Search Results (3,546)

Background: Evidence on the presence of an association between epicardial adipose tissue (EAT) and myocardial perfusion imaging (MPI) as assessed by positron emission tomography (PET) has been reported. This systematic review aimed to synthesize the existing literature investigating this topic. Methods: A comprehensive and systematic search of the PubMed/MEDLINE, Scopus, and Embase databases was performed to identify published studies investigating the association between EAT and myocardial perfusion assessed by PET imaging. Eligible studies included original research articles evaluating EAT and reporting PET MPI outcomes. Data regarding the study design, patient characteristics, imaging protocols, and main findings were extracted and qualitatively analyzed. Results: Ten studies were included in the final analysis. Overall, most studies demonstrated a significant association between increased EAT and impaired myocardial perfusion on PET imaging. In several studies, EAT remained an independent predictor of abnormal PET MPI after adjustment for traditional clinical risk factors. Nonetheless, important methodological differences among studies were observed, including heterogeneity in EAT measurement techniques, quantification methods, and PET tracers used for MPI evaluation, which limit the generalizability of these findings. Conclusions: This systematic review seems to suggest a potential association between increased EAT and impaired myocardial perfusion, as assessed by PET. However, significant methodological heterogeneity across the available studies—including differences in EAT quantification, PET protocols, and tracer selection—limits the strength of this conclusion. Standardized imaging protocols and larger, prospective, multicenter studies are required to validate this relationship, determine its incremental prognostic value, and evaluate its potential for integration into routine clinical risk stratification pathways. Full article

Urinary stone disease is common, recurrent, and increasingly managed through imaging-driven pathways, yet standard-dose CT of the kidneys, ureters, and bladder (CT KUB) raises concerns about cumulative radiation exposure and the limited use of quantitative imaging information for risk stratification. This review synthesizes contemporary evidence on dose-optimized CT, advanced spectral technologies, and artificial intelligence (AI)-enabled analytics that are reshaping diagnosis, treatment selection, and triage. This review summarizes data supporting low-dose and ultra-low-dose CT protocols that preserve diagnostic accuracy while substantially reducing dose, and discusses how dual-energy CT, photon-counting CT, and radiomics facilitate noninvasive stone characterization and extraction of imaging biomarkers beyond size and location. It also reviews AI approaches for automated detection, segmentation, and volumetric quantification across CT, KUB, and ultrasounds, highlighting their potential to standardize stone-burden metrics. It further examines predictive models, including logistic regression, nomograms, and machine learning, for perioperative infectious complications, emergency department admission or intervention, procedure success, and long-term recurrence, and outlines reporting and validation frameworks and implementation considerations, including software as a medical device regulation and human oversight. In contrast to prior reviews that consider imaging and AI separately, this review integrates dose reduction, spectral characterization, and AI-driven analytics within real-world clinical pathways to distinguish established clinical applications from those that remain investigational. Integrating advanced CT and AI outputs into well-validated prediction models embedded in real-world workflows may enable safer imaging, more consistent triage, and more personalized follow-up for urinary stone disease. Full article

