Search Results (676)

Objectives: To optimise surgical treatment of mitral valve disease (MVD), a better understanding of gender-based differences is required. In this study, we analyse the gender-based differences among patients undergoing mitral valve surgery. Methods: Between January 2019 and December 2024, 809 consecutive patients were admitted to our centre for surgery for MVD. We analysed the patient characteristics, surgical details, postoperative and short-term outcomes of these patients. Results: Females (31.8%) undergoing mitral valve (MV) surgery were older (p < 0.001). Females had a higher rate of atrial fibrillation (p < 0.001), Rheumatoid arthritis (RA) (p = 0.002) and malignancy (p = 0.030). Furthermore, females were more often admitted to the intensive care unit (ICU) preoperatively (p = 0.037). Among these patients, 419 patients underwent isolated MV surgery. Furthermore, males underwent minimally invasive MV surgery more often (p = 0.004). Females had higher rates of combined MVD (p < 0.001) and combined MS (p < 0.001). Males had higher rates of severe mitral regurgitation (MR) (p = 0.041) and Left Atrium (LA) dilation (p = 0.004). Females exhibited higher rates of severe Tricuspid Regurgitation (TR) (p = 0.032) and pulmonary hypertension (p < 0.001). males had higher rates of posterior mitral leaflet (PML) prolapse (p < 0.001) and Flail leaflets (p < 0.001). Males underwent mitral valve repair (MVr) more often (p = 0.002). Early MACCE were reported in 5.1% of the patients. Freedom from major adverse cardiac and cerebrovascular events (MACCE) was comparable at 1 year and three years (p = 0.548). Prognosis and freedom from events were comparable between genders. Conclusions: Mitral valve disease presents differently across genders. There exist fundamental differences in the pathophysiological processes and presentation of mitral valve disease. Mitral valve surgery can be carried out with low mortality and morbidity rates irrespective of gender. Full article

Background: The miniaturization of ureterorenoscopes increasingly enables atraumatic primary ureteroscopy, without ureteral dilation or presenting. This study aims to evaluate the feasibility and safety of primary ureteroscopy using the HU30M (6.3 Fr, HugeMed, Shenzhen HugeMed Medical Technical Development Co., Ltd., Shenzhen, China), the smallest currently available ureteroscope. Methods: We analyzed consecutive patients in whom primary ureteroscopy using the HU30M was performed or attempted, using prospectively collected in-hospital and 30-day follow-up data for retrospective evaluation. The primary outcome was the success rate of primary ostial intubation. Secondary outcomes included the stone-free rate (SFR) in patients with urolithiasis, incidence of in-hospital complications (Clavien–Dindo classification) and 30-day emergency readmission. Additionally, we conducted a propensity score-matched comparative analysis of the HU30M versus a contemporary 7.5 Fr digital single-use ureteroscope (PUSEN PU3033AH, Zhuhai Pusen Medical Technology Co., Ltd., Jinhua, China). Results: Between January and April 2025, primary ureteroscopy using the HU30M was performed or attempted in 34 patients, including four bilateral procedures. Primary ureteroscopy was defined as ureteroscopic access without prior stenting or dilation. Indications were diagnostic evaluation in 15 patients (44%), uretreroscopic stone treatment in 10 patients (29%) and endoscopic combined intrarenal surgery (ECIRS) in 9 patients (27%). Successful primary ostial intubation was achieved in 36 of 38 renal units (95%). Among urolithiasis cases, SFR was 17/19 (90%) in-hospital complications were limited to postoperative fever in two patients (6%) and no procedure-related 30-day emergency readmission occurred. In matched analyses, HU30M demonstrated significantly shorter operative times compared with the 7.5 Fr ureteroscope, while postoperative hemoglobin drop, inflammatory parameters and renal function were comparable. Conclusions: Primary ureteroscopy with HU30M is feasible and safe across diverse indications, achieving high success of atraumatic ostial access. Comparative analyses suggest procedural efficiency advantages and overall safety comparable to the current digital single-use ureteroscope standard. Full article

