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Rainfall is the major component of the hydrologic cycle and it is the primary source of runoff. The main purpose of this study was to estimate daily discharge by employing an Adaptive Neuro-Fuzzy Inference System (ANFIS) model using rainfall and soil moisture data at three different depths (5 cm, 100 cm and bedrock) for the Damanganga basin. The length of the data for the study period 1983–2022 is 39 years. The model employed nine membership functions for each variable of soil moisture, rainfall, discharge and 30 rules were optimized. The results were compared considering a range of model performance indicators as correlation coefficient (R2) and Nash–Sutcliffe efficiency (NSE) coefficient. The model application results shows that soil moisture at bedrock gives more precise value of daily discharge with (R2) and NSE value as 0.9936 and 0.9981, respectively, as compared to the soil moisture at depths of 5 and 100 cm. The better results obtained for the measurement of soil moisture in the deeper soil layer are consistent with the hydrological behavior anticipated for the analyzed catchment, where the root-zone soil layer is the driver of the runoff response rather than the surface observations. This study can be helpful to hydrologists in selecting appropriate rainfall–runoff models. HIGHLIGHTS To estimate the daily discharge by employing an Adaptive Neuro-Fuzzy Inference System (ANFIS) model using rainfall and soil moisture data.; The model employed nine membership functions for each variable of soil moisture, rainfall, discharge and 30 rules were optimized.; The results were compared considering a range of model performance indicators such as correlation coefficient (R2) and Nash-Sutcliffe efficiency (NSE) coefficient.;

Abstract Disabled 2 (Dab2), an adaptor protein, is up regulated in the hair follicle stem cells (HFSCs); however, its role in any tissue stem cells has not been studied. In the present study, we have reported that Dab2 conditional knockout (Dab2-cKO) mice exhibited a delay in the HF cycle due to perturbed activation of HFSCs. Further, Dab2-cKO mice showed a reduction in the number of HFSCs and reduced colony forming ability of HFSCs. Dab2-cKO mice showed extended quiescence of HFSCs concomitant with an increased expression of Nfatc1. Dab2-cKO mice showed a decreased expression of anti-aging genes such as Col17a1, decorin, Sirt2 and Sirt7. Dab2-cKO mice did not show full hair coat recovery in aged mice thereby suggesting an accelerated aging process. Overall, we unveil for the first time, the role of Dab2 that regulate activation and self-renewal of HFSCs.

The objective of this study is to determine the optimum size of stone revetment for different water standing durations and critical drawdown rates for 80% height of flood. The model bank was constructed using soil sourced from the Parlalpur ferry ghat of the Ganga River in Kaliachak, West Bengal, with a field density of 1,500 kg/m3 achieved by maintaining a 10% moisture content (MC) in the model bank soil. The model riverbank was prepared considering Froude (F) similitude having a distorted depth scale of 1/20 and a linear scale of 1/200. In this study, effective stone sizes (D10) – 2.33, 3.22, 4.58, 6.31, and 8.84 mm – were used. These stone sizes were investigated in conjunction with three water standing durations: 15, 30, and 45 min. The bank slope was prepared at 1V:1.5H, and a drawdown ratio of 80% was maintained. The effectiveness of stone revetment size was analysed in terms of the percentage loss of stone revetment and the percentage loss of the model bank's cross-sectional area. The outcomes of this study indicate that the 6.31 mm stone size exhibits optimal performance. HIGHLIGHTS Optimized stone size was determined.; This underscores the importance of careful stone selection, considering soil characteristics.; The findings of this study offer valuable insights into the temporal dynamics of bank stability under real-world hydrological conditions, which are beneficial for engineers.; The importance of synergetic impact of drawdown and velocity for designing the stone revetment for river bank protection.;

