Search Results (24,905)

Visual orthopedic gait assessment in dogs is recognized as subjective and is limited by interobserver variability. Objective detection of lameness is offered by biomechanical analysis, where asymmetry between limbs is quantified through kinematic parameters and symmetry indices. However, the minimum number of trials (full stride cycles) required to reliably discriminate lameness has remained a challenge. In this study, six healthy adult dogs were used. Mild, reversible lameness was induced in one forelimb using a cotton pad. Dogs were walked along a straight runway, and kinematic data were captured with a high-speed video camera. Stride length (SLE), support time (ST), and elbow range of motion (ROM) were measured. Symmetry indices (for linear and temporal parameters) and the symmetry angle (for angular parameters) were computed. The asymptotic distribution of these indices was derived using the delta method, which allowed for the construction of confidence intervals (CIs) and hypothesis tests for an asymmetry threshold of 3%. The number of trials required to achieve reliable detection was estimated through statistical simulations. Results indicated that the required number of trials was highly dependent on both the kinematic parameter and the magnitude of asymmetry. While detecting subtle asymmetries (≈4%) required a high number of trials (up to 347 for stride length), the requirements decreased substantially for more pronounced lameness. For a true asymmetry of 6%, 11–39 trials per limb were sufficient to achieve 80–90% power. It is concluded that the collection of only five trials is insufficient for detecting mild asymmetries. A statistical framework and practical recommendations for kinematic gait studies in dogs are provided. Full article

In response to the challenges of target recognition and misjudgment caused by varying target scales, diverse shapes, and interference such as lake surface reflections in river and lake scenarios, this paper proposes the YOLO v11n-DDH model for fast and detection of spatial targets in river and lake environments. The model builds upon YOLO v11n by introducing the Dynamic Snake Convolution (DySnakeConv) to enhance the ability to extract detailed features. It integrates the Deformable Attention Mechanism (DAttention) to strengthen key features and suppress noise, while combining the improved High-Level Screening Feature Pyramid Network (HSFPN) structure for multi-level feature fusion, thus improving the semantic representation of targets at different scales. Experiments on a self-constructed dataset show that the precision, recall, and mAP of the YOLO v11n-DDH model reached 88.4%, 78.9%, and 83.9%, respectively, with improvements of 3.4, 2.9, and 2.5 percentage points over the original model. Specifically, DySnakeConv increased mAP@50 by 0.6 percentage points, DAttention improved mAP@50 by 0.3 percentage points, and HSFPN contributed to a 0.9 percentage point rise in mAP@50. This patrol system can effectively identify and visualize various pollutants in river and lake areas, such as underwater waste, water quality pollution, illegal swimming and fishing, and the “Four Chaos” issues, providing technical support for intelligent river and lake management. Full article

The application of vibration monitoring integrated with sparse Auto-Encoder (SAE) networks is investigated in this paper with the objective of detecting and localizing structural anomalies or damages. Unlike previous studies on SAE networks, the methodology proposed is based on the definition of a single SAE model, trained with the signals simultaneously collected from several sensors. Once the SAE has been trained using measurements that represent the baseline (undamaged) condition of the structure, the network is likely to reconstruct well newly collected data if the structure maintains its intact condition. When damage or structural degradation processes start developing, an increase in the reconstruction error—defined as the residual between the original input and the reconstructed output—has to be expected, so that a deviation from the normal state is highlighted. Moreover, this rise in reconstruction errors is typically more significant near the damaged areas, allowing for precise localization of the affected zones. The performance and robustness of the proposed approach are illustrated and validated using experimental data from two real-world bridge structures. Full article

