Search Results (823)

Background: The candidate therapeutic peptide TnP demonstrates broad, system-level regulatory capacity, revealed through integrated network analysis from transcriptomic data in zebrafish. Our study primarily identifies TnP as a multifaceted modulator of drug metabolism, wound healing, proteolytic activity, and pigmentation pathways. Results: Transcriptomic profiling of TnP-treated larvae following tail fin amputation revealed 558 differentially expressed genes (DEGs), categorized into four functional networks: (1) drug-metabolizing enzymes (cyp3a65, cyp1a) and transporters (SLC/ABC families), where TnP alters xenobiotic processing through Phase I/II modulation; (2) cellular trafficking and immune regulation, with upregulated myosin genes (myhb/mylz3) enhancing wound repair and tlr5-cdc42 signaling fine-tuning inflammation; (3) proteolytic cascades (c6ast4, prss1) coupled to autophagy (ulk1a, atg2a) and metabolic rewiring (g6pca.1-tg axis); and (4) melanogenesis-circadian networks (pmela/dct-fbxl3l) linked to ubiquitin-mediated protein turnover. Key findings highlight TnP’s unique coordination of rapid (protease activation) and sustained (metabolic adaptation) responses, enabled by short network path lengths (1.6–2.1 edges). Hub genes, such as nr1i2 (pxr), ppara, and bcl6aa/b, mediate crosstalk between these systems, while potential risks—including muscle hypercontractility (myhb overexpression) or cardiovascular effects (ace2-ppp3ccb)—underscore the need for targeted delivery. The zebrafish model validated TnP-conserved mechanisms with human relevance, particularly in drug metabolism and tissue repair. TnP’s ability to synchronize extracellular matrix remodeling, immune resolution, and metabolic homeostasis supports its development for the treatment of fibrosis, metabolic disorders, and inflammatory conditions. Conclusions: Future work should focus on optimizing tissue-specific delivery and assessing genetic variability to advance clinical translation. This system-level analysis positions TnP as a model example for next-generation multi-pathway therapeutics. Full article

We introduce a novel portfolio optimization framework—Distributionally Robust Multivariate Stochastic Cone Order (DR-MSCO)—which integrates partial orders on random vectors with Wasserstein-metric ambiguity sets and adaptive cone structures to model multivariate investor preferences under distributional uncertainty. Grounded in measure theory and convex analysis, DR-MSCO employs data-driven cone selection calibrated to market regimes, along with coherent tail-risk operators that generalize Conditional Value-at-Risk to the multivariate setting. We derive a tractable second-order cone programming reformulation and demonstrate statistical consistency under empirical ambiguity sets. Empirically, we apply DR-MSCO to 23 Borsa Istanbul equities from 2021–2024, using a rolling estimation window and realistic transaction costs. Compared to classical mean–variance and standard distributionally robust benchmarks, DR-MSCO achieves higher overall and crisis-period Sharpe ratios (2.18 vs. 2.09 full sample; 0.95 vs. 0.69 during crises), reduces maximum drawdown by 10%, and yields endogenous diversification without exogenous constraints. Our results underscore the practical benefits of combining multivariate preference modeling with distributional robustness, offering institutional investors a tractable tool for resilient portfolio construction in volatile emerging markets. Full article

Diclofenac, an aryl-acetic acid derivative from the non-steroidal anti-inflammatory drug class, is the subject of multiple non-clinical and clinical studies regarding its usefulness in treating some dermatologic pathologies with an inflammatory, auto-immune, or proliferative component. Diclofenac is now approved for the topical treatment of actinic keratoses (AK), pre-malignant entities that have the risk of transformation into skin carcinomas. The hypothesis that diclofenac increases granular layer development in the mice tail model, having an anti-psoriatic effect, was demonstrated in a previous study in which 1% and 2% diclofenac ointment was evaluated. The aim of the present study was to perform experimental research on the topical effect of diclofenac in the mice tail model, by testing 4% and 8% diclofenac ointment, which is presented in the first part of the manuscript. In the second part of the manuscript, we also aimed to conduct a literature review regarding topical diclofenac uses in specific dermatological entities by evaluating the articles published in PubMed and Scopus databases during 2014–2025. The studies regarding the efficacy of topical diclofenac in dermatological diseases such as AK and field cancerization, actinic cheilitis, basal cell carcinoma, Bowen disease, Darier disease, seborrheic keratoses, and porokeratosis, were analyzed. The results of the experimental work showed a significant effect of 4% and 8% diclofenac ointment on orthokeratosis degree when compared to the negative control groups. Diclofenac in the concentration of 4% and 8% significantly increased the orthokeratosis degree compared to the negative control with untreated mice (p = 0.006 and p = 0.011, respectively, using the Kruskal–Wallis test) and to the negative control with vehicle (p = 0.006 and p = 0.011, respectively, using the Kruskal–Wallis test). The mean epidermal thickness was increased for the diclofenac groups, but not significantly when compared to the control groups. The results are concordant with our previous experiment, emphasizing the need for future clinical trials on the use of topical diclofenac in psoriasis. Full article

