Search Results (3,651)

The blood-brain barrier (BBB) is one of the most selective physiological interfaces in the human body. Transendothelial electrical resistance (TEER) has become a widely adopted quantitative metric for assessing its in vitro structural and functional integrity. Although TEER measurements are routinely incorporated into BBB-on-chips, the absence of harmonized electrode architectures, measurement settings, and reporting standards continues to undermine reproducibility and translational reliability among laboratories. This systematic review provides the first comprehensive classification and critical comparison of electrode configurations used for TEER assessment, specifically within BBB-on-chip systems. Eligible studies were analyzed and categorized according to electrode design, fabrication method, integration strategy, and operational constraints. We critically evaluated six principal electrode architectures, highlighting their performance trade-offs in terms of uniformity of current distribution, long-term stability, scalability, and compatibility with dynamic shear conditions. Furthermore, we propose a bioinspired TEER reporting framework that consolidates essential metadata, including electrode specification, temperature control, viscosity effects, and blank resistance correction. Our analysis proposes screen-printed and hybrid silver-indium tin oxide (ITO) electrodes as promising candidates for next-generation BBB platforms. Moreover, our review provides a structured roadmap for standardizing TEER electrode design and reporting practices to facilitate interlaboratory consistency and accelerate the adoption of BBB-on-chip systems as truly biomimetic platforms for predictive neuropharmacological workflows. Full article

Exosomes and other extracellular vesicles (EVs) carry microRNAs, proteins, and lipids that reflect cardiovascular pathophysiology and can enable minimally invasive biomarker discovery. However, EV datasets are highly dimensional and heterogeneous, strongly influenced by pre-analytic variables and non-standardized isolation/characterization workflows, limiting reproducibility across studies. Artificial intelligence (AI), including machine learning (ML), deep learning (DL), and network-based approaches, can support EV biomarker development by integrating multi-omics profiles with clinical metadata. These approaches enable feature selection, disease subtyping, and interpretable model development. Among the AI ​​approaches evaluated, ensemble methods (Random Forest, gradient boosting) demonstrate the most consistent performance for EV biomarker classification (AUC 0.80–0.92), while graph neural networks (GNNs) are particularly promising for path integration but require larger validation cohorts. Evolutionary neural networks applied to EV morphological features yield comparable discrimination but face interpretability challenges for clinical use. Current studies report promising discrimination performance for selected EV-derived panels in acute myocardial infarction and heart failure. However, most evidence remains exploratory, based on small cohorts (n < 50) and limited external validation. For clinical implementation, EV biomarkers need direct comparison against established standards (high-sensitivity troponin and natriuretic peptides), supported by locked-in assay plans, and validation in multicenter cohorts using MISEV-aligned protocols and transparent AI reporting practices. Through a comprehensive, integrative, and comparative analysis of AI methodologies for EV biomarker discovery, together with explicit criteria for reproducibility and translational readiness, this review establishes a practical framework to advance exosomal diagnostics from exploratory research toward clinical implementation. Full article

This study addresses the enduring controversy surrounding the diagnostic criteria for Specific Learning Disabilities (SLD) following the publication of the DSM-5, which is related to their definition. The aim of this study is to review and compare the diagnostic criteria of different classification systems and analyse differences in the identification and evaluation criteria of SLD. To this end, a search of the scientific literature was conducted through ERIC, PsycInfo (Proquest) and Web of Science spanning 2013 to 2024. Fifteen records published in English and focused on school-age children (primary education) were included. The studies address issues in reading, writing and mathematics, using different diagnostic criteria and tools. The findings highlight the need for multidimensional, validated assessments, as well as the importance of early identification to improve access to resources and tackle socio-emotional and motivational factors. Full article

