Search Results (235)

Fujian Tulou, a UNESCO World Heritage Site, is highly vulnerable to environmental and anthropogenic stresses, with its earthen walls prone to surface cracking that threatens both structural stability and cultural value. Traditional manual inspection is inefficient, subjective, and may disturb fragile surfaces, highlighting the need for non-destructive and automated solutions. This study proposes a dual-path framework that integrates lightweight crack detection with independent physical simulation. On the detection side, an improved YOLOv12 model is developed to achieve lightweight and accurate recognition of multiple crack types under complex wall textures. On the simulation side, a two-layer RFPA^3D model was employed to parameterize loading conditions and material thickness, reproducing the four-stage crack evolution process, and aligning well with field observations. Quantitative validation across paired samples demonstrates improved consistency in morphology, geometry, and topology compared with baseline models. Overall, the framework offers an effective and interpretable solution for standardized crack documentation and mechanistic interpretation, providing practical benefits for the preventive conservation and sustainable management of Fujian Tulou. Full article

Ozone (O₃) has re-emerged in periodontology for its antimicrobial, oxygenating, and immunomodulatory actions, yet its role in regeneration remains contentious. This narrative review synthesizes current evidence on adjunctive ozone use in periodontal therapy, delineates cellular constraints—especially in periodontal ligament fibroblasts (PDLFs)—and explores mitigation strategies using bioactive compounds and advanced delivery platforms. Two recent meta-analyses indicate that adjunctive ozone with scaling and root planing yields statistically significant reductions in probing depth and gingival inflammation, with no significant effects on bleeding on probing, plaque control, or clinical attachment level; interpretation is limited by heterogeneity of formulations, concentrations, and delivery methods. Mechanistically, ozone imposes a dose-dependent oxidative burden that depletes glutathione and inhibits glutathione peroxidase and superoxide dismutase, precipitating lipid peroxidation, mitochondrial dysfunction, ATP depletion, and PDLF apoptosis. Concurrent activation of NF-κB and upregulation of IL-6/TNF-α, together with matrix metalloproteinase-mediated extracellular matrix degradation and tissue dehydration (notably with gaseous applications), further impairs fibroblast migration, adhesion, and ECM remodeling, constraining regenerative potential. Emerging countermeasures include co-administration of polyphenols (epigallocatechin-3-gallate, resveratrol, curcumin, quercetin), coenzyme Q10, vitamin C, and hyaluronic acid to restore redox balance, stabilize mitochondria, down-modulate inflammatory cascades, and preserve ECM integrity. Nanocarrier-based platforms (nanoemulsions, polymeric nanoparticles, liposomes, hydrogels, bioadhesive films) offer controlled ozone release and co-delivery of protectants, potentially widening the therapeutic window while minimizing cytotoxicity. Overall, current evidence supports ozone as an experimental adjunct rather than a routine regenerative modality. Priority research needs include protocol standardization, dose–response definition, long-term safety, and rigorously powered randomized trials evaluating bioactive-ozone combinations and nanocarrier systems in clinically relevant periodontal endpoints. Full article