Breast-conserving surgery followed by external beam Radiotherapy (RT) is a standard approach for early-stage Breast Cancer (BC). This retrospective study aims to determine the risk of RT-induced lung cancer for both standard and hypofractionated treatments. Fifty-eight Sicilian women treated at Humanitas Istituto Clinico Catanese (Misterbianco, Italy) between 2015 and 2021 with standard fractionated 3D-CRT (50 Gy in 2 Gy/fraction) were included. All treatment plans were designed using a hypofractionated schedule (42.56 Gy in 2.66 Gy/fraction). An Eclipse™ plug-in script was developed using the Eclipse Scripting Application Programming Interface (ESAPI) to extract patient and treatment data from the Treatment Planning System and compute Organ At Risk (OAR) volume, Organ Equivalent Dose (OED), Excess Absolute Risk (EAR), and Lifetime Attributable Risk (LAR) using the Schneider Mechanistic Model and reference data from regional populations, A-bomb survivors, and patients with Hodgkin’s Disease (HD). The OED distributions exhibited a statistically significant shift toward higher values in standard fractionated plans (p < 0.01, one-tailed paired Student’s t-test), leading to increased EAR and LAR. These results indicate that hypofractionated treatment may lower the risk of radiation-induced lung cancer. The feasibility of a priori risk estimation was evaluated by integrating the script into the TPS, allowing rapid comparison of SF and HF plans during planning. Full article

This paper develops a novel modeling framework that integrates time-varying quantile-based spillover effects into a regime-switching realized volatility model. A dynamic spillover factor is constructed by identifying the most influential contributors to Bitcoin’s realized volatility across different quantile levels. This quantile-layered structure enables the model to capture heterogeneous spillover paths under varying market conditions at a macro level while also enhancing the sensitivity of volatility regime identification via its incorporation into a time-varying transition probability (TVTP) Markov-switching mechanism at a micro level. Empirical results based on the cryptocurrency market demonstrate the superior forecasting performance of the proposed TVTP-MS-HAR model relative to standard benchmark models. The model exhibits strong capability in identifying state-dependent spillovers and capturing nonlinear market dynamics. The findings further reveal an asymmetric dual-tail amplification and time-varying interconnectedness in the spillover effects, along with a pronounced asymmetry between market capitalization and systemic importance. Compared to decomposition-based approaches, the X-RV type of models—especially when combined with the proposed quantile-driven factor—offers improved robustness and predictive accuracy in the presence of extreme market behavior. This paper offers a coherent approach that bridges phenomenon identification, source localization, and predictive mechanism construction, contributing to both the academic understanding and practical risk assessment of cryptocurrency markets. Full article

This study examines the impact of geopolitical risk (GPR) on the connectedness dynamics among the sovereign bonds of the emerging seven (E7) and the Group of Seven (G7) countries. Initially, a quantile-based vector-autoregressive (Q-VAR) connectedness approach is used to calculate the total connectedness index (TCI) among sovereign bonds under different market states. Then, the impact of GPR on the TCI at the median and tails is estimated to examine if GPR affects the TCI among sovereign bonds. Using daily yields from 30 January 2012, to 17 June 2024, the findings show that the GPR is one of the significant determinants of the TCI among sovereign bonds during normal and extreme market conditions. Other determinants of the TCI include yields on Treasury bills (T-bills), the exchange rate, and the financial market volatility index. The impact of GPR on the TCI varies significantly during different GPR episodes and bond market conditions. The effect of GPR on the TCI among sovereign bonds yields is higher during war times and when bond yields are average. These findings can be utilized by investors seeking to achieve international diversification and policymakers aiming to mitigate the effects of heightened geopolitical risk on financial stability. Furthermore, GPR can be used as an early signal tool for systematic tail risk spillovers among sovereign bonds. Full article