The article examines machine learning models for precipitation forecasting in the Ambica River basin, addressing the important requirement for accurate hydrological forecasts in water resource management. Using a comprehensive collection of meteorological variables such as temperature, humidity, wind speed, and precipitation, four separate models are used: Support Vector Regression (SVR), Random Forest (RF), Decision Tree (DT), and Multiple Linear Regression (MLR). These models’ performance is rigorously evaluated using various assessment indicators. The cross-correlation function (XCF) is used in this study to evaluate the correlations between climatic variables and precipitation. The XCF analysis reveals several noteworthy trends, such as a high link between maximum temperature and precipitation, with maxima consistently found at months across all four sites. Furthermore, relative humidity and wind speed have significant connections with precipitation. The findings highlight the value of machine learning approaches in improving precipitation forecast accuracy. The RF and SVR models typically outperform, with values ranging from 0.74 to 0.91. This impressive accuracy underlines their effectiveness in precipitation forecasting, beating competing models in both the training and testing stages. These findings have significant consequences for hydrological processes, notably in the Ambica River basin, where accurate precipitation forecasting is critical for sustainable water resource management. HIGHLIGHTS To create precise precipitation forecasting models for the Ambica River basin and evaluating the efficiency of various machine learning techniques.; To estimate precipitation in the Ambica River basin most accurately, the study will determine the most accurate ML model.;

Morbidity and mortality caused by respiratory syncytial virus (RSV) in older adults and those with underlying health conditions can be potentially alleviated through vaccination. To assist vaccine policy decision-makers and payers, we estimated the annual economic burden of RSV-associated cardiorespiratory hospitalizations among insured US adults aged ≥18 y in the Merative MarketScan claims database from September through August of 2017–2018 and 2018–2019. Negative binomial regression models were used to estimate the number of RSV-associated cardiorespiratory hospitalizations using MarketScan-identified cardiorespiratory diagnosis codes in the presence or absence of RSV circulation per weekly laboratory test positivity percentages from the Centers for Disease Control and Prevention. This number was multiplied by mean cardiorespiratory hospitalization costs to estimate total costs for RSV-associated cardiorespiratory hospitalizations. Number and cost for International Classification of Diseases (ICD)-coded RSV hospitalizations were quantified from MarketScan. In 2017–2018 and 2018–2019, respectively, 18,515,878 and 16,462,120 adults with commercial or Medicare supplemental benefits were assessed. In 2017–2018, 301,248 cardiorespiratory hospitalizations were observed; 0.32% had RSV-specific ICD codes, costing $44,916,324, and 5.52% were RSV-associated cardiorespiratory hospitalizations, costing $734,078,602 (95% CI: $460,826,580–$1,103,358,799). In 2018–2019, 215,525 cardiorespiratory hospitalizations were observed; 0.34% had RSV-specific ICD codes, costing $33,053,105, and 3.14% were RSV-associated cardiorespiratory hospitalizations, costing $287,549,472 (95% CI: $173,377,778–$421,884,259). RSV contributes to substantial economic burden of cardiorespiratory hospitalizations among US adults. Modeling excess risk using viral positivity data provides a comprehensive estimation of RSV hospitalization burden and associated costs, compared with relying on ICD diagnosis codes alone.

Changes in Land Use and Land Cover (LULC) have a significant impact on both urban and environmental planning, especially in the context of rapidly urbanising areas. The largest city in the state of Gujarat, Ahmedabad, has experienced substantial growth. Utilising the powerful tools of Geographic Information System (GIS) and Google Earth Engine (GEE), this study uses a thorough methodology to track and examine changes in LULC over the last ten years. Modifications in LULC have a significant impact on both urban and environmental planning, especially in light of the fast urbanisation cycle. This study uses a comprehensive approach to monitor and analyze Landsat imagery changes in LULC over the past ten years, leveraging the powerful capabilities of Google Earth Engine and GIS. Important insights into LULC patterns are revealed by analysing Landsat imagery from Ahmedabad from 2013 to 2023. Pre-processing with GIS and GEE, accuracy evaluation, and classification of satellite images are all included in the methodology. With a notable 0.85 Kappa and 89 % accuracy in LULC classification, the resulting LULC classifications cover settlements, vegetation, water bodies, arid land, and other relevant features. Results show that over the previous ten years, Ahmedabad has seen a noteworthy decrease in agricultural lands of 13.74 % and a reduction in barren areas of 4.78 %. Meanwhile, the total LULC patterns have shown a significant expansion of 23.56 %. This study is significant because it highlights how urbanisation reduces vegetation cover. The results will helpful for urban planning strategies that take changing environmental dynamics into account.