Recent advances in cardiovascular imaging have revolutionized the assessment and management of abdominal aortic aneurysm (AAA) through the integration of sophisticated haemodynamic biomarkers. This comprehensive review evaluates the clinical utility and mechanistic significance of multiple biomarkers in AAA pathogenesis, progression, and treatment outcomes. Advanced cardiac imaging modalities, including four-dimensional magnetic resonance imaging (4D MRI), computational fluid dynamics (CFD), and specialized echocardiography, enable precise quantification of critical haemodynamic parameters. Wall shear stress (WSS) emerges as a fundamental biomarker, with values below 0.4 Pa indicating pathological conditions and increased risk for aneurysm progression. Time-averaged wall shear stress (TAWSS), typically maintaining values above 1.5 Pa in healthy arterial segments, provides crucial information about sustained haemodynamic forces affecting the vessel wall. The oscillatory shear index (OSI), ranging from 0 (unidirectional flow) to 0.5 (purely oscillatory flow), quantifies directional changes in WSS during cardiac cycles. In AAA, elevated OSI values between 0.3 and 0.4 correlate with disturbed flow patterns and accelerated disease progression. The relative residence time (RRT), combining TAWSS and OSI, identifies regions prone to thrombosis, with values exceeding 2–3 Pa⁻¹ indicating increased risk. The endothelial cell activation potential (ECAP), calculated as OSI/TAWSS, serves as an integrated metric for endothelial dysfunction risk, with values above 0.2–0.3 Pa⁻¹ suggesting increased inflammatory activity. Additional biomarkers include the volumetric perivascular characterization index (VPCI), which assesses vessel wall inflammation through perivascular tissue analysis, and pulse wave velocity (PWV), measuring arterial stiffness. Central aortic systolic pressure and the aortic augmentation index provide essential information about cardiovascular load and arterial compliance. Novel parameters such as particle residence time, flow stagnation, and recirculation zones offer detailed insights into local haemodynamics and potential complications. Implementation challenges include the need for specialized equipment, standardized protocols, and expertise in data interpretation. However, the potential for improved patient outcomes through more precise risk stratification and personalized treatment planning justifies continued development and validation of these advanced assessment tools. Full article

Data-driven modeling methods have been preliminarily applied in the development of tight-gas reservoirs, demonstrating unique advantages in post-fracturing productivity prediction. However, most of the established predictive models are “black-box” models, which provide productivity predictions based on a set of input parameters without revealing the internal prediction mechanisms. This lack of transparency reduces the credibility and practical utility of such models. To address the challenges of poor performance and low trustworthiness of “black-box” machine learning models, this study explores a data-driven approach to “black-box” predictive modeling by integrating ensemble learning with interpretability methods. The results indicate the following: The post-fracturing productivity prediction model for tight-gas reservoirs developed in this study, based on ensemble learning, achieves a goodness of fit of 0.923, representing a 26.09% improvement compared to the best-performing individual machine learning model. The stacking ensemble model predicts post-fracturing productivity for horizontal wells more accurately and effectively mitigates the prediction biases of individual machine learning models. An interpretability method for the “black-box” ensemble learning-based productivity prediction model was established, revealing the ranked importance of factors influencing post-fracturing productivity: reservoir properties, controllable operational parameters, and rock mechanics. This ranking aligns with the results of orthogonal experiments from mechanism-driven numerical models, providing mutual validation and enhancing the credibility of the ensemble learning-based productivity prediction model. In conclusion, this study integrates mechanistic numerical models and data-driven models to explore the influence of various factors on post-fracturing productivity. The cross-validation of results from both approaches underscores the reliability of the findings, offering theoretical and methodological support for the design of fracturing schemes and the iterative advancement of fracturing technologies in tight-gas reservoirs. Full article

Intestinal dysbiosis represents a critical determinant of clinical outcomes in patients undergoing allogeneic hematopoietic stem cell transplantation (allo-HSCT). Distinct microbiota patterns represent potential prognostic biomarkers and therapeutic targets. However, the exponential growth in microbiota research and analytical complexity has created significant interpretive challenges for clinicians. This review provides a synthesis of current literature examining microbiota fingerprints and their clinical implications. We analyzed key studies evaluating the clinical implications of intestinal microbiota fingerprints in allo-HSCT. Additionally, we examined current therapeutic strategies for microbiota modulation and approaches for translating research findings into clinical practice. We identified three major microbiota fingerprints: (1) decreased intestinal microbiota diversity, (2) reduced abundance of short-chain fatty acid-producing bacteria, and (3) Enterococcus domination. These fingerprints are associated with critical clinical outcomes including overall survival, Graft-versus-host disease, transplant-related mortality, and infection-related complications. While fecal microbiota transplantation and dietary interventions appear promising, current studies suffer from limited sample sizes and lack standardized protocols. Despite significant advances in microbiota research, biological, methodological, and logistical challenges continue to hinder its clinical translation. Understanding microbiota fingerprints represents a promising avenue for improving allo-HSCT outcomes. However, successful clinical implementation requires standardized methodologies, mechanistic studies, and multi-center collaborations to translate research into actionable clinical tools. Full article