Mining waste presents significant environmental and public health risks due to the potential release of toxic substances when improperly managed. In this study, four tailings samples were taken to evaluate the environmental risks in the Ponce Enríquez mining area in Ecuador. Chemical characterization and X-ray Fluorescence Spectrometry (XRF) were used to analyze the content of potentially toxic elements (PTEs) of interest (As, Cd, Cr, Cu, Ni, Pb, and Zn), and X-ray Diffraction (XRD) for mineralogical characterization. The contamination index (IC) was calculated to assess the potential hazard associated with the content of PTEs in the mining wastes. To assess environmental risks, leaching tests were carried out to evaluate the potential release of PTEs, and Acid-Base Accounting (ABA) tests were conducted to determine the likelihood of acid mine drainage formation. The results revealed that the PETs concentration exceeded the maximum permissible limits in all samples, according to Ecuadorian regulations: As, Pb, and Cd were identified as critical contaminants. Mineralogically, quartz was the dominant phase, followed by carbonates (calcite, dolomite and magnesite), phyllosilicates (chlorite and illite), and minor amounts of pyrite and talc. The IC indicated high to very high contamination risk levels, with As being the predominant contributor. Although leaching tests met the established limits for non-hazardous mining waste, the ABA test showed that all samples had a high potential for long-term acid generation. These results underscore the need for implementing management strategies to mitigate the environmental impacts and the development of plans to protect local ecosystems and communities from the adverse effects of mining activities. Full article

As investment portfolios become increasingly diversified and financial asset risks grow more complex, accurately forecasting the risk of multiple asset classes through mathematical modeling and identifying their heterogeneity has emerged as a critical topic in financial research. This study examines the volatility and tail risk of gold, crude oil, Bitcoin, and selected stock markets. Methodologically, we propose two improved Value at Risk (VaR) forecasting models that combine the autoregressive (AR) model, Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH) model, Extreme Value Theory (EVT), skewed heavy-tailed distributions, and a rolling window estimation approach. The model’s performance is evaluated using the Kupiec test and the Christoffersen test, both of which indicate that traditional VaR models have become inadequate under current complex risk conditions. The proposed models demonstrate superior accuracy in predicting VaR and are applicable to a wide range of financial assets. Empirical results reveal that Bitcoin and the Chinese stock market exhibit no leverage effect, indicating distinct risk profiles. Among the assets analyzed, Bitcoin and crude oil are associated with the highest levels of risk, gold with the lowest, and stock markets occupy an intermediate position. The findings offer practical implications for asset allocation and policy design. Full article

Life Cycle Impact Assessments (LCIAs) examine the environmental impacts of products using life cycle inventories (LCIs) of quantified inputs and outputs of a product through its life cycle. Currently, estimated impacts from mining are dominated by long-term metal release from tailings due to inaccurate assumptions regarding metal release and transport within and from mine materials. A conceptual model approach is proposed to support the development of a new database of LCI data, applying mechanistic processes required for the release and transport of metals through tailings and categorizing model inputs into ‘bins’. The binning approach argues for accuracy over precision, noting that precise metal release rates are likely impossible with the often-limited data available. Three case studies show the range of forecasted metal release rates, where even after decades of monitoring within the tailings and underlying aquifer, metal release rates span several orders of magnitude (<100 mg/L to >100,000 mg/L sulfate at the Faro Mine). The proposed tool may be useful for the development of a new database of LCI data, as well as to analyze mine’s regional considerations during designs for risk evaluation, management and control prior to development, when data is also scarce. Full article

This study investigates the systemic tail dependence among biodiversity, clean energy, and financial transition assets using a novel partial correlation-based network approach. Analyzing eleven indices from 2019 to 2025, we capture dynamic connectedness across normal and extreme market conditions. Empirical findings indicate that clean energy assets form a central hub of connectedness, while biodiversity-linked instruments increasingly influence systemic behavior under stress. Events such as the COVID-19 vaccine rollout, the Russia–Ukraine war, and El Niño intensify these dynamics. Compared to the traditional Generalized Forecast Error Variance Decomposition (GFEVD) framework, our approach better detects short-term shocks, offering actionable insights for climate-aware investment and risk management. Full article

Assessing the risks associated with waste disposal is essential for environmental protection and sustainable development, especially given concerns about the impact of industrial activities on the environment. This study analyses soil contamination in the Defor Petrila tailings-dump area caused by the deposition of waste material resulting from coal exploitation. To characterise the heavy-metal contamination in detail, we applied a comprehensive methodology that includes the calculation of the geo-accumulation index (I_geo), contamination factor (Cf), and potential ecological risk index (PERI), along with an analysis of the heavy-metal concentration isolines and a statistical analysis using the Pearson correlation coefficient. The results reveal varying levels of heavy-metal concentrations, as indicated by the calculated indices. The findings underscore the need for remediation and ongoing monitoring to mitigate the environmental impacts. This study provides a scientific basis for decision making in environmental management and highlights the importance of assessing mining-waste disposal near human settlements using various contamination-assessment methods. Full article