The rapid expansion of e-commerce has significantly influenced consumer purchasing behavior, making user reviews a critical source of product-related information. However, the large volume of low-quality and superficial reviews limits the ability to obtain reliable insights. This study aims to classify Turkish e-commerce reviews as either useful or useless, thereby highlighting high-quality content to support more informed consumer decisions. A dataset of 15,170 Turkish product reviews collected from major e-commerce platforms was analyzed using traditional machine learning approaches, including Support Vector Machines and Logistic Regression, and transformer-based models such as BERT and RoBERTa. In addition, a novel Multi-Transformer Fusion Framework (MTFF) was proposed by integrating BERT and RoBERTa representations through concatenation, weighted-sum, and attention-based fusion strategies. Experimental results demonstrated that the concatenation-based fusion model achieved the highest performance with an F1-score of 91.75%, outperforming all individual models. Among standalone models, Turkish BERT achieved the best performance (F1: 89.37%), while the BERT + Logistic Regression hybrid approach yielded an F1-score of 88.47%. The findings indicate that multi-transformer architectures substantially enhance classification performance, particularly for agglutinative languages such as Turkish. To improve the interpretability of the proposed framework, SHAP (SHapley Additive exPlanations) was employed to analyze feature contributions and provide transparent explanations for model predictions, revealing that the model primarily relies on experience-oriented and semantically meaningful linguistic cues. The proposed approach can support e-commerce platforms by automatically prioritizing high-quality and informative reviews, thereby improving user experience and decision-making processes. Full article

Background/Objectives: The study aims to provide a comprehensive understanding of personalized treatment for patients with inguinal hernias at our hospital, focusing on complications, recurrence rates, and hospitalization duration to optimize treatment outcomes. Methods: Our center performs inguinal hernia surgery using an algorithm tailored to individual clinical conditions, developed in collaboration with the anesthesiology department. We retrospectively reviewed outcomes of open, totally extraperitoneal (TEP), and transabdominal preperitoneal (TAPP) approaches, with all procedures performed by a single surgeon. Results: A total of 229 patients (213 males; age range, 24–92 years; median age, 69 years) underwent inguinal hernia repair at Busan National University Hospital between January 2018 and April 2024. Patients in the open group had higher age and comorbidity burden (age/ASA American Society of Anesthesiologists physical status classification: open 74/3.5 vs. TAPP 70/2.0 vs. TEP 68/2.0; p = 0.036/< 0.001). There were no statistically significant differences in intraoperative complications (p = 1.000); however, the conversion rate was slightly higher in the TEP group (TEP 2 vs. TAPP 1). Length of hospital stay was longest in the TAPP group (open 3.77 days vs. TAPP 3.98 days vs. TEP 3.27 days; p = 0.817), while postoperative complication rates did not differ significantly among groups (overall complications: open 15.4% vs. TAPP 6.2% vs. TEP 4.3%; p = 0.100). Conclusions: Laparoscopic surgery is recommended when general anesthesia is feasible, with TEP preferred for patients without previous surgeries and TAPP for those with preperitoneal space (PPS) access challenges due to previous surgeries or radiation therapy. Open surgery is suitable for patients unable to undergo general anesthesia. Anesthesia and surgical approaches should be based on patient preferences and individual clinical conditions. Full article

Respiratory diseases and welfare impairments impose substantial economic and ethical burdens on modern broiler production, driven by high stocking density, rapid pathogen transmission, and limited sensitivity of conventional monitoring methods. Because respiratory pathology and stress directly alter vocal behavior, acoustic monitoring has emerged as a promising non-invasive approach for continuous flock-level surveillance. This review synthesizes recent advances in audio classification and artificial intelligence for monitoring respiratory health and welfare in broiler chickens. We have reviewed the anatomical basis of sound production, characterized key vocal categories relevant to health and welfare, and summarized recording strategies, datasets, acoustic features, machine-learning and deep-learning models, and evaluation metrics used in poultry sound analysis. Evidence from experimental and commercial settings demonstrates that AI-based acoustic systems can detect respiratory sounds, stress, and welfare changes with high accuracy, often enabling earlier intervention than traditional methods. Finally, we discuss current limitations, including background noise, data imbalance, limited multi-farm validation, and challenges in interpretability and deployment, and outline future directions for scalable, robust, and practical sound-based monitoring systems in broiler production. Full article

