Search Results (12,594)

Background: Renal cell carcinoma (RCC) is a common malignancy with a rising global incidence. While cytoreductive nephrectomy (CRN) was historically a cornerstone in the management of metastatic RCC (mRCC), its role has been questioned following pivotal trials such as CARMENA and SURTIME. With the advent of immune checkpoint inhibitors (ICIs) and targeted therapies, the contemporary relevance of CRN coupled with first-line immunotherapy and targeted therapy combination regimens warrants re-evaluation. Methods: This retrospective cohort study utilized the TriNetX research network to identify patients aged 18–90 years diagnosed with mRCC between 2005 and 2024 who received first-line systemic therapies. Patients were stratified into two cohorts based on receipt of CRN status within one year of diagnosis. Propensity score matching (1:1) was done to adjust baseline characteristics. Kaplan–Meier survival analysis and Cox proportional hazards modeling were used to compare five-year overall survival between the groups. Results: Among 5960 eligible patients, 1776 (888 CRN matched to 888 who did not) formed the cohort of analysis. The CRN group demonstrated significantly higher five-year survival (57.7% vs. 45.0%, p < 0.0001) with a hazard ratio of 1.56 (95% CI: 1.33–1.83). Subgroup analyses showed consistent survival benefits across all four NCCN-recommended first-line regimens—Axitinib + Pembrolizumab: 64.0% (CRN) vs. 53.3% (no CRN), p = 0.01; Cabozantinib + Nivolumab: 50.1% vs. 40.4%, p = 0.004; Lenvatinib + Pembrolizumab: 37.4% vs. 22.8%, p = 0.012; Nivolumab + Ipilimumab: 56.4% vs. 46.1%, p = 0.005. Conclusions: In the era of modern immunotherapy and targeted agents, CRN remains associated with improved survival in patients with mRCC receiving NCCN-recommended first-line regimens. These findings support the continued evaluation of CRN as a component of multimodal therapy, particularly in patients with favorable risk profiles. Full article

Neuromuscular hip dysplasia (NHD) is a common deformity in children with cerebral palsy (CP). Although some predictive factors of NHD are known, the prediction of NHD is in its infancy. We present a Clinical Decision Support System (CDSS) designed to calculate the probability of developing NHD in children with CP. The system utilizes an ensemble of three machine learning (ML) algorithms: Neural Network (NN), Support Vector Machine (SVM), and Logistic Regression (LR). The development and evaluation of the CDSS followed the DECIDE-AI guidelines for AI-driven clinical decision support tools. The ensemble was trained on a data series from 182 subjects. Inclusion criteria were age between 12 and 18 years and diagnosis of CP from two specialized units. Clinical and functional data were collected prospectively between 2005 and 2023, and then analyzed in a cross-sectional study. Accuracy and area under the receiver operating characteristic (AUROC) were calculated for each method. Best logistic regression scores highlighted history of previous orthopedic surgery (p = 0.001), poor motor function (p = 0.004), truncal tone disorder (p = 0.008), scoliosis (p = 0.031), number of affected limbs (p = 0.05), and epilepsy (p = 0.05) as predictors of NHD. Both accuracy and AUROC were highest for NN, 83.7% and 0.92, respectively. The novelty of this study lies in the development of an efficient Clinical Decision Support System (CDSS) prototype, specifically designed to predict future outcomes of neuromuscular hip dysplasia (NHD) in patients with cerebral palsy (CP) using clinical data. The proposed system, PredictMed-CDSS, demonstrated strong predictive performance for estimating the probability of NHD development in children with CP, with the highest accuracy achieved using neural networks (NN). PredictMed-CDSS has the potential to assist clinicians in anticipating the need for early interventions and preventive strategies in the management of NHD among CP patients. Full article

