Search Results (1,381)

Background/Objectives: The glymphatic system is a recently characterized glial-dependent waste clearance pathway in the brain, which makes use of perivascular spaces for cerebrospinal fluid exchange. Diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) offers a non-invasive method for estimating perivascular flow, but its biological specificity and susceptibility to methodological variation, particularly head position during MRI acquisition, remain as threats to the validity of this technique. This study aimed to assess the prevalence of current DTI-ALPS practices, evaluate the impact of head orientation on ALPS index calculation, and propose a novel computational approach to improve measurement validity. Methods: We briefly reviewed DTI-ALPS literature to determine the use of head-orientation correction strategies. We then analyzed diffusion MRI data from 172 participants in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) to quantify the influence of head orientation on ALPS indices computed using the conventional Unrotated-ALPS, a vecrec-corrected ALPS, and the new LD-ALPS method proposed within. Results: A majority of studies employed Unrotated-ALPS, which does not correct for head orientation. In our sample, Unrotated-ALPS values were significantly associated with absolute head pitch (r₁₆₉ = −0.513, p < 0.001), indicating systematic bias. This relationship was eliminated using either vecreg or LD-ALPS. Additionally, LD-ALPS showed more sensitivity to cognitive status as measured by Mini-Mental State Examination scores. Conclusions: Correcting for head orientation is essential in DTI-ALPS studies. The LD-ALPS method, while computationally more demanding, improves the reliability and sensitivity of perivascular fluid estimates, supporting its use in future research on aging and neurodegeneration. Full article

Background/Objectives: Trigger finger is a common cause of hand pain and functional limitation. Although open A1 pulley release remains the standard surgical treatment, ultrasound-guided percutaneous needle release (UGPR) has emerged as a minimally invasive alternative. This study aimed to compare the clinical effectiveness and safety of UGPR with open surgery. Methods: In this prospective, randomized controlled trial, 146 patients with Green stage 2–4 trigger finger were randomly assigned to UGPR (n = 75) or open release (n = 71). Pain (VAS), functional status (QuickDASH), and symptom severity (Nirschl Phase Rating) were assessed preoperatively and at postoperative day 3, 1, 6, and 12 months. Grip strength was measured with a digital pinchmeter, and ultrasonographic evaluation of A1 pulley and flexor tendon thickness was performed preoperatively and at 12 months. Subgroup analyses were conducted to address the imbalance in thumb distribution. Results: Both groups showed significant postoperative improvements in VAS, QuickDASH, and Nirschl scores (p < 0.05 for intragroup comparisons), with no significant differences between groups at 12 months (p > 0.05). At the one-year follow-up, grip strength was significantly greater in the UGPR group (p = 0.008). Ultrasonographic evaluation revealed greater MCP tendon thickness in UGPR, without clinical impact. Subgroup analyses confirmed comparable functional outcomes in thumb-only and non-thumb cases. Four revisions occurred in the UGPR group (incomplete release, recurrent tenosynovitis, flexor tendon rupture, and neurovascular injury), while none were observed in the open group. Conclusions: UGPR and open release provide comparable long-term outcomes in the treatment of trigger finger. UGPR offers the advantages of being minimally invasive and preserving grip strength, although it carries a small risk of incomplete release and procedure-related complications. Patient preference, surgeon expertise, and digit type should guide treatment selection. Full article