A hazard is a natural occurrence that might harm humans, animals or the environment. It may cause loss of life, illness or other health consequences, property damage, social and economic crisis or environmental degradation. Many places of the world are at risk from one or more disasters. Although many studies have concentrated on single hazards, there is a need for integrated evaluations of multi-hazards for more effective land management. A selection of datasets and methods, such as meteorological data, satellite images, and GIS, were used to create the risk assessment maps. The parameters for multi-hazard assessment are mainly considered as rainfall, slope, elevation, and land use/land cover and create a map in a GIS environment. For a particular region, multi-hazards can be produced by integrating maps of several hazard assessments. The objective of this study is an integration of geospatial and fuzzy-logic techniques for multi-hazard mapping. Extensive parts of Gujarat state (India) experience a wide range of natural hazards: floods, soil erosion, drought, and earthquakes. This research creates and evaluates individual and group multi-hazard maps to visualize the spatial variation of hazards in Gujarat state, India. The calculated four individual hazard maps have been categorised into five classes: very-low, low, moderate, high, and very high. The multi hazard map has been classified into sixteen classes using the GIS unsupervised. This study aims to improve disaster preparedness, enhance land management, or guide decision-making for disaster risk reduction. This study can be helpful in the future to engineers, planners, and local governments in the field of spatial planning and natural disaster management.

Groundnut, being a widely consumed oily seed with significant health benefits and appealing sensory profiles, is extensively cultivated in tropical regions worldwide. However, the yield is substantially impacted by the changing climate. Therefore, predicting stressed groundnut yield based on climatic factors is desirable. This research focuses on predicting groundnut yield based on several combinations of climatic factors using artificial neural networks and three training algorithms. The Levenberg–Marquardt, Bayesian Regularization, and Scaled Conjugate Gradient algorithms were evaluated for their performance using climatic factors such as minimum temperature, maximum temperature, and rainfall in different regions of Sri Lanka, considering the seasonal variations in groundnut yield. A three-layer neural network was employed, comprising a hidden layer. The hidden layer consisted of 10 neurons, and the log sigmoid functions were used as the activation function. The performance of these configurations was evaluated based on the mean squared error and Pearson correlation. Notable improvements were observed when using the Levenberg–Marquardt algorithm as the training algorithm and applying the natural logarithm transformation to the yield values. These improvements were evident through the higher Pearson correlation values for training (0.84), validation (1.00) and testing (1.00), and a lower mean squared error (2.2859 × 10−21) value. Due to the limited data, K-Fold cross-validation was utilized for optimization, with a K value of 5 utilized for the process. The application of the natural logarithm transformation to the yield values resulted in a lower mean squared error (0.3724) value. The results revealed that the Levenberg–Marquardt training algorithm performs better in capturing the relationships between the climatic factors and groundnut yield. This research provides valuable insights into the utilization of climatic factors for predicting groundnut yield, highlighting the effectiveness of the training algorithms and emphasizing the importance of carefully selecting and expanding the climatic factors in the modeling equation.

Precipitation is the most critical element of the hydrological cycle that might influence the frequency of floods or drought. Therefore, for drought and flood forecasting, knowing the precipitation pattern and its trend is mandatory. The research aims to estimate the monthly, seasonal, and annual precipitation trend using statistical (Mann Kendall test & Sen's Slope estimator) and graphical (Innovative Trend Analysis method) for the Hanumangarh district of N-E Rajasthan. The mean monthly precipitation data were gathered from the India-WRIS from 1901 to 2022 (122 years). Seasonal and annual precipitation trend variations were examined by statistical methods and compared with the graphical method. The study results conclude that precipitation trends showed variability for the SW monsoon season from the graphical to the statistical method. For the SW monsoon season, the decreasing trend was seen using statistical methods. In contrast, the graphical method revealed an increasing trend. During the annual precipitation, increasing trends were seen using statistical and graphical methods. It is found that the ITA method is more efficient in finding trends because it does not assume any assumptions like that of the MK test. This study will help water resources engineers and local people with sustainable management, planning, and development of water resources in the Hanumangarh district. HIGHLIGHTS Trends in seasonal and annual precipitation time series are assessed using both statistical and graphical methods and then results are compared.; The results of the statistical method are well-matched with the graphical method.; The ITA method is easier to apply for finding trends.; The ITA method is more accurate because it does not consider any assumptions.;

