Search Results (404)

Background/Objectives: Prolonged remission on low-dose glucocorticoids (GC) is a main goal in patients with systemic lupus erythematosus (SLE). The aim of this study is to assess whether GC ≤ 5 mg/d increases the risk of damage accrual in patients with SLE in prolonged remission. Methods: Observational study of routine clinical care data of the inception Lupus Cruces-Bordeaux cohort. Only patients in DORIS remission during five consecutive yearly visits were included. The endpoint was damage accrual during the 5-year follow-up, either global or specific damage: GC-induced, cardiovascular (CV), lupus and other. Patients no longer on GC therapy by year 5 (GC5-Off) were compared with those who continued GC therapy (GC5-On). Comparisons were made by Cox and Poisson regressions, which were adjusted with propensity score (PE) in order to control for confounding by indication. Results: 132 patients were included, 56 in the GC5-On and 76 in the GC5-Off groups. All patients were on GC ≤ 5 mg/d for the whole follow-up, the mean prednisone dose in the GC5-On group being 2.96 mg/d during the whole study period and 2.6 mg/d during the 5th year. Fourteen patients (10.6%) accrued damage. More patients in the GC5-On group accrued global damage, 16% vs. 7% in the GC5-Off group, p = 0.08, mainly at CV domains (7% vs. 1%, respectively, p = 0.16). In the PS-adjusted Cox and Poisson regressions, the GC5-On group was not significantly associated with global (p = 0.39) or CV damage accrual (p = 0.62), nor with the absolute (p = 0.40) or CV-restricted final SDI scores (p = 0.63). The C-index of the propensity score model was 0.79. Conclusions: Maintaining doses of prednisone < 5 mg/d in lupus patients in prolonged remission is not associated with an increased risk of damage accrual. Full article

Background: Iron deficiency (ID) remains the leading cause of anemia, affects a vast number of persons globally, and continues to be a significant global health burden. Comprehending the patterns of ID burden is essential for developing targeted public health policies. Methods: Using data from the Global Burden of Disease (GBD) 2021 study for the years 1990–2021, the XGBoost model was constructed to predict prevalence and disability-adjusted life years (DALYs) for the period 2022–2050, based on key demographic variables. Shapley Additive exPlanations (SHAP) values were applied to interpret the contributions of each variable to the model’s predictions. Additionally, the Age–Period–Cohort (APC) model was used to evaluate the effects of age, period, and birth cohort on both prevalence and DALYs. The relationship between the Socio-Demographic Index (SDI) and ID’s age-standardized prevalence rate (ASPR) as well as the age-standardized DALYs rate (ASDR) was also analyzed to assess the influence of socioeconomic development on disease burden. Results: The global prevalent cases of ID grew from 984.61 million in 1990 to 1270.64 million in 2021 and are projected to reach 1439.99 million by 2050. Similarly, global DALYs from ID increased from 28.41 million in 1990 to 32.32 million in 2021, with a projected rise to 36.13 million by 2050. The ASPR declined from 18,204/100,000 in 1990 to 16,433/100,000 in 2021, with an estimated annual percentage change (EAPC) of −0.36% over this period. It is expected to decrease further to 15,922 by 2050, with an EAPC of −0.09% between 2021 and 2050. The ASDR was 518/100,000 in 1990 and 424/100,000 in 2021, with an EAPC of −0.68% from 1990 to 2021. It is expected to remain relatively stable at 419/100,000 by 2050, with an EAPC of −0.02% between 2021 and 2050. In 2021, the highest ASPRs were recorded in Senegal (34,421/100,000), Mali (34,233/100,000), and Pakistan (33,942/100,000). By 2050, Mali (35,070/100,000), Senegal (34,132/100,000), and Zambia (33,149/100,000) are projected to lead. For ASDR, Yemen (1405/100,000), Mozambique (1149/100,000), and Mali (1093/100,000) had the highest rates in 2021. By 2050, Yemen (1388/100,000), Mali (1181/100,000), and Mozambique (1177/100,000) are expected to remain the highest. SHAP values demonstrated that gender was the leading predictor of ID, with age and year showing negative contributions. Females aged 10 to 60 consistently showed higher prevalence and DALYs rates compared to males, with the under-5 age group having the highest rates for both. Additionally, men aged 80 and above exhibited a rapid increase in prevalence. Furthermore, the ASPR and ASDR were significantly higher in regions with a lower SDI, highlighting the greater burden of ID in less developed regions. Conclusions: ID remains a significant global health concern, with its burden projected to persist through 2050, particularly in lower-SDI regions. Despite declines in ASPR and ASDR, total cases and DALYs are expected to rise. SHAP analysis revealed that gender had the greatest influence on the model’s predictions, while both age and year showed overall negative contributions to ID risk. Children under 5, women under 60, and elderly men aged 80+ were the most vulnerable groups. These findings underscore the need for targeted interventions, such as improved nutrition, early screening, and addressing socioeconomic drivers through iron supplementation programs in low-SDI regions. Full article

