Abstract: Integrating botanical extracts rich in bioactive compounds, such as bilberry (Vaccinium myrtillus L.) and blackcurrant (Ribes nigrum), may represent an adjunctive nutritional strategy to support the management of chronic low-grade inflammation and, more cautiously, oncology supportive care. Both species contain high levels of anthocyanins and other flavonoids, which are associated with anti-inflammatory effects through modulation of redox-sensitive and inflammatory signaling pathways, reduction of selected pro-inflammatory mediators, and potential contributions to intestinal eubiosis. A growing body of evidence also suggests that anthocyanins can influence host physiology indirectly via gut microbiota-mediated biotransformation and the generation of phenolic metabolites and short-chain fatty acids (SCFAs), which may impact barrier integrity, immune regulation and systemic inflammatory tone. Preclinical studies report antineoplastic mechanisms—such as modulation of proliferation, apoptosis and angiogenesis—however, these findings are largely derived from in vitro and animal models and should be considered hypothesis-generating. Importantly, the use of standardized anthocyanin-rich dry extracts should not be interpreted as an alternative to standard antineoplastic therapies. When clinically used, standardized dry fruit extracts should be framed as nutraceutical/food-based interventions within an overall nutritional plan, with attention to product standardization, dosing, and potential interaction pathways. Overall, bilberry and blackcurrant represent promising candidates in the context of nutritional modulation of inflammation, oxidative stress and gut microbiota. Robust, long-term trials are still needed to clarify efficacy on patient-relevant clinical endpoints and safety during concomitant anticancer therapies.

Abstract: Polyetheretherketone (PEEK) is increasingly attractive for patient-specific maxillofacial reconstruction because its elastic modulus approximates cortical bone, it is fully radiolucent, and it is compatible with additive manufacturing; its principal limitation is bioinertness, as the hydrophobic surface does not support protein adsorption or direct bone apposition. This study aimed to render the surface of fused-deposition-modelling (FDM)-printed, medical-grade PEEK bioactive while preserving these bulk advantages. To this aim, 3D printed specimens of implant-grade PEEK were activated by CO₂ plasma and coated with gelatin methacryloyl (GelMA) via EDC/NHS coupling followed by UV photocrosslinking. Surfaces were characterized by attenuated total reflectance Fourier-transform infrared (ATR-FTIR) spectroscopy, sessile-drop water contact angle measurement, gravimetric analysis, and scanning electron microscopy (SEM). Plasma treatment introduced oxygen-containing functional groups and reduced the water contact angle from 78.3° to 6.6°. Covalent GelMA grafting was confirmed by characteristic amide bands, was most pronounced for the 50 mg/mL formulation, and corresponded to a deposited mass of 11.81 ± 0.47 µg/mm²; SEM revealed a relatively uniform protein film along the printed filaments. These results establish a reproducible route toward bioactive, patient-specific PEEK maxillofacial implants.

Abstract: Featuring considerable tensile ductility and multiple cracking behavior, polyethylene fiber-reinforced engineered cementitious composites (PE-ECC) are promising cement-based materials for engineering construction. However, establishing accurate design models for evaluating the mechanical properties of PE-ECC is a challenging task owing to the complex material components. This study presents an interpretable data-driven framework for predicting the mechanical properties of PE-ECC using mixture-of-experts (MoE) learning. A database comprising 383 deduplicated material-level records from 90 verified literature sources was compiled for modelling the compressive strength, ultimate tensile strain, ultimate tensile strength and first-cracking tensile strength of PE-ECC. An MoE prediction model was developed by integrating XGBoost, LightGBM, CatBoost, WDBPANN and TabPFN through out-of-fold stacking and learned gating. The model achieved coefficient of determination (R²) values of 0.971, 0.950, 0.970 and 0.954 for the four mechanical properties, respectively. SHAP, ALE and response maps were used to examine the fitted nonlinear associations between the reported mixture variables and each target property. Based on these relationships, support-filtered virtual screening was conducted within the database-supported design space to identify candidate mixtures for subsequent experimental verification. The framework links target-specific prediction with mixture-response interpretation and confines screening to regions supported by reported PE-ECC mixtures.