Chrysin (5,7-dihydroxyflavone) is a flavonoid widely distributed in propolis, honey, and various plant sources. It exhibits a wide range of pharmacological activities, including anti-inflammatory, antioxidant, anticancer, antimicrobial, and anti-diabetic effects. However, its clinical translation is hampered by poor aqueous solubility, low bioavailability, and rapid metabolic clearance. To address these limitations and expand the chemical space of this natural scaffold, extensive synthetic efforts have focused on generating structurally diverse chrysin derivatives that possess improved drug-like properties. This review systematically categorizes synthetic methodologies—such as etherification, esterification, transition-metal-mediated couplings, sigmatropic rearrangements, and electrophilic substitutions—and integrates them with corresponding biological outcomes. Particular emphasis is placed on recent (2020–present) advances that directly link structural modifications with pharmacological enhancements, thereby offering comparative structure–activity relationship (SAR) insights. In addition, transition-metal-catalyzed C–C bond-forming reactions are highlighted in a dedicated section, underscoring their growing role in accessing bioactive chrysin analogs previously unattainable by conventional chemistry. Unlike prior reviews that mainly summarized biological activities or broadly covered flavonoid scaffolds, this article bridges synthetic diversification with pharmacological evaluation. It provides both critical synthesis and mechanistic interpretation. Overall, this work consolidates current knowledge and suggests future directions that integrate synthetic innovation with pharmacological validation and address pharmacokinetic challenges in chrysin derivatives. Full article

Integrating artificial intelligence (AI) with the Quantitative Structure-Activity Relationship (QSAR) has transformed modern drug discovery by empowering faster, more accurate, and scalable identification of therapeutic compounds. This review outlines the evolution from classical QSAR methods, such as multiple linear regression and partial least squares, to advanced machine learning and deep learning approaches, including graph neural networks and SMILES-based transformers. Molecular docking and molecular dynamics simulations are presented as cooperative tools that boost the mechanistic consideration and structural insight into the ligand-target interactions. Discussions on using PROTACs and targeted protein degradation, ADMET prediction, and public databases and cloud-based platforms to democratize access to computational modeling are well presented with priority. Challenges related to authentication, interpretability, regulatory standards, and ethical concerns are examined, along with emerging patterns in AI-driven drug development. This review is a guideline for using computational models and databases in explainable, data-rich and profound drug discovery pipelines. Full article

Non-communicable diseases (NCDs), including cancer and autoimmune, metabolic, cardiovascular, and neurodegenerative diseases, represent the leading cause of death globally and a growing healthcare burden. The gut microbiota (GM) has been recognized as a key biological component of host health that contributes to the maintenance of immune regulation, metabolic homeostasis, and epithelial barrier function. Several studies are now demonstrating that biological sex has an influence on both GM composition and function, which might explain sex differences in disease predisposition, course, and treatment response. Evidence from both clinical and experimental studies indicates that sex hormones, genetics, and lifestyle-related exposures interact with GM to influence the development and progression of most common NCDs. Some research suggests that estrogens promote diversity in GM with anti-inflammatory immune responses, while androgens and male-abundant taxa are associated with pro-inflammatory conditions. However, the evidence in humans is largely confounded by other variables (such as age, genetics, and lifestyle) and should be interpreted with caution. Unique GM metabolites, such as short-chain fatty acids and secondary bile acids, can have distinct, sex-specific effects on inflammation, metabolic regulation, and even antitumor immunity. While the existence of a sex–gut microbiota axis is gaining increased support, most studies in humans are cross-sectional epidemiological studies with limited mechanistic evidence and little consideration for sex as a biological variable. Future works should prioritize longitudinal, sex-stratified studies and utilize multi-omics integrated approaches to identify causal pathways. Ultimately, integrating sex differences into GM-based approaches could provide new avenues for personalized strategies for the prevention and treatment of NCDs. Full article

