Search Results (3,096)

Background/Objectives: Male factor infertility contributes to approximately 40–50% of infertility cases globally, yet traditional laboratory-based semen analysis often imposes logistical and psychological barriers. This study aimed to evaluate the analytical performance and diagnostic concordance of a novel smartphone-based point-of-care testing (POCT) system, Hagobogo Pro, compared with a laboratory-based computer-assisted sperm analysis (CASA) reference system. Methods: This retrospective validation study analyzed 520 video microscopy clips obtained from 104 men undergoing infertility evaluation at a tertiary fertility center. Following World Health Organization (WHO) 2021 guidelines, sperm concentration and total motility were measured using the Hagobogo Pro smartphone device and the reference system. Analytical performance was assessed based on intra-assay precision, operational time, and method agreement using Passing–Bablok regression, Bland–Altman analysis, and Spearman correlation. Results: The smartphone-based system demonstrated strong analytical agreement with the CASA reference, with high correlations observed for sperm concentration (ρ = 0.943) and motility (ρ = 0.7335). Bland–Altman analysis indicated minimal systematic bias, and intra-assay precision showed coefficients of variation below 6%. There were no statistically significant differences in mean parameters between the smartphone device, CASA, and manual assessment. Conclusions: The Hagobogo Pro platform enables rapid, reliable, and standardized sperm concentration and motility quantification, and results showed good agreement with laboratory CASA. While not a replacement for holistic laboratory evaluations, this technology improves access to preliminary male fertility screening and may empower patients by mitigating barriers to initial testing. Full article

Public participation is a central component of democratic decision-making processes, particularly in planning and approval procedures. However, increasing data complexity and the growing number of submitted objections significantly raise the effort required for their review and processing. Against this background, this study developed an AI-supported objection management system that uses a large language model (LLM) to automatically pre-sort objections by topic and generate response suggestions based on historical objection texts from previous infrastructure projects. The aim is to increase efficiency in the processing workflow while maintaining consistent response quality without replacing human decision-making. The prototype development is preceded by a literature review to identify key user requirements and derive relevant use cases. Subsequently, four expert workshops with representatives from German road and rail infrastructure administrations at the state and federal level were conducted to evaluate the prototype. The results indicate significant efficiency potential, particularly through automated thematic pre-sorting of objections. However, topic structures must be adapted to the specific procedure. AI currently mainly serves as supportive pre-processing and requires human review (“human-in-the-loop”). Transparent labeling of AI use is also necessary to ensure traceability and acceptance. The findings will be incorporated into the ongoing development of the prototype within the BIM4People research project funded by the German Federal Ministry of Transport (BMV), with the aim of further improving the system’s functionality and exploring additional applications. Full article

Automated pavement distress detection (PDD) is critical for the structural health monitoring (SHM) of transportation infrastructure, yet existing methods struggle with real-time multi-target detection under resource constraints. In this paper, YOLOv8-PDD was constructed based on YOLOv8 by introducing the large separable kernel attention (LSKA) mechanism module into the Spatial Pyramid Pooling—Fast (SPPF) module, replacing Complete-IoU (CIoU) loss with Distance-IoU (DIOU) loss as the loss function, and adopting Soft-Non-Maximum Suppression (NMS) to replace the original NMS algorithm. The proposed YOLOv8-PDD achieved 78.3% mean average precision with intersection over union above 0.5 (mAP@0.5 +8.1%) with a minimal complexity increase of +0.2 GFLOPs compared to the baseline YOLOv8n model. While incurring a negligible increase in latency (+0.09 ms), YOLOv8-PDD significantly outperforms YOLOv8n in detection accuracy (mAP@0.5 +8.1%), offering a superior accuracy–efficiency trade-off for real-time applications. YOLOv8-PDD performed well in detecting all categories, with AP values above 75% except for transverse crack and strip patch. Significant improvements in pothole detection AP@0.5 (+22.1%) and strip patch detection AP@0.5 (+17.7%) indicate superior small target and complex background adaptability. Our model achieved a detection efficiency of 68 frames per second (FPS) on consumer-grade CPUs (OpenVINO-optimized), outperforming 10 models (e.g., YOLOv5n and RTDETR-l) in accuracy–speed balance. Full article