Spatial Data Infrastructure (SDI) is a decision-making tool that is often used in the area of urban planning. At the same time, many other data sources with great utility potential, such as Big Data, can be identified. The aim of the paper is to present the possibility of using mobile Big Data collections with data from Polish SDI, for the purposes of local spatial planning on the example of the tourist town, Mikolajki in Poland. The publication also focuses on assessing the quality of data, as well as the decision-making process supported by these sources. The draft of the local spatial development plan was verified based on integrated data sources. The results showed that the visualization of Big Data as a heat map may be used in urban tasks and as the thematic layer integrated with vector and raster data sets from the SDI in the geographic information system software. The contribution is the practical example how information about users of mobile devices and some information from behavioral profiles may be analyzed for the purposes of verifying planned land use. Full article

A series of 16 (hetero)aryl compounds based on coumarin and equol has been efficiently synthesized by exploring the palladium-catalyzed Suzuki cross-coupling reactions. Polyphenol based on coumarin (4-methyl-7-hydroxy coumarin) was initially converted to corresponding coumarin imidazylate and then subjected to Suzuki coupling reaction with 4-methoxyphenylboronic acid to obtain the coupled product. This modified approach was later developed into a one-pot methodology by directly reacting the polyphenol with 1,1-sulfonyldiimidazole (SDI) and boronic acid in situ to obtain the Suzuki coupled product in one step. Moreover, an array of (poly)phenols based on coumarin and equol were later converted to diverse (hetero)aryl compounds by this optimized step-economic protocol. The synthesized compounds were then subjected to the screening of their potential antioxidant activities by 2,2-diphenyl-1-picrylhydrazyl (DPPH) assay. In our investigation, the compounds 4ah, 4eh, 4gh and 4hh exhibited promising antioxidant potential when compared to the reference standard, butylated hydroxytoluene (BHT). Structure activity relationship (SAR) studies revealed the importance of the presence of electron-donating substituents in enhancing the antioxidant activity of the synthesized compounds. Full article

The forecasting of hydrological flows (rainfall depth or rainfall discharge) is becoming increasingly important in the management of hydrological risks such as floods. In this study, the Long Short-Term Memory (LSTM) network, a state-of-the-art algorithm dedicated to time series, is applied to predict the daily water level of Lake Nokoué in Benin. This paper aims to provide an effective and reliable method to enable the reproduction of the future daily water level of Lake Nokoué, which is influenced by a combination of two phenomena: rainfall and river flow (runoff from the Ouémé River, the Sô River, the Porto-Novo lagoon, and the Atlantic Ocean). Performance analysis based on the forecasting horizon indicates that LSTM can predict the water level of Lake Nokoué up to a forecast horizon of t + 10 days. Performance metrics such as Root Mean Square Error (RMSE), coefficient of correlation (R²), Nash–Sutcliffe Efficiency (NSE), and Mean Absolute Error (MAE) agree on a forecast horizon of up to t + 3 days. The values of these metrics remain stable for forecast horizons of t + 1 day, t + 2 days, and t + 3 days. The values of R² and NSE are greater than 0.97 during the training and testing phases in the Lake Nokoué basin. Based on the evaluation indices used to assess the model’s performance for the appropriate forecast horizon of water level in the Lake Nokoué basin, the forecast horizon of t + 3 days is chosen for predicting future daily water levels. Full article