The purpose of the study was to use hierarchical clustering and Thiessen polygon algorithms to identify the significant rain gauge stations for flood forecasting at Sardar Sarovar Dam. Rainfall data from 2010 to 2018 was utilized to analyze the catchment region between Omkareshwar Dam and Sardar Sarovar Dam. The study identified two clusters of rain gauge stations with similar rainfall patterns and divided the study area into seventeen regions using Thiessen polygons. The land use map showed that the study area was mostly covered by crop lands, and the soil map divided the area into three types of sedimentary claystone soil. A hydrological model, Hydrologic Engineering Center – Hydrologic Modelling System (HEC-HMS), was used for rainfall-runoff modeling, and the computed runoff was compared with observed gauge discharge and inflow of Sardar Sarovar Dam. Regression analysis was performed to assess the performance of the model, and the results showed a good correlation between observed rainfall and estimated runoff values for 2012 and 2016. The study concludes that the existing rain gauge network is sufficient for flood forecasting, and the developed model along with the Thiessen polygon method can provide more accurate predictions of flow. The study highlights the importance of selecting suitable rain gauge stations for reliable flood forecasting in flood-prone basins. The study findings can be useful for future runoff prediction and flood forecasting in the study area.

The natural hazard that has the greatest impact on people and is considered to be the most complicated but least understood is drought. Thus, the projection of drought is extremely necessary to be prepared against possible damage. There are many indices available for the assessment of drought severity, but the result of SPEI is more reliable than any other indices. The objective of the study was to predict drought in the Sirohi district (India) using different meteorological variables like temperature, precipitation, Potential Evapotranspiration (PET), etc. For the projection of drought, the statistical downscaling technique was applied by using observed data and Global Climate Model (GCM) data. Drought years in the period 2020–2099 were identified and the probability of occurrence of the different drought classes has been evaluated. Finally, the relationship between El Niño and droughts was investigated using Sea Surface Temperature (SST) anomaly data. Sirohi district has a 75.0 % chance of drought during El Niño years. The model can predict droughts with a 60 % accuracy using the statistical downscaling technique. From 2019 to 2099, there are 13.75 % chances of moderate droughts and 12.5 % chances of mild droughts. According to this research, there will not be any severe or extreme droughts in the future. This study concludes that the projection of drought can be done accurately by statistical downscaling techniques using GCM data. Results showed that major droughts in the South-West region of Rajasthan occurred during El Niño years. This will help the policymakers to prepare policies, drought contingency plans, and rules according to the drought conditions of the particular region.

Study region: Mahanadi reservoir project complex (MRP), Chhattisgarh, India Study focus: This study assesses and compares the performance of the Coupled Model Intercomparison Project Phase 5 (CMIP5) and CMIP6 models in modeling the hydrological regime. The study compared the performance of 13 CMIP6 GCMs in two Shared Socioeconomic Pathways (SSEPs) with that of 16 CMIP5 GCMs in two Representative Concentration Pathways (RCPs). Before being used to drive the Soil and Water Assessment Tool (SWAT) for streamflow prediction, the raw CMIP outputs were adjusted and downscaled using Bias Correction and Spatial Disaggregation methods (BCSD). In addition, the water deficits were also assessed between the mean monthly released by the optimized model and the mean monthly water demand. Results of this research projected the domestic water demand for future scenarios (2023–2099). New hydrological insights for the study region: The results of this study revealed that the genetic algorithm (GA) outperforms the various optimization techniques for CMIP5, with deficits observed by the GA algorithm. In addition, CCCmaCanESM2 was found to be the most efficient among CMIP5 GCMs, whereas MPI-ESM1–2-HR was used for CMIP6 GCMs. Overall, the CMIP6 multi-model mean ensemble (MMME) outperformed the CMIP5 MMME in simulating streamflow over the study area at annual and seasonal timescales. CMIP6 MMME also reduced maximum and minimum temperature biases over the study region significantly. In overall conclusion, the CMIP6 ensemble offers a lower margin of uncertainty for future climate projections and better credibility for hydrological effect analysis. The results further indicate that satisfactory steam flows can be obtained from the SWAT model while slight underestimations can be seen for higher discharges. In addition, the projected domestic water demand based on future population and irrigation demand clearly showcases significant annual increases.