Introduction: Cutibacterium acnes, a component of normal skin flora and a common commensal Gram-positive bacterium, presents a diagnostic challenge for arthroplasty surgeons. While Cutibacterium acnes (C. acnes) as a source of infection has been well characterized in shoulder surgery, its presentation and clinical significance in total hip (THA) and total knee arthroplasty (TKA) remain less understood. Methods: A retrospective chart review identified patients with C. acnes culture positivity following THA or TKA. Demographics, laboratory values, and microbiologic data were collected. Statistical comparisons were performed using t-tests and chi-squared analysis. One-year outcomes were evaluated using the Musculoskeletal Infection Society Outcome Reporting Tool (MSIS ORT) criteria among patients undergoing further surgical intervention. Results: Twenty-nine patients with C. acnes-positive cultures were identified (21 THA, 8 TKA); 15 (52%) were polymicrobial. Ten THA patients (47.6%) and seven TKA patients (87.5%) met MSIS criteria for infection at the time of presentation. Mean time to culture positivity was similar between THA (6.8 days) and TKA (7.4 days; p = 0.57). Sonicated cultures were positive in 24% of THA and 12.5% of TKA cases. Mean ESR was 36.4 mm/h for THA and 51.5 mm/h for TKA (p = 0.21); mean C-reactive protein (CRP) was 35.2 and 36.8 mg/dL, respectively (p = 0.95). Mean synovial cell counts were 27,055 for THA and 22,194 for TKA, with polymorphonuclear cells (PMN) percentages of 68% and 73.9% (p = 0.72, 0.70). Monomicrobial infections demonstrated a mean cell count of 24,143 with 58.9% PMNs, compared to 25,903 and 78.8% in polymicrobial cases. At one year, 72% of patients undergoing subsequent surgery achieved successful outcomes. Higher ASA classification was the only significant predictor of failure (mean 3.0 vs. 2.75). Conclusions: C. acnes-associated THA and TKA infections often present with delayed culture growth, mild inflammatory markers, and frequent polymicrobial involvement. At one-year, patients with available follow-up who undergo surgical management experience favorable outcomes, with 72% achieving MSIS ORT success. Full article

Primary Sjogren’s Disease (SjD) is characterized by features of dryness arising from inflammation in the secretary glands, particularly the salivary and lachrymal glands. Generalized symptoms of fatigue and limb pain are very common but, in addition, about 40% of patients have one or more features of organ-specific systemic disease. This review goes through the background of SjD including diagnosis and classification, epidemiology, impact and investigations such as ultrasound and lip biopsy. It then focuses in detail on each of the systemic organ-specific features, principally using the European League against Rheumatism (EULAR) Sjogren’s Syndrome Disease Activity Index (ESSDAI) along with some non-ESSDAI domains, before concluding with comments on disease heterogeneity, treatment, vaccination, pregnancy and surgery along with observations on patient perspectives. The aim is to provide a general overview of these aspects of the disease to complement other chapters in this monograph. Full article

There is an increasing need to establish reliable safety controls in the food industry and to protect public health. Consequently, there are numerous efforts to develop sensitive, robust, and selective analytical strategies. As regulatory requirements for food and the concentration for target biomarkers in clinical analysis evolve, the food and health sectors are showing a growing interest in developing non-destructive, rapid, on-site, and environmentally safe methodologies. One alternative that meets the conditions is non-destructive spectroscopic sensors, such as those based on vibrational spectroscopy (Raman, surface-enhanced Raman—SERS, mid- and near-infrared spectroscopy, and hyperspectral imaging built on those techniques). The use of vibrational spectroscopy in food safety and health applications is expanding rapidly, moving beyond the laboratory bench to include on-the-go and in-line deployment. The dominant trends include the following: (1) the miniaturisation and portability of instruments; (2) surface-enhanced Raman spectroscopy (SERS) and nanostructured substrates for the detection of trace contaminants; (3) hyperspectral imaging (HSI) and deep learning for the spatial screening of quality and contamination; (4) the stronger integration of chemometrics and machine learning for robust classification and quantification; (5) growing attention to calibration transfer, validation, and regulatory readiness. These advances will bring together a variety of tools to create a real-time decision-making system that will address the issue in question. This article review aims to highlight the trends in vibrational spectroscopy tools for health and food safety control, with a particular focus on handheld and miniaturised instruments. Full article

Background/Objectives: Central nervous system (CNS) involvement in acute myeloid leukemia (AML) is a rare but important complication linked to poor outcomes. Diagnosing it is difficult because neurological symptoms are often subtle or nonspecific, and conventional cytology and imaging have limitations. This review summarizes current evidence on the neuroradiologic features of CNS infiltration in AML and explores the growing role of artificial intelligence (AI) in enhancing detection and characterization. Methods: A thorough narrative review was conducted using PubMed, Scopus, and Embase, employing key terms related to AML, CNS involvement, MRI, CT, PET, AI, machine learning, deep learning, and radiomics. Of several thousand records, 138 relevant studies were selected and analyzed across four main areas: neuroradiologic patterns, imaging biomarkers, AI and radiomics applications, and emerging computational trends. Results: Imaging findings in AML mainly include myeloid sarcomas (isointense on T1, hyperintense on T2/FLAIR, restricted diffusion) and leptomeningeal enhancement. Secondary ischemic or hemorrhagic lesions may indicate brain leukocytosis. MRI proved more sensitive than CT, while PET/CT helped detect extramedullary disease. Recent AI and radiomics models showed high tumor classification and prognosis accuracy in similar CNS conditions, indicating significant potential for application in AML-CNS. Conclusions: Combining AI-based image analysis with multimodal neuroimaging could significantly improve diagnostic accuracy and personalized treatment for CNS involvement in AML. Progress is still challenged by the rarity of the condition and the lack of large, annotated datasets. Full article