Deep learning is increasingly integrated into oral rehabilitation workflows, particularly in implant planning, prosthodontic design automation, and peri-implant diagnosis. However, reported performance is heterogeneous and difficult to compare across tasks, modalities, and validation designs. The goal of this study was to critically analyze deep learning architecture families applied to oral rehabilitation and to provide task-driven selection guidance supported by an evidence table reporting dataset characteristics, validation strategy, and performance metrics. A focused narrative review was conducted using transparent, database-specific search criteria (final n = 10 included studies), emphasizing implant planning (cone–beam computed tomography [CBCT]-based segmentation), prosthodontic design (intraoral scan [IOS]/mesh inputs), and peri-implant diagnosis (periapical/panoramic radiographs). Evidence certainty for each clinical task was assessed using GRADE-informed ratings (High/Moderate/Low/Very Low). Extracted variables included clinical task, imaging modality, dataset size, architecture, validation strategy (internal vs. internal + external), split level, ground truth protocol, and performance metrics. A structured computational and hardware feasibility analysis was conducted for each architecture family to support real-world deployment planning. Encoder–decoder networks (U-Net/nnU-Net) dominate CBCT segmentation for implant planning, while detection architectures (Faster R-CNN, YOLO) support implant localization and peri-implant assessment on radiographs. Generative models (3D GANs, transformer-based point-to-mesh networks) enable crown design from three-dimensional scans. Hybrid CNN–Transformer architectures show promise for multimodal CBCT–IOS fusion, though direct evidence from the included studies remains limited to a single study. External validation remains uncommon yet essential given the risk of domain shift. In conclusion, architecture selection should be anchored to task geometry (2D vs. 3D), artifact burden, and required clinical output type. Reporting standards should prioritize dataset transparency, validation rigor, multi-center external testing, and uncertainty-aware outputs. Full article

Calcium silicate-based sealers are widely used in contemporary endodontics, but their strong interaction with dentinal substrates may complicate their removal during nonsurgical retreatment and potentially hinder canal disinfection. This ex vivo study evaluated the effectiveness of different irrigation activation techniques in removing a calcium silicate-based sealer from oval-shaped root canals. Sixty extracted single-rooted teeth were instrumented and obturated using the single-cone technique with NeoSealer Flo, followed by retreatment using a reciprocating system. Specimens were randomly assigned to four final irrigation protocols: conventional needle irrigation (CNI) with NaOCl/EDTA, ultrasonic activation (US), diode laser activation (LI), and Er:YAG laser activation using the SWEEPS mode (SW) (n = 15). Residual filling material was quantified before and after final irrigation using stereomicroscopic imaging and ImageJ (version 1.54) analysis. Dentinal surface morphology and residual sealer were further evaluated using SEM–EDS. Statistical analysis included one-way ANOVA and chi-square tests (p < 0.05). All protocols significantly reduced residual filling material compared with mechanical retreatment alone (US 15.08%, CNI 7.89%, LI 8.01%, SW 7.20%) (p < 0.01). US resulted in significantly greater sealer removal compared with CNI, LI, and SW, with mean differences ranging from 7.08% to 7.88% (p < 0.05). These findings indicate that irrigation activation enhances the removal of NeoSealer Flo calcium silicate-based sealer, with ultrasonic activation demonstrating greater effectiveness among the evaluated techniques, under the conditions of this experimental setup. Full article

Sleep quality is closely associated with cardiovascular, metabolic, and mental health outcomes, yet the clinical gold standard, polysomnography (PSG), is costly and intrusive for long-term home monitoring. Ballistocardiography (BCG) enables unobtrusive in-bed sensing and is therefore attractive for low-burden sleep assessment in natural environments. However, most existing BCG studies are PSG-referenced and mainly focus on sleep staging, while movement and out-of-bed episodes are often treated as artifacts rather than modeled jointly. In this study, we propose an interpretable unsupervised proxy-state modeling framework for three-state in-bed monitoring from BCG signals under an unlabeled setting. BCG recordings were segmented into 30 s windows with 50% overlap, and multi-domain features were extracted from waveform morphology, spectral power, heart rate-related dynamics, and wavelet energy distribution. K-means clustering (K = 3) was used to construct cluster-derived proxy labels, TreeSHAP-based feature ranking together with inner-CV-guided Top-N subset selection was used for training-only feature screening, and multiple classifiers were compared under a strict leave-one-subject-out protocol, with an ROA-optimized RBF-SVM achieving the best overall performance. Using data from 32 volunteers, the framework achieved an accuracy of 0.9932 ± 0.0047 (mean ± SD), together with consistently strong Macro-F1 and MCC scores. Overall, it outperformed the alternative methods compared in this study. Full article

