Search Results (219)

Objective: Uncovering the renoprotective and anti-inflammatory effects of atorvastatin treatment in diabetic-and-obese rats by employing traditional renal function indicators (urea and creatinine) and four prototypical cytokines (IL-1β, il-6, IL-17α, TNFα). Method: Twenty-eight male Wistar rats, aged 6 months, 350–400 g, were randomized into four groups. The first group, G-I, the denominated control, were fed standard chow over the whole course of the experiments. The rodents in G-II were exposed to a High-Fat Diet. The last two groups were exposed to Streptozotocin peritoneal injection (35 mg/kg of body weight). A short biochemical assessment was performed before diabetes model induction to ensure appropriate glucose metabolism before experiments. Following model induction, only rodents in group G-IV were gradually introduced to the same High-Fat Diet as received by G-II. Model confirmation 10 days after injections marked the start of statin treatment in group G-IV, by daily gavage of atorvastatin 20 mg/kg of body weight/day for 21 days. At the end of the experiments, the biochemical profile of interest comprised typical renal retention byproducts (urea and creatinine) and the inflammatory profile described using plasma levels of TNFα, IL-17α, IL-6, and IL-1β. Results: Treatment with Atorvastatin was associated with a statistically significant improvement in renal function in G-IV compared to untreated diabetic rodents in G-III. Changes in inflammatory activity showed partial association with statin therapy, TNFα and IL-17α mirroring the trend in urea and creatinine values. Conclusions: Our results indicate that atorvastatin treatment yields a myriad of pleiotropic activities, among which renal protection was clearly demonstrated in this model of diabetic-and-obese rodents. The statin impact on inflammation regulation may not be as clear-cut, but the potential synergy of renal function preservation and partial tapering of inflammatory activity requires further research in severely metabolically challenged models. Full article

In a European context, facing pressure to digitalize public administration, the integration of artificial intelligence (AI) at the local level remains a deeply uneven and empirically poorly understood process. This study investigates the degree of adoption of artificial intelligence (AI) in local public administrations in Germany, exploring territorial disparities and institutional factors influencing this transition. Based on a national sample of 347 municipalities, this research proposes a composite AI adoption index, built by integrating six relevant indicators (including the use of conversational bots and the automation of internal and decision-making processes). In the simulations, local administration profiles were differentiated according to factors such as IT staff (with a weight of 30%), the degree of urbanization (25%), and participation in digital networks (20%), reflecting significant structural variations between regions. The analysis model used is a multilevel one, which highlights the combined influences of local and regional factors. The results indicate a clear stratification of digital innovation capacity, with significant differences between eastern and western Germany, as well as between urban and rural environments. The study contributes to the specialized literature by developing a replicable analytical tool and provides public policy recommendations for reducing interregional digital divides. Full article

Catalpa bungei C. A. Mey, an economically and ecologically important tree species endemic to China, exhibits notable drought resistance; however, the spatial dynamics of its habitat under future climate change have not been thoroughly investigated. We employed a parameter-optimized MaxEnt modeling framework to project current and future suitable habitats for C. bungei under two Shared Socioeconomic Pathway scenarios, SSP126 (low-emission) and SSP585 (high-emission), based on CMIP6 climate data. We incorporated 126 spatially rarefied occurrence records and 22 environmental variables into a rigorous modeling workflow that included multicollinearity assessment and systematic variable screening. Parameter optimization was performed using the kuenm package in R version 4.2.3, and the best-performing model configuration was selected (Regularization Multiplier = 2.5; Feature Combination = LQT) based on the AICc, omission rate, and evaluation metrics (AUC, TSS, and Kappa). Model validation demonstrated robust predictive accuracy. Four primary environmental predictors obtained from WorldClim version 2.1—the minimum temperature of the coldest month (Bio6), annual precipitation (Bio12), maximum temperature of the warmest month (Bio5), and elevation—collectively explained over 90% of habitat suitability. Currently, the optimal habitats are concentrated in central and eastern China. By the 2090s, the total suitable habitats are projected to increase by approximately 4.25% under SSP126 and 18.92% under SSP585, coupled with a significant northwestward shift in the habitat centroid. Conversely, extremely suitable habitats are expected to markedly decline, particularly in southern China, due to escalating climatic stress. These findings highlight the need for adaptive afforestation planning and targeted conservation strategies to enhance the climate resilience of C. bungei under future climate change. Full article

