Search Results (3,584)

Background and Clinical Significance: Primary cutaneous anaplastic large cell lymphoma (C-ALCL) is a CD30-positive T-cell lymphoproliferative disorder that can clinically resemble various non-melanoma skin cancers, making diagnosis challenging. Although histopathology remains the diagnostic gold standard, non-invasive imaging modalities such as dermoscopy and reflectance confocal microscopy (RCM) are increasingly used as complementary tools to support the differential diagnosis. To date, no data on RCM features of C-ALCL have been described. Case Presentation: We report the case of an 80-year-old man presenting with a rapidly enlarging nodule on the lateral aspect of his right eyelid, providing a detailed account of dermoscopic and RCM findings integrated with clinicopathological correlation. Dermoscopy revealed a red-orange homogeneous background with white streaks, and polymorphic vascular structures, while subsequent RCM (Vivascope 3000 probe) demonstrated marked architectural disarray of the epidermis and dermoepidemal junction, with prominent epidermal involvement characterized by aggregates of highly reflective cells. In the absence of alternative diagnostic patterns, these features raised suspicion for a cutaneous lymphoproliferative disorder, which was later confirmed by histopathological and immunohistochemical analyses. Conclusions: Our findings support the value of RCM as a practical tool in guiding differential diagnosis and biopsy, particularly for rapidly growing lesions located in anatomically sensitive areas. Full article

Electroencephalography (EEG) has emerged as a promising non-invasive tool for the diagnosis of neurodegenerative disorders, and artificial intelligence (AI) has shown significant potential in this domain, as demonstrated by recent studies. However, strong inter-subject variability remains a major challenge, limiting the ability of AI-based models to learn disease-specific features that generalize across individuals, thereby hindering the development of clinically deployable subject-independent systems. In this work, we propose a cross-subject, AI-based EEG classification framework to distinguish between Alzheimer’s disease (AD), Creutzfeldt–Jakob disease (CJD), and healthy control subjects using clinical EEG data collected from a local hospital. A lightweight hybrid deep learning model is developed, combining a two-layer one-dimensional convolutional neural network with a two-layer Transformer encoder to capture both local temporal patterns and long-range dependencies in EEG signals. The proposed model achieves an average classification accuracy of $97\%$, representing a $3\%$ improvement over a baseline model evaluated on a cohort of 36 subjects. To assess deployment feasibility in real-time clinical settings, the trained model is implemented and evaluated on an edge-AI platform (NVIDIA Jetson AGX Orin), demonstrating energy efficiency for the inference with a compact model footprint. These results indicate that the proposed approach provides an accurate, efficient, and practically deployable solution for subject-independent EEG-based classification of neurological disorders. Full article

Background: Fiberoptic bronchoscopy (FOB) is a procedure routinely performed in clinical practice for both diagnostic and therapeutic purposes. FOB frequently impairs respiratory function, which may exacerbate respiratory failure. Currently, conventional oxygen therapy (COT) is the most commonly used form of respiratory support; however, non-invasive ventilation (NIV) and high-flow nasal cannula (HFNC) are being used increasingly. The optimal settings and indications for NIV and HFNC in patients with respiratory acidosis undergoing FOB have not yet been determined. Methods: This is a prospective, multicenter, randomized controlled trial including two parallel study populations defined by the indication for bronchoscopy and the type of respiratory acidosis. Therapeutic FOB (Study 1): Patients with decompensated type 2 respiratory failure (pH < 7.35 and PaCO₂ > 45 mmHg) will be randomized to receive one of four methods of respiratory support during bronchoscopy: COT, NIV, HFNC, or invasive mechanical ventilation (IMV) (n = 315). Diagnostic FOB (Study 2): Patients with chronic respiratory acidosis (pH ≥ 7.35, PaCO₂ > 45 mmHg, and/or HCO₃⁻ > 27 mmol/L) will be randomized to receive COT, NIV, or HFNC during bronchoscopy (n = 210). Before FOB, patients in both groups will undergo arterial blood gas (ABG) analysis. During FOB, vital signs will be continuously monitored, including SpO₂, FiO₂, TcCO₂, ECG, and heart rate. After FOB, ABG analysis will be repeated, and study endpoints and complications, if any, will be recorded. The planned study period is from April 2026 to April 2029. Results: Based on the study results, we aim to evaluate the effectiveness and safety of different respiratory support strategies during flexible bronchoscopy, with the primary objective of comparing the rate of treatment failure among COT, HFNC, NIV, and IMV. Treatment failure is defined as the need for endotracheal intubation, premature termination of the procedure, or escalation of respiratory support. Additionally, we aim to identify the optimal NIV and HFNC settings, as well as complication rates in both study groups. Conclusions: The results of this study will help define the role of optimal respiratory support in patients with respiratory acidosis undergoing FOB, potentially leading to a shorter time from admission to diagnosis, better tolerance of the procedure, and faster recovery afterward. Full article

