along a continuum from chronic inflammation to preneoplastic lesions and invasive cancer. This progression is frequently associated with gallstones and chronic cholecystitis and shares common pathogenic mechanisms with systemic inflammatory and metabolic disorders. Despite its relatively low incidence, GBC is characterized by poor prognosis, largely due to late-stage diagnosis and limited understanding of its molecular underpinnings. Methods: We conducted an observational study including 60 adult patients with radiologically suspected gallbladder cancer (GBC). Patients with disseminated disease, ongoing oncologic treatment, or synchronous malignancies were excluded. Fasting venous blood samples were collected to evaluate tumor markers and biochemical parameters, including carcinoembryonic antigen (CEA) and carbohydrate antigen CA 19-9. Surgical specimens were analyzed histopathologically and staged according to the European Society for Medical Oncology TNM classification system. Statistical analysis was performed using SPSS software (version 26.0), with appropriate parametric or non-parametric tests applied based on data distribution, and a p-value < 0.05 considered statistically significant. Results: Based on histological findings, patients were stratified into benign gallbladder disease (GBD) and GBC groups. CA 19-9 demonstrated higher mean serum levels with lower variability compared to CEA, suggesting superior sensitivity and diagnostic stability for gallbladder adenocarcinoma. In contrast, CEA levels exhibited greater fluctuation, limiting its reliability as a standalone biomarker. Importantly, the combined use of CA 19-9 and CEA improved diagnostic accuracy, supporting a multimarker approach for better clinical stratification. Our findings highlight the diagnostic value of CA 19-9 as a robust biomarker in GBC and support the integration of combined biomarker panels. Beyond tumor markers, the study identified a strong interplay between systemic inflammation and metabolic comorbidities, with obesity and hypertension significantly associated with chronic gallbladder pathology, and diabetes mellitus contributing to increased risk of acute inflammatory episodes. Elevated inflammatory markers, leukocytosis, and cholestatic enzyme alterations further supported the presence of a systemic inflammatory milieu. Multivariate analysis revealed that C-reactive protein (CRP), as a marker of systemic inflammation, was significantly influenced by a combination of clinical and biochemical variables, including age, hemoglobin, hypertension, amylase, CA 19-9, and CEA, explaining over 50% of its variability and up to 85% in advanced fibrotic changes. Additionally, platelet counts were significantly reduced in adenocarcinoma and correlated specifically with CA 19-9 levels, suggesting a potential link between tumor burden, inflammation, and platelet dynamics. Conclusions: Therefore, the observed associations between chronic inflammation, metabolic dysregulation, and tumor marker expression suggest a potential link between gallbladder carcinogenesis and systemic cardiometabolic pathways, opening new perspectives for early detection and targeted therapeutic strategies. Full article

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder for which early detection and reliable monitoring remain major clinical challenges. Electroencephalography (EEG) combined with machine learning has attracted growing interest as a scalable and non-invasive approach to AD detection, yet reported classification accuracies vary widely across studies and are rarely comparable or clinically translatable. One important reason is that the analytical pipeline—from data acquisition to model validation—involves numerous methodological choices whose inter-stage dependencies and reproducibility implications are rarely made explicit. In this narrative review, we adopt a methodological chain framework to make these dependencies explicit, organizing EEG-based AD research into five sequential stages: data acquisition, preprocessing, feature representation, modeling, and validation. Choices at each stage can shape downstream analyses, inflate reported performance, and reduce cross-study comparability in ways that are difficult to detect when stages are assessed independently. These effects are particularly consequential in EEG-based AD research, where cohorts are typically small and biomarkers are subtle. We make three primary contributions: (1) we describe inter-stage methodological dependencies that may contribute to reproducibility problems and performance inflation; (2) we synthesize major sources of methodological variability across representative EEG–AD studies and evaluate their differential impact on spectral, connectivity, and complexity features; and (3) we provide practical, stage-aligned recommendations culminating in a minimum reporting checklist. Full article