The vein of Galen malformations (VoGMs) is mainly correlated with the retention of an embryonic pattern of vascularity, inducer of vein of Galen dilation, and formation of arteriovenous communications that give rise to the risk of systemic shunting, causing cardiac dysfunction, vascular steal, and venous hypertension. This is a rare cerebral vascular malformation in the newborn, accounting for 1% of all cerebral arteriovenous malformations and occurring in approximately 1 in 25,000–50,000 live births. We review nine cases of newborns diagnosed with vein of Galen malformations (VoGMs) to assess whether this pathology demonstrates a marked improvement over the past 13 years in diagnostic accuracy, treatment approaches, and patient survival rates within our clinic. Medical treatment was focused on providing inotropic support and tightly controlled peripheral and pulmonary vasodilation with the aim of overriding the effects of high output heart failure. Most of the patients underwent liver failure and flow-mediated pulmonary hypertension, while half of the newborns expressed anomalies of the nervous system due to impaired cerebral hemodynamics. Given the unavailability of endovascular treatment in our unit, which predisposes the newborns to a higher vital risk, we recognize the importance of delivering tailored intensive care aimed at maintaining cardiorespiratory and hemodynamic stability until a curative intervention can be performed in a specialized center. Full article

Although Lempel–Ziv complexity (LZC) can reflect changes in object characteristics by measuring changes in independent patterns in the signal, it can only be applied to one-dimensional time series and cannot be directly applied to two-dimensional images. To address this issue, this paper proposed a two-dimensional Lempel–Ziv complexity by combining the concept of local receptive field in convolutional neural networks. This extends the application scenario of LZC from one-dimensional time series to two-dimensional images, further broadening the scope of application of LZC. First, the pixels and size of the image were normalized. Then, the image was encoded according to the sorting of normalized values within the 4 × 4 region. Next, the encoding result of the image was rearranged into a vector by row. Finally, the Lempel–Ziv complexity of the image could be obtained based on the rearranged vector. The proposed method was further used for defect detection in conjunction with the dilation operator and Sobel operator, and validated by two practical cases. The results showed that the proposed method can effectively identify independent pattern changes in images and can be used for defect detection. The accuracy rate of defect detection can reach 100%. Full article

Background: Dilated cardiomyopathy (DCM) is a major contributor to heart failure-related morbidity and mortality. While type 2 diabetes mellitus (T2DM), obesity, and thyroid dysfunction are individually linked to cardiovascular disease, their combined effects on DCM remain poorly understood. Objective: To evaluate the independent and synergistic associations of diabetes (stratified by treatment), thyroid dysfunction, and obesity with the prevalence of DCM and 30-day hospital readmission. We further examined the utility of a composite Metabolic Burden Score for risk stratification. Methods: In this retrospective cohort study, electronic health record data from 1079 adult patients at a tertiary care center were analyzed. Multivariable logistic regression, including ridge regularization, was used to identify predictors of DCM. Endocrine phenotypes were stratified by diabetes and thyroid status. A Metabolic Burden Score (range: 0–3) based on diabetes, obesity, and thyroid dysfunction was developed and correlated with clinical outcomes. Results: DCM was diagnosed in 46% of the cohort. Non-insulin-treated diabetes (OR: 6.93; 95% CI: 3.78–12.73), hypothyroidism (OR: 1.78; 95% CI: 1.02–3.11), and male sex (OR: 2.33; 95% CI: 1.36–4.00) were independently associated with increased DCM risk. Obesity was not independently predictive but contributed to DCM prevalence when assessed within the Metabolic Burden Score. DCM prevalence increased across burden strata, reaching 50% in the high-risk group. Notably, the moderate-risk group had the highest 30-day readmission rate (42.8%). Conclusions: Non-insulin-treated diabetes and hypothyroidism are key metabolic drivers of DCM. A simple composite burden score offers a clinically useful tool for stratifying risk of DCM and early readmission. These findings support integrated endocrine–cardiac screening strategies to improve early identification and prevention of structural heart disease. Full article

Background/Objectives: After pneumonectomy, the right ventricular stroke volume is pumped into pulmonary vessels whose volume has been reduced by approximately 50%. To sustain conditions for pulmonary flow, the flow reserve is increased in the remaining lung, which is conducive to the development of pulmonary hypertension symptoms. This study sought to examine pulmonary flow in one lung and the size of the right atrium (RA), right ventricle (RV) and pulmonary artery (PA) in patients who had undergone pneumonectomy and to establish the influence of time since pneumonectomy on these parameters, as well as their potential mutual dependencies. Methods: The retrospective analysis included 34 patients who had undergone pneumonectomy. Pulmonary flow was measured by means of perfusion scintigraphy. The diameters of the RA, RV and PA were evaluated based on computed tomography with contrast. Results: We observed complete or near-complete utilization of flow reserve in 38.2% (13/34) of patients, enlarged transversal and longitudinal dimensions of the RA in 17.6% (6/34) and 32.3% (11/34) of patients, respectively, and enlarged transversal and longitudinal dimension of the RV in 67.6% (23/34) and 44.1% (15/34) of patients, respectively. Dilatation of the PA was discovered in 23.5% (8/34) to 26.5% (9/34) of patients, as well as the presence of an extensive complex of radiographic features of pulmonary hypertension (PH) syndrome in 23.5% (8/34) of cases. Conclusions: Radiological features of PH were present in a significant number of patients. These features developed at varying rates but were present in all patients followed >10 years after the procedure. Full article

