Search Results (1,736)

Alzheimer’s disease, an irreversible neurodegenerative disorder, manifests through the progressive deterioration of memory and cognitive functions. While magnetic resonance imaging has become an indispensable neuroimaging modality for Alzheimer’s disease diagnosis and monitoring, current diagnostic paradigms predominantly rely on single-time-point data analysis, neglecting the inherent longitudinal nature of neuroimaging applications. Therefore, in this paper, we propose a multi-task feature selection algorithm for Alzheimer’s disease classification based on longitudinal imaging and hypergraphs (THM2TFS). Our methodology establishes a multi-task learning framework where feature selection at each temporal interval is treated as an individual task within each imaging modality. To address temporal dependencies, we implement group sparse regularization with two critical components: (1) a hypergraph-induced regularization term that captures high-order structural relationships among subjects through hypergraph Laplacian modeling, and (2) a fused sparse Laplacian regularization term that encodes progressive pathological changes in brain regions across time points. The selected features are subsequently integrated via multi-kernel support vector machines for final classification. We used functional magnetic resonance imaging and structural functional magnetic resonance imaging data from Alzheimer’s Disease Neuroimaging Initiative at four different time points (baseline (T1), 6th month (T2), 12th month (T3), and 24th month (T4)) to evaluate our method. The experimental results show that the accuracy rates of 96.75%, 93.45, and 83.78 for the three groups of classification tasks (AD vs. NC, MCI vs. NC and AD vs. MCI) are obtained, respectively, which indicates that the proposed method can not only capture the relevant information between longitudinal image data well, but also the classification accuracy of Alzheimer’s disease is improved, and it helps to identify the biomarkers associated with Alzheimer’s disease. Full article

Background: Ischemic heart disease (CAD), a leading global health burden, arises primarily from atherosclerosis, an inflammatory condition characterized by lipid accumulation and metabolic dysregulation. The precise contribution of inflammatory cytokines (IL-2, IL-6, and TNF-α) to CAD pathogenesis remains an area of significant research. Aim: The primary aim of this study is to examine the IL-2, IL-6, and TNF-α in patients with coronary artery disease (CAD) and compare them with Non-CAD individuals to evaluate their potential as diagnostic biomarkers for CAD. Methodology: A prospective observational study was conducted over 3 years, involving 100 participants divided into CAD and non-CAD groups. Blood samples were isolated and analyzed for IL-2, IL-6, and TNF-α levels utilizing ELISA kits. Biochemical parameters, including lipid profiles, were also assessed. Results: This study observed significantly elevated IL-6 in patients with CAD compared with controls, while IL-2 and TNF-α levels did not reach statistical significance. The CAD group exhibited dyslipidemia characterized by elevated triglycerides and reduced HDL. Furthermore, the CAD group demonstrated alterations in biochemical parameters, including lower albumin and calcium levels, higher urea and uric acid levels, and an elevated erythrocyte sedimentation rate. These findings suggest a systemic inflammatory state and metabolic disturbances in patients with CAD. Conclusions: This study highlights IL-6 as a potential biomarker and key player in CAD pathogenesis. These findings warrant further investigation into the therapeutic potential of targeting inflammatory pathways for cardiovascular risk reduction. Full article

This review paper presents a comprehensive analysis of electroluminescence (EL) imaging techniques for photovoltaic (PV) module diagnostics, focusing on advancements from conventional indoor imaging to outdoor and daylight EL imaging. It examines key challenges, including ambient light interference and environmental variability, and highlights innovations such as infrared-sensitive indium gallium arsenide (InGaAs) cameras, optical filtering, and periodic current modulation to enhance defect detection. The review also explores the role of artificial intelligence (AI)-driven methodologies, including deep learning and generative adversarial networks (GANs), in automating defect classification and performance assessment. Additionally, the emergence of drone-based EL imaging has facilitated large-scale PV inspections with improved efficiency. By synthesizing recent advancements, this paper underscores the critical role of EL imaging in ensuring PV module reliability, optimizing performance, and supporting the long-term sustainability of solar energy systems. Full article