This study developed a preliminary screening framework for identifying candidate Magnolia species potentially resistant to drought and cold conditions, using open access plant specimens and climate data. Based on 969 specimens, a distribution database was constructed to map 35 Magnolia species in China. Nonparametric variance analysis revealed significant interspecific differences in precipitation of the driest quarter (PDQ) and minimum temperature of the coldest month (MTCM). Using the updated climatic thresholds, nine candidate species each were identified as having drought resistance (PDQ < 60.5 mm) and cold tolerance (MTCM < 0.925 °C). In conclusion, the proposed method integrates geocoded specimen information with climate data, providing preliminary candidate species for future physiological validation, conservation planning, and further botanical research. However, the primary focus on climate data and lack of consideration of non-climatic factors warrant cautious interpretation of the results and comprehensive investigations for validation of the present study results. Full article

Background: The relationship between medicinal efficacy and the geographical environment in Thesium chinense Turcz. (T. chinense Turcz.), a traditional Chinese herb, remains systematically unexplored. This study integrates metabolomics, machine learning, and ecological factor analysis to elucidate the geographical variation patterns and regulatory mechanisms of secondary metabolites in T. chinense Turcz. from Anhui, Henan, and Shanxi Provinces. Methods: Metabolomic profiling was conducted on T. chinense Turcz. samples collected from three geographical origins across Anhui, Henan, and Shanxi Provinces. Machine learning algorithms (Random Forest, LASSO regression) identified region-specific biomarkers through intersection analysis. Metabolic pathway enrichment employed MetaboAnalyst 5.0 with target prediction. Antioxidant activity (DPPH/hydroxyl radical scavenging) was quantified spectrophotometrically. Environmental correlation analysis incorporated 19 WorldClim variables using redundancy analysis, Mantel tests, and Pearson correlations. Results: We identified 43 geographical marker compounds (primarily flavonoids and alkaloids). Random forest and LASSO regression algorithms determined core markers for each production area: Anhui (4 markers), Henan (6 markers), and Shanxi (3 markers). Metabolic pathway enrichment analysis revealed these markers exert pharmacological effects through neuroactive ligand–receptor interaction and PI3K-Akt signaling pathways. Redundancy analysis demonstrated Anhui samples exhibited significantly higher antioxidant activity (DPPH and hydroxyl radical scavenging rates) than other regions, strongly correlating with stable low-temperature environments (annual mean temperature) and precipitation patterns. Conclusions: This study established the first geo-specific molecular marker system for T. chinense Turcz., demonstrating that the geographical environment critically influences metabolic profiles and bioactivity. Findings provide a scientific basis for quality control standards of geo-authentic herbs and offer insights into plant–environment interactions for sustainable cultivation practices. Full article

Cotton is the most widely consumed natural fiber globally and emits fewer greenhouse gases compared to synthetic alternatives. Brazil is currently the largest cotton exporter, and understanding its potential for sustainable expansion is crucial. This study developed agroclimatic zoning maps for cotton (Gossypium hirsutum L.) across Brazil under current and future climate conditions using data from the World-Clim and MapBiomas platforms. Four climate change scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5) were assessed over multiple time periods. Results showed that rising temperatures and reduced rainfall will likely reduce cotton suitability in traditional producing regions such as Bahia. However, areas with potential for cotton cultivation, especially in Mato Grosso, which currently accounts for 90% of national production, remain extensive, with agroclimatic conditions indicating a theoretical expansion potential of up to 40 times the current cultivated area. This projection must be interpreted with caution, as it does not account for economic, logistical, or social constraints. Notably, Brazilian cotton is cultivated with minimal irrigation, low fertilizer input, and high adoption of no-till systems, making it one of the least carbon-intensive globally. Full article