Abstract: Background: Type 2 inflammation is a hallmark of allergic asthma, and the central role of CD4 Th2 cells in this process is well established. Nevertheless, how CD4 Th2 cells spatially organize and interact with structural cells within tissues to promote airway microenvironment remodeling remains unclear. Methods: In this research, we employed 10x Genomics Visium spatial transcriptomics technology to examine spatial gene expression patterns in lung tissues from a mouse model of asthma induced by house dust mite (HDM). By integrating the SPOTlight algorithm with single-cell transcriptomic reference data, we conducted cell type deconvolution of spatial spots, developed spatial feature scores for CD4 Th2 cells and fibroblasts, and utilized spatially constrained CellChat analysis to deduce ligand-receptor communication between these two cell types in the bronchial microenvironment. Results: Our findings revealed that in asthmatic conditions, CD4 Th2 cells transitioned from a scattered distribution to notable clustering around the bronchi, exhibiting specific spatial co-localization with fibroblasts in the bronchial area (correlation coefficient R=0.19, P=0.0023). In contrast, no positive correlation was found between these cells in non-bronchial regions (R=-0.1, P=0.0001). Further cell communication analysis showed that under normal conditions, interactions between CD4 Th2 cells and fibroblasts were mainly driven by extracellular matrix (ECM)-related signals, particularly the Collagen-Integrin pathway. However, in asthma, this interaction pattern shifted to a pathological form characterized by Cdh1-Cdh1 mediated cell adhesion signaling. Conclusion: This study illustrates that CD4 Th2 cells and fibroblasts establish region-specific spatial cooperative relationships within the asthmatic bronchial microenvironment, with their communication patterns evolving from maintaining tissue structure to facilitating immune cell anchoring and adhesion. These insights offer a novel perspective on the spatial coupling mechanisms underlying type 2 inflammation and airway remodeling.

Abstract: We present a unified thermodynamic framework in which the flow of quantum information — described covariantly by the entropy current vector s^μ-- couples to fermionic matter and simultaneously provides the microphysical origin of spacetime geometry. The Dirac equation is modified as (iℏγ^μ ∂_μ-mc+λγ^μ s_μ)ψ=0, where dimensional analysis identifies λ as a coupling constant of mass dimension [λ]=-2, naturally placing the theory as a low-energy effective field theory. The modified dispersion relation E=-λs^0 c±c√(p^2+m^2 c^2 ) shows that uniform entropy flow produces only an overall energy shift, while non-uniform flows induce a complete gauge-like structure in the non-relativistic limit: the spatial entropy current s acts as a vector potential, and its curl generates an effective magnetic field B_eff=(λ/m)∇×s, driving spin precession via ω_entropy=(λ/m)∇×s. Extending the Proca-type dynamics of s^μ to curved spacetime with non-minimal curvature coupling, we quantize the entropy flow field and integrate out its quantum fluctuations. In this framework, the Einstein-Hilbert action emerges as the low-energy effective action, with Newton's constant Gdetermined by the entropy field's vacuum expectation value. The linearized theory yields a massive spin-2 excitation -- the emergent graviton -- with dispersion E^2=p^2+m_s^2, where the entropy field mass m_s is constrained by LIGO/Virgo gravitational-wave observations to m_s≲10^(-20)eV. Using STAR data on Λ hyperon global spin polarization in heavy-ion collisions, we estimate λ∼10^(-7)-10^(-5) GeV^(-2), corresponding to an entropy-flow energy scale Λ_λ∼10^2-10^3GeV. This framework naturally connects to Jacobson's thermodynamic gravity, Verlinde's entropic gravity, and the generalized second law ∇_μ s^μ≥0, providing a quantum-information theoretic foundation for the emergence of spacetime and a concrete, testable realization of gravity as an entropic phenomenon.