Perovskites (ABX₃) exhibit remarkable potential in optoelectronic conversion, catalysis, and diverse energy-related fields. However, the tunability of A, B, and X-site compositions renders conventional screening methods labor-intensive and inefficient. This review systematically synthesizes the roles of physical simulations and machine learning (ML) in accelerating perovskite discovery. By harnessing existing experimental datasets and high-throughput computational results, ML models elucidate structure-property relationships and predict performance metrics for solar cells, (photo)electrocatalysts, oxygen carriers, and energy-storage materials, with experimental validation confirming their predictive reliability. While data scarcity and heterogeneity inherently limit ML-based prediction of material property, integrating high-throughput computational methods as external mechanistic constraints—supplementing standardized, large-scale training data and imposing loss penalties—can improve accuracy and efficiency in bandgap prediction and defect engineering. Moreover, although embedding high-throughput simulations into ML architectures remains nascent, physics-embedded approaches (e.g., symmetry-aware networks) show increasing promise for enhancing physical consistency. This dual-driven paradigm, integrating data and physics, provides a versatile framework for perovskite design, achieving both high predictive accuracy and interpretability—key milestones toward a rational design strategy for functional materials discovery. Full article

The widespread adoption of deep learning (DL) models in neuroscience research has introduced a fundamental epistemological paradox: while these models demonstrate remarkable performance in pattern recognition and prediction tasks, their inherent opacity contradicts neuroscience’s foundational goal of understanding biological mechanisms. This review article examines the growing trend of using DL models to interpret neural dynamics and extract insights about brain function, arguing that the black box nature of these models fundamentally undermines their utility for mechanistic understanding. We explore the distinction between computational performance and scientific explanation, analyze the limitations of current interpretability techniques, and discuss the implications for neuroscience research methodology. We propose that the field must critically evaluate whether DL models can genuinely contribute to our understanding of neural processes or whether they merely provide sophisticated curve-fitting tools that obscure rather than illuminate the underlying biology. Full article

Thyroid hormone (TH) system disruption by chemicals poses a significant concern due to the key role the TH system plays in essential body functions, including the metabolism, growth, and brain development. Animal-based testing methods are resource-demanding and raise ethical issues. Thus, there is a recognised need for new approach methodologies, such as quantitative structure–activity relationship (QSAR) models, to advance chemical hazard assessments. This review, covering the scientific literature from 2010 to 2024, aimed to map the current landscape of QSAR model development for predicting TH system disruption. The focus was placed on QSARs that address molecular initiating events within the adverse outcome pathway for TH system disruption. A total of thirty papers presenting eighty-six different QSARs were selected based on predefined criteria. A discussion on the endpoints and chemical classes modelled, data sources, modelling approaches, and the molecular descriptors selected, including their mechanistic interpretations, was provided. By serving as a “state-of-the-art” of the field, existing models and gaps were identified and highlighted. This review can be used to inform future research studies aimed at advancing the assessment of TH system disruption by chemicals without relying on animal-based testing, highlighting areas that require additional research. Full article

Considering that the ferritic stainless steel Z10C13 support plate material in nuclear power equipment tends to undergo fretting wear during service, this paper systematically investigates the effect of varying normal loads (10–50 N) and displacement amplitudes (15–75 μm) on its fretting response and wear mechanisms. Through ball-on-flat fretting wear experiments, together with macro- and micro-scale observations of wear scars, it is revealed that normal load primarily controls the contact intensity and the extent of adhesion, whereas displacement amplitude mainly affects the slip amplitude and features of fatigue damage. The results show that the fretting system’s dissipated energy increases nonlinearly with both load and amplitude, and their coupled effect significantly exacerbates interfacial damage. The wear scar morphology evolves from a shallow bowl shape to a structure characterized by multiple spalling pits and propagating fatigue cracks. An equivalent hardness-corrected Archard model is proposed based on the experimental data. The model captures the nonlinear dependence of equivalent material hardness on both load and amplitude. As a result, it accurately predicts wear volume ($R^2=0.9838$), demonstrating its physical consistency and modeling reliability. Overall, this study elucidates the multi-scale damage evolution mechanism of Z10C13 under fretting conditions and provides a theoretical foundation and methodological support for wear-resistant design, life prediction, and safety evaluation of nuclear power support structures. Full article

