Search Results (6,015)

This study proposes a combination of optical and thermal methods to investigate the structural integrity of two 16th–17th centuries wooden panel paintings at the early stages of restoration. Well-established techniques, such as 3D scanning, technical photography, and active thermography, are combined with the less conventional shearography, which has recently gained increasing relevance in the diagnostics of cultural heritage materials. The proposed methodology enables the identification and spatial localization of different forms of degradation within the multilayered structure of the artworks, including physical-structural alterations, insect damage, localized hygroscopic degradation, nails, interlayer deterioration, and craquelure. This approach provides a comprehensive insight into the state of the panel painting structure and highlights potentially critical areas which were undetectable by visual inspection alone, demonstrating the ability to guide restoration interventions. Full article

Image dehazing is a fundamental visual restoration task for improving visual perception under low-visibility weather conditions, especially in UAV-based remote sensing, traffic monitoring, and surveillance scenarios. Existing convolutional neural networks are effective in local feature extraction but remain limited in long-range dependency modeling, while Transformer-based methods improve global modeling at the cost of high computational complexity. To address these issues, this paper proposes an efficient image-dehazing framework termed FSSM, which integrates frequency-enhanced State Space Modeling with a hierarchical encoder–decoder architecture. Specifically, an FFT-based State Space Block (FFTSSB) is designed to reformulate state propagation as frequency-domain two-sided non-causal convolution, enabling efficient bidirectional global dependency modeling without explicit recursive scanning. Furthermore, a Frequency-Aware Discriminative Enhancement Block (FDEB) is introduced to enhance local textures, edges, and structural details through spatial gating and lightweight block-wise frequency modulation. Based on these two components, a Frequency-Aware State Interaction (FASI) block is constructed to progressively couple global state propagation and local frequency-aware enhancement. Experimental results on the HazyDet dataset demonstrate that FSSM achieves favorable restoration accuracy, structural consistency, and perceptual quality compared with representative dehazing methods. Ablation studies further validate the effectiveness of the proposed two-sided FFT-based state modeling, frequency-aware enhancement, and hierarchical multi-scale design. Full article

Understanding trade-offs and synergies among ecosystem services (ESs) along environmental gradients is crucial for sustainable oasis management. This study investigated four key ESs—carbon storage (CS), habitat quality (HQ), water yield (WY), and soil conservation (SC)—in three typical oases along water–heat gradients in arid northwestern China. The InVEST model was used to quantify ESs in 1990, 2005, and 2022, and Pearson correlation, geographically weighted regression, K-means clustering, and random forest models were applied to analyze service relationships, ecosystem service bundles (ESBs), and driving factors. The results showed that CS and HQ maintained strong synergies, while the WY–SC relationship shifted from weak trade-offs under drier conditions to stronger synergies under more favorable water–heat conditions. Geographically weighted regression revealed spatial heterogeneity and directional asymmetry in ES relationships. Four ESB types were identified: ecologically fragile zones, ecological transition or buffer zones, agricultural production zones, and core ecological source zones. Driving-factor analysis indicated that vegetation-related services were mainly associated with land-cover structure and vegetation growth, whereas hydrological and erosion-related services were more closely linked to precipitation, potential evapotranspiration, temperature, and topography. These findings support differentiated oasis management through ecological restoration, development regulation, water-saving agriculture, and strict ecological protection. Full article