Prostate cancer (PCa) is one of the leading causes of cancer-related morbidity and mortality in men worldwide, and early detection remains crucial for ensuring effective treatment and improving patient outcomes. In this context, the development of non-invasive, accurate, and cost-effective screening strategies is of paramount importance. One particularly promising and innovative approach is the analysis of volatile organic compounds (VOCs), a field known as volatolomics. VOCs, which are metabolic by products released by the body, reflect underlying biochemical processes and offer a valuable, non-invasive source of diagnostic information. Recent advances have highlighted the potential of VOC profiling in PCa detection. A variety of biological systems have demonstrated remarkable sensitivity and specificity in recognizing disease-associated VOC signatures. Notably, trained dogs, selected invertebrates, and artificial sensing platforms have all shown the ability to identify PCa-related olfactory patterns. Among technological approaches, electronic noses (eNoses), which combine chemical sensor arrays with pattern recognition algorithms such as neural networks, represent a rapidly evolving diagnostic tool. Together, these biologically inspired and technology-driven strategies are reshaping the landscape of cancer diagnostics. They offer a compelling foundation for the development of rapid, non-invasive, and clinically translatable methods for PCa detection. This narrative review summarizes recent advances in using VOCs for PCa diagnosis and evaluates the reproducibility and clinical robustness of these approaches, focusing on challenges such as standardizing sampling, storage, and analysis, small cohort sizes, and the need for external validation and regulatory integration. Full article

Ketosis is a prevalent metabolic disorder in early-lactation dairy cows, significantly affecting animal health, milk production, and farm profitability. Developing accurate and non-invasive methods for early risk detection is therefore of critical importance. In this study, a hybrid optimization framework integrating an Improved Whale Migration Algorithm (IWMA) with a Light Gradient Boosting Machine (LightGBM) is proposed to predict ketosis risk based on the milk fat-to-protein ratio (F/P) using Dairy Herd Improvement (DHI) records. The proposed IWMA enhances optimization performance through cubic chaotic initialization, elite opposition-based learning, and a Cauchy–Gaussian hybrid mutation strategy, enabling improved global exploration and convergence stability. A dataset comprising 25,155 DHI records collected from multiple commercial dairy farms over seven months was used for model development and evaluation. Experimental results demonstrate that the IWMA–LightGBM model achieves a classification accuracy of 0.8997 and a mean squared error of 0.289, consistently outperforming six benchmark optimization methods. Feature analysis identifies Herd Within Index (WHI), Energy Corrected Milk (ECM), Days in Milk (DIM), Milk Urea Nitrogen, and Foremilk as key predictors associated with metabolic risk. Overall, the proposed approach provides a robust and effective non-invasive solution for early-stage metabolic risk screening at the herd level, offering practical value for precision dairy management. It should be noted that the model is intended for risk assessment rather than clinical diagnosis of ketosis. Full article

Background/Objectives: To explore potential dry eye-related ocular surface functional alterations in women at the time of first diagnosis of endometriosis or adenomyosis in a real-world clinical setting. Methods: This was a cross-sectional case–control study. Patients were evaluated at the time of initial diagnosis, prior to initiation of any hormonal therapy, to reflect real-world clinical conditions. Participants underwent a standardized ocular surface assessment comprising the Ocular Surface Disease Index (OSDI) questionnaire, Schirmer test, and multimodal TearCheck^® analysis, including Non-Invasive Break-Up Time (NIBUT), Tear Film Stability Evaluation (TFSE), Meibography, and Abortive Blinking^®. Results: A total of 71 women were included: 41 with endometriosis or adenomyosis and 30 without known gynecological disease. Patients reported significantly higher OSDI scores than controls (p < 0.05). Objective testing demonstrated lower Schirmer values, reduced tear film stability, and more pronounced Meibomian gland dropout in the patient group (all p < 0.05). Differences were consistently observed across both subjective and objective parameters. Conclusions: Women with endometriosis and/or adenomyosis exhibited significantly altered ocular surface parameters compared with women without known gynecological disease. These findings suggest a possible association between gynecological disease and ocular surface dysfunction. Greater awareness of potential ocular involvement may encourage closer collaboration between gynecology and ophthalmology in the care of affected patients. Full article