In modern smart dairy farms, precise feed management and accurate monitoring of dairy cows’ feeding behavior are crucial for improving production efficiency and reducing feeding costs. However, in practical applications, complex environmental factors such as varying illumination, frequent occlusion, and dense multi-targets pose significant challenges to real-time visual perception. To address these issues, this paper proposes a lightweight multi-target detection model, BFDet-YOLO, for the joint detection of dairy cows’ feeding behavior and feed density levels in pasture environments. Based on the YOLOv10 framework, the model incorporates four targeted improvements: (1) a bidirectional feature fusion network (BiFPN) to address the insufficient multi-scale feature interaction between dairy cows (large targets) and feed particles (small targets); (2) a lightweight downsampling module (Adown) to preserve fine-grained features of feed particles and reduce the risk of small target miss detection; (3) an attention-enhanced detection head (SEAM) to mitigate occlusion interference caused by cow stacking and feed accumulation; (4) an improved bounding box regression loss function (DIoU) to optimize the localization accuracy of non-overlapping small targets. Additionally, this paper constructs a pasture-specific dataset integrating dairy cows’ feeding behavior and feed distribution information, which is annotated and expanded by combining public datasets with on-site monitoring data. Experimental results demonstrate that BFDet-YOLO outperforms the original YOLOv10 and other mainstream target recognition models in terms of detection accuracy and robustness while maintaining a significantly streamlined model scale. On the constructed dataset, the model achieves 95.7% mAP@0.5 and 70.7% mAP@0.5:0.95 with only 1.85 M parameters. These results validate the effectiveness and deployability of the proposed method, providing a reliable visual perception solution for intelligent feeding systems and smart pasture management. Full article

Background/Objectives. Changes in CO₂-derived variables during a fluid challenge have been proposed as markers of fluid responsiveness. We investigated whether, instead of fluid administration, passive leg raising (PLR)-induced changes in the CO₂-derived variables, namely central venous-arterial carbon dioxide partial pressure (P(cv-a)CO₂) and the ratio between P(cv-a)CO₂ and the arterial-central venous oxygen content (P(cv-a)CO₂/C(a-cv)O₂), could detect preload responsiveness in critically ill patients. Methods. We studied 30 mechanically ventilated patients in whom a PLR test was performed due to acute circulatory failure. Routine hemodynamic variables, velocity-time integral (VTI), in the left ventricular outflow tract, and CO₂-derived variables, were measured before, during, and after a PLR test. A PLR-induced increase in VTI of ≥10% defined preload responsiveness. The differences (Δ) of P(cv-a)CO₂ and P(cv-a)CO₂/C(a-cv)O₂ between PLR and pre-PLR were calculated. The predictive values of PLR-induced changes in the CO₂-derived variables was determined by receiver operating characteristic area under curves (ROC-AUCs). Results. Fifteen patients (50%) were classified as preload responsive. ΔP(cv-a)CO₂ and ΔP(cv-a)CO₂/C(a-cv)O₂ were correlated with VTI changes and differed significantly between responders and non-responders −1.3 (−2–−0.6) vs. 0.6 (−0.1–1.1) mmHg, p < 0.001, and −0.38 (−0.97–−0.34) vs. 0.1 (−0.15–0.57) mmHg/mL O₂, p < 0.001, respectively. The PLR-induced decrease in P(cv-a)CO₂ was significantly associated with preload responsiveness (OR 0.48, CI 0.20–0.89, p = 0.016, bootstrap CI 0–0.85). The AUC curves for both ΔP(cv-a)CO₂ and ΔP(cv-a)CO₂/C(a-cv)O₂ ratio to predict preload responsiveness were 0.89 (CI 0.74–1), p < 0.001, and 0.85 (CI 0.70–1), p < 0.001, respectively. Conclusions. In mechanically ventilated ICU patients with circulatory shock, PLR-induced changes in P(cv-a)CO₂ and P(cv-a)CO₂/C(a-cv)O₂ ratio were correlated with VTI changes. The change in P(cv-a)CO₂ was the only variable detecting preload responsiveness assessed by PLR; therefore, it could serve as an indirect marker, useful to guide fluid resuscitation when cardiac output measurement is not feasible. Full article