In this study, we analyzed the predictions of hydrological droughts in the Lam Chiang Kri Watershed (LCKW) by using the Soil and Water Assessment Tool (SWAT) and streamflow data for 2010–2021. The objective was to assess the streamflow drought index (SDI) for 5-, 10-, 25-, and 50-year return periods (RPs) in 2029 and 2039 in two representative concentration pathway (RCP) scenarios: the moderate climate change scenario (RCP 4.5) and the high-emission scenario (RCP 8.5). The SWAT model showed high accuracy (R² = 0.82, NSE = 0.78). In RCP4.5, streamflow is projected to increase by 34.74% for 2029 and 18.74% for 2039, while in RCP8.5, a 37.06% decrease is expected for 2029 and 55.84% for 2039. A historical analysis indicated that there were frequent short-term droughts according to SDI-3 (3-month-period index), particularly from 2014 to 2015 and 2020 to 2021, and severe droughts according to SDI-6 (6-month-period index) in 2015 and 2020. The RCP8.5 projections indicate worsening drought conditions, with critical periods from April to June. A wavelet analysis showed that there is a significant risk of severe hydrological drought in the LCKW. Drought characteristic analysis indicated that high-intensity events occur with low frequency in the 50-year RP. Conversely, high-frequency droughts with lower intensity are observed in RPs of less than 50 years. The results of this study highlight an increase in severe drought risk in high emission scenarios, emphasizing the need for water management. Full article

Effective drought management requires precise measurement, but this is challenging due to the variety of drought indices and indicators, each with unique methods and specific uses, and limited ground data availability. This study utilizes remote sensing data from 2001 to 2020 to compute drought indices categorized as meteorological, agricultural, and hydrological. A Gaussian kernel convolves these indices into a denoised, multi-band composite image. Further refinement with a Gaussian kernel enhances a single drought index from each category: Reconnaissance Drought Index (RDI), Soil Moisture Agricultural Drought Index (SMADI), and Streamflow Drought Index (SDI). The enhanced index, encompassing all bands, serves as a predictor for classification and regression tree (CART), support vector machine (SVM), and random forest (RF) machine learning models, further improving the three indices. CART demonstrated the highest accuracy and error minimization across all drought categories, with root mean square error (RMSE) and mean absolute error (MAE) values between 0 and 0.4. RF ranked second, while SVM, though less reliable, achieved values below 0.7. The results show persistent drought in the Sahel, North Africa, and southwestern Africa, with meteorological drought affecting 30% of Africa, agricultural drought affecting 22%, and hydrological drought affecting 21%. Full article

Efforts to establish Pakistan’s National Spatial Data Infrastructure (NSDI) have been underway for the past 15 years, and therefore it is necessary to gauge the current progress to channelize efforts into areas that need improvement. This article assessed Pakistan’s NSDI implementation efforts through well-established approaches, including the SDI readiness model, organizational aspects, and state of play. The data were collected from Spatial Data Infrastructure (SDI) and Geographic Information System (GIS) experts. The findings underscored challenges related to human resources, SDI education/culture, long-term vision, lack of awareness of geoinformation (GI), sustainable funding, metadata availability, online geospatial services, and geospatial standards hindering NSDI development in Pakistan. However, certain factors exhibit favorable standings, such as the legal framework for NSDI establishment, web connectivity, geospatial software availability, the unavailability of core spatial datasets, and institutional leadership. Thus, to enhance the development of NSDI in Pakistan, recommendations include bolstering financial and human resources, improving online geospatial presence, and fostering a long-term vision for NSDI. Full article

Drought monitoring is a critical environmental challenge, particularly in regions where irrigated agricultural intensification and urban expansion pressure water resources. This study assesses the impact of these activities on drought dynamics in Morocco’s Oum Er-Rbia (OER) watershed from 2002 to 2022, using the newly developed Watershed Integrated Multi-Drought Index (WIMDI), through Google Earth Engine (GEE). WIMDI integrates several drought indices, including SMCI, ESI, VCI, TVDI, SWI, PCI, and SVI, via a localized weighted averaging model (LOWA). Statistical validation against various drought-type indices including SPI, SDI, SEDI, and SMCI showed WIMDI’s strong correlations (r-values up to 0.805) and lower RMSE, indicating superior accuracy. Spatiotemporal validation against aggregated drought indices such as VHI, VDSI, and SDCI, along with time-series analysis, confirmed WIMDI’s robustness in capturing drought variability across the OER watershed. These results highlight WIMDI’s potential as a reliable tool for effective drought monitoring and management across diverse ecosystems and climates. Full article