Metabolic dysfunction-associated steatotic liver disease (MASLD) is rapidly emerging as the leading cause of chronic liver disease, closely tied to rising global obesity rates. Endoscopic bariatric therapies (EBTs), including endoscopic sleeve gastroplasty (ESG), intragastric balloons (IGB), duodenal-jejunal bypass liners (DJBL), and duodenal mucosal resurfacing (DMR), offer minimally invasive interventions that target metabolic dysfunction and weight loss. This review synthesizes current evidence on the mechanisms and hepatic outcomes of EBTs in MASLD, highlighting improvements in hepatic steatosis, liver stiffness, and fibrosis biomarkers across multiple modalities. ESG is consistently associated with reductions in hepatic steatosis and fibrosis scores across multiple studies. IGB therapy improves liver stiffness and reduces hepatic fat as assessed by imaging modalities such as MRI- Proton Density Fat Fraction and ultrasound. DJBL lowers liver enzymes and improves non-invasive markers of steatohepatitis like the Fibroscan-AST score, although its effect on fibrosis appears limited. DMR demonstrates reductions in liver fat, particularly in patients with type 2 diabetes, but evidence for histological improvement in MASLD remains inconsistent. Despite their promise, most EBT studies remain limited by small sample sizes and short follow-up. Further randomized trials are needed to validate long-term efficacy and position EBTs alongside or as alternatives to surgical interventions for MASLD. Full article

Background/Objectives: A variant of autism spectrum disorder (ASD) known as Asperger syndrome (AS) shows increasing incidence worldwide, affecting between 0.02% and 0.03% of children. Patients display abnormal conduct, are limited in social interaction and communication, and are more often affected by micturition disorders, incontinence, and voiding symptoms than typically developing children. Methods: The present study aimed to review the literature related to the current management of lower urinary tract conditions in children with Asperger syndrome and to present a case of a 14-year-old girl with ASD, with characteristic impairments, including communication challenges, stereotyped, repetitive behaviors, and chronic constipation with concomitant bladder dysfunction, presenting recurrent urinary tract infections (UTIs) and lower urinary tract symptoms (LUTS), including voiding and filling storage symptoms. For the AS, she was treated with a selective serotonin reuptake inhibitor (Sertraline). An abdominal ultrasound, PLUTTS—pediatric lower urinary symptoms scoring (21); QL-quality of life (3); voiding diary; and uroflowmetry were performed, revealing an incomplete urinary retention (incomplete bladder emptying of 120 mL), a prolonged and interrupted curve, a maximum urinary flow rate (Qmax) 7 mL/s, and a UTI with Enterococcus. Results: Besides psychiatric reevaluation and antibiotic therapy, functional magnetic stimulation (FMS) sessions were performed. After eight sessions (20 min, 35 MHz, every second day), the ultrasound control and the uroflowmetry showed no residual urine, and the Qmax was 17 mL/s. The curve continued to be interrupted: PLUTSS-11, QL-1. FMS was continued at two sessions per week. At the 3-month follow-up, no residual urine was detected, and Qmax reached 24 mL/s. Conclusions: ASD is an incapacitating/debilitating condition that significantly impairs social functioning. In many cases, in addition to psychological symptoms, other conditions such as LUTS and constipation may coexist. Antipsychotics and antidepressants are frequently prescribed for these patients, often leading to various side effects, including micturition disorders. Therefore, screening for LUTS is recommended, and, if indicated, treatment—especially non-pharmacological and non-invasive approaches, such as FMS—should be considered. Full article