This study details the validation of a novel microbead beating extraction (MBE) technique for the analysis of polycyclic aromatic compounds (PACs) in sediments. The method’s performance was evaluated against international analytical validation criteria, including trueness, precision, measurement uncertainty and robustness. Limits of detection and quantitation were consistently low (≤6.5 and 21 ng g⁻¹, respectively), trueness for the majority of analytes fell within accepted performance criteria, and repeatability values for most analytes were generally <10%. Analytical data confirm the method’s reliability, with more than 80% of certified analytes in two certified reference materials (CRMs) meeting the satisfactory z-score (∣z∣ ≤ 2.0). Furthermore, the method’s inter-laboratory repeatability, as measured by HorRat values, fell within the range recommended by the Association for Official Analytical Chemist for most analytes, and combined measurement uncertainties showed no statistical difference from the certified uncertainties of the CRMs. Incorporating an in situ cleanup step enabled the MBE method to substantially reduce extraction times (<15 min) and reduces solvent consumption by ~60% compared with conventional pressurize fluid extraction while maintaining good quality data objectives. By meeting or exceeding well-established metrics for good laboratory performance, the MBE method demonstrates reliability, efficiency, and a greener approach for the routine analysis of PACs in sediments. Full article

Background/Objectives: Wernicke encephalopathy (WE) is an acute neurological syndrome caused by severe thiamine deficiency. Early detection is challenging due to the low sensitivity of the classic triad. Methods: This retrospective observational study used the Spanish Minimum Basic Data Set, including hospital admissions with a primary diagnosis of WE (2016–2022). Demographic, clinical, and economic variables were also analyzed. Severity of illness (SOI) and risk of mortality (ROM) were assessed using the All Patient Refined Diagnosis-Related Groups (APR–DRG) system. Results: A total of 2477 WE episodes were included (1864 men; mean age, 58.2 years; standard deviation [SD], 11.0). The hospital admission rate increased by an average of 16% per year (incidence rate ratio [IRR], 1.16; p < 0.001). The proportion of foreign-born patients increased significantly over the study period. Most patients were discharged home (1868; 75.4%), whereas transfers to residential care facilities increased over time. The mean hospital stay was 19.0 days (SD 36.5). In-hospital mortality was 3.7%. In multivariable analysis, malnutrition (odds ratio [OR] 1.64), cancer (OR 2.11), and active infection (OR 5.79) were independently associated with mortality. The incorporation of ROM into the mortality model markedly improved discrimination, and mortality increased progressively with higher ROM categories: moderate (OR 3.45), major (OR 11.76), and extreme (OR 38.76) (all p < 0.001). Conclusions: WE is an increasingly frequent cause of neurological hospitalization in Spain, associated with a substantial clinical and economic burden. In-hospital mortality is driven mainly by overall clinical complexity and comorbidity burden rather than by WE in isolation. Full article

While the rapid development and widespread application of drone technology have brought about significant advancements, they have also introduced security challenges, making anti-UAV technology a key research focus. However, existing methods still face severe challenges when dealing with UAV tracking in complex scenarios. To address this, this paper proposes an integrated Motion-associated Detection and Tracking Collaboration (MDTC) system for anti-UAV applications. To better handle the perception of target existence states, we designed a motion association module that dynamically senses the presence of targets and responds quickly to target disappearance. Simultaneously, to address the issue of feature degradation in small targets, we optimized the detection branch to enhance robust perception of multi-scale targets. Additionally, the proposed verification matching mechanism can infer the integrity and reliability of targets in occluded scenarios, ensuring stable tracking. Compared to existing methods, our approach achieves superior performance across three benchmark datasets. On Anti-UAV600, it attains IoU, ACC, and SR scores of 0.525, 0.427, and 0.641, respectively—surpassing the second-best method, GlobalTrack, by 6.2%, 6.4%, and 5.9%. These gains highlight the method’s strengths in prompt target response, scale adaptability, and occlusion awareness, underscoring its reliability and practicality for real-world deployment. Full article