Background/Objectives: Chronic rhinosinusitis with nasal polyps (CRSwNP) is a heterogeneous inflammatory disease in which eosinophilic subclassification is widely used for clinical stratification. However, it remains unclear how closely nominal histologic eosinophilic labels reflect the broader molecular organization of diseased tissue. Methods: We performed an inference-based integrative analysis of public datasets spanning discovery single-cell RNA sequencing (scRNA-seq), independent scRNA-seq validation, GeoMx digital spatial profiling, and bulk transcriptomic replication cohorts. A sample-level molecular burden framework was constructed using four dimensions: type 2 inflammation, epithelial injury/remodeling, extracellular-matrix remodeling, and barrier/defense impairment. Composite burden and component-level features were then examined across nominal eosinophilic categories, epithelial states, spatial compartments, and independent bulk cohorts. Results: Nominal eosinophilic labels were directionally informative but incompletely concordant with molecular burden. In the discovery cohort, eosinophilic CRSwNP samples were enriched toward the higher-burden end, whereas nominally non-eosinophilic CRSwNP samples extended across the intermediate-to-high burden range. Across discovery and validation scRNA-seq datasets, GeoMx spatial profiling, and independent bulk cohorts, the most reproducible burden-associated signals centered on epithelial injury/remodeling-like programs and related remodeling features. In the epithelial compartment, higher burden was associated with epithelial state reorganization, stronger wounding-associated activity, and trajectory-linked glandular/secretory remodeling. Independent validation and spatial analyses further supported epithelial wounding-, barrier-, and myeloid remodeling-related features, whereas type 2 context signals were directionally consistent but less uniform across platforms. In bulk replication, composite burden, epithelial wounding, and myeloid remodeling were more consistent across cohorts than type 2 context alone. Conclusions: Nominal eosinophilic labels in CRSwNP capture clinically relevant but incomplete information about underlying tissue biology. Epithelial injury/remodeling-like programs and remodeling-linked myeloid features emerged as the most stable organizational axes of molecular burden across public multimodal datasets. These findings support a graded, multidimensional view of CRSwNP and may complement, rather than replace, conventional pathology-based eosinophilic subclassification. Full article

Background: Total knee arthroplasty in patients with hemophilia remains the most effective surgical intervention for end-stage hemophilic arthropathy, yet it poses unique surgical and perioperative challenges that are rarely encountered in standard osteoarthritis cases. This article synthesizes technical, anatomical, and perioperative considerations specific to hemophilic patients and integrates prospective clinical data derived exclusively from the hemophilic cohort of our long-term study (twenty patients, twenty knees; 2015–2024). Emphasis is placed on deformity correction, bone loss management, implant selection, hemostatic strategies, transfusion patterns, and perioperative pitfalls. The objective is to provide a comprehensive narrative reference for surgeons managing complex hemophilic knees, consolidating both evidence-based recommendations and practical perioperative “tips and tricks” accumulated across more than a decade of clinical experience. Methods: This prospective observational study evaluated twenty consecutive male patients with hemophilia who underwent primary total knee arthroplasty for advanced hemophilic arthropathy between 2015 and 2024 at our institution. The following variables were collected: operative time measured from skin incision to skin closure, postoperative transfusion requirement, length of hospitalization measured in days, early postoperative complications, and functional recovery as assessed by the Knee Society Score. Early complications included postoperative bleeding or hematoma, superficial or deep infection, and stiffness requiring intensive physiotherapy or manipulation under anesthesia. Results: The mean age at the time of surgery was 44.8 years with a standard deviation of 7.2 years, ranging from 31 to 59 years. The mean operative time in the hemophilic cohort was 154.54 min with a standard deviation of 18.36 min. The range of operative time was from 120 to 180 min. Nine of the twenty patients, representing 45 percent, required postoperative blood transfusion. The mean length of hospital stay in the hemophilic cohort was 12.3 days with a standard deviation of 2.38 days, ranging from 9 to 17 days. The mean Knee Society Score improved from 38 points preoperatively to 82 points at final follow-up, representing a mean increase of 44 points. Conclusions: Total knee arthroplasty in hemophilic patients is safe and effective when specialized surgical techniques, comprehensive synovectomy, precise deformity correction, optimized hemostasis, and structured postoperative coagulation factor replacement are implemented. Functional outcomes and prosthetic survival are excellent in experienced centers. Full article