Today, the rapid progress in non-invasive light–matter interaction analysis is transforming the landscape of biomedical and life sciences driven by low-intensity light detection technologies. As the complexity of photonic applications continues to grow, the importance of single-photon detection techniques becomes pivotal. Among them, Time-Correlated Single-Photon Counting (TCSPC) has become the gold standard for precise, time-resolved reconstruction of rapid and faint optical signals. However, TCSPC has long been constrained by pile-up distortion, which worsens with increasing acquisition speed, typically limiting it to 5% of the excitation frequency. To overcome the operational constraints of conventional implementations, a novel TCSPC acquisition methodology has been introduced, independent of photodetector dead time, excitation intensity, and prior optical signal knowledge, still enabling distortion-free reconstruction of the measured light profiles. In this context, the development of single-photon detectors with short dead time and low timing jitter becomes crucial. This work presents a single-photon detection module based on a Silicon Photomultiplier, which delivers 750 ps FWHM output pulses with a 33.5 ps RMS IRF. Its performance is showcased through fluorescence measurements employing the constraint-free TCSPC methodology, achieving a photon count rate up to 166% of the excitation frequency with a minimal lifetime estimation error of just −1.46%. Full article

Background: Southeast Asian Americans (SEAAs) experience a disproportionately high burden of hepatocellular carcinoma (HCC), with incidence in several subgroups (i.e., Cambodian, Laotian, and Vietnamese individuals) reaching up to nine times that of non-Hispanic Whites. HCC in SEAAs is largely driven by chronic hepatitis B (HBV), hepatitis C (HCV), metabolic dysfunction–associated steatotic liver disease (MASLD), and alcohol-associated liver disease (ALD). Despite established screening guidelines, under-detection and delayed diagnosis remain common. Objective: To summarize epidemiologic patterns, risk factors, screening challenges, and potential interventions aimed at reducing HCC disparities among SEAAs. Design and Methods: This narrative review synthesized evidence from population based epidemiologic studies, community-based interventions, health services research, and policy analyses. Attention was given to studies reporting disaggregated SEAA subgroup data. Findings derived from SEAA specific studies were distinguished from evidence drawn from broader Asian American or general cirrhosis populations, with inferential steps explicitly noted where subgroup specific data were limited. Key Findings: HCC incidence varies widely across SEAA subgroups, with elevated HBV- and HCV-related HCC in Vietnamese, Cambodian, and Laotian communities, and increasing MASLD-related HCC including among lean individuals who fall outside many surveillance frameworks. Screening and surveillance remain suboptimal, with fewer than 30% of patients with cirrhosis receiving recommended semiannual HCC surveillance and even lower uptake among SEAAs. Barriers include low HBV/HCV screening rates, limited disease awareness, language barriers, underinsurance, provider knowledge gaps, and lack of automated EHR-based reminders. Structural challenges such as poverty, transportation barriers, and limited access to specialty care further delay diagnosis. Proposed Interventions: Culturally tailored outreach programs, bilingual navigators, and community-based screening initiatives have demonstrated improved HBV/HCV testing and linkage to care. AI-enabled EHR tools may enhance identification of high-risk patients, streamline follow-up, and increase surveillance adherence. Expanded use of non-invasive fibrosis assessment and recognition of MASLD-related risk in non-obese individuals may support earlier detection. Policy priorities include mandatory Asian subgroup data disaggregation, expanded insurance coverage, and strengthened community-level healthcare infrastructure. Conclusions: SEAAs face a substantial and preventable HCC burden. A coordinated approach combining culturally tailored community engagement, improved provider support systems, and policy reforms is essential to improving early detection and reducing HCC disparities in this diverse population. Full article

Background: Prostate cancer remains a significant global health burden, yet current diagnostic reliance on PSA screening is heavily hampered by limited specificity and high rates of overdiagnosis. Methods: To address this clinical bottleneck, we utilized a highly sensitive Complete360^®-MyMeta targeted-metabolomics platform to perform high-resolution profiling of 43 metabolites across the carnitine, polyamine, and methylation networks in plasma from a discovery cohort of all-stage (I–IV) PCa patients and healthy controls. Results: Our analysis identified 28 significantly altered metabolites (p < 0.05), revealing profound systemic metabolic reprogramming characterized by the depletion of circulating TML and putrescine, alongside the elevation of L-acetylcarnitine and sarcosine. These systemic shifts are consistent with a localized tumoral “metabolic sink”, wherein upregulated mitochondrial TML import via the SLC25A45 transporter actively fuels fatty acid oxidation, while parallel androgen signaling drives massive polyamine synthesis. Translating these mechanistic insights into a clinical tool, we developed a multivariable diagnostic signature utilizing mathematically stable bipartite metabolic ratios. An optimized, cross-validated model combining L-acetylcarnitine/TML and sarcosine/putrescine effectively mitigated physiological noise to achieve robust diagnostic separation, yielding an area under the curve (AUC) of 0.99. Conclusions: Ultimately, this study provides a discovery-phase proof-of-concept for the SLC25A45-TML axis as a mechanistically grounded, stage-independent liquid biopsy, offering a rational, non-invasive framework to significantly improve PCa detection. Full article