The accurate assessment of muscle morphology and function is crucial for medical diagnostics, rehabilitation, and biomechanical research. This study presents a novel methodology for constructing volumetric models of forearm muscles based on three-dimensional ultrasound imaging integrated with a robotic system to ensure precise probe positioning and controlled pressure application. The proposed ultrasound scanning approach combined with a collaborative six-degrees-of-freedom robotic manipulator enabled reproducible and high-resolution imaging of muscle structures in both relaxed and contracted states. A custom-built phantom, acoustically similar to biological tissues, was developed to validate the method. The cross-sectional area of the muscles and the coordinates of the center of mass of the sections, as well as the volume and center of gravity of each muscle, were calculated for each cross-section of the reconstructed forearm muscle models at contraction. The method’s feasibility was confirmed by comparing the reconstructed volumes with anatomical data and phantom measurements. This study highlights the advantages of robotic-assisted ultrasound imaging for non-invasive muscle assessment and suggests its potential applications in neuromuscular diagnostics, prosthetics design, and rehabilitation monitoring. Full article

Cardiovascular diseases (CVDs) are among the leading causes of mortality worldwide, particularly in Mexico, where rural regions face challenges due to limited access to medical equipment. This preliminary study proposes a low-cost cardiovascular disease classifier, Buazduino-001, which integrates machine learning (ML) techniques with Arduino-based technology to provide accessible and non-invasive risk assessment. Three classical ML models—logistic regression, random forest, and support vector machine—were implemented and evaluated using a dataset of 303 patients from the UCI Machine Learning Repository. This study introduces a six-stage methodology, including a novel step that prioritizes non-invasive attributes to optimize diagnostic time and cost. The random forest model demonstrated the best performance, achieving 87% classification accuracy, with a reduced feature set of five attributes (sex, age, chest pain, heart rate, and exercise-induced angina). In this preliminary study, the system was validated experimentally with 30 patients, confirming an 85% accuracy and an 80% reduction in diagnostic time compared to traditional medical assessments. The results highlight the practicality of combining ML with low-cost electronics to address healthcare gaps in resource-limited settings. While this study is preliminary, the Buazduino-001 system demonstrates potential for early CVD risk detection and could serve as a screening tool in rural clinics, complementing conventional diagnostic methods. Full article

Background/Objectives: Cerebral amyloid angiopathy (CAA) is one of the most prevalent small vessel diseases (SVDs). Its diagnostic criteria rely mainly on neuroimaging markers, in particular using Magnetic Resonance Imaging (MRI), as pathology-based diagnoses are only occasionally available. Amyloid PET is frequently used to assess parenchymal amyloid deposition in Alzheimer’s disease (AD), but amyloid tracers are not specific to vascular and parenchymal amyloids. The aim of this scoping review is to assess the usefulness of amyloid PET imaging in CAA. Methods: A systematic literature search was performed, aiming to assess amyloid PET performance in the following situations: (I) CAA-related intracerebral hemorrhage (ICH) and convexal subarachnoid hemorrhage; (II) pathology-proven CAA; (III) CAA-related inflammation; (IV) hereditary CAA. Results: A total of 52 studies were retrieved, including three systematic reviews, and from these, a specific selection was taken according to each objective, confirming the diagnostic value of amyloid PET added to MRI and clinical information in all the selected situations, although with some limitations. Conclusions: Amyloid PET reliably detects increased global and region-specific amyloid deposition in CAA patients, with a characteristic occipital-predominant pattern. Continued advancements in tracer development and imaging methodologies are needed to increase specificity. Full article