Background/Objectives: Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by complex and interconnected pathophysiological mechanisms, including mitochondrial dysfunction, oxidative stress, neuroinflammation, lysosomal impairment, and altered neurotransmitter metabolism. Unlike cerebrospinal fluid or blood, urine offers a truly non-invasive source of biomarkers, reflecting systemic metabolic changes and renal protein excretion linked to neurodegeneration. This review aims to critically synthesize current evidence on urinary biomarkers in PD and to organize this heterogeneous literature into pathophysiologically meaningful domains. Methods: A comprehensive literature search of human studies investigating urinary biomarkers in PD was performed. Eligible studies were comprehensively analyzed and classified according to dominant biological pathways. To facilitate interpretation, findings were organized into six thematic domains: genetic and protein-based biomarkers; metabolic pathways and mitochondrial dysfunction; oxidative stress and neuroinflammation; gut–brain-axis-related metabolites; hormonal and systemic biomarkers; and emerging exploratory markers. Results were summarized in domain-specific tables and integrated using a conceptual framework. Results: A total of 32 human studies met the inclusion criteria, revealing diverse urinary molecular signatures associated with PD across multiple biological domains. Genetic and protein-based markers, including LRRK2-related proteins, α-synuclein species, and lysosomal lipids, showed potential for disease stratification. Metabolomic studies consistently identified alterations in acylcarnitines, organic acids, and amino acid metabolism, reflecting mitochondrial dysfunction. Biomarkers related to oxidative stress, immune activation, gut microbiota metabolism, and hormonal regulation further highlighted the systemic nature of PD. However, most individual biomarkers lacked disease specificity and exhibited methodological heterogeneity. Conclusions: Current evidence supports urine as a valuable source of systemic biomarkers reflecting multiple pathophysiological processes in PD. While single urinary markers remain insufficient for clinical application, integrated omics-based approaches—particularly metabolomics and peptidomics/proteomics—hold promise for identifying combinatorial biomarker signatures. Future longitudinal and standardized studies are required to enhance specificity and translational potential for non-invasive diagnosis and disease monitoring in PD. Full article

At present, the gold standard for gastric cancer (GC) confirmation relies mostly on histopathology, an invasive procedure. Noninvasive detection methods using serum for large-scale screening may be useful for the early diagnosis of GC. Helicobacter pylori (HP) infection and chronic atrophic gastritis are major GC risk factors. We recently developed a noninvasive test called the DSC test-based on the patient’s age, sex, their serum PGI and PGII, anti-HP immunoglobulin (IgG), and gastrin G17 levels-predicting GC risk as low (score 0, S0) or high (score 2, S2). The comparative investigation at the serum protein level of the two different patient groups detected by our DCS test (S0 and S2) may undoubtedly help to identify gastric disease-dependent proteins, resulting from bacterial infection or gastric mucosa inflammation, as well as get better insight into the molecular scenario associated with pre-cancerous conditions. We used an untargeted liquid chromatography–tandem mass spectrometry (LC-MS/MS)-based proteomic profiling approach, followed by univariate statistical analysis to compare the different DSC groups across two patient cohorts (exploratory and validation). Significantly differentially abundant proteins differing more than 1.5-fold between S0 and S2 groups were selected and validated, and their putative role(s) in gastritis and GC were discussed. In both the exploratory and the validation cohorts, four proteins (beta-2-microglobulin, EGF-containing fibulin-like extracellular matrix protein 1, complement factor D, and cystatin-C) were more abundant, while two (sex hormone-binding globulin and pregnancy zone protein) were less abundant in the sera of S2 individuals (|fold change| ≥ 0.6, p < 0.05, t-test). The higher presence of beta-2-microglobulin (B2M) and the lower content of pregnancy zone protein (PZP) in S2 sera were validated by immunoblotting. Replacing age and sex in our DSC model with two specific candidate biomarkers can lead to a refined, albeit modest, improvement in classification accuracy. This study identified a proteomic signature that was differentially associated with the sera of patients with a different risk to develop advanced atrophy/GC according to the DSC test. Moving from a demographic model to a proteomic-driven model can better reflect the personalized biology of pathological processes associated with DSC. Full article

