Search Results (132,200)

Background: Temporomandibular disorders (TMDs) are common musculoskeletal conditions associated with pain, functional limitation, and reduced quality of life (QoL). Despite the widespread use of conservative and minimally invasive treatments, the available evidence remains fragmented across heterogeneous interventions, diagnostic criteria, and outcome measures, limiting comparative interpretation and clinical applicability. Objectives: The primary objective of this systematic review is to evaluate the effectiveness of conservative and minimally invasive interventions for pain reduction in adult patients with temporomandibular disorders. Secondary objectives include assessing effects on mandibular function and QoL and exploring differences across intervention categories, TMD subtypes, diagnostic criteria, and follow-up durations. Methods: This protocol is registered in the International Prospective Register of Systematic Reviews (PROSPERO; CRD420251250251) and adheres to the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) guidelines. A systematic search will be conducted in PubMed/MEDLINE, Web of Science, Scopus, Embase, and Cochrane Central Register of Controlled Trials (CENTRAL) for randomized controlled trials (RCTs) published from 1 January 2015, up to the date of study initiation, using controlled vocabulary terms and free-text keywords combined with Boolean operators. Eligible studies will include adult patients (≥18 years) diagnosed with temporomandibular disorders using validated diagnostic criteria and treated with conservative or minimally invasive interventions, compared with placebo/sham, no treatment or usual care, or active comparators, in accordance with the PICOS framework. Two reviewers will independently screen studies and extract data, with disagreements resolved by consensus or consultation with a third reviewer; the study selection process will be documented using a PRISMA 2020 flow diagram. Interventions will be synthesized within predefined clusters (e.g., physical and manual therapies, occlusal splint therapy, physical agent modalities, and minimally invasive joint procedures). Risk of bias will be assessed using the revised Cochrane Risk of Bias tool (RoB 2). The primary outcome will be pain intensity, while secondary outcomes will include mandibular function and QoL. Where appropriate, meta-analysis using a random-effects model will be performed; otherwise, a structured narrative synthesis will be provided. Expected Impact: The systematic review is expected to deliver an updated and methodologically rigorous synthesis of evidence on conservative and minimally invasive interventions for TMDs. By addressing existing research gaps such as the fragmentation of evidence across intervention types, heterogeneity in diagnostic criteria, and variability in outcome measures, this review will support evidence-based clinical decision-making and identify priorities for future research. Full article

Decellularization removes immunogenic intracellular components of fish tissues while keeping the extracellular matrix (dECM) structure, mechanical integrity, and bioactivity. Fish-derived dECM retains native bioactive components, exhibiting high biocompatibility, low immunogenicity, and biodegradability, while supporting cell adhesion, proliferation, and tissue regeneration. Due to its abundance, minimal ethical concerns, and low zoonotic risks, fish wastes are emerging as sustainable sources of dECM, offering an eco-friendly alternative to mammalian biomaterials. This review highlights advances in decellularizing fish wastes such as skin, scales, bones, viscera, and swim bladders from species including tilapia, tuna, milkfish, carp, goldfish, and sturgeon. Physical, chemical, biological, and hybrid decellularization methods are assessed for cell removal, ECM preservation, and mechanical performance. Recent advances in polymer-dECM composites, crosslinking, and 3D bioprinting have significantly improved scaffold performance, making fish-derived dECM applicable for healing of wounds, regeneration of bone and cartilage, and repair of soft tissues. Despite its potential, challenges remain in optimizing perfusion rates, temperature variations, and tissue-specific protocols, as well as developing eco-friendly decellularization techniques using biodegradable reagents. Future perspectives include expanding decellularized fish tissue sources, innovating bio-inks for 3D bioprinting, and refining tissue-specific processing methods to maximize the potential of fish-derived dECM in regenerative medicine and tissue engineering. Full article

To address the challenge of purifying bioactive polyphenols from the complex matrix of Ziziphus jujuba Mill. var. spinosa pulp, this study established an integrated purification protocol combining Deep Eutectic Solvent (DES) extraction with macroporous adsorption resin (MAR) enrichment. Among five screened resins, AB-8 exhibited superior selectivity, achieving a maximum adsorption capacity of 62.48 mg polyphenols/g dry resin and a desorption ratio of 83.40%. Kinetic analysis revealed that the adsorption process strictly followed a pseudo-second-order model (R² = 0.999), indicating a mechanism dominated by chemisorption. Through dynamic optimization, optimal column parameters were determined as a loading concentration of 2.4 mg/mL, a flow rate of 1.0 mL/min, and elution with 70% (v/v) ethanol. Structural characterization via UV-Vis and FT-IR confirmed the effective removal of polysaccharide and protein impurities, while High-Performance Gel Permeation Chromatography (HPGPC) indicated a low-molecular-weight distribution (M_w approx. 1073 Da). Furthermore, HPLC-MS profiling definitively identified eight key constituents, including chlorogenic acid, catechin, rutin, and quercetin. Collectively, this work elucidates the adsorption mechanism and provides a scalable, efficient technical foundation for the high-purity preparation of jujube polyphenols. Full article