Background/Objectives: Flatfoot is a common pediatric foot deformity characterized by a reduced or absent medial longitudinal arch (MLA). The condition can lead to altered gait, pain, and potential long-term morbidity if untreated. Identifying potential risk factors—such as body mass index (BMI), ligamentous or joint instability, shoe choices, and physical activity—is crucial for prevention and management. The objectives are to systematically review and synthesize current evidence on how flatfoot severity correlates with BMI and other risk factors in children and adolescents, and to highlight methodological considerations essential for future research. Methods: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we searched five electronic databases from inception to February 2024. Flatfoot severity was measured by various clinical or radiographic indices. Two reviewers independently screened and assessed the risk of bias. Results: Thirty-seven studies met the inclusion criteria. Children with high BMI had increased odds of flatfoot (pooled Odds Ratio = 2.3, 95% Confidence Interval: 1.6–3.1), with one outlier reporting an OR of 9.08. Heterogeneity (I² up to 70%) stemmed from varied diagnostic methods. Other factors, including joint instability, shoe choices, and physical activity, showed mixed associations. Conclusions: Elevated BMI strongly correlates with pediatric flatfoot severity, highlighting the importance of proactive weight management and foot assessments. Future standardized, longitudinal studies are needed to clarify causality and refine interventions. Full article

Winding deformities in distribution transformers pose significant risks to operational reliability and system safety. Frequency response analysis (FRA) is a well-established technique for identifying mechanical faults; however, its diagnostic reliability is hindered by subjectivity in interpreting response signatures. This study proposes a novel diagnostic technique, termed FRA6σ, which integrates Six Sigma (6σ) statistical tools with FRA to enable objective fault detection. The methodology employs control charts ($\overline{X}$ chart, $\overline{R}$-chart) to monitor deviations from baseline signatures and utilizes process capability indices ($C_p$ and $C_{pk}$) to quantify the severity of deviations. Three transformer cases were evaluated across five defined frequency regions (10 Hz to 2 MHz), each associated with distinct physical fault types. The FRA6σ approach successfully identified early-stage faults across all cases. In one instance, axial and radial winding deformation was detected with a $C_p$ of 1.0 and corresponding range chart violations, preceding any visible damage. Another case revealed inter-turn insulation degradation in the 100 kHz–1 MHz band with $C_{pk}$ values below 0.9, prompting immediate intervention. Compared to traditional FRA interpretation, the proposed method improved diagnostic sensitivity by 31.25% and enabled fault detection earlier based on retrospective physical inspection benchmarks. The integration of Six Sigma with FRA provides a structured, quantifiable, and repeatable approach to transformer fault diagnostics. FRA6σ enhances early detection of winding deformities and dielectric issues, offering a robust alternative to subjective analysis and supporting predictive maintenance strategies in power systems. Full article

Oral Allergy Syndrome (OAS) is an allergic reaction that occurs upon contact of the mouth and throat with food, leading to symptoms primarily affecting the oral mucosa. In patients with allergic rhinitis, OAS may develop due to cross-reactivity between the pollen allergens responsible for allergic rhinitis, and specific plant-derived foods. This particular type of OAS is known as Pollen Food Allergy Syndrome (PFAS). The difference in prevalence of PFAS across different regions of the world is attributed to various factors, including environmental exposure and dietary habits. Southern Europe’s temperate climate favors the blooming of many allergenic plants, making respiratory allergies and PFAS significant public health concerns. There is a regional variation in pollen in Southern Europe, contributing to differences in the presence of panallergens—such as profilins, pathogenesis-related class 10 (PR-10) proteins and lipid transfer proteins (LTPs)—which mediate PFAS. In order to examine the epidemiology, pathogenesis, and diagnostic approaches of OAS and PFAS, focusing on their prevalence and impact in Southern European adults, a narrative review was performed. Data from Portugal, Spain, France, Italy, Albania, Greece, and Türkiye were retrieved. The main outcome of this review was that the frequency of PFAS varies across studies, not only between countries but also within the same country, due to vegetation variability across regions as well as methodological differences and the year of study. However, despite these differences, PFAS emerges as a common issue in Southern Europe, underscoring the need for effective diagnosis and management. Full article