Background: Pancreatic adenocarcinoma (PDAC) with liver oligometastases (LOM) presents a therapeutic challenge, with optimal management strategies remaining uncertain. This study evaluates the long-term outcomes, patterns of disease progression, and potential factors influencing prognosis in this patient subset. Methods: Patients diagnosed with PDAC and LOM were retrospectively analyzed. Disease progression patterns, causes of death, and predictors of long-term outcomes were assessed. Results: Among 1442 patients diagnosed with metastatic PDAC between November 2009 and July 2024, 129 (9%) presented with LOM, defined as ≤3 liver lesions each measuring <2 cm. Patients with LOM had significantly improved overall survival (OS) compared to those with high-burden disease (p = 0.026). The cause of death (local regional disease vs. systemic disease) could be determined in 74 patients (57%), among whom age at diagnosis, history of smoking, and white blood cell (WBC) count differed significantly between groups. However, no significant difference in OS was observed between the two groups (p = 0.64). Sixteen patients (22%) died from local complications of the primary tumor, including 6 patients (7%) who showed no evidence of new or progressive metastases. In competing risk and multivariable analysis, a history of smoking remained the only factor significantly associated with death due to local complications. Conclusions: Approximately one in five patients with PDAC-LOM died from local tumor-related complications—some without metastatic progression—highlighting a potential role for surgical intervention. Further multicenter studies are warranted to refine diagnostic criteria and better identify patients who may benefit from surgery. Full article

Canine visceral leishmaniasis (CVL) is a major zoonosis that poses a growing challenge to public health services, as successful disease management requires sensitive, specific, and rapid diagnostic methods capable of identifying infected animals even at a subclinical level. The objective of this study was to evaluate the performance of the recombinant chimeric protein rMELEISH3 as an antigen in ELISA assays for the robust diagnosis of CVL. The protein was expressed in a bacterial system, purified by affinity chromatography, and evaluated through a series of serological assays using serum samples from dogs infected with Leishmania infantum. ROC curve analysis revealed a diagnostic sensitivity of 96.4%, a specificity of 100%, and an area under the curve of 0.996, indicating excellent discriminatory power. Furthermore, rMELEISH3 was recognized by antibodies present in the serum of dogs with low parasite loads, reinforcing the diagnostic potential of the assay in asymptomatic cases. It is concluded that the use of the recombinant antigen rMELEISH3 could significantly contribute to the improvement of CVL surveillance and control programs in endemic areas of Brazil and other countries, by offering a safe, reproducible and effective alternative to the methods currently recommended for the serological diagnosis of the disease. Full article

The Internet of Medical Things (IoMT) is revolutionizing healthcare by enabling continuous patient monitoring, early diagnosis, and personalized treatments. However, the het-erogeneity of IoMT devices and the lack of standardized protocols introduce serious security vulnerabilities. To address these challenges, we propose a hybrid BiLSTM-DNN intrusion detection system, named HBiLD-IDS, that combines Bidirectional Long Short-Term Memory (BiLSTM) networks with Deep Neural Networks (DNNs), leveraging both temporal dependencies in network traffic and hierarchical feature extraction. The model is trained and evaluated on the CICIoMT2024 dataset, which accurately reflects the diversity of devices and attack vectors encountered in connected healthcare environments. The dataset undergoes rigorous preprocessing, including data cleaning, feature selection through correlation analysis and recursive elimination, and feature normalization. Compared to existing IDS models, our approach significantly enhances detection accuracy and generalization capacity in the face of complex and evolving attack patterns. Experimental results show that the proposed IDS model achieves a classification accuracy of 98.81% across 19 attack types confirming its robustness and scalability. This approach represents a promising solution for strengthening the security posture of IoMT networks against emerging cyber threats. Full article

The complex topology of shipboard DC microgrids and the strong coupling between positive and negative poles during faults pose significant challenges for faulted-pole identification, especially under high-resistance conditions. To address these issues, this paper proposes a novel faulted-pole identification method based on S-Transformation and convolutional neural networks (CNNs). Single-ended voltage and current measurements from the generator side are used to generate time–frequency spectrograms via S-Transformation, which are then processed by a CNN trained to classify the faulted pole. This approach avoids reliance on complex threshold settings. Simulation results on a representative shipboard DC microgrid demonstrate that the proposed method achieves high accuracy, fast response, and strong robustness, even under high-resistance fault scenarios. The method significantly enhances the selectivity and reliability of fault protection, offering a promising solution for advanced marine DC power systems. Compared to conventional fault-diagnosis techniques, the proposed model achieves notable improvements in classification accuracy and computational efficiency for line-fault detection. Full article

