Search Results (12,601)

Inflammatory Bowel Diseases (IBDs) are complex, multifactorial disorders with no known cure, necessitating lifelong care and often leading to surgical interventions. This ongoing healthcare requirement, coupled with the increased use of biological drugs and rising disease prevalence, significantly increases the financial burden on the healthcare systems. Thus, a number of novel technological approaches have emerged in order to face some of the pivotal questions still associated with IBD. In navigating the intricate landscape of IBD, biosensors act as indispensable allies, bridging the gap between traditional diagnostic methods and the evolving demands of precision medicine. Continuous progress in biosensor technology holds the key to transformative breakthroughs in IBD management, offering more effective and patient-centric healthcare solutions considering the One Health Approach. Here, we will delve into the landscape of biomarkers utilized in the diagnosis, monitoring, and management of IBD. From well-established serological and fecal markers to emerging genetic and epigenetic markers, we will explore the role of these biomarkers in aiding clinical decision-making and predicting treatment response. Additionally, we will discuss the potential of novel biomarkers currently under investigation to further refine disease stratification and personalized therapeutic approaches in IBD. By elucidating the utility of biosensors across the spectrum of IBD care, we aim to highlight their importance as valuable tools in optimizing patient outcomes and reducing healthcare costs. Full article

Sexually transmitted infections (STIs) are a significant global health concern, affecting millions of people worldwide, particularly sexually active adolescents and young adults. These infections, caused by various pathogens, including bacteria, viruses, parasites, and fungi, can have profound implications for women’s reproductive health and fertility. This review explores the role of vaginal and uterine infections in women’s infertility, focusing on the most common pathogens and their impact on reproductive outcomes. Bacterial infections, such as those caused by intracellular bacteria (Mycoplasma, Ureaplasma, and Chlamydia), Neisseria gonorrhoeae, and bacterial vaginosis, are among the most prevalent causes of infertility in women. Studies have shown that these infections can lead to pelvic inflammatory disease, tubal occlusion, and endometrial damage, all of which can impair fertility. Mycobacterium tuberculosis, in particular, is a significant cause of genital tuberculosis and infertility in high-incidence countries. Viral infections, such as Human papillomavirus (HPV) and Herpes simplex virus (HSV), can also affect women’s fertility. While the exact role of HPV in female infertility remains unclear, studies suggest that it may increase the risk of endometrial implantation issues and miscarriage. HSV may be associated with unexplained infertility. Parasitic infections, such as trichomoniasis and schistosomiasis, can directly impact the female reproductive system, leading to infertility, ectopic pregnancy, and other complications. Fungal infections, such as candidiasis, are common but rarely have serious outcomes related to fertility. The vaginal microbiome plays a crucial role in maintaining reproductive health, and alterations in the microbial balance can increase susceptibility to STIs and infertility. Probiotics have been proposed as a potential therapeutic strategy to restore the vaginal ecosystem and improve fertility outcomes, although further research is needed to establish their efficacy. In conclusion, vaginal and uterine infections contribute significantly to women’s infertility, with various pathogens affecting the reproductive system through different mechanisms. Early diagnosis, appropriate treatment, and preventive measures are essential to mitigate the impact of these infections on women’s reproductive health and fertility. Full article

Backgrounds: The incidence of gastrointestinal cancers (GICs), though decreased in recent years, still accounts for 35% of all cancer-related mortality. The proper identification of risk factors, early diagnosis, and therapy optimization represent the three cornerstones of GIC treatment. In four-stage diseases, chemotherapy embodies target therapy that may prolong patients’ expectancy when suitably applied. Patients and Methods: There were 133 (82 (62%) male and 51 (38%) female) consecutive patients with a median age of 70 (64–74) years who underwent palliative treatment due to four-stage colorectal cancer (CRC) between 2022 and 2024. The demographic, clinical, and laboratory data and applied chemotherapeutic protocols were evaluated regarding the response to applied therapy, resulting in complete or partial tumor regression. The advancement of the tumor was based on computed tomography (CT) performed before and 6 months after the chemotherapy. Results: The multivariable model revealed red cell distribution width (RDW) from peripheral blood analysis (OR: 0.81, 95% CI: 0.65–1.00, p = 0.049) as a possible predictor for systemic treatment response in colorectal cancer. The receiver operating characteristic curve revealed a predictive value of male sex and RDW prior to systemic therapy, with an area under the curve of 0.672, yielding a sensitivity of 70.0% and specificity of 58.1%. Conclusions: The results of our analysis point out the possible modulatory impact of RDW on six-month systemic therapy in colorectal terminal cancer management. Further studies are required to confirm the presented results. Full article