Background: Emergency triage systems using machine learning traditionally rely on structured tabular data (vital signs), creating a “contextual blind spot” that ignores diagnostic information embedded in unstructured clinical narratives. Hybrid AI models that fuse tabular and text data may improve predictive discrimination, but the magnitude and conditions under which fusion adds value remain unclear. Methods: Five databases (PubMed, Scopus, Web of Science, IEEE Xplore, ACM Digital Library) were searched from 1 January 2015 to 15 December 2025. Eligible studies employed Hybrid AI models integrating structured and unstructured emergency department data with quantitative baseline comparisons. Twenty-five studies (N ≈ 4.8 million encounters) met inclusion criteria. We extracted marginal performance gains (ΔAUC), calibration metrics, and demographic reporting. Synthesis followed SWiM principles with subgroup meta-regression testing our novel “Complexity Gradient” hypothesis. Results: Hybrid models demonstrated superior discrimination compared to tabular baselines, with effect magnitude dependent on clinical task complexity. Low-complexity tasks (tachycardia prediction) showed minimal gains (median ΔAUC + 0.036, IQR: 0.02–0.05), while high-complexity tasks (hypoxia, sepsis) demonstrated substantial improvement (median ΔAUC + 0.111, IQR: 0.09–0.13). Meta-regression confirmed complexity significantly moderated effect size (R² = 0.42, p = 0.003). Only 12% (3/25) of studies reported calibration metrics (Brier scores: 0.089–0.142). Zero studies stratified performance by race/ethnicity; 88% (22/25) failed to report training data demographics. Discussion: The complexity gradient framework explains when multimodal fusion adds predictive value: tasks where diagnostic signal resides in narrative features (temporality, negation) rather than physiological measurements. However, systematic absence of calibration reporting and fairness auditing prevents clinical deployment. Seventy-two percent of studies had high risk of bias in the analysis domain due to retrospective designs without temporal validation. Conclusions: Hybrid triage models show promise for complex diagnostic tasks but require mandatory calibration reporting and demographic performance stratification before clinical implementation. We propose minimum reporting standards including Brier scores, race-stratified metrics, and temporal validation protocols. Full article

Background: Highly lipidic tissues (e.g., adipose tissue, brain, and liver) are challenging for transcript and protein extraction and for next-generation sequencing. Lipids can clog filters, columns, and pipettes; cause autofluorescence and quenching in imaging; and interfere with centrifugation-based separation. Aim: To identify the most suitable method for extracting total RNA for RT-qPCR and an alternative method for extracting total protein for quantification in mice fed a regular or high-fat diet. Methods: We compared three total RNA extraction methods and two total protein extraction methods. Results: The highest total RNA yield and purity were obtained with TRIzol and chloroform, with optimized steps added to the original protocol to address the challenges posed by highly lipid-rich tissues. For total protein extraction, an adipose tissue-specific kit from Invent Biotechnologies yielded higher protein levels than the classical RIPA-based method. Among lipid-rich tissues, we observed that adipose tissue was more challenging to process than the brain and the liver. Conclusions: Adipose tissue, particularly under a high-fat diet, is the most challenging lipid-rich tissue, followed by the brain and then the liver. We highlight protocols that improve total RNA and protein yields and purity, which may benefit other researchers working with these tissues. Full article