Abstract: Coal self-heating in longwall goaf areas results from strongly coupled gas flow, heat transfer, mass transport, and chemical reactions occurring within a porous medium containing residual coal. This study presents a mathematical and numerical model for analysing these transient and non-isothermal processes with spatially variable permeability based on in-situ mining data. The model accounts for gas filtration through the porous goaf, heat and mass transfer between the gas and solid phases, heterogeneous coal oxidation, homogeneous gas-phase reactions, continuous methane emission, and the possibility of nitrogen inertisation. The governing equations form a strongly coupled non-linear system and are solved using the finite volume method. Numerical simulations were performed for U-type and Y-type ventilation layouts. The results provide spatial distributions of methane, oxygen, and carbon monoxide concentrations, gas temperature, solid-phase temperature, pressure, and gas velocity. The simulations demonstrate that ventilation configuration affects oxygen penetration, gas composition, and temperature development within the goaf. In particular, the Y-type ventilation system promotes deeper oxygen ingress into the porous zone, which may increase the extent of regions susceptible to coal self-heating. The proposed approach provides a framework for analysing coupled thermal and transport phenomena associated with spontaneous coal combustion and for assessing the influence of ventilation conditions on the development of thermal hazards in longwall goaf areas.

Abstract: Multi-Key Searchable Encryption (MKSE) enables data owners to outsource their data to a cloud server, while supporting fine-grained data sharing with other authorized users. Existing most MKSE schemes can protect the date user’s search query privacy against collusion attacks between malicious data owners and the server. However, the server is not fully trusted and may maliciously return forged or incomplete search result. To address this issue, Verifiable Multi-Key Searchable Encryption (VMKSE) is proposed by leveraging Garbled Bloom Filter(GBF), which can support verifiability even when the search result is empty. Unfortunately, due to the massive native storage redundancy of Garbled Bloom Filters (GBF), the storage and computation costs of verification evidence generated during the sharing phase rise sharply with the quantity of shared documents. Therefore, in this paper, we present a novel VMKSE scheme by adopting Binary Fuse Filter(BFF), VMKSE-BFF, which can simultaneously support verifiability of search result and secure data sharing in multi-user setting. We provide a comparison with the existing VMKSE schemes. Experimental results on real-world datasets show a significant performance improvement of VMKSE-BFF.

Abstract: Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by amyloid-β plaques, neurofibrillary tau tangles, neuroinflammation, and progressive cognitive decline. Beyond these classical pathological features, emerging evidence implicates microbial dysbiosis as a contributing factor, with the gut and oral microbiomes currently providing the strongest evidence for microbiome AD associations. In contrast, the ocular microbiome represents a biologically plausible but largely unexplored candidate whose potential contribution to AD pathogenesis remains hypothetical and requires rigorous investigation. The ocular surface shares embryological, anatomical, and functional connections with the central nervous system, and the retina has emerged as a non-invasive window into neurodegenerative brain changes. Bacterial taxa detected in ocular specimens, including Cutibacterium acnes and Acinetobacter johnsonii, overlap with those reported in some studies of AD brain tissue, prompting speculation about an ocular–brain microbial interface. However, this overlap does not establish microbial trafficking, and no direct evidence currently supports the proposition that ocular microorganisms translocate to, colonize, or contribute causally to AD brain pathology. This review critically appraises the existing evidence, graded by methodological rigor and evidence strength, across gut, oral, nasal, ear, and ocular microbiome compartments. We address the substantial methodological challenges inherent in low-biomass microbiome research, emphasize the importance of distinguishing contamination artifacts from biological signals, and delineate the evidence gaps that separate association from causation. We conclude by proposing a research framework that places the ocular microbiome as an emerging hypothesis warranting experimental validation, rather than an established contributor to AD.

Abstract: This study evaluates the effects of sampling interval and averaging period on the assessment of extreme mean wind speeds for structural design, focusing on correction factors for the standard meteorological observations that are archived for more than 35,000 stations around the globe. A literature review reveals insights and flaws in past works that influenced the design of the two phases of the study. The first phase uses 2-minute mean wind speeds at 1-minute intervals over, typically, 20 years from 642 well-exposed stations distributed over the contiguous USA that are averaged to the World Meteorological Organization standard ten-minute and hourly means. Informed by the first stage results, which confirms good representation by the Weibull distribution with a von Karman autocorrelation, the second phase uses simulated 1,000-year timeseries to investigate the parameters governing the effects, which are: sampling interval, averaging period, integral timescale of the autocorrelation, and the return period of the extremes. The effects of these four parameters, in any combination, consolidate into a single analytical equation with empirically calibrated coefficients which is a good match well to the empirical analysis of the first phase, when using the appropriate integral time scale.