(1) Background: Invasive micropapillary carcinoma (IMPC) is a rare, aggressive breast cancer subtype marked by high lymph node metastasis rates. While Oncotype DX recurrence score (RS) offers prognostic information for patients with hormone-receptor-positive (HR+) breast cancer, its utility in IMPC—a histology with distinct biologic behavior—remains unvalidated. This study evaluates whether Oncotype DX offers prognostic information with respect to overall survival (OS) in non-metastatic, early-stage patients with IMPC of the breast. (2) Methods: The National Cancer Database (2004–2020) was queried to select for women with ER+/HER2−, T1-T2N0-N1 IMPC who underwent Oncotype DX testing and received no neoadjuvant therapy. Patients were stratified by RS: low (≤11), intermediate (12–25), and high (>25). Kaplan–Meier survival curves and log-rank tests compared 5-year OS between groups. Multivariable Cox proportional hazards models assessed RS as an independent predictor, adjusting for age, race, comorbidities, grade, radiation, and insurance status. (3) Results: A total of 1325 women met the selection criteria. The cohort demonstrated significant survival disparities by RS (log-rank p = 0.017). Five-year OS rates were 97.5%, 97.5%, and 93.7% for low, intermediate, and high-risk patients, respectively. Adjusted multivariate analysis confirmed RS as an independent prognosticator: low (HR = 0.31, 95% CI: 0.15–0.75) and intermediate (HR = 0.32, 95% CI: 0.15–0.75) scores correlated with reduced mortality versus high RS. Omission of radiation therapy (HR = 2.68, 95% CI: 1.05–6.86) and higher comorbidity burden (0 comorbidities vs. ≥2: HR = 0.25, 95% CI: 0.10–0.61) were significantly associated with worse survival. (4) Conclusions: Oncotype DX is predictive for OS in IMPC, with high RS (>25) portending poorer outcomes. The survival detriment associated with RT omission aligns with prior studies demonstrating RT benefit in higher-risk cohorts. These findings validate RS as a prognostic tool in IMPC and underscore its potential to refine adjuvant therapy, particularly RT utilization. Future studies should explore RS-driven treatment personalization in IMPC, including comorbidity management and adjuvant radiation to improve outcomes in this distinct patient population. Full article

This paper introduces the Gesture-Based Physical Stability Classification and Rehabilitation System (GPSCRS), a low-cost, non-invasive solution for evaluating physical stability using an Arduino microcontroller and the DFRobot Gesture and Touch sensor. The system quantifies movement smoothness, consistency, and speed by analyzing “up” and “down” hand gestures over a fixed period, generating a Physical Stability Index (PSI) as a single metric to represent an individual’s stability. The system focuses on a temporal analysis of gesture patterns while incorporating placeholders for speed scores to demonstrate its potential for a comprehensive stability assessment. The performance of various machine learning and deep learning models for gesture-based classification is evaluated, with neural network architectures such as Transformer, CNN, and KAN achieving perfect scores in recall, accuracy, precision, and F1-score. Traditional machine learning models such as XGBoost show strong results, offering a balance between computational efficiency and accuracy. The choice of model depends on specific application requirements, including real-time constraints and available resources. The preliminary experimental results indicate that the proposed GPSCRS can effectively detect changes in stability under real-time conditions, highlighting its potential for use in remote health monitoring, fall prevention, and rehabilitation scenarios. By providing a quantitative measure of stability, the system enables early risk identification and supports tailored interventions for improved mobility and quality of life. Full article

Background: Type 2 diabetes mellitus (T2DM) is a global health concern driven by aging, lifestyle, and socio-economic disparities. Early detection is key, with tools like FINDRISC, QDScore, and CANRISK providing non-invasive screening. Yet, the combined effects of sociodemographic factors, healthy habits, and perceived quality of life on diabetes risk remain insufficiently studied in working populations. Objectives: To evaluate the association between sociodemographic variables, lifestyle habits (smoking, physical activity, adherence to the Mediterranean diet), and health-related quality of life (HRQoL) with the risk of developing type 2 diabetes, using three validated screening tools in a large cohort of Spanish workers. Methods: A cross-sectional study was conducted among 100,014 Spanish workers aged 18 to 69 years who underwent standardized medical evaluations between January 2021 and December 2023. Diabetes risk was assessed using the FINDRISC, QDScore, and CANRISK tools. Lifestyle variables and HRQoL (measured via the SF-12 questionnaire) were evaluated through validated instruments. Multivariate logistic regression models were used to examine the association of independent variables with moderate-to-high diabetes risk according to each score. Results: Among the strongest predictors, poor adherence to a Mediterranean diet (OR = 1.45, 95% CI: 1.32–1.58) and low physical activity (OR = 1.39, 95% CI: 1.27–1.52) were independently associated with higher diabetes risk. Poor HRQoL was also significant (OR = 1.33, 95% CI: 1.22–1.47). Conclusions: Sociodemographic factors, lifestyle behaviors, and perceived health status are independently associated with increased type 2 diabetes risk in Spanish workers. The integration of HRQoL assessments into occupational health surveillance may enhance early identification of at-risk individuals and guide tailored prevention strategies. Full article