Irreversible covalent enzyme inhibitors, including targeted covalent inhibitors (TCIs) and mechanism-based enzyme inhibitors (MBEIs), play an increasingly important role in drug discovery. Their pharmacological behavior is governed by intrinsic inactivation parameters, typically described by the inactivation constant K_I, the maximal inactivation rate constant k_inact, and their ratio k_inact/K_I. However, no consensus exists regarding how these parameters should be experimentally determined and interpreted, particularly in high-throughput screening environments where IC₅₀ values are often used as primary descriptors. This article presents a critical survey of the kinetic methodologies employed to characterize irreversible enzyme inhibition. Continuous progress-curve analysis, discontinuous end-point assays, IC₅₀-based estimation strategies, direct mass-spectrometric monitoring of covalent modification, and numerical approaches required by pre-incubation protocols are examined and compared. Attention is given to the statistical robustness of parameter estimation under realistic experimental error, including bootstrap-based uncertainty analysis. For mechanism-based enzyme inhibitors, the kinetic consequences of branching between productive turnover and irreversible inactivation are analyzed, and limitations of classical half-life-based linearization methods are discussed. Intrinsic inactivation parameters are distinguished from protocol-dependent observables, and experimental conditions that may compromise reliable parameter extraction are identified. The objective is to clarify how irreversible inhibitors should be kinetically characterized when the goal is mechanistic understanding and rational drug design. By bridging classical enzymology with current discovery practices, this review provides practical guidance on what experimental data can legitimately support and where caution is required. Full article

Global demand for sustainable protein has intensified amid environmental, public health, and ethical concerns surrounding conventional animal agriculture. Edible mushrooms have emerged as promising next-generation protein sources, delivering 19–35% protein (dry weight) with complete essential amino acid profiles and digestibility rates of 60–80%. Beyond protein, mushrooms provide bioactive compounds, including β-glucans, ergothioneine, phenolic acids, and vitamin D₂, supporting immunomodulatory, antioxidant, and anti-inflammatory functions. Enzymatically derived bioactive peptides further demonstrate antihypertensive and antimicrobial activity. This review systematically examines mushroom protein properties, processing technologies, and product performance across three application categories: meat analogs, functional snacks, and beverages. Advanced processing technologies including high-moisture extrusion, ultrasonic-assisted extraction, and microencapsulation have improved bioactive preservation and digestibility. From an environmental perspective, mushroom cultivation requires 85–90% less water and land than animal agriculture, with 80% fewer greenhouse gas emissions. However, critical gaps remain: extraction efficiency varies 3-fold across studies, only 15–23% of commercial products are supported by clinical trials, and techno-economic analyses are largely absent. Standardized processing protocols, large-scale clinical validation, and harmonized quality standards are essential to establish mushrooms as viable, commercially scalable protein alternatives. Full article

Accurate landslide segmentation from remote sensing imagery is important for geohazard assessment and emergency response, yet it remains challenging because landslide regions are often spectrally confused with bare soil, riverbeds, shadows, and disturbed surfaces while also suffering from severe foreground–background imbalance. To address these issues, we propose an Efficient Spectrally Guided Hierarchical Fusion Network (SGH-Net) for multi-modal landslide segmentation. Instead of directly concatenating heterogeneous inputs at the image level, SGH-Net adopts an asymmetric encoder–decoder design in which a pretrained EfficientNet-B4 extracts RGB features, while two lightweight guidance encoders capture complementary multispectral band and DEM-derived terrain cues. These guidance features are progressively injected into the RGB backbone through multi-stage Guided Attention Blocks, enabling selective feature recalibration and reducing cross-modal interference. In addition, a hybrid Dice–Focal loss is used to alleviate class imbalance. Experiments on the Landslide4Sense dataset show that SGH-Net achieves the best overall performance among the compared methods under the adopted evaluation protocol, reaching 81.15% IoU and a 77.86% F1-score. Compared with representative multi-modal baselines, the proposed method delivers more accurate boundary delineation and fewer false alarms while maintaining favorable model complexity. These results indicate that modality-guided hierarchical fusion is an effective and efficient strategy for multi-modal landslide segmentation. Full article