There are different sources of groundwater pollution among them industrial water disposal, seawater ingress, usage of pesticides and fertilizers in agricultural fields, and municipal and residential wastewater disposal. The aim of the study is to assess groundwater vulnerability in terms of quality using the GOD method using the Geographical Investigation System approach for Surat and its surroundings. Groundwater confinement, overlying strata and depth to water table are the three parameters that are used in the assessment of groundwater vulnerability. In this study, all three parameters are given by the conventional weights which are suggested by Foster 1987. Based on the study Mahuva, Mandvi, Umarpada and some parts of the Bardoli talukas lie in very high-vulnerability zones whereas another part of the study area lies in a high-vulnerability zone. Almost 35.98% of the area of the district lies in the higher-vulnerability zone. Depth to the water table and the overlying strata are very important parameters that effectively cause groundwater vulnerability. This study may help in groundwater quality management, and watershed management as well as it is very much useful for the policymakers and local authorities as well as the government. HIGHLIGHTS To assess the groundwater vulnerability in Surat district, the GIS approach-based GOD method is effectively used.; This study helps to improve the quality of the groundwater in the study area.; This study helps to improve groundwater management policies.; Effective and authentic data provided by the government bodies are used for the assessment.; From this study, we can achieve sustainable water use management.;

This research work aimed to project and analyze climatic variability using GCMs and RCMs in the Hathmati River Basin, which happens to be one of the most important tributaries of Western India. The analysis included a baseline period from 1980 to 2014 and a future scenario (2050s) under a representative concentration pathway (RCP4.5). Following the evaluation of bias correction methods, distribution mapping and power transformation were used for temperature and precipitation projection, respectively. The 2050 s RCP4.5 simulations showed an increase in precipitation to 1,015.54 mm from 936.91 mm giving around a 8.45% increase in average precipitation. For temperature, the maximum temperature was around a 7.05% increase taking the average temperature to 34.21 from 33.97. The minimum temperature was 20.24 from 20.41 showing a positive change of around 8.4%. The future precipitation and temperature change projected might worsen the water stress and increase the probability of the occurrence of severe events, and hence mitigation strategies and management options to reduce this negative impact should be encouraged. HIGHLIGHTS Work is on one of the most important tributaries of Western India.; Includes latest technology and method.; GCMs are used.; RCP4.5 projections are used for forecasting.; BIAS correction is incorporated.;

Rainfall is an important part of the hydrological cycle, as well as its variability, and is relevant to drought and floods. Long-term changes in hydrological processes are referred to as climate change for an area. Urbanization, population growth, and economic growth are all having negative effects on the environment. The study of trends in the long term is essential from climatic change and socioeconomic perspectives. Examining the temporal variations in rainfall is crucial because changes in rainfall patterns and distribution can have a significant impact on the amount of water available in a watershed. The objective of the study is to assess the long-term (1902–2021) temporal trends in seasonal (winter, pre-monsoon, monsoon, and post-monsoon) and annual rainfall for the Rajsamand district of Rajasthan state using the innovative trend analysis (ITA) technique. Due to its ability to provide results in graphical form, the ITA approach is a very useful tool for detecting patterns in rainfall time series data. This technique is also used to detect trends as ‘low,’ ‘medium,’ and ‘high,’ which should be considered in future studies on floods ‘high’ and drought ‘low’, respectively. Based on the above study, it is observed that no trend is detected for the annual season, a positive trend is detected in the winter season and a negative trend is detected for pre-monsoon and post-monsoon seasons in some regimes respectively. The ITA of the S-W monsoon also specifies that in the low regime the nature of the trend is increasing and in the high regime nature of the trend is decreasing. This research will serve as a scientific foundation for assessing and mitigating the effect of climate change on the environment to reduce the risk of weather patterns. HIGHLIGHTS The innovative trend analysis (ITA) method is used in the study.; To apply the ITA approach to examine the annual and seasonal patterns of various precipitation intensities in Rajsamand.; To discuss the patterns of seasonal and annual rainfall variability across the region.; ITA method is a very useful tool for detecting patterns in rainfall time series.; ITA method is both simple and effective.;