Background/Objectives: The toxic side effects and resistance-associated limitations of conventional chemotherapeutic agents necessitate the development of more effective and selective combination strategies incorporating naturally derived compounds. In this study, the cytotoxic, apoptotic, oxidative stress-associated, and immunomodulatory effects of thymoquinone (TQ), a bioactive compound derived from Nigella sativa, and docetaxel (Dos), a taxane-based chemotherapeutic agent, were investigated alone and in combination in OVCAR3 ovarian cancer cells using integrated two-dimensional (2D) and three-dimensional (3D) experimental models. Materials and Methods: Cell viability was evaluated following treatment with TQ (10–500 µM), Dos (1–500 nM), and the TQ + Dos combination, and synergistic interactions were assessed by IC₅₀⁻ and combination index-based analyses. Apoptosis and cell cycle distribution were analyzed by flow cytometry. Cytokine levels were determined using ELISA, whereas apoptosis- and cell cycle-associated gene expression profiles were evaluated by RT-qPCR. Active caspase-3 expression was assessed by immunocytochemistry. Intracellular reactive oxygen species (ROS) accumulation was examined using DCFH-DA-based fluorescence imaging and antioxidant rescue experiments using N-acetyl-L-cysteine (NAC). In addition, the antitumor activity of the combination was further evaluated in OVCAR3-derived 3D tumor spheroid models using spheroid morphology, ATP-based viability, and live/dead fluorescence imaging analyses. Results: The TQ + Dos combination demonstrated enhanced cytotoxic and apoptotic activity in OVCAR3 cells compared with single-agent treatments and induced marked G2/M cell cycle arrest. Combination treatment increased pro-apoptotic gene expression and was associated with reduced expression of anti-apoptotic markers and modulated inflammatory cytokine profiles. Fluorescence-based analyses demonstrated marked intracellular ROS accumulation following TQ + Dos treatment, whereas NAC pretreatment partially attenuated oxidative stress and restored viability, suggesting partial involvement of ROS-associated mechanisms in treatment-induced cytotoxicity. Importantly, the combination maintained stronger cytotoxic and growth-inhibitory effects than either monotherapy in 3D ovarian cancer spheroids, where combination treatment induced pronounced spheroid shrinkage, viability loss, and structural disruption. Relatively lower toxicity observed in HaCaT cells suggested partial selectivity toward cancer cells. Conclusions: Collectively, these in vitro findings suggest that the TQ + Dos combination produces greater cytotoxic, apoptotic, and growth-inhibitory effects than either agent alone in ovarian cancer models and is associated with alterations in apoptosis-, cell cycle-, and oxidative stress-related responses. The observation of these effects in 3D spheroid models supports further investigation of this combination in more advanced preclinical systems. Full article

This paper develops a unified asymptotic theory for conditional single-index U-statistics and the associated conditional U-processes in the setting of locally stationary functional time series subject to missing-at-random response mechanisms. The proposed framework addresses, within a single nonparametric inferential architecture, three major sources of complexity in modern functional data analysis: infinite-dimensional covariates, smoothly time-varying stochastic dynamics, and incomplete response observations. The methodology is based on a class of kernel-type estimators combining temporal localization, functional single-index smoothing, and inverse-propensity correction. Temporal localization captures the gradual evolution of the underlying regression structure, the single-index projection provides an effective dimension-reduction mechanism for functional covariates, and the propensity adjustment restores the target conditional functional under the MAR sampling scheme. The principal contribution of the paper is the establishment of weak convergence, in a suitable space of bounded functions, for the resulting propensity-adjusted conditional U-process indexed by a general class of measurable kernels. Under absolute regularity conditions, local stationarity assumptions, small-ball probability requirements, entropy restrictions of VC type, and uniform consistency of the propensity-score estimator, the normalized process is shown to converge weakly to a tight centered Gaussian process. The limiting covariance structure explicitly reflects the interaction between temporal smoothing, functional concentration, dependence, and the random loss of responses. In parallel, uniform convergence rates are derived for the associated conditional single-index U-statistic estimators, thereby quantifying the respective contributions of smoothing bias, stochastic fluctuation, local-stationarity approximation error, and missingness-induced variance inflation. A substantial part of the analysis is devoted to the technical difficulties created by the simultaneous presence of dependence, nonstationarity, functional covariates, and incomplete observations. The proofs combine Hoeffding-type decompositions adapted to weighted incomplete data, blocking and coupling arguments for absolutely regular triangular arrays, refined entropy bounds for kernel-indexed function classes, and small-ball probability techniques for functional covariates. The MAR mechanism is incorporated via inverse-propensity weighting, and its effects on the effective sample size, asymptotic variance, and bias structure are made explicit. The theory also provides a rigorous foundation for bandwidth selection through blocked, propensity-adjusted cross-validation and clarifies its relation to the corresponding oracle risk. The proposed framework encompasses a broad class of statistical learning and inference problems involving pairwise or higher-order functionals of functional time series. In particular, it applies to conditional Kendall-type functionals, discrimination problems, metric learning with incomplete labels, and conditional independence testing under local stationarity. A simulation study illustrates the finite-sample behavior of the proposed estimators and supports the theoretical findings across varying regimes of temporal nonstationarity, serial dependence, functional concentration, and response missingness. Overall, the results provide a mathematically rigorous and methodologically flexible foundation for inference from evolving functional data when dependence, infinite dimensionality, and incomplete observation are present simultaneously. Full article