Background/Objectives: Although research has demonstrated the positive impacts of hearing aid (HA) digital noise reduction (DNR), limited research is available on the impacts of the strength of DNR on listening effort. This study evaluated the effects of changes in the strength of HA DNR on listening effort, measured, behaviorally, using a self-report rating scale, and, physiologically, using pupillometry. The agreement between both measures was also examined. Methods: Eleven young adults with normal hearing completed a sentence-in-noise recognition task. Stimuli were processed through four noise reduction conditions (off, minimum, medium, maximum) using DNR algorithms found in conventional digital HAs. After sentence presentation, participants subjectively rated their perceived listening effort. Pupillometry was recorded during the task to assess changes in pupil size (a proxy of listening effort) during sentence recognition. Results: Participants’ perceived listening effort reduced as the noise reduction strength increased from off to medium DNR and then plateaued for the maximum DNR condition. Pupil dilation increased from off to medium DNR and then reduced for the maximum condition. Correlation analyses suggested no agreement between self-report and pupillometry measures of listening effort. Conclusions: Both self-report and pupillometry measures demonstrated changes in listening effort, with changes in the DNR strength indicating that noise reduction systems do provide benefit in reducing listening effort to a certain extent. Lack of agreement between the measures suggests that both methods might be assessing different constructs of listening effort and care should be taken while making methodological decisions to assess listening effort in individuals wearing HAs. Full article

Background: Virtual reality has been shown to reduce pain during labour. We aimed to determine whether virtual reality reduces analgesia use and shortens labour duration. Methods: A non-randomised pilot study was conducted, using a matched case–control design (1:2 ratio). Cases were women who voluntarily used virtual reality alongside standard intrapartum pain management, including non-pharmacological methods and/or epidural analgesia. Controls received standard intrapartum pain management. Results: A total of 108 women were included for analysis (36 cases vs. 72 controls). Perceived pain scores before and after virtual reality use did not differ significantly (p = 0.43, p = 0.73), suggesting a limited immediate analgesic effect under current conditions. Epidural analgesia rates and cervical dilation at initiation of analgesia did not show significant differences between cases and controls (p = 0.13, p = 0.42). After adjusting for induction of labour and cervical dilation at admission, there were no significant differences for duration of epidural analgesia (p = 0.86, p = 0.56), duration of labour (p = 0.64, p = 0.55), or vaginal birth (p = 0.23). Adjusted models indicated a non-significant trend toward shorter durations of labour, birth, and epidural exposure for cases. Conclusions: Our pilot study did not reveal a decrease in perceived pain or epidural analgesia use or an effect on duration of labour and vaginal birth. Full article

This study investigates the occurrence of dystocia in sheep farming, particularly focusing on the Batna region in eastern Algeria. Dystocia, or difficulty in parturition, represents a significant economic challenge, contributing to high perinatal mortality rates, maternal death, and reduced reproductive efficiency in sheep. This study aimed to identify the primary causes and risk factors associated with dystocia through surveys conducted with local veterinarians. The research findings suggest that fetal malposition, fetal oversize, and incomplete cervical dilation are the leading causes of dystocia. Additionally, maternal factors such as incomplete cervical dilation and uterine torsion were also a common cause. The risk of dystocia increases with parity and litter size and decreases with body score. Early veterinary intervention, including cesarean sections, reduced lamb mortality and greatly improved reproductive outcomes. These findings underline the importance of effective management strategies in preventing and managing dystocia in sheep farming. Full article