Stimuli-responsive hydrogels are an emerging class of smart materials with immense potential across biomedical engineering, soft robotics, environmental systems, and advanced manufacturing. In this review, we present an in-depth exploration of their material design, classification, fabrication strategies, and real-world applications. We examine how a wide range of external stimuli—such as temperature, pH, moisture, ions, electricity, magnetism, redox conditions, and light—interact with polymer composition and crosslinking chemistry to shape the responsive behavior of hydrogels. Special attention is given to the growing field of 4D printing, where time-dependent shape and property changes enable dynamic, programmable systems. Unlike existing reviews that often treat materials, stimuli, or applications in isolation, this work introduces a multidimensional comparative framework that connects stimulus-response behavior with fabrication techniques and end-use domains. We also highlight key challenges that limit practical deployment—including mechanical fragility, slow actuation, and scale-up difficulties—and outline engineering solutions such as hybrid material design, anisotropic structuring, and multi-stimuli integration. Our aim is to offer a forward-looking perspective that bridges material innovation with functional design, serving as a resource for researchers and engineers working to develop next-generation adaptive systems. Full article

Background: The aim of this study is to investigate the potential link between the use of specific medications and oral adverse drug reactions. Methods: The 100 most frequently prescribed drugs in Germany in 2023 were compiled using the “PharMaAnalyst” database. According to the descriptions of adverse drug reactions (ADRs) in the patient information leaflets the ADRs were selected, analyzed and weighted with scores according to a classification system that distinguishes four groups of ADRs by frequency: ‘very common’ (4), ‘common’ (3), ‘uncommon’ (2) and ‘rare’ (1). The objective was to summarize the scores of the oral ADRs and define the ‘oral side effect score’ (OSES). Results: After accounting for duplication due to various brand names, 49 medications were reviewed. A total of 65% of the medications exhibited oral ADRs. The number of oral ADRs per medication ranged from one to seven. Xerostomia and dysgeusia were the most prevalent oral side effects, accounting for 37% of cases. Overall, 34% of side effects were classified as either ‘very common’ or ‘common’. The medication groups with the highest OSES were antidepressants, antibiotics and analgesics. Of the individual medications, azithromycin, gabapentin and pregabalin exhibited the highest OSES. Conclusions: This study provides a comprehensive overview of oral side effects associated with the 100 most frequently prescribed drugs. Patients with polypharmacy are particularly likely to experience oral side effects such as xerostomia and dysgeusia. Due to their high OSES combinations, antibiotics, analgesics or antidepressants may trigger multiple oral ADRs. It is essential that the medical community is continuously updated on pharmacological knowledge to raise awareness of oral ADRs. Full article

Recent developments in machine learning (ML) and deep learning (DL) algorithms have introduced a new approach to the automatic detection of plant diseases. However, existing reviews of this field tend to be broader than maize-focused and do not offer a comprehensive synthesis of how ML and DL methods have been applied to image-based detection of maize leaf disease. Following the PRISMA guidelines, this systematic review of 102 peer-reviewed papers published between 2017 and 2025 examined methods and approaches used to classify leaf images for detecting disease in maize plants. The 102 papers were categorized by disease type, dataset, task, learning approach, architecture, and metrics used to evaluate performance. The analysis results indicate that traditional ML methods, when combined with effective feature engineering, can achieve classification accuracies of approximately 79–100%, while DL, especially CNNs, provide consistent, superior classification performance on controlled benchmark datasets (up to 99.9%). Yet in “real field” conditions, many of these improvements typically decrease or disappear due to dataset bias, environmental factors, and limited evaluation. The review provides a comprehensive overview of emerging trends, performance trade-offs, and ongoing gaps in developing field-ready, explainable, reliable, and scalable maize leaf disease detection systems. Full article