Satellite–terrestrial spectrum sensing plays a crucial role in enhancing spectrum efficiency through reusing spectra. However, in a satellite–terrestrial converged system, the large SNR range, non-Gaussian signal characteristics and noise uncertainty pose significant challenges for spectrum sensing. In this paper, we investigate a downlink spectrum sensing framework where multi-terrestrial BSs act as a secondary system to sense idle satellite spectra through a multi-domain feature-level sensing signal fusion. To enhance the characterization of signal/noise features, we provide a fusion strategy of multi-features including energy, power spectral density, cyclic autocorrelation function, higher-order moments, sparse ratio, and I/Q samples, constructing two feature tensors of statistical features and an I/Q component. Then, we propose a deep-learning-enabled multi-feature fusion spectrum sensing method (DL-MFFSSnet) based on a dual-branch deep neural network architecture with the constructed two feature tensors as inputs. In the statistical feature processing branch, CNN and channel self-attention are incorporated to capture intra-channel correlations and inter-channel relative contributions of different feature modalities. In the I/Q branch, multi-scale dilated convolutions and spatial self-attention are introduced to analyze dependencies across different temporal positions and multi-scale spatial features. The feature map extracted from both branches passed through fully connected layers for deepwise feature fusion, achieving accurate spectrum sensing. Extensive simulation results demonstrate that the DL-MFFSSnet method outperforms the existing state-of-the-art algorithms. Full article

High-precision localization of Mars rovers is essential for safe path planning and efficient navigation toward scientific targets. As planetary rovers traverse the surface, their positional uncertainty accumulates, which can be corrected through global localization by registering rover images to orbital maps. To date, image-based solutions are widely adopted; however, substantial manual intervention is often required, which is time-consuming and limits the range of autonomous navigation. To address these challenges, we propose a two-stage localization framework, comprising the Mars cross-view few-shot training paradigm (MarsCVFP), Mars cross-view feature extraction network (MCVN) trained under MarsCVFP, and a robust template matching algorithm. Specifically, the MarsCVFP model can leverage implicit cross-view feature as guidance without relying on a large amount of high-precision location-level supervision and explicitly annotated, specific learning targets in the scene. MCVN can capture discriminative fine-grained features on the weakly textured and unstructured surface of Mars by constructing the multi-scale feature pyramid structure (MSFPS) and the feature interaction module (FIM). We validate our framework on 85 unit-planned sites and 20 panoramic sites, respectively, as traversed by the Zhurong rover. The experimental results demonstrate that our framework consistently outperforms both the traditional approaches and the representative learning-based methods across diverse terrains, including dunes, bedrock, craters, and flat plains, achieving a localization success rate above 82% while maintaining a localization accuracy of better than 4 pixels, even under coarse prior positions uncertainties spanning 40m×40m. Full article

Selenium (Se) biofortification of vegetables can improve dietary Se intake; however, the dose-dependent balance between inorganic Se retention and organic Se assimilation following foliar selenate application remains insufficiently resolved across species. Five leafy vegetable species (garden rocket, wild rocket, dandelion, and two chicory cultivars) were grown under controlled greenhouse conditions and treated twice with foliar sodium selenate at increasing application rates (1 + 1, 2 + 2, 5 + 5, 10 + 0, 10 + 10, and 10 + 50 mg Se L⁻¹) across two experiments. Total Se and Se species were determined by HPLC-UV-HG-AFS following enzymatic extraction and cross-checked on selected extracts by HPLC-ICP-MS. Foliar selenate induced substantial Se accumulation in all species, reaching up to 102 µg g⁻¹ DW in garden rocket. At moderate application rates (notably 2 + 2 and 5 + 5 mg Se L⁻¹), a considerable proportion of extracted Se was converted into organic forms, with selenomethionine (SeMet) accounting for up to ~40% of total extracted Se. In contrast, at the highest application rate (10 + 50 mg Se L⁻¹), inorganic Se(VI) became predominant (often >40%), while SeMet proportion declined sharply to ~2–4%, indicating a saturation of metabolic assimilation capacity under high Se exposure. Leaf biomass was promoted at intermediate treatments (e.g., 5 + 5 and 10 + 0/10 + 10 mg Se L⁻¹), whereas the highest rate reduced growth. Overall, foliar selenate effectively biofortifies chicory, rocket, and dandelion leaves, but excessive application rates shift Se speciation toward inorganic storage and markedly suppress SeMet formation. These findings highlight the importance of dose optimization to maximize nutritional quality while avoiding metabolic overload. Full article