Background/Objectives:Helicobacter pylori infection is traditionally associated with gastrointestinal diseases; however, increasing evidence suggests that it may have systemic effects involving inflammatory, metabolic, and hematological pathways. Despite this, integrated evaluations of these domains remain limited, particularly in Middle Eastern populations. This study aimed to assess the impact of H. pylori infection on inflammatory, metabolic, and hematological parameters among adults. Methods: A case–control study was conducted including 100 participants (50 H. pylori-positive patients and 50 healthy controls) recruited from Qassim Health Cluster, Saudi Arabia. Demographic and clinical data were collected, and blood samples were analyzed for random blood sugar (RBS), glycated hemoglobin (HbA1c), C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), hemoglobin, ferritin, and white blood cell count (WBC). Statistical analyses included group comparisons, Spearman correlation, logistic regression, and receiver operating characteristic (ROC) curve analysis. Results: The infected group showed significantly higher levels of RBS and HbA1c, indicating impaired glycemic control. Inflammatory markers (CRP and ESR) were also significantly elevated compared to controls (p < 0.001). Hemoglobin and ferritin levels were significantly lower in the infected group (p < 0.001), suggesting disturbed iron metabolism. Correlation analysis revealed positive associations between inflammatory markers and glycemic indices, and negative associations with hemoglobin and ferritin. Multivariable logistic regression identified CRP (adjusted OR = 1.33, 95% CI: 1.04–1.71) and ESR (adjusted OR = 1.09, 95% CI: 1.02–1.16) as independent predictors of H. pylori infection after adjustment for smoking status and fast-food consumption. The combined model demonstrated acceptable discriminatory performance with an AUC of 0.82 (95% CI: 0.74–0.90). Conclusions:Helicobacter pylori infection was associated with significant inflammatory, metabolic, and hematological alterations, supporting its potential role as a systemic condition beyond the gastrointestinal tract. These associations remained significant after adjustment for major lifestyle-related confounders, including smoking status and fast-food consumption. Although the combined inflammatory model demonstrated acceptable discriminatory performance, it should currently be considered mainly for research or preliminary screening purposes and not as a replacement for established diagnostic methods for active H. pylori infection. Further large-scale longitudinal and interventional studies are warranted to clarify causality and evaluate the impact of eradication therapy on systemic outcomes. Full article

Background: Celiac disease (CeD) requires lifelong adherence to a gluten-free diet (GFD). However, there is evidence that a GFD may lead to an unhealthy cardiometabolic risk profile and potentially increase the risk of cardiovascular disease in some patients. Incorporating plant-based foods (primarily derived from plants) into a GFD may offer a solution to improve cardiometabolic health. Thus, this study aimed to identify the attitudes toward and barriers to adopting a more plant-dominant diet among Danish patients with CeD. Methods: A cross-sectional survey was distributed to 2861 members of the Danish Celiac Society. Data from 959 patients with confirmed CeD were included. Results: Most participants (58.5%) reported adapting their diet after diagnosis by combining gluten-free products with plant-based foods, while 31.2% relied solely on gluten-free replacements. Dietary adaptation was primarily shaped by the limited availability of gluten-free plant-based foods (64%), taste/texture (55%), and cost (51%). More than half of the patients (56.8%) considered ‘eating more plant-based foods’, with ‘health’ being the primary motivator (70%), followed by ‘climate’ (50%) and ‘taste’ (36%). However, several barriers to a more plant-dominant diet were identified. Most notably, ‘taste and texture’ (71%), ‘limited availability of gluten-free plant-based foods’ (68%), ‘nutritional concerns’ (56%), and ‘cost’ (54%) were reported as barriers. Conclusions: Most Danish patients with CeD were generally positive about increasing their intake of plant-based foods; however, barriers to such dietary changes remain. Ongoing follow-up, practical guidance from dietitians, and accessible evidence-based resources may help patients maintain a nutritionally balanced, plant-dominant GFD that supports long-term health. Full article