Despite advances in Convolutional Neural Networks (CNNs) for intelligent fault diagnosis in CNC machine tools, bearing fault diagnosis in CNC feed systems remains challenging, particularly in multi-scale feature extraction and generalization across operating conditions. This study introduces an enhanced multi-scale feature network (MSFN) that addresses these limitations through three integrated modules designed to extract critical fault features from vibration signals. First, a Soft-Scale Denoising (S2D) module forms the backbone of the MSFN, capturing multi-scale fault features from input signals. Second, a Multi-Scale Adaptive Feature Enhancement (MS-AFE) module based on long-range weighting mechanisms is developed to enhance the extraction of periodic fault features. Third, a Dynamic Sequence–Channel Attention (DSCA) module is incorporated to improve feature representation across channel and sequence dimensions. Experimental results on two datasets demonstrate that the proposed MSFN achieves high diagnostic accuracy and exhibits robust generalization across diverse operating conditions. Moreover, ablation studies validate the effectiveness and contributions of each module. Full article

The integration of machine learning (ML) into industrial automation is fundamentally reshaping how manufacturing systems are monitored, inspected, and optimized. By applying machine learning to real-time sensor data and operational histories, advanced models enable proactive fault prediction, intelligent inspection, and dynamic process control—directly enhancing system reliability, product quality, and efficiency. This review explores the transformative role of ML across three key domains: Predictive Maintenance (PdM), Quality Control (QC), and Process Optimization (PO). It also analyzes how Digital Twin (DT) and Edge AI technologies are expanding the practical impact of ML in these areas. Our analysis reveals a marked rise in deep learning, especially convolutional and recurrent architectures, with a growing shift toward real-time, edge-based deployment. The paper also catalogs the datasets used, the tools and sensors employed for data collection, and the industrial software platforms supporting ML deployment in practice. This review not only maps the current research terrain but also highlights emerging opportunities in self-learning systems, federated architectures, explainable AI, and themes such as self-adaptive control, collaborative intelligence, and autonomous defect diagnosis—indicating that ML is poised to become deeply embedded across the full spectrum of industrial operations in the coming years. Full article

Sleep disorders and stroke are intricately linked through a complex, bidirectional relationship. Sleep disturbances such as obstructive sleep apnea (OSA), insomnia, and restless legs syndrome (RLS) not only increase the risk of stroke but also frequently emerge as consequences of cerebrovascular events. OSA, in particular, is associated with a two- to three-fold increased risk of incident stroke, primarily through mechanisms involving intermittent hypoxia, systemic inflammation, endothelial dysfunction, and autonomic dysregulation. Conversely, stroke can disrupt sleep architecture and trigger or exacerbate sleep disorders, including insomnia, hypersomnia, circadian rhythm disturbances, and breathing-related sleep disorders. These post-stroke sleep disturbances are common and significantly impair rehabilitation, cognitive recovery, and quality of life, yet they remain underdiagnosed and undertreated. Early identification and management of sleep disorders in stroke patients are essential to optimize recovery and reduce the risk of recurrence. Therapeutic strategies include lifestyle modifications, pharmacological treatments, medical devices such as continuous positive airway pressure (CPAP), and emerging alternatives for CPAP-intolerant individuals. Despite growing awareness, significant knowledge gaps persist, particularly regarding non-OSA sleep disorders and their impact on stroke outcomes. Improved diagnostic tools, broader screening protocols, and greater integration of sleep assessments into stroke care are urgently needed. This narrative review synthesizes current evidence on the interplay between sleep and stroke, emphasizing the importance of personalized, multidisciplinary approaches to diagnosis and treatment. Advancing research in this field holds promise for reducing the global burden of stroke and improving long-term outcomes through targeted sleep interventions. Full article