Industrial and municipal wastes, agricultural contamination owing to pesticides and chemical hazards, seawater intrusion in coastal areas, and other factors damage groundwater. In several towns and industrial clusters across India, this is becoming a rising subject of concern. Groundwater is difficult to contaminate, but once contaminated, it is difficult to clean up. It is critical to attain this goal using a variety of aquifer vulnerability assessment approaches. All of these strategies rely on process models as well as statistical or overlay index methodologies. Groundwater vulnerability is a major topic of discussion due to declining groundwater levels and rising contamination, posing a serious threat to the environment and water sources. To identify the risk and to assess the vulnerability, extensive research has been carried out among all the methods based on different parameters and different indexes. The DRASTIC method is one of the most important and accurate of the overlay and index methods for the assessment of groundwater vulnerability. This research study is a systematic analysis of the available research articles on the applications of the DRASTIC and Modified DRASTIC (DRASTIC-L) performance management process on Geographical Information Systems (GIS). This research also reveals research gaps in the various groundwater vulnerability assessment approaches, as well as their limits and hypotheses. This study discovered that integrating GIS with DRASTIC is the most effective and accurate way for determining groundwater vulnerability. In agricultural, arid, semi-arid, and basaltic zones, the modified DRASTIC model also outperforms the traditional DRASTIC model. HIGHLIGHTS Helpful in taking management decisions so as to afford early warning of quality degradation.; Effective strategy for sustainable groundwater management.; Increase the chances of reducing the gap between policy enhancement and enforcement.; Improve the quality of groundwater and safe supply for use.; Achieve sustainable water-use management.;

Drought forecasting is being considered an important tool to help understand the rainfall pattern and climate change trend. Drought is a prolonged period of months or years in which an area, whether surface water or groundwater, becomes insufficient in its water supplies. Drought is considered as the most difficult but least known environmental phenomenon, impacting more persons than any other. There are several indices used to classify droughts. For this study, precipitation-based drought indices are considered (i.e., SPI, RAI and Percentage Departure of Rainfall). The objective of the research is to examine and determine the possible rainfall trends over the Jalore district of South-West Rajasthan in Luni river basin. In this research, trend analysis using the rainfall data from the years 1901 to 2021 was carried out on a monthly, seasonal and annual basis. To define the current trend path, the Mann-Kendall test and Sen's slope estimator test were used. In order to detect the trend and its change in magnitude over a particular period of time, Sen's slope estimator was used. During the southwest monsoon, declining rainfall leads to short-term meteorological droughts, which have severe effect on the agriculture sector and Jalore district's water supplies, while rising rainfall during other seasons tends to mitigate the severity of drought. The result of research reveals that there is rise of pre-monsoon and post-monsoon rainfall, but it also depicts a fall in the annual rainfall which reflects in reduced Winter and S-W monsoon rainfall. HIGHLIGHTS To assess possible drought severity and duration in the Jalore district of S-W Rajasthan region.; The study also deals with identification of drought patterns, and changes with the 121 years of the time span.; Trend analysis is carried out using Mann-Kendall test to quantify the rainfall pattern & characteristics.; To estimate the magnitude of trend using Sen's slope.;

Well-being is a multifaceted construct that is used across disciplines to portray a state of wellness, health, and happiness. While aspects of well-being seem universal, how it is depicted in the literature has substantial variation. The aim of this scoping review was to identify conceptual and operational definitions of well-being within the field of occupational health. Broad search terms were used related to well-being and scale/assessment. Inclusion criteria were (1) peer-reviewed articles, (2) published in English, (3) included a measure of well-being in the methods and results section of the article, and (4) empirical paper. The searches resulted in 4394 articles, 3733 articles were excluded by reading the abstract, 661 articles received a full review, and 273 articles were excluded after a full review, leaving 388 articles that met our inclusion criteria and were used to extract well-being assessment information. Many studies did not define well-being or link their conceptual definition to the operational assessment tool being used. There were 158 assessments of well-being represented across studies. Results highlight the lack of a consistent definitions of well-being and standardized measurements.