Background/Objectives: Stevens–Johnson Syndrome (SJS) and toxic epidermal necrolysis (TEN) are rare, potentially fatal immunological conditions that affect cutaneous and mucosal surfaces and have the potential to involve the genitourinary tract. While genital involvement is common, urological manifestations are under-recognised clinically and there is a paucity of clear, evidence-based management pathways specific to urological manifestations of SJS/TEN. To map the spectrum of urological manifestations of SJS/TEN, to describe the short- and long-term outcomes of these manifestations, and to synthesise management and prevention strategies to inform clinical practice. Methods: This was a scoping review conducted in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guideline. Data sources: Medline and PubMed articles published in English with publication date up to December 2025. Study selection: Eligible studies included case reports, case series, observational studies, clinical guidelines, and review articles describing urological manifestations, outcomes, management, or prevention strategies for patients with SJS/TEN. Articles limited to renal or isolated gynaecological involvement were excluded. Data extraction and synthesis: Articles were screened independently by two reviewers using a pre-defined data extraction template covering four domains: urological manifestations, outcomes and sequelae, management strategies, and prevention strategies. This criterion was refined after a pilot of 20 studies. Discrepancies were resolved by consensus with a third reviewer. Formal risk-of-bias assessment was not performed, consistent with scoping review methodology. Results: One hundred and four studies published between 1987 and 2025 were included in this review. Selected articles included case reports (n = 63), retrospective cohort studies (n = 23), prospective studies (n = 2), guidelines (n = 5), and summary articles (n = 11). Reported urological involvement ranged from genital cutaneous and mucosal disease including erosions, adhesions, and balanitis to urethral manifestations such as urethritis, stenosis, and strictures, as well as scarce upper urinary tract involvement including ureteric stricture and ureteric mucosal sloughing. Whie some manifestations resolved with supportive care, others progressed to chronic sequelae including persistent urethral strictures, voiding dysfunction, sexual dysfunction, recurrent infection, and in rare cases, obstructive uropathy. A multidisciplinary approach was recommended for all patients with SJS/TEN. Urological management centred around early and repeated urogenital examination, manual lysis of adhesions, urinary catheterisation, and timely intervention for urethral or ureteric obstruction. Long-term urological follow-up of 12 months was recommended for patients with significant urogenital involvement. Conclusions: Urological manifestations of SJS/TEN are diverse, clinically significant, and frequently under-recognised. Early urological involvement, systematic genital and urinary tract assessment, and proactive preventative measures may reduce long-term morbidity. This review provides a comprehensive synthesis of knowledge and recommendations to support urologists’ role in multidisciplinary care of patients with this pathology. This review also highlights the need for prospective research to guide further evidence-based management of urological complications of SJS/TEN. Full article

Background: Bone Mineral Density (BMD) plays a crucial role in diagnosing osteoporosis, and early detection is essential to preventing complications such as osteoporotic fractures. However, access to dual-energy X-ray absorptiometry (DXA) screening remains limited in many healthcare settings. Objective: This study presents a fully automated artificial intelligence pipeline for BMD prediction from lumbar spine radiographs to enable opportunistic osteoporosis screening. Methods: The proposed system integrates automatic vertebral segmentation and a machine learning-based regression model for BMD prediction. A YOLO-based instance segmentation model was trained to automatically segment four lumbar vertebrae, achieving a high Intersection over Union (IoU) of 0.9. Radiomic features were extracted from the segmented vertebrae to capture advanced image characteristics and combined with clinical features from 2875 female patients. An eXtreme Gradient Boosting (XGBoost) regressor was trained to provide opportunistic BMD estimation. Results: The model achieved a mean absolute percentage error (MAPE) of 6% for BMD prediction. A classification model built from segmented vertebrae distinguished between osteoporosis, osteopenia, and normal bone with approximately 90% accuracy. Strong agreement between predicted and ground-truth BMD values was confirmed using Pearson correlation coefficient and Bland–Altman analysis. Conclusions: The proposed fully automated system demonstrates strong agreement with DXA measurements and potential for opportunistic osteoporosis screening in settings with limited DXA access. Further validation and refinement are needed to achieve clinical-grade precision for diagnostic applications. Full article