Foodborne pathogens such as Klebsiella aerogenes pose a threat to food safety, highlighting the need for rapid, reliable detection methods amid rising contamination risks in production chains. In this study, a loop-mediated isothermal amplification (LAMP) assay was developed and validated to detect the histidine decarboxylase (HDC) gene of K. aerogenes. The assay was optimized for specificity and sensitivity, tested on pure bacterial genomic DNA and artificially contaminated food matrices (vegetables and meats), and evaluated against real-time PCR (qPCR). To evaluate performance under different DNA quality conditions and simulate laboratory versus on-site workflows, two extraction approaches were compared: a standard laboratory protocol yielding high-purity DNA and a crude extraction method producing low-purity DNA, mimicking the presence of inhibitors commonly encountered in routine analysis and enabling practical on-site detection where commercial kits are not feasible. The developed LAMP assay achieved maximum specificity with no cross-reactivity to related species, limits of detection of 240 fg/reaction for pure bacterial DNA and 0.4 pg/µL in K. aerogenes artificially contaminated food samples, and a reaction time under 30 min—outperforming real-time PCR in speed and robustness. This cost-effective method provides a scalable tool for near-real-time monitoring of K. aerogenes in food production, enhancing safety and enabling early outbreak detection. Full article

Code smells are indicators of potential design problems in software systems and are commonly used to guide refactoring activities. Recent advances in representation learning have enabled the use of embedding-based models for analyzing source code, offering an alternative to traditional approaches based on manually engineered metrics. However, the effectiveness of different embedding representations for multiclass code smell detection remains insufficiently explored. This study presents an empirical comparison of embedding models for the automatic detection of three widely studied code smells: Long Method, God Class, and Feature Envy. Using the Crowdsmelling dataset as an empirical basis, source code fragments were extracted from the original projects and transformed into vector representations using two embedding approaches: a general-purpose embedding model and the code-specialized CodeBERT model. The resulting representations were evaluated using several machine learning classifiers under a stratified group-based validation protocol. The results show that CodeBERT consistently outperforms the general-purpose embeddings across multiple evaluation metrics, including balanced accuracy, macro F1-score, and Matthews correlation coefficient. Dimensionality reduction analyses using PCA and t-SNE further indicate that CodeBERT organizes code smell instances in a more structured latent representation space, which facilitates the separation of smell categories. In particular, CodeBERT achieved a macro F1-score of 0.8619, outperforming general-purpose embeddings (0.7622) and substantially surpassing a classical TF-IDF baseline (0.4555). These findings highlight the value of this study as a controlled multiclass evaluation of embedding representations and demonstrate the practical value of domain-specific representations for improving automated code smell detection and class separability in real-world software engineering scenarios. Full article

With the growing interest in personalized medicine, alternative biological matrices to blood are increasingly explored as sources of diagnostic information. Saliva has emerged as a promising diagnostic matrix due to its non-invasive collection, suitability for home sampling, and minimal requirements for personnel training. Numerous studies have demonstrated the presence of metabolites in saliva that enable disease diagnosis and monitoring. However, the influence of pre-analytical and analytical factors on salivary metabolomics outcomes remains insufficiently characterized. In this study, we investigated factors potentially affecting the number and abundance of detected metabolites in untargeted salivary metabolomics using liquid chromatography coupled with mass spectrometry (LC–MS). The impact of chromatographic column type, extraction protocol, and saliva type (stimulated versus resting) was evaluated. Additionally, the effect of swab type on analyte recovery was assessed. The use of a synthetic swab for saliva collection yielded results most comparable to those obtained without swabs, for both resting and stimulated saliva samples, indicating minimal pre-analytical interference. The greatest metabolite coverage was obtained using ACN:MeOH (1:1, v/v), with a ZIC-HILIC column for polar metabolites and a C18 column for non-polar metabolite separation. These findings demonstrate that swab type, chromatographic column, extraction solvent, and saliva type critically shape metabolite coverage in untargeted salivary metabolomics. Importantly, the distinct metabolic profiles of resting and stimulated saliva suggest that these matrices may provide complementary clinical insights, underscoring the need for saliva type selection tailored to specific diagnostic and biomarker discovery objectives. Full article