Extrapulmonary tuberculosis (EPTB) is an infectious disease characterized by the invasion of Mycobacterium tuberculosis beyond the lungs. Diagnosis is frequently delayed due to nonspecific clinical presentations that vary by organ system, making diagnostic imaging essential for disease detection, characterization, and treatment monitoring. The objective of this review is to examine and summarize imaging-based approaches for the diagnostic evaluation of EPTB across multiple body systems, including the central nervous system, spine, cardiovascular system, lymphatic system, abdominal and hepatic organs, genitourinary tract, cutaneous and soft tissue, and other rare sites. While computed tomography, magnetic resonance imaging, positron emission tomography, and ultrasound are widely used in the evaluation of EPTB, their ability to provide a definitive diagnosis is often limited by nonspecific radiologic findings. Emerging techniques, including perfusion-weighted MRI, contrast-enhanced ultrasound, and machine learning, have been discussed, as they improve lesion characterization and EPTB differentiation. By organizing imaging findings according to affected organ systems, this review highlights both shared diagnostic challenges and site-specific patterns that can inform clinical suspicion. Together, these developments underscore the value of a multimodal, organ-specific imaging approach integrated with the clinical context to improve the recognition and management of EPTB. Full article

Background/Objectives: Chest radiography remains a fundamental diagnostic tool for evaluating thoracic disease, yet its interpretation requires considerable time and specialized expertise. Worldwide shortages of trained radiologists can lead to lengthy turnaround times and delayed treatment. This study introduces the Multi-label Chest Abnormality Detection System (MCADS), a deep-learning-driven platform designed to automatically identify and interpret 18 distinct radiographic abnormalities to address these diagnostic challenges. Methods: MCADS integrates a pre-trained DenseNet121 convolutional neural network (via TorchXRayVision) to balance broad pathology coverage with rapid inference. Images are processed asynchronously on a central server to avoid the interruption of clinical workflows. To enhance transparency and clinician confidence, the system employs Gradient-weighted Class Activation Mapping (Grad-CAM) to overlay heatmaps pinpointing image regions most influential to each predicted abnormality. The system was evaluated using eight large, publicly available datasets. Results: When evaluated on diverse datasets, MCADS achieved high area-under-the-curve performance metrics across all 18 target conditions. The platform consistently produced accurate, multi-condition analyses in under thirty seconds per image, demonstrating both reliability and speed suitable for clinical environments. Conclusions: MCADS demonstrates the potential to accelerate chest X-ray interpretation by delivering fast, reliable, and explainable multi-abnormality screening. Its deployment could reduce radiologist workload and mitigate diagnostic delays, offering a pathway to improve patient care within data-driven healthcare environments. Full article

Neosporosis, caused by Neospora caninum, is a major protozoal disease responsible for reproductive disorders and economic losses in livestock. Swine are susceptible to N. caninum infection, as evidenced by serological and experimental studies, but the impact of natural infection on reproduction failure remains poorly defined. The objective of this study was to investigate N. caninum transplacental transmission in naturally infected sows by detecting an active fetal immune response in their stillborn piglets. Paired maternal blood and umbilical cord blood (UCB) samples were collected from 247 sows and stillborn piglets across 39 farrow-to-finish farms in mainland Greece. Sera were tested for anti-N. caninum IgG and IgM antibodies using an indirect fluorescence antibody test. An IgG and IgM seropositivity for N. caninum of 8.91% and 3.64%, respectively, was reported in sows, while lower percentages of IgG and IgM antibodies (3.24% and 0.81%, respectively) were detected in UCB samples. Overall, antibodies were detected in 4.05% of UCB samples, indicative of in utero antibody production. Positive samples were more frequently encountered on smaller farms with up to 250 sows, possibly due to lower biosecurity standards. The detection of antibodies in UCB resulting from the fetal immune response to intrauterine N. caninum infection is indicative of the potential involvement of N. caninum parasitism in reproductive system disorders. Testing of UCB for the presence of anti-Neospora antibodies elucidates the dynamics of parasite transmission within the farm and provides evidence for the implementation of more efficient biosecurity and preventative measures. Full article