When solving partial differential equations (PDEs), traditional Physics-Informed Neural Networks (PINNs) often encounter difficulties in capturing critical physical features and addressing information bias between subdomains. To overcome these limitations, this paper proposes a Dual Adaptive Domain Decomposition Physics-Informed Neural Network (DADD-PINN). The core of this method lies in the construction of a dual-driven architecture that facilitates intra-subdomain feature extraction and inter-subdomain feature coordination. Within each subdomain, the solver’s precision is significantly enhanced by integrating a multi-criterion adaptive sampling strategy with a dynamic weighting mechanism. Experimental results demonstrate that DADD-PINN reduces the optimal $L^2$ error by 1–2 orders of magnitude compared to existing baselines. The model exhibits superior generalization and robustness across various physical fields, offering a new route toward accurate and efficient solutions for complex PDEs. Full article

Under the new water control philosophy, reclaimed water utilization is an important strategic measure to increase water supply and reduce water environmental pollution. This has important implications for addressing the water crisis in urban development. The scientific and reasonable price of reclaimed water has a positive effect on promoting the utilization of reclaimed water and improving the utilization rate of urban reclaimed water. Therefore, this study extracted the influencing factors of reclaimed water price through grounded theory and used the ISM method to elucidate the logical hierarchy and investigate their influencing mechanisms. The results indicate that the structural system of factors affecting the price of reclaimed water was composed of 16 factors, which could be divided into four hierarchical levels. Among them, the external value of reclaimed water utilization, the technical level of reclaimed water processes, the regional economic development level, and the quality differences between reclaimed water and conventional water were the deep-rooted factors that affect the price of reclaimed water. In the end, the policy implications regarding the management of reclaimed water prices were proposed from the perspectives of surface-level direct factors, middle-level indirect factors, and deep-rooted factors. Full article

This study aimed to investigate the emotional changes in the brain activity of 34 special education teachers using electroencephalography (EEG) signals in response to common problem behaviours observed in students with Autism Spectrum Disorder (ASD), such as self-harm, aggression, tantrums, and stereotyped behaviours. Vignettes with Turkish narration and stimulus videos were used for each behaviour type to trigger emotions. EEG data were collected from the frontal, temporal, parietal, and occipital regions, and subjected to pre-processing steps such as band-pass filtering (0.5–40 Hz) and Independent Component Analysis (ICA), and various spectral and statistical features were extracted. To improve classification performance, feature selection was performed using the Least Absolute Shrinkage and Selection Operator (LASSO) method, and Support Vector Machine (SVM), Artificial Neural Network (ANN), Linear Discriminant Analysis (LDA), and Random Forest (RF) algorithms were used for classification. The machine learning techniques used achieved success rates of up to 97.66% F1 score in classifying teachers’ brain activity in response to different behavioural patterns. Teachers showed strong negative emotional responses to self-harm, aggression, and tantrums, while showing less response to the stereotypical behaviours. It is recommended that the study be replicated with different signals and teachers. Full article

Background: Periprosthetic joint infection (PJI) after hip arthroplasty for proximal femur fracture is a severe complication, and early postoperative identification remains challenging. This study developed and validated machine learning (ML) models for the early prediction of 90-day EBJIS 2021 “confirmed” PJI using routinely available perioperative data. Methods: We performed a single-center retrospective study including 1182 consecutive adults undergoing primary hip arthroplasty for proximal femur fracture (2015–2022). Forty-seven perioperative candidate predictors were extracted, including early postoperative laboratory values (postoperative day 1–2 and maxima within 72 h). Six algorithms were trained and compared (logistic regression, random forest, support vector machine, multilayer perceptron, XGBoost, and stacking ensemble) using a stratified 80/20 training–test split with 10-fold cross-validation, grid-search hyperparameter tuning, and class weighting. A sensitivity-prioritizing classification threshold was derived using training data only and applied unchanged to evaluation cohorts. Uncertainty was estimated via 1000 bootstrap iterations. Calibration was assessed using the Brier score and calibration intercept/slope. Temporal validation was conducted in a same-center 2023 cohort (n = 147). Model explainability used SHAP. Results: EBJIS-confirmed 90-day PJI occurred in 58/1182 (4.9%) patients. In held-out testing, the final XGBoost model demonstrated good discrimination (AUC 0.889, 95% CI 0.804–0.960) with good overall calibration (Brier score 0.043). Using a prespecified sensitivity-prioritizing threshold selected in the training set, test-set sensitivity was 100%, specificity 58.5%, PPV 11.4%, and NPV 100%. The stacking ensemble yielded the highest discrimination (AUC 0.937; 95% CI 0.89–0.98). In temporal validation (same-center 2023 cohort; n = 147), model performance remained stable (AUC 0.892; sensitivity 85.7%; NPV 99.1% at the prespecified threshold). Calibration was favorable in the development cohort (Brier 0.041; intercept −0.04; slope 0.96) and in 2023 (Brier 0.038; intercept −0.06; slope 0.94). SHAP identified postoperative C-reactive protein, operative duration, body mass index, ASA class, and serum sodium as the most influential predictors. Conclusions: ML models, particularly XGBoost, supported early postoperative risk stratification for 90-day EBJIS-confirmed PJI after fracture-related hip arthroplasty, with a consistently high NPV and stable calibration in a temporally independent same-center cohort. Prospective multi-center validation and impact evaluation are needed before clinical implementation. Full article