Acromegaly is caused by an excessive secretion of growth hormone and the secondary elevation of IGF-1 levels, leading to progressive changes in multiple body systems, including the craniofacial region and oral cavity. Dental manifestations such as mandibular overgrowth, macroglossia, malocclusion, periodontal disease, and prosthetic difficulties represent not only a clinical component of the disease but also a significant therapeutic and diagnostic challenge. The aim of this review is to present the current state of knowledge on the relationship between acromegaly and oral health and to analyze the role of interdisciplinary collaboration between endocrinologists and dentists in patient care. For this narrative review, a literature search was conducted in the PubMed, Scopus, and Web of Science databases covering the period from 2000 to 2025. Sixty-two peer-reviewed publications meeting the methodological and thematic criteria were included in the analysis, including original studies, meta-analyses, systematic reviews, and case reports. The results indicate significant correlations between disease activity and the severity of periodontal and microbiological changes, while effective endocrine treatment only results in the partial regression of morphological changes. Particular attention was given to the role of the dentist in recognizing the early symptoms of the disease, planning prosthetic and surgical treatment, and monitoring therapy-related complications. Interdisciplinary collaboration models, including integrated clinics and co-managed care, were also described as optimal systemic solutions for improving treatment quality. The conclusion drawn from the analysis are as follows: there is a need for the permanent integration of dentistry into the standard of interdisciplinary care for patients with acromegaly, in both diagnostic and therapeutic dimensions. Increasing awareness among dentists and developing integrated collaboration models may reduce the time to diagnosis, improve patients’ quality of life, and enable the more effective management of craniofacial complications in the course of this rare disease. Full article

Temperature is a key physiological indicator of plant health, influenced by factors including water status, disease and developmental stage. Monitoring changes in multiple factors is helpful for early diagnosis of plant growth. However, there are a variety of complex light interference phenomena in the greenhouse, so traditional detection methods cannot meet effective online monitoring of strawberry health status without manual intervention. Therefore, this paper proposes a leaf soft-sensing method based on a thermal infrared imaging sensor and adaptive image screening Internet of Things system, with additional sensors to realize indirect and rapid monitoring of the health status of a large range of strawberries. Firstly, a fuzzy comprehensive evaluation model is established by analyzing the environmental interference terms from the other sensors. Secondly, through the relationship between plant physiological metabolism and canopy temperature, a growth model is established to predict the growth period of strawberries based on canopy temperature. Finally, by deploying environmental sensors and solar height sensors, the image acquisition node is activated when the environmental interference is less than the specified value and the acquisition is completed. The results showed that the accuracy of this multiple sensors system was 86.9%, which is 30% higher than the traditional model and 4.28% higher than the latest advanced model. It makes it possible to quickly and accurately assess the health status of plants by a single factor without in-person manual intervention, and provides an important indication of the early, undetectable state of strawberry disease, based on remote operation. Full article

Background/Objectives: Diabetic retinopathy is a leading cause of vision impairment worldwide, and the development of reliable automated classification systems is crucial for early diagnosis and clinical decision-making. This study presents a comprehensive comparative evaluation of two state-of-the-art deep learning families for the task of classifying diabetic retinopathy using retinal fundus images. Methods: The models were trained and tested in both binary and multi-class settings. The experimental design involved partitioning the data into training (70%), validation (20%), and testing (10%) sets. Model performance was assessed using standard metrics, including precision, sensitivity, specificity, F1-score, and the area under the receiver operating characteristic curve. Results: In binary classification, the ResNeXt101-64x4d model and RegNetY32GT model demonstrated outstanding performance, each achieving high sensitivity and precision. For multi-class classification, ResNeXt101-32x8d exhibited strong performance in early stages, while RegNetY16GT showed better balance across all stages, particularly in advanced diabetic retinopathy cases. To enhance transparency, SHapley Additive exPlanations were employed to visualize the pixel-level contributions for each model’s predictions. Conclusions: The findings suggest that while ResNeXt models are effective in detecting early signs, RegNet models offer more consistent performance in distinguishing between multiple stages of diabetic retinopathy severity. This dual approach combining quantitative evaluation and model interpretability supports the development of more robust and clinically trustworthy decision support systems for diabetic retinopathy screening. Full article