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

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

The clinical cases described in this text add to the limited literature on chronic and allergic rhinosinusitis caused by dematiaceous fungi, particularly Curvularia spicifera. These cases highlight the growing recognition of fungal infections as a significant factor in the etiology of rhinosinusitis, a condition traditionally attributed to bacterial causes It has become evident that a comprehensive clinical approach, involving diagnostic imaging and laboratory examinations, particularly culture-based analysis, has been crucial in identifying the specific fungal pathogen responsible for the infection. Additionally, molecular biology techniques have proven indispensable in enhancing diagnostic accuracy and the understanding of such infections. Importantly, these types of infections are commonly observed in immunocompetent individuals, distinguishing them from other fungal infections that typically affect immunocompromised patients. This study underlines the importance of integrating microbiological findings with clinical, radiological, and histopathological data for the accurate diagnosis of non-invasive fungal rhinosinusitis, particularly given the lack of serological assays specific for this species. The available literature on these infections remains limited, and diagnosis continues to rely on an integrated multimodal approach. 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: Parkinson’s disease (PD) is a progressive neurodegenerative disorder that affects millions globally, particularly those in the elderly population. Several occupational exposures typical of maritime environments are recognized or suspected risk factors for PD, warranting attention within occupational health frameworks. The disease is characterized by motor symptoms such as tremor, rigidity, and bradykinesia, as well as non-motor impairments including speech abnormalities. Objective: Early diagnosis is crucial for effective disease management but remains challenging due to symptoms overlapping with normal aging and other neurological conditions. This study presents a machine learning (ML)-based approach for the early diagnosis of PD using speech signal analysis. Methods: We employed six supervised ML classifiers to differentiate between PD patients and healthy controls based on vocal features. The experimental dataset, MDVR-KCL, consists of speech recordings from both reading tasks and spontaneous dialogs, collected via mobile devices. From these recordings, we extracted Mel-Frequency Cepstral Coefficients (MFCCs), Gammatone Frequency Cepstral Coefficients (GTCCs), and acoustic features such as jitter, shimmer, and harmonic-to-noise ratio. These features capture a broad range of prosodic, spectral, and articulatory characteristics associated with PD-related speech impairments. Speaker diarization was applied in spontaneous dialog recordings to separate participant speech. Hyperparameter tuning was performed using GridSearchCV with 10-fold cross-validation, while final model evaluation was conducted using Leave-One-Subject-Out Cross-Validation (LOSOCV) to ensure subject-independent performance assessment. Results: In the read-text task, the SVM model performed exceptionally, yielding 95.45% accuracy, 94.62% sensitivity, 95.97% specificity, an F1-score of 94.12%, and an AUC of 0.98 with an MCC value of 0.90, for GTCCs with the acoustic features. In the spontaneous dialog task, the XGB model demonstrated the highest overall performance across all metrics, with a test accuracy of 83.7%, a sensitivity of 76.3.9%, a specificity of 88.9%, an F1-score of 79.5%, an AUC value of 0.88, and an MCC value of 0.66. Conclusions: Comparable results were obtained on both spontaneous dialog and reading speech subsets, demonstrating the robustness of the approach across different speaking contexts. These results demonstrate the effectiveness of integrating cepstral and acoustic features with machine learning models for non-invasive PD classification. The findings support the use of speech-based digital biomarkers in early PD detection and highlight the potential for developing scalable tools. This work highlights the potential of speech-based digital diagnostics to support clinical decision-making and improve patient outcomes. Full article

Background/Objectives: Metastatic breast cancer (MBC) remains a daunting clinical challenge, accounting for approximately 90% of all breast cancer-related deaths. The management of MBC has shifted from traditional chemotherapy to a sophisticated, biomarker-driven model of precision oncology. This evolution is predicated on the ability of biomarkers to provide prognostic and predictive information. The objective of this review is to provide a comprehensive synthesis of the current landscape of biomarker testing in MBC, detailing which biomarkers to test and the clinical rationale for doing so. Methods: This is a comprehensive review based on current international clinical practice guidelines, peer-reviewed literature, and evidence regarding clinically actionable and emerging biomarkers in metastatic breast cancer. Results: Key biomarkers currently in routine use include the established estrogen receptor (ER), progesterone receptor (PR), and HER2, alongside newer, clinically actionable alterations such as mutations in PIK3CA and ESR1, germline/somatic BRCA1/2, and PD-L1 expression. Furthermore, liquid biopsy, particularly the analysis of circulating tumor DNA (ctDNA), is rapidly gaining prominence as a non-invasive tool for real-time disease monitoring and resistance detection, highlighting the critical need for re-testing at metastasis due to tumor heterogeneity. Conclusions: The future of personalized oncology in MBC will be defined by the seamless integration of dynamic biomarkers and cutting-edge technologies. The integration of AI and spatial transcriptomics will move the field of pathology beyond a static diagnosis to a more dynamic and predictive model, reinforcing the pathologist’s role as the “molecular gatekeeper” for adaptive and personalized cancer care. Full article