Objectives: Syncope is a common cause of the transient loss of consciousness, with neurally mediated syncope (NMS) and particularly vasovagal syncope (VVS) being the most prevalent types among older children and adolescents. VVS is primarily caused by heightened parasympathetic activity triggered by emotional or postural stimuli, resulting in a temporary disruption of circulation. Although anamnesis and physical examination play key roles in diagnosing VVS, additional diagnostic methods are necessary in unclear cases. This study aims to evaluate the long-term outcomes of pediatric patients with syncope, focusing on clinical characteristics, diagnosis, and treatment approaches. Methods: A retrospective analysis was conducted on 455 pediatric patients aged 8–21 years who presented with syncope at our cardiology clinic between 2005 and 2023. Patients diagnosed with cardiac syncope, epilepsy, or postural orthostatic tachycardia syndrome (POTS) were excluded. The remaining 283 patients were categorized into two groups: those with confirmed VVS—based on a comprehensive evaluation, including medical history, physical examination, and electrocardiography—and those suspected of VVS who lack a confident diagnosis after an initial assessment requiring tilt table testing. Clinical features, diagnostic methods, and treatment outcomes were analyzed. Results: The study cohort had a mean age of 13.5 ± 1.6 years, with a female predominance of 69%. Among patients who underwent tilt table testing (TTT), 74.8% exhibited a positive response, with mixed-type syncope being the most prevalent (51%). Syncope recurrence was significantly higher in the TTT group (54%) compared to the clinically diagnosed group (15%) (p < 0.001). Relapse risk was strongly associated with the syncope subtype, particularly cardioinhibitory type 2B (OR: 2.3, 95% CI: 1.1–4, p < 0.01), and episode frequency (OR: 1.7, 95% CI: 1.3–2.5, p = 0.03). Beta-blocker therapy was selectively administered and demonstrated a reduced relapse risk in a univariate analysis. Conclusions: VVS is a significant health issue in pediatric patients and the therapeutic modalities available encompass various interventions, including modifications to lifestyle, adequate hydration, and pharmacological therapies. TTT was found to be an effective diagnostic tool for identifying high-risk patients and is recommended for appropriate cases in pediatric VVS diagnosis in accordance with the guidelines, with the objective of refining therapeutic methodologies and ultimately augmenting patient prognoses. Full article

Health indicators (HIs) are quantitative indices that assess the condition of engineering systems by linking sensor data with monitoring, diagnostic, and prognostic methods to estimate the remaining useful life (RUL). Digital twins (DTs), which serve as digital representations of physical assets, enhance system monitoring, diagnostics, and prognostics by operationalizing analytic capabilities derived from sensor data. This paper explores the integration of HIs and DTs, illustrating their roles in condition-based maintenance and structural health monitoring. The methodologies discussed span data-driven and physics-based approaches, emphasizing their applications in rotary machinery, including bearings and gears. These approaches not only detect anomalies but also predict system failures through advanced modeling and machine learning (ML) techniques. The paper provides examples of HIs derived from vibration analysis and soft sensors and maps future research directions for improving health monitoring systems through hybrid modeling and uncertainty quantification. It concludes by addressing the challenges of data labeling and uncertainties and the role of HIs in advancing performance engineering, making DTs a pivotal tool in predictive maintenance strategies. Full article

Energy and production efficiency are critical for achieving sustainability and competitiveness in polymer processing plants. A system with high energy efficiency and performance enhances productivity while reducing greenhouse gas emissions. While Monitoring and Targeting (M&T) methodologies are widely used for energy control in Energy Accounting Centers (EACs), they do not provide a diagnostic framework. The Energy Gap Method (EGM), introduced in 2018, addresses this gap by identifying the origin and magnitude of energy inefficiencies through a hierarchical model that defines six levels of specific energy consumption (SEC). Inspired by M&T strategies, the EGM has led to the development of diagnostic tools, including the Performance Characteristic Line for Diagnostics (PCLD), the Activity-Based Target from Diagnostics (ABTD), and the Performance Characteristic Curve for Diagnostics (PCCD). These tools enable manufacturers to determine optimal production batch sizes, establish minimum productivity requirements, identify molds and product references requiring intervention, and support the design of energy-efficient components. By integrating these tools, manufacturers can optimize energy consumption, achieve cost savings, and enhance environmental sustainability. This paper presents the methodology and two case studies demonstrating the analytical capabilities of the developed tools in improving energy efficiency within polymer production processes. Full article