Background: Radiomics has emerged as a promising approach to non-invasively characterize the molecular landscape of gliomas, providing quantitative, high-dimensional data derived from routine MRI. Given the recent shift toward molecularly driven classification, radiomics may support precision oncology by predicting key genomic, epigenetic, and phenotypic alterations without the need for invasive tissue sampling. This systematic review aimed to synthesize current radiomics applications for the non-invasive prediction of molecular biomarkers in gliomas, evaluating methodological trends, performance metrics, and translational readiness. Methods: This review followed the PRISMA 2020 guidelines. A systematic search was conducted in PubMed, Ovid MEDLINE, and Scopus on 10 January 2025, and updated on 1 February 2025, using predefined MeSH terms and keywords related to glioma, radiomics, machine learning, deep learning, and molecular biomarkers. Eligible studies included original research using MRI-based radiomics to predict molecular alterations in human gliomas, with reported performance metrics. Data extraction covered study design, cohort size, MRI sequences, segmentation approaches, feature extraction software, computational methods, biomarkers assessed, and diagnostic performance. Methodological quality was evaluated using the Radiomics Quality Score (RQS), Image Biomarker Standardization Initiative (IBSI) criteria, and Newcastle–Ottawa Scale (NOS). Due to heterogeneity, no meta-analysis was performed. Results: Of 744 screened records, 70 studies met the inclusion criteria. A total of 10,324 patients were included across all studies (mean 140 patients/study, range 23–628). The most frequently employed MRI sequences were T2-weighted (59 studies, 84.3%), contrast-enhanced T1WI (53 studies, 75.7%), T1WI (50 studies, 71.4%), and FLAIR (48 studies, 68.6%); diffusion-weighted imaging was used in only 7 studies (12.8%). Manual segmentation predominated (52 studies, 74.3%), whereas automated approaches were used in 13 studies (18.6%). Common feature extraction platforms included 3D Slicer (20 studies, 28.6%) and MATLAB-based tools (17 studies, 24.3%). Machine learning methods were applied in 47 studies (67.1%), with support vector machines used in 29 studies (41.4%); deep learning models were implemented in 27 studies (38.6%), primarily convolutional neural networks (20 studies, 28.6%). IDH mutation was the most frequently predicted biomarker (49 studies, 70%), followed by ATRX (27 studies, 38.6%), MGMT methylation (8 studies, 11,4%), and 1p/19q codeletion (7 studies, 10%). Reported AUC values ranged from 0.80 to 0.99 for IDH, approximately 0.71–0.953 for 1p/19q, 0.72–0.93 for MGMT, and 0.76–0.97 for ATRX, with deep learning or hybrid pipelines generally achieving the highest performance. RQS values highlighted substantial methodological variability, and IBSI adherence was inconsistent. NOS scores indicated high-quality methodology in a limited subset of studies. Conclusions: Radiomics demonstrates strong potential for the non-invasive prediction of key glioma molecular biomarkers, achieving high diagnostic performance across diverse computational approaches. However, widespread clinical translation remains hindered by heterogeneous imaging protocols, limited standardization, insufficient external validation, and variable methodological rigor. Full article

Osteoarthritis (OA) is a prevalent joint disorder characterized by symptoms such as pain and stiffness, often leading to loss of function and disability. Knee osteoarthritis (KOA) represents the most prevalent type of osteoarthritis. KOA is usually detected using X-ray radiographs of the knee; however, the classification of disease severity remains subjective and varies among clinicians, motivating the need for automated assessment methods. In recent years, deep learning–based approaches have shown promising performance for KOA classification tasks, particularly when applied to structured imaging datasets. This review analyzes convolution neural network (CNN)-based approaches reported in the literature and compares their performance across multiple criteria. Studies were identified through systematic searches of IEEE Xplore, SpringerLink, Elsevier (ScienceDirect), Wiley Online Library, ACM Digital Library, and other sources such as PubMed and arXiv, with the last search conducted in March 2025. The review examines datasets used (primarily X-ray and MRI), preprocessing strategies, segmentation techniques, and deep learning architectures. Reported classification accuracies range from 61% to 98%, depending on the dataset, imaging modality, and task formulation. Finally, this paper highlights key methodological limitations in existing studies and outlines future research directions to improve the robustness and clinical applicability of deep learning–based KOA classification systems. Full article