Background: Neuroendocrine neoplasms are frequently diagnosed after the detection of liver metastases, often when the primary tumor remains occult. Accurate non-invasive differentiation of neuroendocrine liver metastases (NELMs) from other focal hepatic lesions is therefore crucial. This study aimed to characterize the magnetic resonance imaging (MRI) features of NELMs using hepatocyte-specific contrast agents and to identify a potential radiologic “signature” that may suggest a neuroendocrine origin. Methods: This retrospective study included three cohorts: patients with histologically confirmed NELMs (n = 51; 146 lesions), patients with colorectal cancer liver metastases (n = 18; 46 lesions), and patients with benign hepatic hemangiomas (n = 28; 51 lesions). All subjects underwent standardized liver MRI with Gd-EOB-DTPA. Lesions were evaluated for size, diffusion-weighted imaging characteristics, apparent diffusion coefficient values, arterial-phase enhancement, T2-weighted signal, hepatobiliary-phase appearance, and hemorrhagic components. Statistical analyses included univariate and multivariate testing and receiver operating characteristic curve analysis. Results: NELMs commonly demonstrated arterial hyperenhancement, diffusion restriction, and variable T2 and hepatobiliary-phase signal heterogeneity. Compared with colorectal metastases and hemangiomas, NELMs showed distinctive patterns, particularly higher rates of hepatobiliary-phase heterogeneity and arterial enhancement. Lesion size, ADC metrics, T2 heterogeneity, and hemorrhage were significant discriminators. Conclusions: Hepatocyte-specific MRI enables identification of characteristic imaging features of NELMs. An integrated assessment of morphologic, diffusion, and hepatobiliary-phase findings may facilitate early recognition of neuroendocrine metastases, even when the primary tumor is unknown, improving diagnostic confidence and clinical management. Full article

Background/Objectives: The A-Palp enables a calibrated anatomical systems technique (CAST) approach. Previous studies have demonstrated repeatability and concurrent validity for selected spinal curvature angles in patients with scoliosis. However, the inter-operator reproducibility, temporal repeatability, and reliability of sagittal spinal curvature measurements and spinopelvic parameters remain to be established. Methods: Eighteen healthy adults without spinal pathology were assessed. Two operators sampled sagittal spinal profiles with the A-Palp in a 14-camera optoelectronic setup, applying reflective markers and palpating spinous processes. One operator repeated measurements after seven days. Marker data were processed in MATLAB (R2019b) to smooth trajectories, fit curvature arcs, and compute extracorporeal kyphosis, lordosis, and pelvic parameters. Reliability and repeatability were evaluated using Bland & Altman analysis, intraclass correlations (ICCs), standard error of measurement (SEM), mean detectable change (MDC₉₅), root-mean-squared errors (RMSEs), and Statistical Parametric Mapping (SPM). Results: Reliability and repeatability were strong. For global spinal angles, ICCs exceeded 0.90 across operators and sessions. The tangent method yielded low SEM (1–2°) and MDC₉₅ (3–6°) values, whereas the circle-fit/trigonometric methods showed larger errors. Most spinopelvic angles had moderate-to-excellent ICCs (0.65–0.98) with SEM/MDC₉₅ values ≈2.1–4.5°/5.9–12.4°. Ground reaction force-referenced distances showed good ICCs and small intra-operator error (SEM: 3.8–4.8 mm; MDC₉₅: 10.7–13.4 mm) but wider inter-session thresholds (SEM: 10.3–11.6 mm; MDC₉₅: 28.6–32.8 mm). Bland & Altman biases were ~0, with narrower limits for the tangent (≈±5°) than circle-fit/trigonometric (≈±8–12°) methods. Curve tracking was consistent (RMSE: 2.7–3.7 mm, <5% amplitude), and SPM detected no point-wise differences. Conclusion: The A-Palp method demonstrated high reliability and repeatability for extracorporeal sagittal spinal and sacro-spinal evaluation. Variability was low across operators and sessions, supporting its use as a robust, non-invasive clinical and research tool. Full article