Education is another field which generative artificial intelligence has made its way into, intervening in students’ learning processes. This article explores students’ perspectives on the use of generative AI tools, specifically ChatGPT-3.5 (free version) and ChatGPT-4 (with a subscription). The results of the survey revealed a correlation between the use of ChatGPT and the perception of grade improvement by students. In addition, this article proposes an architecture for an adaptive learning system based on generative artificial intelligence (AI). To develop the architectural proposal, we incorporated the results of the student survey along with insights gained from analyzing the architectures of other learning platforms. The proposed architecture is based on a study of adaptive learning platforms with classically virtual assistants. The main question from which the current research started was how artificial intelligence can be integrated into a learning system to improve student outcomes based on their experience with generative AI. This has been sectioned into two more specific questions: 1. How do students perceive the use of generative artificial intelligence tools, such as ChatGPT, in enhancing their learning journey? 2. Is it possible to integrate generative AI into a learning system used in education? Consequently, this article concludes with a proposed architecture for a learning platform incorporating generative artificial intelligence technologies. This article aims to present a way to understand how generative AI technologies support education and contribute to improving academic performance. Full article

A major challenge for the viticulture and oenology sector is to understand the impact of climate change on grapevine agronomic performance and wine quality. Genetic variability offers a key tool for adaptation, as some grape varieties may better withstand changing conditions while maintaining wine quality. As part of the WineClimAdapt research project (PDR2020-101-031010), a study was conducted on the adaptability of 18 white grape varieties to hot and dry conditions in Portugal. These grape varieties from Herdade do Esporão (Alentejo, Portugal) were vinified in duplicate at the INIAV winery, the result being 36 wines. The wines underwent physicochemical and sensory analyses, including gas chromatography–mass spectrometry (GC-MS) and gas chromatography–flame ionization detection (GC-FID), to assess their composition and sensory profiles. Tasters evaluated the wines using a structured scale (0–10) and rated their overall quality (0–20). Results from analysis of variance (ANOVA) revealed significant differences in the physicochemical composition and sensory profiles of the wines. Notably, some white wines displayed high acidity, which is advantageous for hot regions. The study also highlighted clear varietal differentiation across physicochemical, volatile and sensory analyses. Among the tested varieties, “Cayetana Blanca” and “Fernão Pires” achieved the highest average quality ratings, indicating promising potential for future studies and adaptation to climate change. Full article

This research investigates how historical architectural practices offer valuable solutions for contemporary climate adaptation challenges. Through systematic documentary analysis, we examine how European builders across centuries developed sophisticated construction techniques to address climate variability—techniques that remain relevant as we face increasingly extreme climate conditions. Our study focuses mainly on La Aljafería Palace in Zaragoza, Spain, a remarkable 11th-century Islamic structure that exemplifies bioclimatic design principles. We analyze its ingenious architectural elements—strategic courtyards, thermal mass management, passive ventilation systems, and innovative water features—that collectively create comfortable interior environments despite the region’s harsh summer climate. Similar analyses were conducted on historical structures in Italy, Greece, Portugal, and Cyprus as part of the ClimAid European project. Our findings reveal that these ancestral building practices utilized locally available materials and passive design strategies that required minimal energy inputs while providing effective climate regulation. We conclude that modern architects, conservationists, and policymakers face a dual challenge: developing strategies to reduce the vulnerability of historical structures to current climate impacts while also learning from and adapting these time-tested techniques to contemporary sustainable design. This research demonstrates how cultural heritage can serve not merely as an object of preservation but as a valuable knowledge repository for addressing present-day environmental challenges. Full article