Early, non-invasive detection of Parkinson’s Disease (PD) using speech analysis offers promise for scalable screening. In this work, we propose a multi-channel spectro-temporal deep-learning approach for PD detection from sentence-level speech, a clinically relevant yet underexplored modality. We extract and fuse three complementary time–frequency representations—mel spectrogram, constant-Q transform (CQT), and gammatone spectrogram—into a three-channel input analogous to an RGB image. This fused representation is evaluated across CNNs (ResNet, DenseNet, and EfficientNet) and Vision Transformer using the PC-GITA dataset, under 10-fold subject-independent cross-validation for robust assessment. Results showed that fusion consistently improves performance over single representations across architectures. EfficientNet-B2 achieves the highest accuracy (84.39% ± 5.19%) and F1-score (84.35% ± 5.52%), outperforming recent methods using handcrafted features or pretrained models (e.g., Wav2Vec2.0, HuBERT) on the same task and dataset. Performance varies with sentence type, with emotionally salient and prosodically emphasized utterances yielding higher AUC, suggesting that richer prosody enhances discriminability. Our findings indicate that multi-channel fusion enhances sensitivity to subtle speech impairments in PD by integrating complementary spectral information. Our approach implies that multi-channel fusion could enhance the detection of discriminative acoustic biomarkers, potentially offering a more robust and effective framework for speech-based PD screening, though further validation is needed before clinical application. Full article

The creatinine-to-cystatin C ratio (CCR), a surrogate for skeletal muscle mass, may also be associated with liver fibrosis due to the strong link between sarcopenia and liver disease progression. We aimed to evaluate the utility of CCR as a noninvasive marker of liver fibrosis in metabolic-dysfunction-associated steatotic liver disease (MASLD). This retrospective study included 104 patients with biopsy-proven MASLD. CCR was calculated using serum creatinine and cystatin C levels. Liver fibrosis was staged histologically (F0–F4), and skeletal muscle mass was assessed using the skeletal muscle index (SMI) on computed tomography. Associations between CCR and liver fibrosis, SMI, and nonalcoholic fatty liver disease activity score were analyzed. ROC analysis evaluated CCR performance alone and in combination with FIB-4 and enhanced liver fibrosis (ELF) scores. CCR values were significantly lower in patients with significant fibrosis (≥F2). The AUROC of CCR for detecting ≥F2 fibrosis was 0.621 (95% CI: 0.509–0.733), with an optimal cutoff of 0.664. CCR alone yielded an AUC of 0.815 for predicting ≥F2 fibrosis. Combining CCR with FIB-4 and ELF substantially improved diagnostic accuracy, increasing the AUROC from 0.621 (CCR alone) to 0.820 for the combined model. CCR correlated positively with SMI (r = 0.451, p < 0.001). CCR is a simple, cost-effective biomarker reflecting muscle mass and liver fibrosis in MASLD. Combining CCR with established markers may enhance risk stratification and reduce the need for liver biopsy in selected cases. Full article

Intracranial aneurysms (IAs) are potentially devastating cerebrovascular lesions, and predicting rupture risk remains a major clinical challenge. Conventional radiological and clinical scores offer only partial risk stratification, highlighting the need for complementary approaches. Liquid biopsy represents a promising non-invasive strategy to identify circulating biomarkers that reflect aneurysm biology and instability. We conducted a systematic review according to PRISMA 2020 guidelines, screening PubMed, Scopus, and Web of Science up to August 2025. Forty-eight eligible studies, encompassing 3515 IA patients, evaluated circulating RNA species, including microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs) in serum, plasma, blood, or cerebrospinal fluid. Multiple candidates emerged as consistently dysregulated: upregulation of miR-21, miR-126, and miR-200a-3p, and downregulation of miR-143 and let-7b-5p were recurrently observed across independent cohorts. LncRNAs, such as MALAT1 and MIAT, and circRNAs, including circ_0000690 and circ_0021001, demonstrated diagnostic and prognostic potential, with some correlating with rupture status and clinical severity indices. Despite encouraging findings, heterogeneity in study design, sample handling, and analytic methods limits reproducibility. Large-scale, multicenter validation studies are essential to translate these biomarkers into clinical practice. Full article