Cracks are among the most common pavement distresses, and their timely detection is crucial for road maintenance. Existing methods struggle to completely capture elongated and irregular cracks, often resulting in fragmented detection outputs, which leads to the inaccurate assessment of crack length and affects the reliability of pavement condition evaluation. To address this issue, this paper proposes a dual-path crack segmentation network that integrates CNN and Transformers. The CNN branch incorporates a dynamic multi-branch convolution module to enhance the directional perception and structural modeling of elongated cracks. The Transformer branch employs a lightweight DCNv4 module to replace traditional self-attention mechanisms, effectively capturing long-range dependencies while reducing computational complexity. A multi-path fusion module is designed to achieve the collaborative enhancement of dual-path features, improving the semantic representation of continuous crack regions. Additionally, a combined loss function of BCE and Dice is adopted to alleviate the severe class imbalance between crack and background pixels, further improving the completeness of crack segmentation. Experiments on four datasets, including CFD, DeepCrack537, Gaps384, and Crack500, demonstrate that the proposed model outperforms all compared methods in terms of F-score and mIoU. Ablation studies further validate the effectiveness of the dual-path architecture and its key modules in improving performance. Furthermore, in field validation on real road scenarios, the pavement condition index (PCI) calculated based on the proposed method shows an average deviation of only 0.81 compared to manually interpreted ground truth, demonstrating the practical value of continuous crack detection for pavement maintenance assessment. Full article

In contiguous seam mining, the sudden large-scale collapse of a hard roof in an overlying goaf generates violent impact airflow, driving hazardous gases into the underlying working face and seriously threatening production safety. However, quantitative analysis of airflow responses under such transient impacts is rare for conventional exhaust ventilation systems, and proactive control strategies remain lacking. This study hypothesized that replacing exhaust ventilation with a forced ventilation system builds a sufficient counter-pressure gradient across the working face to block the downward migration of hazardous gases. Taking the Longhua Coal Mine as the engineering background, this study combines a theoretical velocity model of roof-collapse-induced impact airflow with numerical simulations and subsequently implements a forced ventilation system on site. Results show that under exhaust ventilation, roof collapse greatly intensifies air leakage in the goaf, causing the CO concentration at the return corner to spike to 5000 ppm within only 0.2 s. In contrast, the field-deployed forced ventilation system effectively suppresses this impact: by keeping the pressure difference across the air regulator within 338–417 Pa, the CO concentration drops from 36 ppm to below 15 ppm. Complemented by a real-time monitoring system for goaf pressure surges and hazardous gases, this strategy successfully shifts disaster control from passive ventilation to active aerodynamic suppression. This study provides a robust theoretical foundation and practical engineering reference for disaster prevention in contiguous seam mining. Full article

Background: Early external validation studies demonstrated the robust and consistent predictive performance of the International IgA Nephropathy Prediction Tool (IIgAN-PT) across diverse ethnic populations. However, emerging evidence suggests that, in contemporary cohorts of patients with IgA nephropathy, the IIgAN-PT increasingly tends to overestimate the risk of adverse renal outcomes. Subclassification of segmental glomerulosclerosis (S lesions) in the Oxford Classification system (MEST-C) could identify high-risk IgAN patients, with evidence that different S subclassifications respond differently to treatment. Our study aimed to evaluate the predictive performance of the IIgAN-PT in a contemporary Chinese external validation cohort and to optimize its prognostic accuracy by incorporating the most severe and prevalent pathological subclassification of S lesions, NOS⁺Adh⁺, into the original model. Methods: A total of 746 Chinese patients were included with biopsy-proven IgAN in this study. Major adverse kidney events (MAKEs) were defined as death from any cause, initiation of renal replacement therapy, or a 50% decline in eGFR. This study evaluated the discrimination and model fit of three predictive models. The performance of the original and modified IIgAN-PT models was compared and evaluated through reclassification, survival analysis, calibration, decision curve analyses and subgroup analyses. Results: In the study cohort, the median follow-up duration was 4.2 years, during which 77 patients experienced MAKEs. The discriminative ability of the three original models was relatively limited. In contrast, the modified IIgAN-PT incorporating the NOS⁺Adh⁺ subtype of S subclassification demonstrated improved global performance for predicting 5-year risk, achieving a C-index of 0.808 (95% CI, 0.756–0.861). Kaplan–Meier survival curves showed clear risk stratification, particularly between low- and intermediate-risk categories. Reclassification analyses (continuous NRI and IDI) and decision curve analysis further supported enhanced predictive performance, while calibration curves corrected the original model’s risk overestimation. The modified model maintained stable performance across clinically relevant subgroups, including patients with hypertension, proteinuria, or receiving immunosuppression. Conclusions: This study further confirms the independent and clinically relevant prognostic value of the S pathological subclassification. The modified IIgAN-PT model, incorporating the NOS⁺Adh⁺ subtype of S subclassification, demonstrated consistent performance in individualized risk assessment for patients with IgA nephropathy. Full article