Background/Objectives: Thrombotic thrombocytopenic purpura (TTP) is a microangiopathic hemolytic anemia exhibiting 90% mortality without prompt treatment. The aim of this study was to investigate the association of therapeutic plasma exchange (TPE)-treated TTP in end-stage renal disease (ESRD) patients with mortality, demographics, and clinical comorbidities. We queried the United States Renal Data System for ESRD patients starting dialysis between 1 January 2005 and 31 December 2018, using International Classification of Diseases (ICD)-9 and ICD-10 codes for thrombotic microangiopathy, with a TPE procedure code entered within 7 days. Methods: Cox proportional hazards models were used to assess mortality, adjusting for demographic and clinical factors. Results: Among 1,155,136 patients, increased age [adjusted odds ratio (OR) = 0.96, 95% confidence interval (CI): 0.94–0.96]; black race (OR = 0.67, CI: 0.51–0.89); and Hispanic ethnicity (OR = 0.43, CI: 0.28–0.66) were associated with a lower risk of TPE-treated TTP diagnosis, whereas female sex (OR = 1.59, CI: 1.25–2.02) and tobacco use (OR = 2.08, CI: 1.58–2.75) had a higher risk. A claim for TPE-treated TTP carried a lower risk of death (adjusted hazard ratio = 0.024, CI: 0.021–0.028). Female sex, black race, Hispanic ethnicity, and hypothyroidism were also associated with decreased all-cause mortality. Conclusions: These findings suggest that ESRD patients with TPE-treated TTP are significantly protected from mortality compared with ESRD patients without this diagnosis. Full article

Internet of Things (IoT)-based solar insecticidal lamps (SIL-IoTs) offer an eco-friendly alternative by merging solar energy harvesting with intelligent sensing, advancing sustainable smart agriculture. However, SIL-IoTs encounter practical challenges, e.g., hardware aging, electromagnetic interference, and abnormal data patterns. Therefore, developing an effective fault detection and diagnosis (FDD) system is essential. In this survey, we systematically identify and address the core challenges of implementing FDD of SIL-IoTs. Firstly, the fuzzy boundaries of sample features lead to complex feature interactions that increase the difficulty of accurate FDD. Secondly, the category imbalance in the fault samples limits the generalizability of the FDD models. Thirdly, models trained on single scenarios struggle to adapt to diverse and dynamic field conditions. To overcome these challenges, we propose a multi-level solution by discussing and merging existing FDD methods: (1) a data augmentation strategy can be adopted to improve model performance on small-sample datasets; (2) federated learning (FL) can be employed to enhance adaptability to heterogeneous environments, while transfer learning (TL) addresses data scarcity; and (3) deep learning techniques can be used to reduce dependence on labeled data; these methods provide a robust framework for intelligent and adaptive FDD of SIL-IoTs, supporting long-term reliability of IoT devices in smart agriculture. Full article

Introduction: Subcutaneous panniculitis-like T-cell lymphoma (SPTCL) is a very rare subtype of cutaneous T-cell lymphoma. It is characterized by the neoplastic infiltration of subcutaneous adipose tissue. Its clinical presentation, including subcutaneous nodules, fever, and systemic symptoms, often mimics inflammatory panniculitis, making diagnosis difficult. Case Presentation: This case report describes a 14-year-old female presenting with fever, limb pain, swelling, and subcutaneous nodules, who was ultimately diagnosed with SPTCL via punch biopsy and BIOMED-2 clonality assays, confirming positive T-cell receptor-γ chain gene rearrangement. Positron emission tomography–computed tomography revealed diffuse subcutaneous involvement across multiple body regions. Methylprednisolone and cyclosporine A treatment rapidly resolved her symptoms, with laboratory parameters, including ferritin and inflammatory markers, showing significant improvement. Next-generation sequencing identified a heterozygous C9 gene mutation (c.346C>T, p.Arg116Ter), adding a novel genetic dimension to the case. Following a tapered discontinuation of immunosuppressive therapy, the patient achieved sustained remission without relapse for over 1 year. Conclusions: We report a case of adolescent SPTCL treated with immunosuppressive therapy and suggest that immunosuppressive therapy should be considered before chemotherapy in pediatric patients with SPTCL but without HLH. Full article