Background/Objectives: Accurate diagnosis of thyroid cancer is critical but challenging due to overlapping ultrasound (US) features of benign and malignant nodules. This study aimed to evaluate the diagnostic performance of non-invasive and minimally invasive US techniques, including B-mode US, shear wave elastography (SWE), color Doppler, superb microvascular imaging (SMI), and TI-RADS, in patients with suspected thyroid lesions and to assess their reliability and cost effectiveness compared with fine needle aspiration (FNA) biopsy. Methods: A prospective, single-center study (October 2023–February 2025) enrolled 300 patients with suspected thyroid cancer at a Spanish tertiary hospital. Of these, 296 patients with confirmed diagnoses underwent B-mode US, SWE, Doppler, SMI, and TI-RADS scoring, followed by US-guided FNA and Bethesda System cytopathology. Lasso-penalized logistic regression and a bootstrap analysis (1000 replicates) were used to develop diagnostic models. A utility function was used to balance diagnostic reliability and cost. Results: Thyroid cancer was diagnosed in 25 patients (8.3%). Elastography combined with SMI achieved the highest diagnostic performance (Youden index: 0.69; NPV: 97.4%; PPV: 69.1%), outperforming Doppler-only models. Intranodular vascularization was a significant risk factor, while peripheral vascularization was protective. The utility function showed that, when prioritizing cost, elastography plus SMI was cost effective (α < 0.716) compared with FNA. Conclusions: Elastography plus SMI offers a reliable, cost-effective diagnostic rule for thyroid cancer. The utility function aids clinicians in balancing reliability and cost. SMI and generalizability need to be validated in higher prevalence settings. Full article

Multiple sclerosis (MS), a chronic disease of the central nervous system, is known to cause structural and vascular changes in the retina. Although optical coherence tomography (OCT) and fundus photography can detect retinal thinning and circulatory abnormalities, these findings are not specific to MS. This study explores the potential of Infrared Scanning-Laser-Ophthalmoscopy (IR-SLO) imaging to uncover vascular morphological features that may serve as MS-specific biomarkers. Using an age-matched, subject-wise stratified k-fold cross-validation approach, a deep learning model originally designed for color fundus images was adapted to segment optic disc, optic cup, and retinal vessels in IR-SLO images, achieving Dice coefficients of 91%, 94.5%, and 97%, respectively. This process included tailored pre- and post-processing steps to optimize segmentation accuracy. Subsequently, clinically relevant features were extracted. Statistical analyses followed by SHapley Additive exPlanations (SHAP) identified vessel fractal dimension, vessel density in zones B and C (circular regions extending 0.5–1 and 0.5–2 optic disc diameters from the optic disc margin, respectively), along with vessel intensity and width, as key differentiators between MS patients and healthy controls. These findings suggest that IR-SLO can non-invasively detect retinal vascular biomarkers that may serve as additional or alternative diagnostic markers for MS diagnosis, complementing current invasive procedures. Full article

Teledermatology relies on digital transfer of dermatological images, but compression and resolution differences compromise diagnostic quality. Image enhancement techniques are crucial to compensate for these differences and improve quality for both clinical assessment and AI-based analysis. We developed a customized image degradation pipeline simulating common artifacts in dermatological images, including blur, noise, downsampling, and compression. This synthetic degradation approach enabled effective training of DermaSR-GAN, a super-resolution generative adversarial network tailored for dermoscopic images. The model was trained on 30,000 high-quality ISIC images and evaluated on three independent datasets (ISIC Test, Novara Dermoscopic, PH²) using structural similarity and no-reference quality metrics. DermaSR-GAN achieved statistically significant improvements in quality scores across all datasets, with up to 23% enhancement in perceptual quality metrics (MANIQA). The model preserved diagnostic details while doubling resolution and surpassed existing approaches, including traditional interpolation methods and state-of-the-art deep learning techniques. Integration with downstream classification systems demonstrated up to 14.6% improvement in class-specific accuracy for keratosis-like lesions compared to original images. Synthetic degradation represents a promising approach for training effective super-resolution models in medical imaging, with significant potential for enhancing teledermatology applications and computer-aided diagnosis systems. Full article

This study develops and validates an artificial intelligence (AI)-assisted internship learning platform for automotive electronics based on the Llama 3 large language model, aiming to enhance pedagogical effectiveness within vocational training contexts. Addressing critical issues such as the persistent theory–practice gap and limited innovation capability prevalent in existing curricula, we leverage the natural language processing (NLP) capabilities of Llama 3 through fine-tuning based on transfer learning to establish a specialized knowledge base encompassing fundamental circuit principles and fault diagnosis protocols. The implementation employs the Hugging Face Transformers library with optimized hyperparameters, including a learning rate of 5 × 10⁻⁵ across five training epochs. Post-training evaluations revealed an accuracy of 89.7% on validation tasks (representing a 12.4% improvement over the baseline model), a semantic comprehension precision of 92.3% in technical question-and-answer assessments, a mathematical computation accuracy of 78.4% (highlighting this as a current limitation), and a latency of 6.3 s under peak operational workloads (indicating a system bottleneck). Although direct trials involving students were deliberately avoided, the platform’s technical feasibility was validated through multidimensional benchmarking against established models (BERT-base and GPT-2), confirming superior domain adaptability (F1 = 0.87) and enhanced error tolerance (σ² = 1.2). Notable limitations emerged in numerical reasoning tasks (Cohen’s d = 1.15 compared to human experts) and in real-time responsiveness deterioration when exceeding 50 concurrent users. The study concludes that Llama 3 demonstrates considerable promise for automotive electronics skills development. Proposed future enhancements include integrating symbolic AI modules to improve computational reliability, implementing Kubernetes-based load balancing to ensure latency below 2 s at scale, and conducting longitudinal pedagogical validation studies with trainees. This research provides a robust technical foundation for AI-driven vocational education, especially suited to mechatronics fields that require close integration between theoretical knowledge and practical troubleshooting skills. Full article

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