Under green mine construction and efficient resource utilization, non-pillar mining has been increasingly applied. However, surrounding rock control remains difficult in traditional gob-side entry retaining under large mining height conditions. To address this problem, a cooperative control method combining roof cutting and pressure relief with a pre-placed backfill strip for coal pillar replacement is proposed. Taking the 15,108 and 15,110 working faces of Wangzhuang Coal Industry as the engineering background, a mechanical model and FLAC3D simulations were used to analyze the effects of roof cutting height and backfill strip width. The results show that roof cutting shortens the goaf-side suspended roof, weakens lateral abutment pressure, and improves the stress state of the strip. When the roof cutting height increases from 11 m to 13 m, the peak vertical stress of the strip decreases from 16.2 MPa to 13.9 MPa, with a reduction of 14.2%. When the strip width increases from 1.0 m to 1.5 m, the peak stress decreases by about 12.0%. Thus, the reasonable roof cutting height and strip width are determined to be 13 m and 1.5 m. Field monitoring shows maximum roof-to-floor and rib-to-rib convergences of 178.5 mm and 143.5 mm, respectively, with no obvious strip instability. Full article

Background/Objectives: AA amyloidosis is a serious complication of chronic inflammation, which may arise in the setting of inborn errors of immunity (IEIs) due to recurrent or persistent infections. Common variable immunodeficiency (CVID) is the most frequent symptomatic IEI in adults, yet its association with secondary AA amyloidosis remains rarely reported. Case presentation: We describe a 37-year-old male with a history of recurrent pneumonia, chronic sinusitis, and osteomyelitis with sepsis since childhood. At age 33, he developed bilateral pneumonia after COVID-19, followed by repeated lower respiratory tract infections. At age 36, nephrotic syndrome (proteinuria 10.69 g/day, hypoalbuminemia) led to kidney and gastric mucosa biopsies, which confirmed AA amyloidosis. Immunological workup revealed panhypogammaglobulinemia (IgG 0.1 g/L, IgA 0.01 g/L, IgM 0.28 g/L), markedly reduced switched memory B cells, and an inverted CD4⁺/CD8⁺ ratio. Chest CT showed bronchiectasis, bronchiolitis, and mediastinal lymphadenopathy. Whole-exome sequencing excluded known monogenic IEIs, autoinflammatory, or hereditary amyloidosis genes; a heterozygous likely pathogenic variant in ODAD2 (associated with primary ciliary dyskinesia) was considered incidental. A diagnosis of CVID with secondary AA amyloidosis was established. Conclusions: This case illustrates that CVID may remain undiagnosed for decades and present with secondary AA amyloidosis as the first major complication. In any patient with nephrotic syndrome and a history of recurrent or unusual infections, an IEI should be actively excluded. Early recognition of CVID and appropriate immunoglobulin replacement therapy can prevent infectious exacerbations and potentially halt amyloid progression. Full article