Background/Objectives: Low back pain (LBP) is one of the most common musculoskeletal conditions globally and a leading cause of disability. University populations may be particularly vulnerable due to prolonged sitting, academic stress, and frequently suboptimal ergonomics, especially in rapidly expanding higher education systems such as those in Saudi Arabia. This systematic review and meta-analysis aimed to synthesize evidence on the prevalence of LBP among university attendants in Saudi Arabia and to quantify its associations with key demographic and environmental risk factors. Methods: We systematically reviewed observational studies reporting LBP prevalence and/or risk factors among university students and faculty in Saudi Arabia published in English, following Cochrane methodological guidance and PRISMA 2020 reporting recommendations. The protocol was prospectively registered in PROSPERO (CRD420250654048). We searched PubMed, Embase and CINAHL from inception to February 2025. Two reviewers independently screened studies, extracted data, and assessed risk of bias using the Joanna Briggs Institute checklist for analytical cross-sectional studies. Random effects meta-analyses were used to pool prevalence estimates across recall periods, regions, populations, and measurement tools, and to calculate pooled odds ratios (ORs) for age, sex, smoking, family history of LBP, and college seating conditions. Heterogeneity, subgroup, and sensitivity analyses were undertaken. Results: Thirteen cross-sectional studies were included. The overall pooled prevalence of LBP was 57% (95% confidence interval [CI] approximately 43–71), with substantial heterogeneity. Prevalence varied by recall period, region, population group, and measurement instrument; pooled prevalence was 58% among students and 50% among faculty. Increasing age (OR 1.17, 95% CI 1.01–1.34) and poor college seating conditions (OR 1.42, 95% CI 1.07–1.76) were significantly associated with LBP. Male gender, smoking, and family history showed non-significant pooled effects. These estimates are limited by substantial between-study heterogeneity, variable measurement tools, and exclusively cross-sectional designs, which restrict causal inference. Conclusions: LBP is prevalent among university attendants in Saudi Arabia, affecting both students and faculty. The consistent associations with age and seating ergonomics highlight the need for ergonomic classroom redesign and age-sensitive preventive strategies. Future work should adopt standardized LBP measures and longitudinal designs to clarify causal pathways and evaluate targeted interventions. Funding: This work was supported by the Deanship of Graduate Studies and Scientific Research at Jouf University (grant DGSSR-2026-NF-01-002). Full article

Objective: This review is to systematically evaluate the clinical effectiveness and safety of acupuncture therapy for patients with post-stroke neurogenic bladder (PSNB). Methods: We included randomized controlled trials (RCTs) evaluating any type of acupuncture treatment for PSNB. Data extraction and quality assessment using Cochrane Risk of Bias 2.0 were performed. Meta-analysis was conducted for total effective rate (TER) and urodynamic parameters. Results: Ten RCTs involving 727 participants were included. Meta-analysis showed that acupuncture was associated with a reduction in residual urine volume (RUV), and increases in maximum cystometric capacity (MCC), and maximal urinary flow rate (Qmax). Acupuncture also showed a higher TER compared to control groups (RR = 1.23, 95% CI [1.15, 1.33], p < 0.001). However, wide 95% prediction intervals for urodynamic parameters indicated substantial uncertainty for future clinical applications. Adverse events were mild and infrequent, but only partly reported in two studies among included trials. Conclusions: Acupuncture as an adjunctive therapy suggests potential trends for improving clinical efficacy and urodynamic parameters in PSNB patients. However, no definitive conclusions can be drawn regarding its clinical efficacy or safety due to the very low certainty of evidence, high methodological heterogeneity, and limited reporting of adverse events. Therefore, these results must be interpreted with extreme caution. Further high-quality, large-scale randomized controlled trials with standardized protocols are essential to establish robust evidence regarding its clinical effectiveness and safety. Protocol registration: PROSPERO CRD42025643092. Full article