Deforestation and forest degradation are important drivers of global warming, yet their implications on regional temperature and precipitation patterns are more elusive. In the Yucatán Peninsula, forest cover loss and deterioration has been rapidly advancing over the past decades. We applied local indicators of spatial association (LISA) cluster analysis and spatial autoregressive models (SAR) to evaluate the spatial relationships between tree cover and regional temperature and precipitation. We integrated NASA’s Global Forest Cover Change (GFCC) and WorldClim’s historical monthly weather datasets (2000–2015) to assess the effects of deforested, degraded, and dense forest land cover on temperature and precipitation distributions on the Yucatán Peninsula. LISA cluster analyses show warmer and drier conditions geographically coincide with deforested and degraded tree cover, but outliers allude to the potential influence of forest cover impacts on regional climate. Controlling spatial dependencies and including covariates, SAR models indicate that deforestation is associated with higher annual mean temperatures and minimum temperatures during dry and wet seasons, and decreased precipitation in the dry season. Degraded tree cover was related to higher maximum temperatures but did not relate to precipitation variability. We highlight the complex interactions between forest cover and climate and emphasize the importance of forest conservation for mitigating regional climate change. Full article

Introduction: Lipid metabolism plays a crucial role in breast cancer’s progression, treatment response, and prognosis. Alterations in triglycerides (TGs), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C) have been implicated in tumor aggressiveness and chemotherapy outcomes. This review examines the relationship between dyslipidemia and breast cancer, with a focus on chemotherapy-induced lipid alterations and their prognostic significance. Methods: A comprehensive literature search was conducted in PUBMED, Web of Science, and Google Scholar, identifying 108 unique studies. After applying the inclusion criteria, 21 studies were selected for analysis, covering lipid profile changes before, during, and after chemotherapy, as well as their impact on treatment response and clinical outcomes. Results: Breast cancer patients exhibited lower baseline TC, TG, and LDL-C levels compared to healthy controls; however, chemotherapy significantly increased these markers while decreasing HDL-C from 1.1 to 0.9 mmol/L. The incidence of dyslipidemia rose from 42.98% pre-treatment to 58.28% post-treatment. Chemotherapy-induced lipid alterations were most pronounced in anthracycline- and taxane-based regimens, leading to a 38% increase in TGs and a 23% reduction in HDL-C. While some studies reported that lipid levels normalized post-treatment, others indicated persistent dyslipidemia up to 12 months later. High baseline HDL-C was associated with a better chemotherapy response, whereas elevated TGs and LDL-C correlated with increased tumor aggressiveness, lower pathological complete response rates, and a higher relapse risk. Patients with persistently high post-treatment TGs had significantly worse disease-free survival, with a 30% relapse rate compared to 18% in those with normal TG. Preliminary evidence suggests that lipid-lowering therapies, such as statins, may offer therapeutic benefits in breast cancer by targeting the cholesterol synthesis pathways involved in tumor growth, though further clinical trials are required. Conclusions: Dyslipidemia is a key metabolic factor influencing breast cancer’s progression, treatment response, and long-term prognosis. Chemotherapy-induced lipid alterations may persist, increasing cardiovascular risk and potentially affecting therapeutic efficacy. Routine lipid monitoring and metabolic interventions could enhance treatment outcomes and survivorship. Future research should focus on developing lipid-targeted strategies to optimize breast cancer management. Full article

The article addresses the current and future potential distribution of Drosophila suzukii (Diptera: Drosophilidae), commonly known as spotted wing Drosophila (SWD). This invasive pest affects various fruit crops worldwide. Native to Southeast Asia, the species has rapidly expanded due to its high adaptability to climates and ability to infest ripe fruits. SWD occurrence data were collected from multiple databases, pseudo-absences were selected from the background area, and climatic variables were downloaded from WorldClim. The Random Forest algorithm was employed to model the current distribution and project future scenarios, categorizing environmental suitability into high, moderate, and low levels. The analysis of bioclimatic variables indicated that factors such as isothermality, maximum temperature of the warmest month, and precipitation of the driest month are the most significant for pest distribution. The results revealed high climatic suitability for the species in North America, Europe, and Asia, with projections indicating expansion under climate change scenarios in the Northern Hemisphere, including new areas in Europe and North America. Regions with higher suitability are expected to require management and monitoring strategies, particularly in vulnerable agricultural areas. Furthermore, the study underscores the importance of climatic data in predicting pest distribution and formulating effective control and mitigation policies. Full article