Nowadays, remote sensing images are characterized by significant scale variations, a high density of small targets, and complex background conditions, which pose substantial challenges for small-object detection. To address these issues, we propose EMWMS-YOLO, a lightweight and efficient detection framework built upon YOLOv11n. Specifically, an Efficient Multi-Scale Cross-Layer Extraction (EMSCLE) backbone is designed by integrating the Dual-Branch Feature Extraction (DBFE), Multi-Scale Feature Perception (MSFP), and Spatial Pyramid Pooling Fast with Large Separable Kernel Attention (SPPF-LSKA) modules, enabling effective multi-scale feature extraction and cross-channel interaction. Furthermore, a Multi-Scale Adaptive Feature Fusion (MSAFF) neck architecture, composed of the Channel-Enhanced Convolution (CEC) and Multi-Scale Gated Feature Fusion (MSGFF) modules, is introduced to dynamically fuse cross-scale features and enhance salient target responses while suppressing background noise. In addition, the WaveletPool module replaces conventional pooling operations to reduce information loss and feature aliasing while preserving structural details. A Detect-MultiSEAM detection head is constructed by embedding a multi-scale spatial enhancement attention mechanism, which improves feature representation under complex conditions and reduces missed detections and false positives. Finally, the ShapeIoU loss function is employed to better model geometric and morphological properties, thereby improving localization accuracy. Experimental results on the VEDAI and NWPU-VHR-10 datasets demonstrate that the proposed method achieves improvements of 9.8% and 4.1% in mAP50 over the YOLOv11n baseline, respectively, verifying its effectiveness in small-object detection. Full article

Background: Complete blood count (CBC) phenotypes are routinely assigned using deterministic rule-based thresholds. While operationally efficient, such rules may lead to unstable phenotype assignments for results close to clinical cutoffs in the presence of analytical variability. Methods: We analyzed routine CBC data from a tertiary care hospital laboratory. Rule-based phenotypes for anemia subtype, white blood cell (WBC) status, and platelet (PLT) status were assigned using established laboratory thresholds. A patient-independent development and holdout split was applied. A multi-output gradient boosting model was trained to reproduce rule-based labels and provide probabilistic outputs. Phenotype stability was assessed by perturbing CBC parameters under realistic analytical noise. Instability was defined as any change in phenotype assignment across perturbations. Distances to decision boundaries were grouped into quantile-based bins. Model uncertainty was evaluated for the triage of unstable cases. Results: Phenotype instability was strongly concentrated near decision boundaries. Under medium analytical variability, samples closest to hemoglobin cutoffs exhibited the highest instability, with the highest instability in the bin closest to the cutoff, a sharp decrease in the adjacent bin, and lower instability across more distant bins. Model uncertainty was enriched among unstable cases, enabling prioritization of borderline samples while reviewing only a subset of all cases. Conclusions: Rule-based CBC phenotyping exhibits intrinsic instability near decision thresholds. Uncertainty-aware machine learning supports a practical framework to identify and prioritize borderline cases without replacing existing laboratory rules, supporting workload-controlled post-analytical decision support. Full article

Background/Objectives: Acute kidney injury (AKI) is a common complication after liver transplantation. Although intraoperative hypotension has been associated with its development, the impact of shock persistence and its hemodynamic profile remains poorly defined. Methods: This was a single-center retrospective observational study including 226 adult patients undergoing liver transplantation. Intraoperative shock was defined as a mean arterial pressure < 60 mmHg or a ≥30% decrease from baseline and was classified as hypovolemic, distributive, cardiogenic, or mixed based on pulmonary artery catheter data. AKI was defined according to Kidney Disease: Improving Global Outcomes (KDIGO) criteria within the first 7 postoperative days. Associations were assessed using adjusted logistic regression models. Results: Intraoperative shock occurred in 35.8% of patients, and the incidence of AKI was 52.2%. The presence of shock was not independently associated with AKI (adjusted OR 1.66; 95% CI 0.94–2.95). However, shock occurring in multiple phases of the procedure was associated with a higher incidence of AKI (81.8% vs. 50%; p = 0.010), greater severity, and higher mortality (27.3% vs. 3.4%; p = 0.002). In exploratory analyses, mixed shock was associated with an increased need for renal replacement therapy within 30 days (p = 0.006), persistent renal dysfunction at day 30 (p = 0.048), and higher mortality (p = 0.01), while hypovolemic shock was associated with moderate AKI (OR 6.60; p = 0.011). Conclusions: The presence of intraoperative shock alone is not independently associated with AKI. In contrast, its persistence is strongly associated with AKI development and worse clinical outcomes. Full article