Shallow eutrophic lakes recover from nutrient loading on time scales ranging from less than one year to many decades, yet whether this range is set by the lake or by the biological response group has rarely been quantified within a single monitoring framework. We assembled a five-year (2020–2025) quarterly monitoring panel from three shallow Yangtze-floodplain lakes (Lake Changhu, Lake Liangzihu, and Lake Honghu; 15 stations, 21 quarters) and applied a panel mixed-effect distributed lag model (PME-DLM) to estimate the lag-response windows of phytoplankton and benthic macroinvertebrate densities against five water-quality drivers. Cross-lake consistency was tested with a station-resampled bootstrap, and the contributions of water quality, season, and lake identity to community variation were resolved by three-table variation partitioning. The PME-DLM resolved a 3-month temperature window for phytoplankton and 9–15 month chlorophyll a and temperature windows for benthic communities, while total nitrogen and total phosphorus were non-significant in either group. Cross-lake bootstrap intervals on window width overlapped substantially across the three lakes, whereas cross-group differences in window centre and shape were an order of magnitude greater. Variation partitioning further showed a mirror-image structure in which phytoplankton variation was dominated by the pure water-quality fraction (12.2%) and benthic variation by the water-quality × season joint fraction (5.8%). Within the resolution of this five-year, three-lake panel, group-level differences in lag-response time scale were more apparent than lake-level differences and provide a quantitative basis for matching restoration assessment cadence to pelagic versus benthic recovery. Full article

Dental caries remains the most prevalent chronic disease worldwide, yet early enamel lesions are reversible if managed with appropriate remineralizing agents. This narrative review synthesizes current evidence on remineralizing agents, their mechanisms of action, and clinical applications, with a focus on dental materials used in preventive and minimally invasive dentistry. Traditional fluoride-based approaches enhance remineralization through fluorapatite formation; however, their effectiveness is limited when calcium and phosphate bioavailability is insufficient. Biomimetic agents, including casein phosphopeptide–amorphous calcium phosphate (CPP-ACP), bioactive glasses, tricalcium phosphate, and nano-hydroxyapatite, provide these bioavailable ions and demonstrate superior performance under challenging clinical conditions. Emerging therapies such as probiotics, photodynamic therapy, and laser-assisted mineralization show promise but require further clinical validation. Based on the primary mechanism of action, an original classification of remineralizing agents is proposed, grouping them into fluoride-based agents, calcium-phosphate systems, nanotechnology-based systems, biofilm modifiers, biomimetic and emerging systems, and adjunctive antimicrobial therapies. The review concludes that bioavailable calcium represents a critical limiting factor in remineralization under certain conditions, and that combination protocols incorporating multiple remineralizing agents, tailored to individual patient risk profiles, achieve superior outcomes compared to single-agent approaches. Clinicians are encouraged to adopt minimally invasive, patient-tailored remineralization strategies that arrest lesions before cavitation, preserving natural tooth structure and reducing the lifelong restorative burden. Full article