Semantic point cloud maps play a pivotal role in smart agriculture. They provide not only core three-dimensional data for orchard management but also empower robots with environmental perception, enabling safer and more efficient navigation and planning. However, traditional point cloud maps primarily model surrounding obstacles from a geometric perspective, failing to capture distinctions and characteristics between individual obstacles. In contrast, semantic maps encompass semantic information and even topological relationships among objects in the environment. Furthermore, existing semantic map construction methods are predominantly vision-based, making them ill-suited to handle rapid lighting changes in agricultural settings that can cause positioning failures. Therefore, this paper proposes a positioning and semantic map reconstruction method tailored for orchards. It integrates visual, LiDAR, and inertial sensors to obtain high-precision pose and point cloud maps. By combining open-vocabulary detection and semantic segmentation models, it projects two-dimensional detected semantic information onto the three-dimensional point cloud, ultimately generating a point cloud map enriched with semantic information. The resulting 2D occupancy grid map is utilized for robotic motion planning. Experimental results demonstrate that on a custom dataset, the proposed method achieves 74.33% mIoU for semantic segmentation accuracy, 12.4% relative error for fruit recall rate, and 0.038803 m mean translation error for localization. The deployed semantic segmentation network Fast-SAM achieves a processing speed of 13.36 ms per frame. These results demonstrate that the proposed method combines high accuracy with real-time performance in semantic map reconstruction. This exploratory work provides theoretical and technical references for future research on more precise localization and more complete semantic mapping, offering broad application prospects and providing key technological support for intelligent agriculture. Full article

Background/Objectives: D-dimer, a fibrin degradation product, is associated with tumor growth and metastasis. In breast cancer, high concentrations of D-dimer are linked to more advanced disease stages and metastatic spread. This research aimed to examine the relevance of D-dimer levels in estrogen and progesterone hormone receptor (HR)-positive breast cancer. Methods: This retrospective single-center cohort study included patients with HR-positive breast carcinoma who underwent adjuvant or palliative radiotherapy in Türkiye. Pre- and post-radiotherapy blood test results, including D-dimer levels, were required. D-dimer, lymphocyte percentage, and interleukin-6 levels were measured for evaluation. All statistical analyses were performed using R software (version 4.4.2) to evaluate associations between D-dimer levels and other laboratory parameters. Univariate and multivariate Cox proportional hazards regression were performed to identify prognostic factors for progression-free survival (PFS) and overall survival (OS). Statistical significance was defined as p < 0.05. Results: Elevated D-dimer levels were associated with worse Eastern Cooperative Oncology Group performance status, advanced disease stages, metastasis, elevated IL-6 and CRP levels, and lower lymphocyte counts. Pre-RT D-dimer was a strong prognostic factor. Patients with D-dimer ≤ 0.3 µg/mL showed significantly superior OS and PFS (>60 months; p < 0.001), with only one event, and this remained significant in multivariate analysis (OS: HR 4.55, 95% CI 1.89–11.3; p = 0.002; PFS: HR 3.43, 95% CI 1.54–7.8; p = 0.004). Similarly, D-dimer ≤ 0.5 µg/mL was associated with improved OS (4/72 vs. 19/40 events; p < 0.001) and longer PFS, confirmed in multivariate analysis (OS: HR 4.37, 95% CI 1.72–9.86; p = 0.002; PFS: HR 3.88, 95% CI 1.67–9.1; p = 0.003), whereas levels > 0.5 µg/mL predicted worse outcomes. Using a 0.65 µg/mL cutoff, patients with D-dimer > 0.65 µg/mL had significantly shorter OS (median 25.5 months; 95% CI, 18–NA) compared with those ≤0.65 µg/mL (median not reached; p < 0.001), and this remained independently significant (OS: HR 5.10, 95% CI 1.9–13.6; p < 0.001; PFS: HR 4.68, 95% CI 1.83–11.9; p = 0.002). Conclusions: D-dimer is an accessible, non-invasive biomarker with predictive and prognostic significance in HR-positive breast cancer. Elevated D-dimer levels are suggestive of a more aggressive disease and poorer survival outcomes. This has the potential to facilitate early assessment of treatment efficacy and disease progression. This study has several limitations. Its retrospective, single-center design may introduce selection bias and limit generalizability. Although the sample size was sufficient to detect significant associations, validation in larger, multi-center cohorts is warranted to confirm the prognostic value of D-dimer. Full article