Considering the prevalence of diet-related diseases, new ways of preventing them are being sought. One of them is the addition of polyphenols to high-starch products to inhibit their digestibility and reduce their glycemic index. Therefore, this study aimed to investigate the differences between polyphenols popular in food ((+)catechin, epigallocatechin gallate, quercetin, kaempferol, naringenin, hesperidin, trans-ferulic acid, and p-coumaric acid), in terms of their impact on wheat, rice, potato, and maize starch digestibility. Polyphenols were added to starch separately, before and after its pasting, in one of the following doses: 5, 10, and 20 mg. Starch was digested in the presence of single polyphenols to measure RDS (rapidly digestible starch), SDS (slowly digestible starch), RS (resistant starch), and TS (total starch) content. On that basis, the SDI (starch digestion index) was calculated, and the GI (glycemic index) was estimated. The results show that polyphenols inhibit starch digestion at different levels depending on the type of tested starch and the time of polyphenol addition. However, in terms of RDS, TS, and eGI (estimated glycemic index), the greatest impact was observed for epigallocatechin gallate in a dose of 20 mg most frequently, independently of the kind of tested starch and the time of polyphenol addition. Full article

The increasing adoption of batteries in a variety of applications has highlighted the necessity of accurate parameter identification and effective modeling, especially for lithium-ion batteries, which are preferred due to their high power and energy densities. This paper proposes a comprehensive framework using the Levenberg–Marquardt algorithm (LMA) for validating and identifying lithium-ion battery model parameters to improve the accuracy of state of charge (SOC) estimations, using only discharging measurements in the N-order Thevenin equivalent circuit model, thereby increasing computational efficiency. The framework encompasses two key stages: model parameter identification and model verification. This framework is validated using experimental measurements on the INR 18650-20R battery, produced by Samsung SDI Co., Ltd. (Suwon, Republic of Korea), conducted by the Center for Advanced Life Cycle Engineering (CALCE) battery group at the University of Maryland. The proposed framework demonstrates robustness and accuracy. The results indicate that optimization using only the discharging data suffices for accurate parameter estimation. In addition, it demonstrates excellent agreement with the experimental measurements. The research underscores the effectiveness of the proposed framework in enhancing SOC estimation accuracy, thus contributing significantly to the reliable performance and longevity of lithium-ion batteries in practical applications. Full article

The relationship between natural resource development and sustainable development has long been a focus in academia. In the context of a new global economic cooperation system, many scholars argue that such cooperation can lead to a “resource curse” effect in partner countries, hindering their sustainable development. This study analyzed panel data from 64 countries from 2008 to 2020, using the Belt and Road Initiative as a representative of multilateral economic cooperation (MEC) policies. The aim was to examine the actual impact of multilateral economic cooperation on the sustainable development levels of partner countries and to explore the underlying mechanisms influencing these outcomes. First, we measured and identified the sustainable development index (SDI) under natural resource development schemes and the “resource curse” effect in these countries. Then, we employed a double machine learning approach to evaluate the policy effects of MEC on sustainable resource development. We constructed an interactive double machine learning model to examine and validate the specific mechanisms of resource development effects. The results indicate that the level of sustainable resource development in MEC countries is relatively low, and a “resource curse” effect already exists. However, participating in MEC suppresses this “curse” effect. By promoting innovation cooperation, institutional improvement, structural optimization, trade openness, and pollution reduction, MEC effectively enhances the sustainable development levels of partner countries. Full article