Parkia speciosa (P. speciosa), a plant utilized in traditional medicine, has shown promise in various therapeutic applications and contains multiple bioactive components (saponins, alkaloids, flavonoids, polyphenols, and terpenoids). These bioactive compounds have attracted increasing scientific interest due to their ability to modulate key cancer-associated pathways, including the inhibition of cell proliferation and migration and the suppression of oxidative stress and inflammation mechanisms. However, despite P. speciosa’s historically long and wide-ranging usage, a comprehensive investigation of these properties has not been conducted for its pod. This study investigated the effects of P. speciosa empty pod extract (PSET) on human colorectal cancer cells. The extract demonstrated significant dose-dependent inhibition of colorectal cell migration, invasion, and colony formation while exhibiting no cytotoxicity toward normal colon epithelial cells. Western blot analysis confirmed reduced expression of Matrix metalloproteinases 2 (MMP2), Matrix metalloproteinases 9 (MMP9), and N-cadherin, indicating suppression of the epithelial–mesenchymal transition (EMT). These findings demonstrate that the PSET effectively inhibits metastasis in colorectal cancer cells through the EMT pathway, suggesting its potential as a dietary supplement or therapeutic agent for colorectal cancer treatment. Our research provides support for the development of natural, less toxic alternative cancer treatments. Therefore, PSET shows potential for development as a dietary supplement or therapeutic agent for the treatment of colon cancer. Full article

In vertical mine hoisting systems, the rigid guide serves as a critical safety component whose failure may induce severe dynamic disturbances and potentially trigger cascading safety incidents. Existing data-driven diagnosis methods for rigid guides often lack robustness under variable operating conditions and require substantial labeled data. Yet in practical mine hoisting operations, variations in hoisting speed and lifting mass are inevitable, and acquiring sufficient fault samples is challenging due to safety constraints. To address these problems, this paper proposes a novel fault diagnosis framework that integrates a physics-informed feature-extraction pipeline with the zero-space observer theory. Vibration signals are processed to extract dimensionless and relative features, which are deliberately designed based on the dynamic mechanisms underlying different fault states. These features rely solely on the geometric characteristics of the waveform at the fault location, rendering them sensitive to fault types while remaining robust to variations in operating conditions. The feature set is subsequently optimized using the minimum redundancy maximum relevance (mRMR) algorithm to enhance computational efficiency, mitigate overfitting, and improve the generalization ability of the method. A set of zero-space observers is then constructed to perform efficient fault classification through geometric operations in the feature space, with each observer specifically sensitive to its corresponding health state while remaining insensitive to others. Experimental validation across multiple health states and operational variations demonstrates that the proposed method outperforms four widely used intelligent models in both classification accuracy and computational efficiency, showing strong suitability for real-world deployment in coal mining applications. Full article

To investigate the dynamic changes in driver attention in complex road traffic scenarios, this paper proposes a driver attention prediction method based on cross-modal adaptive feature fusion (DAFNet). First, semantic segmentation is applied to the input image sequences, and a dual-branch encoder using a 3D residual network is designed to extract spatio-temporal features from both RGB images and semantic information in parallel. Next, a 3D deformable attention mechanism is introduced to enhance the traditional Transformer algorithm, which focuses on the key salient regions through spatio-temporal offset prediction and adaptive fusion of cross-modal features. Subsequently, a predictive recurrent neural network is employed to forecast the fused spatio-temporal features and improve the stability of long-term sequence prediction. Finally, the driver attention results are predicted by a lightweight decoder. Experimental results demonstrate that the proposed method outperforms the comparative methods in overall performance. The predictions not only capture salient regions in driving scenes in a bottom-up manner but also track the driver’s intent in a top-down manner. Thus, our method exhibits strong adaptability to various complex traffic scenarios. Additionally, the method achieves an inference speed of 53.73 frames per second, satisfying the real-time performance requirement of on-vehicle systems. Full article