Gas chromatography–mass spectrometry (GC-MS) plays a crucial role in analyzing complex water samples due to its high sensitivity, selectivity, and robustness. Recent developments have transformed GC-MS into a powerful chemosensor platform, capable of generating detailed chemical fingerprints for targeted or untargeted environmental analysis. This review highlights the integration of GC-MS with atomistic modeling approaches, including quantum chemical calculations and molecular simulations, to enhance the interpretation of mass spectra and support the identification of emerging contaminants and transformation products. These computational tools offer mechanistic insight into fragmentation pathways, molecular reactivity, and pollutant behavior in aqueous environments. Emphasis is placed on recent trends that couple GC-MS with machine learning, advanced sample preparation, and simulation-based spectrum prediction, forming a synergistic analytical framework for advanced water contaminant profiling. The review concludes by addressing current challenges and outlining future perspectives in combining experimental and theoretical tools for intelligent environmental monitoring. Full article

Neurogranin (NRGN), a synaptic protein essential for plasticity and memory function, is gaining recognition as a promising biomarker for mild traumatic brain injury (mTBI). This systematic review brings together findings from six studies that measured neurogranin levels in biofluids—including serum, cerebrospinal fluid (CSF), plasma, and exosomes—during both the acute and chronic phases following injury. In the acute phase of mTBI, elevated levels of neurogranin were consistently observed in serum samples, suggesting its potential as a diagnostic marker. These increases appear to reflect immediate synaptic disturbances caused by injury. In contrast, studies focusing on the chronic phase reported a decrease in exosomal neurogranin levels, pointing to ongoing synaptic dysfunction well after the initial trauma. This temporal shift in neurogranin expression highlights its dual utility—both as an early indicator of injury and as a longer-term marker of synaptic integrity. However, interpreting these findings is not straightforward. The studies varied considerably in terms of sample type, timing of measurements, and control for potential confounding factors such as physical activity. Such variability makes direct comparisons difficult and may influence the outcomes observed. Additionally, none of the studies included proteoform-specific analyses of neurogranin, an omission that limits our understanding of the molecular changes underlying mTBI-related synaptic alterations. Due to heterogeneity across study designs and outcome measures, a meta-analysis could not be performed. Instead, a narrative synthesis was conducted, revealing consistent patterns in neurogranin dynamics over time and underscoring the influence of biofluid selection on measured outcomes. Overall, the current evidence supports neurogranin’s potential as both a diagnostic and mechanistic biomarker for mTBI. Yet, to fully realize its clinical utility, future research must prioritize standardized protocols, the inclusion of proteoform profiling, and rigorous longitudinal validation studies. Full article

In response to the recent meta-analysis by Tassi et al. on hormonal contraception and bone metabolism, we raise critical concerns regarding the interpretation of bone turnover markers as surrogates for bone mineral density (BMD). While bone turnover markers can offer early insights into bone remodeling, they do not directly predict long-term BMD changes, which require 12–24 months to detect. The assumption that equivalent percentage changes in bone formation and resorption markers reflect stable BMD is not supported by current evidence. Bone metabolism varies significantly across life stages, particularly during adolescence and early adulthood, when peak bone mass is still accruing—underscoring the need for age-specific analyses. Additionally, biomarker interpretation is limited by assay variability, inconsistent sampling protocols, and uncertain clinical implications, especially for formation markers. Mechanistically, estrogen supports bone integrity by inhibiting resorption and promoting formation; thus, combined hormonal contraceptives (CHCs) containing estrogen may help preserve bone health. In contrast, progestin-only methods can suppress endogenous estrogen production, potentially compromising skeletal development. We advocate for longitudinal studies incorporating both BMD and turnover markers, stratified by age and contraceptive formulation, to guide safer and more informed contraceptive choices that protect long-term bone health. Full article