Osteoarthritis (OA) is a chronic degenerative joint disease characterized by persistent low-grade inflammation and progressive cartilage destruction. Macrophage-driven inflammatory responses contribute to extracellular matrix (ECM) degradation and accelerate disease progression. Here, we investigated the therapeutic potential of a DHA-derived lipid mediator mixture (LM), generated via soybean lipoxygenase and composed of 17S-hydroxydocosahexaenoic acid, resolvin D5, and protectin DX (3:47:50), in regulating macrophage–chondrocyte crosstalk and OA progression. LM significantly reduced IL-6, IL-1β, and TNF-α production in lipopolysaccharide-induced THP-1 macrophages. Conditioned medium from LM-treated macrophages attenuated ECM degradation in primary chondrocytes by suppressing MMP13 and ADAMTS5 while restoring COL2A1 and ACAN expression, indicating that LM may indirectly protects ECM by modulating the inflammatory microenvironment. In parallel, LM directly protected chondrocytes against IL-1β-induced inflammatory and catabolic responses, and restored ECM homeostasis. Mechanistically, LM significantly increased SIRT1 expression and deacetylation activity, as demonstrated by reduced NF-κB p65 acetylation. Both pharmacological inhibition by EX527 and siRNA-mediated SIRT1 knockdown abolished the protective effects of LM on ECM preservation. In vivo, LM oral administration alleviated cartilage destruction, improved joint structure and suppressed OA progression in a monosodium iodoacetate-induced OA model. Notably, micro-CT studies have demonstrated that LM significantly improved subchondral bone architecture, as evidenced by increased bone volume fraction and improved trabecular parameters. Histological analyses confirmed that LM attenuated inflammation and maintained cartilage integrity. Consistently, immunohistochemical findings showed reduced MMP13 expression, restoration of collagen II and aggrecan, and upregulation of SIRT1 in the LM-treated group compared to OA rats. Collectively, these findings suggest that LM mitigates OA progression by reducing inflammation, preserving ECM homeostasis, and attenuating subchondral bone deterioration. Full article

Post-COVID-19 syndrome (PCS), also referred to as post-acute sequelae of SARS-CoV-2 infection (PASC), represents a heterogeneous set of persistent clinical manifestations developing after acute infection. These conditions are associated with immune dysregulation, autonomic imbalance, impaired thymic function, and possible viral persistence. Objective: This study aims to systematically synthesise current evidence on the immunopathogenesis of PCS and to critically evaluate the application of artificial intelligence (AI) and machine learning (ML) approaches for its prediction and clinical stratification. Methods: A PRISMA 2020–informed systematic review was conducted using PubMed/MEDLINE, Scopus, Web of Science, elibrary.ru and Embase databases (January 2020–December 2025). Studies addressing immunopathological mechanisms and AI/ML applications in PCS were selected based on predefined eligibility criteria. Risk of bias in prediction studies was assessed using the PROBAST tool. Due to heterogeneity, a structured qualitative synthesis was performed. Current evidence indicates that PCS may result from sustained systemic inflammation, cytokine dysregulation, autoimmunity, and delayed restoration of T-cell homeostasis, including reduced thymic output of naïve T lymphocytes. Persistent thymic dysfunction may contribute to prolonged immune imbalance, increased susceptibility to secondary infections, and reactivation of latent viruses. AI/ML approaches—including gradient boosting, ensemble learning, deep neural networks, and natural language processing—have demonstrated promising performance across multimodal datasets. However, significant limitations were identified, including small sample sizes, overfitting, lack of external validation, and heterogeneity in outcome definitions. Conclusions: The integration of immunopathological insights with data-driven modelling highlights the potential of combined approaches for improving PCS risk stratification. However, current AI models remain insufficiently validated for clinical implementation. Future research should prioritise methodological standardisation, external validation, and incorporation of mechanistically informed biomarkers. Full article

Natural disasters frequently cause significant building damage, necessitating timely and accurate damage assessment for effective rescue operations and post-disaster reconstruction. Traditional building damage assessment methods commonly rely on paired pre- and post-disaster remote sensing images, which often face practical challenges in data acquisition and image pairing during emergency situations. To overcome these limitations, a hybrid swin–mamba U-shaped network (UNet) is developed for building damage assessment using only post-disaster remote sensing imagery. The proposed framework employs a Swin Transformer as the encoder to extract multi-scale features and capture long-range contextual information, while a Parallelized Patch-Aware Attention (PPA) convolution module is introduced in the decoder to restore spatial details and improve feature reconstruction. In addition, a Visual State Space (VSS) module is incorporated in the bottleneck layer to effectively model both global contextual dependencies and local structural information, thereby improving the representation of building damage characteristics from single-temporal imagery. Experiments conducted on the xBD dataset show that the proposed method outperforms the Swin–Unet by 1.7% in overall F1-score, achieving an overall F1-score of 55.2%. In addition, qualitative visualization results suggest that the proposed method has favorable generalization capability across different disaster scenarios. These results highlight the practical potential of the proposed framework for rapid post-disaster building damage assessment, particularly in emergency response scenarios where only post-disaster imagery is available. Full article