Abstract: Artificial intelligence (AI) is entering audit workflows while sustainability reporting and assurance expand the volume, variety and uncertainty of information subject to professional evaluation. The policy question is whether firms disclose governance arrangements that keep AI-assisted work human-led, reviewable and accountable. This exploratory study analyses the complete cross-section of the 2024 UK transparency reports of Deloitte, EY, KPMG and PwC, coded against seven pre-specified dimensions of AI–judgment governance and aggregated into a transparent, replicable AI–Judgment Governance Disclosure Index (AI-JGDI). The analysis is triangulated with the UK Financial Reporting Council's 2024 inspection results and interpreted against the ISAs, the IESBA Code, the EU Artificial Intelligence Act, the NIST AI Risk Management Framework, CSRD/ESRS, IFRS S1 and S2, and ISSA 5000. All four firms dis-close deployed AI capabilities and explicitly retain human professional responsibility; disclosure is strongest for human oversight, governance ownership and training, and least consistent for AI-specific validation and for explanations that would allow an external reader to reconstruct how an AI output affected an audit judgment. AI-JGDI scores range from 71.4 to 100.0. The study contributes a public-document method, a disclosure index and a governance framework for accountable AI-assisted judgment, and identifies limited explicit integration between AI governance and sustainability-assurance methodology.

Abstract: Hectorite intercalated with octadecyl trimethylammonium ions was synthesized with one-pot synthesis and the octadecyl trimethylammonium modified hectorite was used as adsorbent to remove phenol from aqueous solution. The pH and content of adsorbent were studied to obtain optimized condition to the adsorption of phenol. As for the 50 ml of 100 mg/L initial phenol solution at pH 12, the phenol removal rate attains about 92.3% when 0.5 g adsorbent is used. As for the adsorption isotherm, the Langmuir and Freundlich model were appropriate. The adsorption kinetic was in accord with the pseudo-second-order and the activation energy (Ea) was about 11.15 kJ/mol. The modified hectorite could be recycled and reused, maintaining a high adsorption amount after five times.

Abstract: Machine learning research in intelligent transportation systems is still largely evaluated through predictive accuracy, often treated as the primary indicator of model quality and practical value. Although this emphasis has supported substantial technical progress, it overlooks critical human and societal concerns, including interpretability, unequal impacts across user groups, uncertainty in high-stakes decisions, and the limited ability of practitioners to translate predictions into safe interventions. This Perspective proposes a human-centric decision-support framework for machine learning in safer intelligent transportation systems. The framework retains task-appropriate predictive performance as a prerequisite and complements it with six human-centric dimensions: relevance, explainability, fairness, uncertainty, human oversight, and actionability. Together, these dimensions provide a basis for evaluating whether predictive systems are not only accurate, but also understandable, trustworthy, and usable in real operational contexts. The framework has direct implications for researchers designing transport models, municipalities deploying data-driven safety policies, and transport operators integrating automated recommendations into daily decision-making. It argues that progress in intelligent mobility should be measured not only by prediction quality, but also by the quality of the decisions that predictive systems enable.