Background: Sarcopenia is a progressive muscle disorder associated with metabolic syndrome (MS), in which early impairments in muscle strength and quality precede muscle mass loss. Simple, non-invasive measures such as handgrip strength, estimated appendicular skeletal muscle mass (eASM), and phase angle (PA) may aid early detection, while adipokines link muscle dysfunction to metabolic regulation. Objective: In the present study, we aimed to evaluate the association between sarcopenia markers and PA in patients with MS. Methods: A cross-sectional study was conducted in patients with MS, at a third-level hospital in Mexico City. Sarcopenia was assessed by handgrip strength and eASM; body composition and PA were measured using bioelectrical impedance; and plasma adipokines were quantified by ELISA. Results: Seventy-four (mean age, 57.7 years; 75% female; BMI, 32.5 kg/m²) participants with MS were included. Handgrip strength correlated with eASM (r = 0.64; p < 0.01) and PA (rho = 0.43; p < 0.01), and eASM also correlated with PA (rho = 0.40; p < 0.01) and predicted higher PA values (OR = 2.74; p = 0.042). The sarcopenic subgroup had lower brachial circumference and plasma adiponectin. Conclusions: Sarcopenia is frequent in MS and associated with lower adiponectin, suggesting a vulnerable condition. Functional/structural markers of sarcopenia showed significant correlation with PA, whereas combined methods may enhance the early detection and management of muscle deterioration in metabolic disease. Full article

Background/Objectives: Ampullary adenomas are neoplasms of the gastrointestinal tract with malignant potential. They are thought to develop through pathways similar to those involved in colorectal neoplasia. This study aimed to determine the prevalence of colonic polyps in patients with ampullary adenoma. Methods: This retrospective study included a total of 35 patients with ampullary adenoma diagnosed between 2023 and 2024 and 105 sex-matched controls. Colonoscopic findings of the patient and control groups were compared with respect to polyp prevalence. In addition, the effects of dysplasia grade of the ampullary adenoma and patient age on polyp prevalence were evaluated. Results: The study included 35 patients (57% male) and 105 controls (59% male). The mean age was 67.06 ± 13.32 years in patients and 61.28 ± 8.42 years in controls. Colonic polyps were detected in 13 (57%) patients in the low-grade dysplasia (LGD) group, 6 (66%) patients in the high grade dysplasia (HGD) or adenocarcinoma group, and 54 (51%) patients in the control group (p = 0.02). After adjusting for age, colonic polyps remained significantly more frequent in the adenoma group than in controls (p = 0.05). Polyp prevalence was not associated with dysplasia grade on ampullary biopsy, and no significant differences were observed between groups regarding polyp histopathology, location, or size. Conclusions: In conclusion, our study indicates that colorectal polyp prevalence is increased among patients with ampullary adenomas and that this association may be independent of age as well as dysplasia severity. Therefore, colonoscopic evaluation may be recommended for all patients diagnosed with ampullary adenoma. Full article