The utilization of iron tailings in road construction poses significant environmental risks due to the complex release mechanisms of pollutants and varying regional conditions. This study integrates an exponential decay model with an instantaneous pollutant transport model, employing Monte Carlo simulations to assess risks and regional characteristics. Results show high Potential Hazard Indices (PHIs) for arsenic, manganese, barium, nickel, and lead, with PHI values between 4.2 and 22.7. Simulations indicate that manganese and nickel concentrations may exceed groundwater standards, particularly in humid areas. The study recommends controlling the iron tailings mixing ratio based on climate, suggesting limits of 35% in humid, 60% in semi-humid, and more lenient ratios in arid and semi-arid regions. It also underscores the need for improved risk assessment methodologies and region-specific management strategies at the national level. Full article

Route planning under uncertain traffic conditions requires accounting for not only expected travel times but also the risk of late arrivals. This study proposes a mean-upper partial moment (MUPM) framework for pathfinding that explicitly considers travel time unreliability. The framework incorporates a benchmark travel time to measure the upper partial moment (UPM), capturing both the probability and severity of delays. By adjusting a risk parameter ($\theta$), the model reflects different traveler risk preferences and unifies several existing reliability measures, including on-time arrival probability, late arrival penalty, and semi-variance. A bi-objective model is formulated to simultaneously minimize mean travel time and UPM. Theoretical analysis shows that the MUPM framework is consistent with the expected utility theory (EUT) and stochastic dominance theory (SDT), providing a behavioral foundation for the model. To efficiently solve the model, an SDT-based label-correcting algorithm is adapted, with a pre-screening step to reduce unnecessary pairwise path comparisons. Numerical experiments using GPS probe vehicle data from Louisville, Kentucky, USA, demonstrate that varying $\theta$ values lead to different non-dominated paths. Lower $\theta$ values emphasize frequent small delays but may overlook excessive delays, while higher $\theta$ values effectively capture the tail risk, aligning with the behavior of risk-averse travelers. The MUPM framework provides a flexible, behaviorally grounded, and computationally scalable approach to pathfinding under uncertainty. It holds strong potential for applications in traveler information systems, transportation planning, and network resilience analysis. Full article

Gastrointestinal (GI) parasitic infections in non-human primates are of growing concern due to their implications for both veterinary and public health. Long-tailed macaques (Macaca fascicularis), commonly found in peri-urban and temple environments in Southeast Asia, may act as reservoirs for zoonotic parasites, posing risks to humans and domestic animals. This study investigated the prevalence and species diversity of GI parasites in free-ranging macaques from four provinces in Northeast Thailand (Loei, Khon Kaen, Bueng Kan, and Sisaket). A cross-sectional study was conducted between April and May 2025. A total of 445 fecal samples were examined using two parasitological techniques: agar plate culture (APC) and the formalin–ethyl acetate concentration technique (FECT). The overall prevalence of parasitic infection was 86.5%, with Strongyloides sp. (65.2%) as the most prevalent helminth and Balantioides coli-like (29.5%) and Entamoeba histolytica-like (28.8%) as the predominant protozoa. Other parasites identified included helminths (Trichuris sp., Ascaris sp.) and protozoa (Blastocystis sp., Iodamoeba bütschlii, Entamoeba coli, and Chilomastix mesnili). Mixed infections were frequently observed, with both helminths and protozoa co-occurring in 37.3% of cases. The high infection rates and parasite diversity reflect substantial environmental contamination and sustained transmission cycles. These findings underscore the importance of integrated surveillance in wildlife populations and the need for One Health-based approaches to minimize zoonotic transmission risks at the human–animal–environment interface. Full article

The accelerated industrialization in China has precipitated a dramatic surge in solid waste generation, causing severe land resource depletion and posing substantial environmental contamination risks. Simultaneously, the cement industry has become characterized by the intensive consumption of natural resources and high carbon emissions. This review aims to investigate the current technological advances in utilizing industrial solid waste for cement production, with a focus on promoting resource recycling, phase transformations during hydration, and environmental management. The feasibility of incorporating coal-based solid waste, metallurgical slags, tailings, industrial byproduct gypsum, and municipal solid waste incineration into active mixed material for cement is discussed. This waste is utilized by replacing conventional raw materials or serving as active mixed material due to their content of oxygenated salt minerals and oxide minerals. The results indicate that the formation of hydration products can be increased, the mechanical strength of cement can be improved, and a notable reduction in CO₂ emissions can be achieved through the appropriate selection and proportioning of mineral components in industrial solid waste. Further research is recommended to explore the synergistic effects of multi-waste combinations and to develop economically efficient pretreatment methods, with an emphasis on balancing the strength, durability, and environmental performance of cement. This study provides practical insights into the environmentally friendly and efficient recycling of industrial solid waste and supports the realization of carbon peak and carbon neutrality goals. Full article