Abstract: Tidal stream turbines operate under highly demanding marine environments, where blades are subjected to extreme hydrodynamic loads, fatigue, and cavitation risks. As a result, designing hydrofoil profiles that simultaneously maximize aerodynamic/hydrodynamic efficiency (C_L/C_D) and maintain structural integrity is critical. This study presents an automated, multi-objective design optimization framework for two-dimensional hydrofoil sections of horizontal axis tidal stream turbines. The profiles are parameterized using Class Shape Transformation (CST) and evaluated using a coupled aerodynamic-structural model. Hydrodynamic coefficients are computed via XFOIL, and structural bending stresses are modeled using a double-clamped Euler-Bernoulli beam representation under distributed hydrodynamic pressure loading. We applied NSGA-II multi-objective genetic search to navigate the inherent trade-off between maximizing lift-to-drag efficiency and minimizing the root bending stress coefficient. Two optimal designs—the Best-Efficiency hydrofoil (Opt. Best-η) and the Knee-Point compromise hydrofoil (Opt. Knee)—were selected from the Pareto front. To validate the optimization results, two-dimensional steady-state Reynolds Averaged Navier-Stokes (RANS) simulations with the k-ω Shear Stress Transport (SST) turbulence model were conducted in OpenFOAM 2412. The CFD results confirm that the Optimized Knee profile achieves a lift-to-drag ratio (L/D) of 19.58 at the design angle of attack (α = 0°), representing a 30.3% improvement over the baseline NACA 63-815 profile (L/D = 15.03), while maintaining a favorable stress profile. This study demonstrates the utility of coupling fast-evaluation panel methods with structural models for rapid multi-objective screening, followed by high-fidelity RANS validation for marine hydrofoil development.

Abstract: The genus Inga Mill. (Fabaceae: Mimosoideae) comprises approximately 300 species distributed across the American tropics, playing key ecological roles in Andean forest ecosystems. In this study, occurrence data for 17 Inga species in Imbabura Province, Ecuador, were compiled through the review of five national and international herbaria (HUTN, QCNE, MO, AAU, F) and field expeditions conducted during 2024–2026, yielding a total of 181 georeferenced records. Potential distribution models were generated using the Maximum Entropy algorithm (MaxEnt v.3.4.4) for 10 species with sufficient records (≥ 6 presence points), integrating bioclimatic (WorldClim 2.1), edaphic (SoilGrids 2.0), and land-cover variables, pre-selected after multicollinearity screening (Pearson |r| < 0.75). Model validation was performed using the Area Under the ROC Curve (AUC), True Skill Statistic (TSS), and Jackknife tests. Training AUC values ranged from 0.9519 (I. cocleensis) to 0.9968 (I. punctata); TSS values ranged from 0.72 (I. striata) to 0.98 (I. feuillei), confirming high discriminative capacity. Seven species were classified as habitat specialists restricted to altitudinal ranges ≤ 400 m, and three as generalists spanning > 600 m of elevation. Productive land-use systems, water vapor pressure, geopedological units, and bioclimatic temperature and precipitation variables were the most influential predictors. Areas of high suitability for multiple species simultaneously (≥ 40% overlap) were concentrated in the cantons of Cotacachi, Otavalo, and Ibarra, identifying priority zones for conservation and sustainable agroforestry management in the Ecuadorian Andes.

Abstract: Background: Affective and somatic symptoms in girls and women arise within interacting biological, psychological, relational, and structural contexts. Premenstrual syndrome (PMS) and premenstrual dysphoric disorder (PMDD) are clinically recognized menstrual-cycle-related conditions, yet premenstrual language is also used socially to trivialize legitimate distress. Broader determinants—including menstrual stigma, reproductive transitions, unpaid care, violence, economic dependence, and health-care bias—remain insufficiently integrated in many clinical formulations. Objective: To clarify the clinical boundaries of PMS, PMDD, and premenstrual exacerbation; synthesize evidence on social determinants of affective-somatic distress across the life course; and propose a practical, non-diagnostic framework for menstrual-informed and gender-responsive assessment. Methods: A structured narrative search of PubMed/MEDLINE and the websites of major professional and public-health organizations was conducted for English-language literature available through July 2026. Search concepts included PMS, PMDD, premenstrual exacerbation, menstrual health, adolescent mental health, perinatal and menopausal mental health, gender norms, unpaid and cognitive care work, violence, child marriage, pain bias, and gender-responsive care. Priority was given to clinical guidelines, consensus statements, systematic reviews, and major epidemiological reports. No meta-analysis or formal certainty-of-evidence grading was undertaken. Synthesis: PMS and PMDD should be evaluated through symptom timing, postmenstrual remission, functional impairment, prospective daily ratings, and differential diagnosis. Hormonal sensitivity may influence vulnerability, but biological mechanisms do not invalidate the meaning of emotions or social stressors. Across the life course, symptom expression and access to care are shaped by menstrual stigma, body surveillance, unequal care responsibilities, reproductive-role expectations, violence, financial dependence, and differential credibility within health systems. These influences may coexist with, exacerbate, or be mistaken for menstrual and reproductive-stage disorders. Conclusions: Clinical assessment should distinguish established menstrual-cycle disorders from non-cyclical psychiatric, medical, and sociostructural sources of distress. The proposed concept of embodied female affective-somatic distress is a heuristic, not a diagnosis, intended to organize assessment of timing, reproductive stage, medical differential diagnoses, safety, care burden, and social context. Such an approach supports diagnostic precision while reducing both hormonal dismissal and social reductionism.