(1) Background: Mandibular condylar fractures continue to be a subject of debate, traditionally framed as a choice between open and conservative management. However, this binary approach fails to adequately account for fracture-level anatomy, Temporomandibular joint (TMJ) involvement, and functional outcomes. (2) Purpose: To present an imaging-guided, fracture-level-based algorithm that integrates radiologic evaluation, surgical approach selection, fixation biomechanics, and functional rehabilitation. (3) Review Strategy: This invited review combines current evidence with a 13-year institutional experience involving 495 joints. High-resolution Computed Tomography (CT) Imaging was used to assess fracture morphology, displacement, and ramal height, while Magnetic Resonance Imaging (MRI) was selectively employed in intracapsular fractures to evaluate disc–condyle relationships when intra-articular involvement was suspected. Management decisions, including surgical approach and fixation strategy, were guided by an institutional algorithm tailored to fracture characteristics. (4) Results: Implementation of this approach yielded consistent and predictable outcomes. Mouth opening improved from approximately 18.77 mm preoperatively to 40 mm at 6 months. Lateral excursions became symmetrical (~9.6 mm), occlusion was restored in all patients, and bite force returned to near-physiological levels. Pain scores showed near complete resolution within 1 month. Postoperative morbidity remained low, with predominantly transient facial nerve weakness. (5) Conclusions: This imaging-guided, algorithmic framework provides reproducible functional outcomes and signifies a shift toward structured, anatomically driven management of condylar fractures. Full article

Oral health has significant implications for pulmonary outcomes, particularly among individuals with dysphagia who are at risk for aspiration. Moreover, oral health and condition affect nutrition accessibility and status. Inadequate oral hygiene promotes bacterial colonization, plaque accumulation, and aspiration-related respiratory complications. This narrative review aimed to explore current evidence and expert perspectives across palliative medicine, pulmonary and critical care, and dentistry on the role of oral hygiene in supporting pulmonary health and maintaining opportunities for oral nutrition. A comprehensive literature search was conducted through the Texas Tech University Health Sciences Center digital library using Cochrane Library (Wiley), EBSCO Discovery, Embase, Ovid databases, PubMed, SCOPUS, ScienceDirect, Web of Science, and Google Scholar between 14 January 2026 and 1 April 2026. From 1287 identified records, 70 studies were selected to be highlighted in the manuscript after duplicate removal and eligibility screening. Relevant literature was reviewed to examine associations among dysphagia, oral health and condition, oral hygiene and care protocols, feeding route, salivary composition and function, and respiratory outcomes. Emphasis was placed on studies addressing pneumonia, oral versus tube feeding, and evidence-based oral care practices. Findings indicate that pneumonia, depression, and mortality rates are higher in patients receiving tube feeding compared to oral feeding. Evidence-based oral care practices inclusive of mechanical plaque disruption, oral cleansing products (Chlorhexidine, hydrogen peroxide, and sodium bicarbonate), and structured oral hygiene protocols can reduce pulmonary consequences of aspiration and support safer/least risk oral intake. Saliva plays a pivotal role in plaque breakdown, microbial defense, and host immunity; oral feeding helps to preserve salivary function. Results of this review highlight the importance of oral hygiene in both restorative and palliative care contexts. This review establishes a framework for embedding oral cleansing agents and protocols into a nutrition-focused health care infrastructure. Based on the literature analysis and inter- and multidisciplinary clinical expertise of the author group, the manuscript proposes consensus statements intended as expert guidance rather than formal clinical practice guidelines. Adherence to best practices in oral care can mitigate pulmonary consequences of aspiration amid dysphagia, make oral nutrition more accessible and comfortable, sustain opportunities for least risk oral feeding across diagnoses and health care settings, and improve quality of life for patients with dysphagia amid life-limiting illness. Full article