The fracture evolution of brittle rock under 3D stress states remains poorly understood, particularly the role of deviatoric stress symmetry as characterized by the Lode angle (θ_σ). To address this knowledge gap, we systematically investigated sandstone using a true triaxial loading apparatus under a constant mean stress (100 MPa) while varying θ_σ from −30° to +30°, integrated with real-time acoustic emission (AE) monitoring and post-failure X-ray computed tomography. Our results demonstrate a critical linear reduction in peak strength with increasing θ_σ. This strength reduction is accompanied by a corresponding transition in failure mechanism, from a progressive mode dominated by dilation to an abrupt mode characterized by shear localization. Innovatively, we introduce a novel AE-based parameter (C), derived from the coupled evolution of AE energy and hit rates, which quantifies fracturing intensity in real-time without pre-defined lithological and monitoring scale. Furthermore, digital reconstruction reveals that consistent X-shaped polymodal fault networks are governed by sequential tensile–shear interactions. This key mechanistic insight prompts us to propose a new “deflected shear–tensile hybrid kinked fracture” classification, which more accurately captures the topological complexity of fractures under 3D stresses than conventional monomodal schemes. This study provides fundamental insights into 3D rock fracture mechanics and practical tools for enhancing safety and efficiency in deep geo-engineering. Full article

Leaves are the key organs in photosynthesis and nutrient production, and leaf counting is an important indicator of banana plant health and growth rate. However, in complex orchard environments, leaves often overlap, the background is cluttered, and illumination varies, making accurate segmentation and detection challenging. To address these issues, we propose a lightweight banana leaf detection and counting method deployable on embedded devices, which integrates a space–depth-collaborative reasoning strategy with multi-scale feature enhancement to achieve efficient and precise leaf identification and counting. For complex background interference and occlusion, we design a multi-scale attention guided feature enhancement mechanism that employs a Mixed Local Channel Attention (MLCA) module and a Self-Ensembling Attention Mechanism (SEAM) to strengthen local salient feature representation, suppress background noise, and improve discriminability under occlusion. To mitigate feature drift caused by environmental changes, we introduce a task-aware dynamic scale adaptive detection head (DyHead) combined with multi-rate depthwise separable dilated convolutions (DWR_Conv) to enhance multi-scale contextual awareness and adaptive feature recognition. Furthermore, to tackle instance differentiation and counting under occlusion and overlap, we develop a detection-guided space–depth position modeling method that, based on object detection, effectively models the distribution of occluded instances through space–depth feature description, outlier removal, and adaptive clustering analysis. Experimental results demonstrate that our YOLOv8n MDSD model outperforms the baseline by 2.08% in mAP50-95, and achieves a mean absolute error (MAE) of 0.67 and a root mean square error (RMSE) of 1.01 in leaf counting, exhibiting excellent accuracy and robustness for automated banana leaf statistics. Full article

Pediatric dilated cardiomyopathy (DCM) is a rare but important cause of heart failure (HF) and a major indication for cardiac transplantation. Early detection of subclinical myocardial dysfunction is essential for risk stratification and management. This study aimed to evaluate left ventricular (LV) systolic function in children with DCM using conventional echocardiographic parameters and speckle-tracking echocardiography (STE) and to explore the relationship between deformation indices, clinical severity and biomarkers. Methods: We conducted a case–control study including 29 children diagnosed with DCM and 27 healthy controls matched by age and sex. All participants underwent clinical evaluation, NT-proBNP measurement, and transthoracic echocardiography. LV systolic function was assessed using conventional echocardiographic parameters, while STE was used to measure LV global longitudinal strain (GLS) and strain rate (SR) from all apical views. Results: GLS and SR were significantly reduced in the DCM group across all apical views (Global GLS: −11.13 ± 6.79% vs. −19.98 ± 3.25%, Global SR: −0.74 ± 0.39 s⁻¹ vs. −1.12 ± 0.16 s⁻¹; p < 0.01). GLS strongly correlated with functional indices (LV ejection fraction, shortening fraction, S′ lateral wave), LV end-diastolic diameter Z-score and NT-proBNP (p < 0.05), but not with MAPSE. In the primary model, GLS was associated with NYHA/Ross III–IV (OR 1.54 per 1% increase; 95% CI 1.14–2.07; p = 0.005); adding systolic blood pressure (p = 0.798) or heart rate (p = 0.973) did not materially change the GLS estimate (Δ ≤ 2%). In separate collinearity-avoiding models, LVEF (OR 1.12 per 1% decrease; 95% CI 1.03–1.22; p = 0.009), LVSF (OR 1.19 per 1% decrease; 95% CI 1.04–1.36; p = 0.011), and NT-proBNP (≈OR 1.11 per 100 units; p = 0.013) were also associated with advanced class. ROC analysis showed excellent discrimination for NT-proBNP (AUC 0.948) and GLS (AUC 0.906), and good–excellent performance for LVEF (AUC 0.869) and LVSF (AUC 0.875). Conclusions: Speckle-tracking derived parameters such as GLS and SR are sensitive and clinically relevant markers of LV dysfunction in pediatric DCM. Global longitudinal strain demonstrated a strong association with both clinical and biochemical markers of disease severity, after accounting for heart rate and blood pressure, supporting its integration into routine evaluation and risk stratification in pediatric DCM. Full article