Abstract: Submarine telecommunication cables carry over 99% of international data traffic yet remain persistently vulnerable to geophysical, anthropogenic, and oceanographic hazards. Existing risk assessment methods , GIS-based least-cost path analysis and cable-body condition monitoring , operate in isolation, cannot fuse heterogeneous multi-modal data, and yield neither uncertainty estimates nor physics-consistent predictions. We present MarineGuard-GNN, a physics-informed multimodal heterogeneous graph neural network that unifies four hazard modalities , bathymetric, oceanographic, vessel traffic, and seismic , in a single relational graph over four semantic node types and four typed relation edges. Three novel technical contributions distinguish the framework: (i) the Cross-Modal Spatiotemporal Tokenizer (CMST), which jointly encodes GEBCO bathymetric raster fields via a SatMAE-pretrained ViT-S and NOAA AIS vessel trajectories via a Transformer-XL into a unified 256-dimensional token space, addressing the modality-heterogeneity gap; (ii) a Physics-Informed Heterogeneous Graph Transformer with a differentiable Mohr–Coulomb geomechanical shear-stress regularization loss Lphys that prevents physically inconsistent risk assignments at steep bathymetric gradients; and (iii) a Bayesian Uncertainty-Aware Risk Head using Monte Carlo Dropout (K=50, p=0.3) to produce calibrated epistemic uncertainty maps for inspection prioritization. Under strict 4-fold geographic cross-validation across the Pacific, Atlantic, Indian, and Mediterranean basins using five publicly available datasets, MarineGuard-GNN achieves the highest mean AUC-ROC (0.7228±0.0665) and Average Precision (0.6418±0.1040) among five baselines. A Dijkstra-based spatial route optimizer reduces mean predicted fault risk by 0.0035 absolute on the Atlantic New York–UK corridor, avoiding one additional high-risk node at a distance overhead of only +7.4 km (+0.56%). All code, preprocessing pipelines, and datasets are publicly available.

Abstract: Background: Dengue is one of the most important mosquito-borne viral diseases in Mexico, where transmission is shaped by complex interactions among environmental, demographic, urban, land-use, and climatic conditions. However, long-term national ecological analyses integrating these factors remain limited. This study evaluated ecological associations between environmental, demographic, urban, land-use, and climatic indicators and dengue incidence trends in Mexico from 1990 to 2021. Methods: A national ecological time-series study was conducted using annual aggregated data obtained from official public databases. Dengue fever and severe dengue incidence rates were analyzed in relation to environmental, land-use, urban, demographic, and climatic indicators. Exploratory simple and multiple linear regression analyses were performed, and autoregressive integrated moving average models with exogenous variables (ARIMAX) were used to evaluate temporal associations while accounting for autocorrelation. Results: In simple regression analyses, urban population, population density, renewable internal freshwater resources, forest area, and average annual temperature were significantly associated with dengue incidence. For severe dengue, renewable internal freshwater resources showed the highest explanatory capacity in simple regression analyses (R² = 0.33). In multivariable models, forest area remained positively associated with severe dengue incidence (β = 64.29; p = 0.006), whereas renewable internal freshwater resources showed an inverse association (β = −0.087; p = 0.002). After accounting for temporal autocorrelation, none of the environmental or demographic indicators remained statistically significant in ARIMAX models; however, renewable internal freshwater resources showed the strongest association with severe dengue incidence (p = 0.062). The severe dengue model included a significant first-order moving-average component (MA(1), p < 0.001), indicating short-term temporal dependence in annual incidence rates. Conclusions: Environmental, demographic, urban, land-use, and climatic indicators were associated with long-term national dengue incidence trends in Mexico. Renewable internal freshwater resources emerged as one of the most consistent ecological indicators across analyses, highlighting the relevance of water availability and management within integrated dengue prevention strategies. The temporal dependence observed for severe dengue suggests that ecological time-series analyses may contribute to improving epidemiological surveillance and generating hypotheses for future regional studies.