The standard treatment for breast cancer typically includes surgery, often followed by systemic therapy and individualized treatment regimens. However, there is growing interest in identifying pre-therapeutic biomarkers that can predict tumor response to neoadjuvant chemotherapy (NACT). This study systematically evaluated various analytical parameters, including age, TNM stage, histological type, molecular subtype, and several biomarker ratios, such as the platelet-to-lymphocyte ratio (PLR), neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), systemic immune-inflammatory index (SII), and prognostic nutritional index (PNI). We aimed to assess the predictive value of these parameters regarding the tumor’s response rate to NACT. The analysis revealed a statistically significant association between the pathological complete response—pCR (absence of any detectable cancer cells in the tissue following neoadjuvant chemotherapy (NACT))—rate and NLR in the subgroup with values between 1 and 3 (p = 0.001). The optimal cut-off for PLR was determined to be 120.45, with 80.55% of patients achieving pCR showing PLR values below this threshold (p = 0.000). Similarly, the LMR cut-off was found to be 12.34, with 77.77% of patients with pCR having LMR values below this threshold (p = 0.002). Additionally, lower pre-therapeutic values of NLR (p < 0.001), PLR (p = 0.002), SII (p = 0.001), and LMR (p = 0.001) were significantly correlated with pCR compared to the non-pCR subgroup (p < 0.005). These findings highlight the predictive potential of these biomarkers for achieving pCR following NACT. Our study supports the hypothesis that pre-therapeutic values of NLR, PLR, SII, and LMR can serve as predictive biomarkers for pCR in breast cancer patients undergoing NACT. However, the PNI did not demonstrate predictive potential in relation to pCR. These biomarkers may provide valuable insights into patient prognosis and guide personalized treatment strategies. Full article

Understanding the response of species to past climate change provides great opportunities to know their adaptive capacity for resilience under future climate change. Since the Late Pleistocene, dramatic climate fluctuations have significantly impacted the distribution of giant pandas (Ailuropoda melanoleuca). However, how the spatial distribution and climatic niche of giant pandas shifted in response to past climate change remain poorly understood. Based on the known distribution records (fossil and present day) and the most updated climate projections for the Last Interglacial (LIG; ~120 ka), Last Glacial Maximum (LGM; ~22 ka), Mid-Holocene (MH; ~6 ka), and the present day, we predicted and compared the distribution and climatic niche of giant pandas. The results show that giant pandas have undergone a considerable range contraction (a 28.27% reduction) followed by a marked range expansion (a 75.8% increase) during the LIG–LGM–MH period, while its climatic niche remained relatively stable. However, from the MH to the current, both the distribution area and climatic niche of giant pandas have undergone significant changes. Our findings suggest that the giant panda may adjust its distribution to track stable climatic niches in response to future climate change. Future conservation planning should designate accessible areas for giant pandas and adjust priority conservation areas as needed. Full article

Land-Cover and Land-Use Change (LCLUC) is a dynamic process affected by the combination and mutual interaction of climatic and socioeconomic drivers. Field studies and surveys, which are typically time- and resource-consuming, have been employed by researchers to better understand LCLUC drivers. However, remotely sensed data may provide the same trustworthy outcomes with less time and expense. This study aimed to assess the relationship between LCLUC and changes in socioeconomic and climatic factors in the Dallas-Fort Worth (DFW) metropolitan area, Texas, USA, between 2000 and 2020. The LCLU, socioeconomic, and climatic data were obtained from the National Land Cover Database of Multi-Resolution Land Characteristics Consortium, NASA’s Socioeconomic Data and Applications Center (SEDAC), and the global climate and weather data website (WorldClim), respectively. Change detection calculated from these data was used to analyze spatial and statistical relationships between LCLUC and changes in socioeconomic and climatic factors. Results showed that LCLUC was significantly predicted by population change, housing and transportation, household and disability change, socioeconomic status change, monthly average minimum temperature change, and monthly mean precipitation change. While socioeconomic factors played a predominant role in driving LCLUC in this study, the influence of climatic factors should not be overlooked, particularly in regions where climate sensitivity is more pronounced, such as arid or transitional zones. These findings highlight the importance of considering regional variability when assessing LCLUC drivers. Full article