Background/Objectives: Intravesical Bacillus Calmette–Guérin (BCG) is the standard adjuvant treatment for high-risk non-muscle-invasive bladder cancer (NMIBC), but treatment-related urinary toxicity may compromise quality of life (QoL) and adherence. The neutrophil-to-lymphocyte ratio (NLR), a marker of systemic inflammation, has been linked to oncologic outcomes in bladder cancer, but its association with urinary symptom burden during BCG therapy remains unclear. We aimed to assess whether baseline NLR is associated with early deterioration in urinary symptoms and urinary QoL following BCG induction. Methods: This prospective study included male patients with NMIBC treated with intravesical BCG. Baseline demographics, comorbidities, laboratory parameters, and urinary symptoms were recorded. Patients were stratified into two groups according to baseline NLR (<3 vs. ≥3). Urinary outcomes were assessed at baseline and 8 weeks using the International Prostate Symptom Score (IPSS) and the IPSS-related QoL item. Univariable and multivariable linear regression analyses were performed. Results: A total of 96 patients were analyzed. Median baseline NLR was 2.6 (IQR: 2.1–3.8). Patients with NLR ≥ 3 (n = 34) and NLR < 3 (n = 62) had comparable baseline characteristics and urinary scores. At 8 weeks, patients with NLR ≥ 3 experienced a greater worsening of urinary symptoms (median IPSS 24 vs. 21, p = 0.02; median change +5 vs. +2, p = 0.01) and QoL (median 5 vs. 4, p = 0.03). Univariable regression confirmed the association of NLR ≥ 3 with worse QoL (β = +0.74; p = 0.003) and higher IPSS (β = +2.20; p = 0.021). Modeled as a continuous variable, each one-unit increase in NLR corresponded to a +0.20 worsening in QoL (p = 0.008). In the multivariable analyses adjusted for baseline IPSS and concomitant CIS, NLR remained independently associated with QoL decline. Conclusions: Baseline NLR was independently associated with worsening urinary symptoms and QoL during BCG induction in NMIBC patients. NLR may represent a simple and accessible biomarker for early risk stratification during BCG induction, warranting validation in larger, longer-term prospective trials. Full article

A vision-based autonomous system for emasculating okra enhances agriculture by enabling precise flower bud identification, overcoming the labor-intensive, error-prone challenges of traditional manual methods with improved accuracy and efficiency. This study presents a framework for an adaptive, automated bud identification method to assist the emasculation process, hybridized optical coherence tomography (OCT). Three YOLOv8 variants were evaluated for accuracy, detection speed, and frame rate to identify the most efficient model. To strengthen the findings, YOLO was hybridized with OCT, enabling non-invasive sub-surface verification and precise quantification of the emasculated depth of both sepal and petal layers of the flower bud. To establish a solid benchmark, gold standard color histograms and a digital imaging-based method under optimal lighting conditions with confidence scoring were also employed. The results demonstrated that the proposed method significantly outperformed these conventional frameworks, providing superior accuracy and layer differentiation during emasculation. Hence, the developed YOLOv8 hybridized OCT method for flower bud identification and emasculation offers a powerful tool to significantly improve both the precision and efficiency of crop breeding practices. This framework sets the stage for implementing scalable, artificial intelligence (AI)-driven strategies that can modernize and optimize traditional crop breeding workflows. Full article