To address the limitations of CNNs and RNNs in handling complex operating conditions, multi-scale degradation patterns, and long-term dependencies—with attention mechanisms often failing to highlight key degradation features—this paper proposes a remaining useful life (RUL) prediction framework based on a multi-scale dilated fusion attention (MDFA) module. The MDFA leverages parallel dilated convolutions with varying dilation rates to expand receptive fields, while a global-pooling branch captures sequence-level degradation trends. Additionally, integrated channel and spatial attention mechanisms enhance the model’s ability to emphasize informative features and suppress noise, thereby improving overall prediction robustness. The proposed method is evaluated on NASA’s C-MAPSS and N-CMAPSS datasets, achieving MAE values of 0.018–0.026, RMSE values of 0.021–0.032, and R² scores above 0.987, demonstrating superior accuracy and stability compared to existing baselines. Furthermore, to verify generalization across domains, experiments on the PHM2012 bearing dataset show similar performance (MAE: 0.023–0.026, RMSE: 0.031–0.032, R²: 0.987–0.995), confirming the model’s effectiveness under diverse operating conditions and its adaptability to different degradation behaviors. This study provides a practical and interpretable deep-learning solution for RUL prediction, with broad applicability to aero-engine prognostics and other industrial health-monitoring tasks. Full article

The increasing demand for portable health monitoring has highlighted the need for automated sleep staging systems that are both accurate and computationally efficient. However, most existing deep learning models for electroencephalogram (EEG)-based sleep staging suffer from parameter redundancy, fixed dilation rates, and limited generalization, restricting their applicability in real-time and resource-constrained scenarios. In this paper, we propose ADG-SleepNet, a novel lightweight symmetry-aware multi-scale dilation-gated temporal convolutional network enhanced with adaptive attention mechanisms for EEG-based sleep staging. ADG-SleepNet features a structurally symmetric, parallel multi-branch architecture utilizing various dilation rates to comprehensively capture multi-scale temporal patterns in EEG signals. The integration of adaptive gating and channel attention mechanisms enables the network to dynamically adjust the contribution of each branch based on input characteristics, effectively breaking architectural symmetry when necessary to prioritize the most discriminative features. Experimental results on the Sleep-EDF-20 and Sleep-EDF-78 datasets demonstrate that ADG-SleepNet achieves accuracy rates of 87.1% and 85.1%, and macro F1 scores of 84.0% and 81.1%, respectively, outperforming several state-of-the-art lightweight models. These findings highlight the strong generalization ability and practical potential of ADG-SleepNet for EEG-based health monitoring applications. Full article

To address the limitations of traditional segmentation algorithms in processing complex agricultural scenes, this paper proposes an improved YOLOv8n-seg model. Building upon the original three detection layers, we introduce a dedicated layer for small object detection, which significantly enhances the detection accuracy of small targets (e.g., people) after processing images through fourfold downsampling. In the neck network, we replace the C2f module with our proposed C2f_CPCA module, which incorporates a channel prior attention mechanism (CPCA). This mechanism dynamically adjusts attention weights across channels and spatial dimensions to effectively capture relationships between different spatial scales, thereby improving feature extraction and recognition capabilities while maintaining low computational complexity. Finally, we propose a C3RFEM module based on the RFEM architecture and integrate it into the main network. This module combines dilated convolutions and weighted layers to enhance feature extraction capabilities across different receptive field ranges. Experimental results demonstrated that the improved model achieved 1.4% and 4.0% increases in precision and recall rates on private datasets, respectively, with mAP@0.5 and mAP@0.5:0.95 metrics improved by 3.0% and 3.5%, respectively. In comparative evaluations with instance segmentation algorithms such as the YOLOv5 series, YOLOv7, YOLOv8n, YOLOv9t, YOLOv10n, YOLOv10s, Mask R-CNN, and Mask2Former, our model achieved an optimal balance between computational efficiency and detection performance. This demonstrates its potential for the research and development of small intelligent precision operation technology and equipment. Full article