BODIPY (Boron-Dipyrromethene) dyes have emerged as versatile fluorescent probes in cellular imaging and therapeutic applications owing to their unique chemical properties, including high fluorescence quantum yield, strong extinction coefficients, and remarkable photostability. This review synthesizes the recent advancements in BODIPY dyes, focusing on their deployment in biological imaging and therapy. The exceptional ability of BODIPY dyes to selectively stain cellular structures enables precise visualization of lipids, proteins, and nucleic acids within live and tumor cells, thereby facilitating enhanced understanding of biochemical processes. Moreover, BODIPY derivatives are increasingly utilized in Photodynamic therapy (PDT) and Photothermal therapies (PTT) for targeting cancer cells, where their capability to generate cytotoxic reactive oxygen species upon light activation offers a promising approach to tumor treatment. Recently, BODIPY derivatives have been used for Boron Neutron Capture Therapy (BNCT) for various tumors, and it is a growing research field. Advancements in nanotechnology have allowed the fabrication of BODIPY dye-based nanomedicines, either alone or with the use of metallic nanoparticles as a matrix offering the development of a new class of bioimaging and theragnostic agents. This review also discusses innovative BODIPY-based formulations and strategies that amplify therapeutic efficacy while minimizing adverse effects, underscoring the potential of these dyes as integral components in next-generation diagnostic and therapeutic modalities. By summarizing current research and future perspectives, this review highlights the critical importance of BODIPY dyes in advancing the fields of cellular imaging and treatment methodologies. Full article

Background: Among rheumatologic diseases following therapy with immune checkpoint inhibitors (ICIs), the cases of cancer patients diagnosed as having polymyalgia rheumatica (PMR), particularly with nivolumab and pembrolizumab, has been steadily rising in published reports. Objectives: We performed a systematic review of published case reports with the aim of answering these questions: (1) Is PMR following therapy with nivolumab and pembrolizumab an adverse drug reaction (ADR)? (2) Is there a difference between cases of PMR following therapy with nivolumab and those following therapy with pembrolizumab? Methods: Based on Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines, a comprehensive literature search in three main bibliographic databases: MEDLINE (Ovid interface), EMBASE, and COCHRANE Library was carried out on 27 December 2024. This systematic review has no registration number. Results: Data were extracted from 12 patients. Namely, 5 cases followed treatment with nivolumab and 7 with pembrolizumab. Validated scales for ADR assessment—such as Naranjo’s scale—were not used in 10 out of the 12 patients. Additionally, validated diagnostic or classification criteria for PMR were used in the majority of case reports related to nivolumab. On the contrary, clinical judgment alone was the rule in almost all case reports on pembrolizumab. Finally, the time interval between PMR manifestations and nivolumab/pembrolizumab therapy ranged from one to 14 cycles (fully compatible with pharmacokinetics). Conclusions: Our literature review highlighted significant methodological blurred lines in the categorization of PMR following therapy with nivolumab or pembrolizumab. Full article

Peptide-functionalized nanomedicine has emerged as a transformative approach in precision therapeutics and diagnostics, leveraging the specificity of peptides to enhance the performance of nanocarriers, including gold nanoparticles, polymeric nanoparticles, liposomes, mesoporous silica nanoparticles, and quantum dots. These systems enable targeted drug delivery, molecular imaging, biosensing, and regenerative medicine, offering unparalleled advantages in bioavailability, cellular uptake, and therapeutic selectivity. This review provides a comprehensive analysis of peptide-functionalization strategies, nanocarrier design, and their applications across oncology, neurodegenerative disorders, inflammatory diseases, infectious diseases, and tissue engineering. We further discuss the critical role of physicochemical characterization, in vitro and in vivo validation, and regulatory considerations in translating these technologies into clinical practice. Despite the rapid progress in peptide-functionalized platforms, challenges related to stability, immune response, off-target effects, and large-scale reproducibility remain key obstacles to their widespread adoption. Addressing these through advanced peptide engineering, optimized synthesis methodologies, and regulatory harmonization will be essential for their clinical integration. By bridging fundamental research with translational advancements, this review provides an interdisciplinary roadmap for the next generation of peptide-functionalized nanomedicines poised to revolutionize targeted therapy and diagnostics. Full article