We aimed to investigate the utility of peritumoral edema-derived radiomic features from magnetic resonance imaging (MRI) image weights and fused MRI sequences for enhancing the performance of machine learning-based glioma grading. The present study utilized the Multimodal Brain Tumor Segmentation Challenge 2023 (BraTS 2023) dataset. Laplacian Re-decomposition (LRD) was employed to fuse multimodal MRI sequences. The fused image quality was evaluated using the Entropy, standard deviation (STD), peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM) metrics. A comprehensive set of radiomic features was subsequently extracted from peritumoral edema regions using PyRadiomics. The Boruta algorithm was applied for feature selection, and an optimized classification pipeline was developed using the Tree-based Pipeline Optimization Tool (TPOT). Model performance for glioma grade classification was evaluated based on accuracy, precision, recall, F1-score, and area under the curve (AUC) parameters. Analysis of fused image quality metrics confirmed that the LRD method produces high-quality fused images. From 851 radiomic features extracted from peritumoral edema regions, the Boruta algorithm selected different sets of informative features in both standard MRI and fused images. Subsequent TPOT automated machine learning optimization analysis identified a fine-tuned Stochastic Gradient Descent (SGD) classifier, trained on features from T₁Gd+FLAIR fused images, as the top-performing model. This model achieved superior performance in glioma grade classification (Accuracy = 0.96, Precision = 1.0, Recall = 0.94, F1-Score = 0.96, AUC = 1.0). Radiomic features derived from peritumoral edema in fused MRI images using the LRD method demonstrated distinct, grade-specific patterns and can be utilized as a non-invasive, accurate, and rapid glioma grade classification method. Full article

Cardiac arrhythmia is a leading cause of sudden cardiac death. Its early detection and continuous monitoring hold significant clinical value. Photoplethysmography (PPG) signals, owing to their non-invasive nature, low cost, and convenience, have become a vital information source for monitoring cardiac activity and vascular health. However, the inherent non-stationarity of PPG signals and significant inter-individual variations pose a major challenge in developing highly accurate and efficient arrhythmia classification methods. To address this challenge, we propose a Fusion Deep Multi-domain Attention Network (Fusion-DMA-Net). Within this framework, we innovatively introduce a cross-scale residual attention structure to comprehensively capture discriminative features in both the time and frequency domains. Additionally, to exploit complementary information embedded in PPG signals across these domains, we develop a fusion strategy integrating interactive attention, self-attention, and gating mechanisms. The proposed Fusion-DMA-Net model is evaluated for classifying four major types of cardiac arrhythmias. Experimental results demonstrate its outstanding classification performance, achieving an overall accuracy of 99.05%, precision of 99.06%, and an F1-score of 99.04%. These results demonstrate the feasibility of the Fusion-DMA-Net model in classifying four types of cardiac arrhythmias using single-channel PPG signals, thereby contributing to the early diagnosis and treatment of cardiovascular diseases and supporting the development of future wearable health technologies. Full article

Background and Objectives: Maternal telomere length (TL) has been proposed as a potential biomarker of biological aging and pregnancy outcomes, yet evidence in Central and Eastern European populations remains scarce. This study aimed to investigate the association between maternal TL and neonatal parameters in a clinically healthy cohort. Materials and Methods: We conducted a prospective observational study including 134 mother–infant pairs at the “Pius Brînzeu” Emergency County Clinical Hospital, Timișoara. All deliveries were performed by cesarean section for maternal indications unrelated to fetal condition. Maternal blood samples were collected at admission, and relative TL was measured by quantitative PCR. Neonatal outcomes included birth weight, length, head circumference, gestational age, and Apgar scores. Results: Longer maternal TL was positively correlated with birth weight (r = 0.515, p < 0.001), length (r = 0.559, p < 0.001), head circumference (r = 0.468, p < 0.001), gestational age (r = 0.444, p < 0.001), and Apgar scores at 1 (r = 0.714, p < 0.001) and 5 min (r = 0.684, p < 0.001). Logistic regression showed that shorter maternal TL independently predicted suboptimal 1 min Apgar (<8), with an adjusted odds ratio of 0.68 (95% CI: 0.51–0.91). Conclusions: Maternal TL is strongly associated with neonatal growth and vitality measures, supporting its potential as a simple, non-invasive biomarker for perinatal risk assessment. Full article