Personalized reconstruction of missing-tooth morphology is a key problem in digital prosthodontics. The main challenge is to generate results that are consistent with the patient’s local dentition, the morphology of the contralateral teeth, and anatomically plausible occlusal details. Although several deep learning-based methods have been proposed for dental restoration, existing approaches still have limitations, including insufficient use of patient-specific contextual information, oversmoothed boundary structures in the generated results, and relatively high model complexity. To address these limitations, this study proposes a CBAM-Sobel conditional Pix2Pix framework, termed CS-cPix2Pix, for predicting the height map of a missing tooth from height projection maps of the contralateral teeth and adjacent teeth. The framework uses height projection maps of a three-tooth contralateral region and an adjacent-tooth region as conditional inputs. A U-Net generator is adopted to learn the mapping from the input conditions to the target missing-tooth height map, and a convolutional block attention module is introduced in the encoder to enhance feature representation in key morphological regions. Furthermore, a Sobel edge loss is incorporated in addition to the adversarial loss and L1 reconstruction loss to constrain the local gradient structure of the generated height map and reduce oversmoothing of occlusal edges, grooves, and ridges. Experimental results show that CS-cPix2Pix achieves better overall quantitative performance than the baseline Pix2Pix model and multiple ablation models, especially in terms of PSNR, FSIM, IoU, and Sobel-L1. Under the current experimental setting, the proposed method generates missing-tooth height maps with clearer boundaries and more continuous structures, and it supports relatively stable reconstruction of three-dimensional occlusal surface meshes from the predicted height maps. However, the present model development still mainly relies on a single public orthodontic dental dataset and focuses primarily on teeth numbered 4, 5, and 6. Therefore, the generalization of the proposed method to other tooth positions, other scanners, different populations, and different acquisition conditions still requires further verification. Full article

Intervertebral disk degeneration (IVDD) is widely recognized as a major contributor to discogenic low back pain (LBP), imposing a substantial burden on global public health and socioeconomic systems. Growing evidence confirms that disrupted redox homeostasis, excessive reactive oxygen species (ROS) accumulation, and oxidative stress act as major convergent mechanisms that propagate inflammatory cascades, nucleus pulposus cell dysfunction, and extracellular matrix degradation. Although conventional conservative therapies and surgical interventions are clinically effective in relieving macrostructural compression, they remain limited in resolving localized molecular dysregulation. In recent years, nanotechnology has emerged as a promising strategy for overcoming the limitations of traditional therapy for IVDD. This review provides an analysis of four categories of antioxidant nanotherapies for IVDD, including inorganic functional nanozymes, bioactive nanomaterials, stimuli-responsive nanosystems, and nanocomposite scaffolds. We elaborate on their mechanisms in scavenging excessive ROS, restoring redox equilibrium, protecting mitochondrial function, and ameliorating oxidative stress-induced degeneration. Integrating structural biomimicry with microenvironmental responsiveness enables the engineering of composite nanosystems with multi-pathway ROS-scavenging capabilities. Therefore, these platforms emerge as promising therapeutic strategies for arresting IVDD progression. Finally, we discuss the key obstacles to clinical translation. Overall, this review provides insights into the development of redox-targeted therapies. Full article

Landscape Ecological Risk (LER) reflects the potential adverse effects of landscape change on ecological structure, function, and stability. In rapidly urbanizing megacities such as Dhaka, vegetation loss and built-up expansion have intensified environmental pressure over recent decades. This study examines the spatiotemporal dynamics of LER in Dhaka from 2004 to 2024 under the combined influence of vegetation change and urban expansion. Multi-temporal remote sensing data were used to generate land cover maps, derive Fractional Vegetation Cover (FVC), and quantify urbanization intensity using Nighttime Light (NTL) data. The Landscape Ecological Risk Index (LERI) was calculated using landscape pattern metrics, while bivariate spatial autocorrelation and geographically weighted regression (GWR) were applied to examine spatial associations and local spatial heterogeneity. The results show that vegetation degradation affected 34.39% of the study area during 2004–2024, while high-risk zones increased from 24.36% in 2004 to 42.95% in 2024. Land cover analysis further indicates a substantial expansion of built-up areas, accompanied by the contraction and fragmentation of vegetation, agricultural land, and lowland classes. Spatial analyses reveal that the relationships among vegetation cover, urbanization intensity, and ecological risk vary across the city and became increasingly spatially differentiated over time. These findings suggest that vegetation loss and urban expansion are spatially associated with increasing ecological risk in Dhaka. However, the results should be interpreted with caution because of uncertainties related to remotely sensed data, unsupervised land cover classification, resampling procedures, and limited ground validation. Despite these limitations, the study provides a spatially explicit framework for understanding ecological risk dynamics and offers useful evidence for green-space conservation, ecological restoration, and sustainable urban planning in rapidly urbanizing regions. Full article