The water footprint is an increasingly demanded environmental sustainability indicator for certifications and labels in agricultural production. Processing tomatoes are highly water-intensive, and existing studies on water footprint have uncertainties and do not consider the impact of different irrigation configurations (e.g., surface drip irrigation (SDI) and subsurface drip irrigation (SSDI)) and irrigation strategies. This study presents a two-year experimental investigation to determine the water footprint of processing tomatoes grown in southern Spain (Andalusia) and the impact of SSDI and deficit irrigation. Five irrigation treatments were established: SDI₁ (surface drip irrigation without water limitation), SDI₂ (surface drip irrigation without water limitation adjusted by soil moisture readings), SSDI₁ (subsurface drip irrigation without water limitation and a dripline depth of 15 cm), SSDI₂ (similar to SSDI₁ but with mild/moderate water deficit during the fruit ripening stage), and SSDI₃ (subsurface drip irrigation without water limitation and a dripline depth of 35 cm (first year) and 25 cm (second year)). Measurements included crop vegetative growth, leaf water potential, leaf gas exchange, nitrate concentration in soil solution, and crop yield and quality. The soil water balance components (actual evaporation, actual transpiration, deep drainage), necessary for determining the total crop water footprint, were simulated on a daily scale using Hydrus 2D software. Results indicated that SSDI makes more efficient use of irrigation water than SDI. The water footprint of SSDI₁ was 20–35% lower than that of SDI₁. SSDI₂ showed similar water footprint values to SDI₁ under highly demanding environmental conditions and significantly lower values (≈40%) in a year with lower evaporative demand. The dripline depth in SSDI was critical to the water footprint. With a 35 cm installation depth, SSDI₃ had a significantly higher water footprint than the other treatments, while the values were similar to SSDI₁ when the depth was reduced to 25 cm. Full article

Hydrological droughts occur as a result of various hydrometeorological conditions, such as precipitation deficits, reduced snow cover, and high evapotranspiration. Droughts caused by precipitation deficits and occurring during warm seasons are usually longer in duration. This important observation raises the question that climate change associated with global warming may increase drought conditions. Consequently, it is important to understand changes in the processes leading to dry periods in order to predict potential changes in the future. This study is a scientific analysis of the impact of climate change on drought conditions in the Zhaiyk–Caspian, Tobyl–Torgai, Yesil, and Nura–Sarysu water management basins using the standardized precipitation index (SPI) and streamflow drought index (SDI). The analysis methods include the collection of hydrometeorological data for the entire observation period up to and including 2021 and the calculation of drought indices to assess their intensity and duration. The results of this study indicate an increase in the intensity and frequency of drought periods in the areas under consideration, which is associated with changes in climatic conditions. The identified trends have serious implications for agriculture, ecological balance, and water resources. The conclusions of this scientific study can be useful for the development of climate change adaptation strategies and the sustainable management of natural resources in the regions under consideration. Full article

Aim: Sphingolipids are a class of complex and bioactive lipids that are involved in the pathological processes of cardiovascular disease. Fabry disease is an X-linked storage disorder that results in the pathological accumulation of glycosphingolipids in body fluids and the heart. Cardiac dyssynchrony is observed in patients with Fabry disease and left ventricular (LV) hypertrophy. However, little information is available on the relationship between plasma sphingolipid metabolites and LV remodelling after acute myocardial infarction (AMI). The purpose of this study was to assess whether the baseline plasma sphingomyelin/acid ceramidase (aCD) ratio predicts LV dyssynchrony at 6M after AMI. Methods: A total of 62 patients with AMI undergoing primary angioplasty were recruited. Plasma aCD and sphingomyelin were measured prior to primary angioplasty. Three-dimensional echocardiographic measurements of the systolic dyssynchrony index (SDI) were performed at baseline and 6 months of follow-up. The patients were divided into three groups according to the level of aCD and sphingomyelin above or below the median. Group 1 denotes lower aCD and lower sphingomyelin; Group 3 denotes higher aCD and higher sphingomyelin. Group 2 represents different categories of patients with aCD and sphingomyelin. Trend analysis showed a significant increase in the SDI from Group 1 to Group 3. Logistic regression analysis showed that the sphingomyelin/aCD ratio was a significant predictor of a worsening SDI at 6 months. Conclusions: AMI patients with high baseline plasma sphingomyelin/aCD ratios had a significantly increased SDI at six months. The sphingomyelin/aCD ratio can be considered as a surrogate marker of plasma ceramide load or inefficient ceramide metabolism. Plasma sphingolipid pathway metabolism may be a new biomarker for therapeutic intervention to prevent adverse remodelling after MI. Full article