Abstract: Fisetin is a natural flavonoid that possesses antioxidant, anti-inflammatory, anti-senescence, and neuroprotective effects in preclinical studies. This double-blind randomized clinical trial investigated the effects of two fisetin dosing regimens on senescent gene expression in peripheral blood mononuclear cells (PBMCs) in elderly humans (> 55 years old). Participants were randomized to Daily dose (100mg/day, N = 41) or Bolus dose (20mg/kg for two consecutive days on and 28 days off, N = 40) for 60 days. Blood draw, PBMC isolation, RNA extraction and quantitative polymerase chain reaction (Q-PCR) for 5 target genes were performed at baseline, and days 15, 35, 45 and 60. At the primary endpoint, day 45, both fisetin dosing regimens significantly decreased galactosidase beta 1 (GLB1) and P16 ^INK4A (P16) gene expression. Bolus dose also significantly reduced macrophage migration inhibition factor (MIF) and alpha-L-fucosidase 1 (FUCA1) compared to baseline and decreased MIF compared to daily dose. Further, Bolus dose significantly decreased GLB1 and MIF at days 15, 35, 60, and P16 at days 15, 35 as well as FUCA1 at day 35 compared to baseline. Hence, Bolus dosing of fisetin is more effective than Daily dosing for reducing cellular senescent genes in PBMCs isolated from elderly human blood.

Abstract: Before Cyclone Senyar made landfall on Sumatra in November 2025 it drew moisture from across the South China Sea, Gulf of Thailand and Indian Ocean. The substantial damage its heavy rainfall caused on the NE and W coasts of Sumatra urges us to rethink the relationships between climate, forests, hydrology, land use, human presence, and vulnerability; the simple ‘deforestation causes floods’ narrative is no longer adequate to guide a building-back-better strategy for the areas affected. We reviewed key concepts and framing of the links between ocean temperature, atmospheric moisture transport (‘rivers in the sky’), rainfall extremes, saturation of the existing buffers, river flow and flooding as they account for the space-time pattern of Senyar effects with its multiple landfalls. Atmospheric roughness (slowing down sky rivers and unloading precipitation), surface infiltration and water retention in the soil profile, and mid- and downstream flow delays due to water retention depend on land cover beyond what a simple forest—nonforest terminology can represent. Rather than indiscriminate tree planting efforts, future adaptation and disaster avoidance efforts should balance the reduction of human exposure through effective land use planning and efforts to reduce hazard by restoring and managing vegetation cover and drainage systems.

Abstract: Background/Objectives: To analyze the profile of global scientific production on Mpox, highlighting authors, sources, keywords, and trends across different fields of knowledge. Methods: A bibliometric study conducted between December 2024 and March 2025 using the Web of Science, Scopus, Cochrane, and PubMed databases, applying Bradford’s, Lotka’s, and Zipf’s laws, in addition to scientific mapping. Results: A total of 1,146 documents were identified, with a predominance of journal articles (724; 62.9%). According to Bradford’s Law, 22 journals concentrated 381 (33.2%) publications. Lotka’s Law revealed author concentration, with one author responsible for 33 (0.5%) articles. Based on Zipf’s Law, 47 (0.74%) words comprised the most frequent “trivial zone.” Co-authorship and keyword mapping demonstrated an interdisciplinary character involving public health, virology, and epidemiology. Conclusions: There was notable progress in research related to predictive factors, transmission, vaccines, and treatments, as well as a growing use of mathematical models. However, gaps remain regarding specific therapies, long-term impacts, and effective preventive measures, underscoring the need for further investigations to ensure rapid responses to future outbreaks.