Extracellular vesicles (EVs) are lipid membrane bound-cell-derived structures that are a key player in intercellular communication and facilitate numerous cellular functions such as tumor growth, metastasis, immunosuppression, and angiogenesis. They can be used as a drug delivery platform because they can protect drugs from degradation and target specific cells or tissues. With the advancement in the technologies and methods in EV research, EV-therapeutics are one of the fast-growing domains in the human health sector. Therapeutic translation of EVs in clinics requires assessing the quality, safety, and efficacy of the EVs, in which pharmacokinetics is very crucial. We report here the application of physiologically based pharmacokinetic (PBPK) modeling as a principal tool for the prediction of absorption, distribution, metabolism, and excretion of EVs. To create a PBPK model of EVs, researchers would need to gather data on the size, shape, and composition of the EVs, as well as the physiological processes that affect their behavior in the body. The PBPK model would then be used to predict the pharmacokinetics of drugs delivered via EVs, such as the rate at which the drug is absorbed and distributed throughout the body, the rate at which it is metabolized and eliminated, and the maximum concentration of the drug in the body. This information can be used to optimize the design of EV-based drug delivery systems, including the size and composition of the EVs, the route of administration, and the dose of the drug. There has not been any dedicated review article that describes the PBPK modeling of EV. This review provides an overview of the absorption, distribution, metabolism, and excretion (ADME) phenomena of EVs. In addition, we will briefly describe the different computer-based modeling approaches that may help in the future of EV-based therapeutic research.

Land use/land cover (LULC) and climate are two crucial environmental factors that impact watershed hydrology worldwide. The current study seeks to comprehend how the evolving climate and LULC patterns are impacting the hydrology of the Mahanadi Reservoir catchment. A semi-distributed Soil and Water Assessment Tool (SWAT) model was utilized to simulate various water balance elements. Twelve distinct scenarios were developed by combining three different climatic data periods (1985–1996, 1997–2008, and 2009–2020) with four sets of land use maps (1985, 1995, 2005, and 2014). The SWAT model demonstrated strong performance in simulating monthly stream flows throughout the calibration and validation phases. The study reveals that changes in LULC have a distinct effect on the environment. Specifically, the changes in LULC lead to heightened streamflow and reduced evapotranspiration (ET). These changes are mainly attributed to amplified urbanization and the diminished presence of water bodies, forest cover, and barren land within the Mahanadi Reservoir catchment. The combined impact of climate change and LULC shifts reveals complex interactions. Therefore, the present study offers an understanding of how changes in climate and land use over the past few decades have influenced the hydrological behavior of the Mahanadi Reservoir catchment in Chhattisgarh. The findings of this study have the potential to offer advantages to governmental bodies, policymakers, water resource engineers, and planners seeking effective strategies for water resource management. These strategies would be particularly relevant in the context of climate change and land use/land cover changes in ecological regions similar to those of the Mahanadi Reservoir catchment. In addition, a rational regulatory framework for land use patterns is essential for assisting stakeholders in managing water resources and appropriately developing the entire catchment.

The causes of many bridge failures have been reported to be local scour around abutments. This study examines roughening elements as devices with which to intercept the downflow responsible for the formation of the principal vortex, which is what triggers local scour around abutments. Two vertical wall abutments with different widths were examined under four different hydraulic conditions in a clear-water regime. Elements with different thicknesses (t) and protrusions (P) with the same dimensions, (P = t = 0.05 L, 0.1 L, 0.2 L, and 0.3 L, where L is the length of the abutment) and with varying depths of installation (Z) were considered. Elements were installed in two positions: between the sediment surface and water elevation and buried within the sediment. To determine the optimum depth of installation, one element was first installed on the sediment surface, and the number of elements was increased in each subsequent test. The results show that installing elements between water surface elevation and the sediment’s initial level did not show any defined trend on scour depth reduction. However, the optimum installation depth of the elements is 0.6–0.8 L below the initial bed level. Moreover, the roughening elements with thickness and protrusion of P = t = 0.2 L resulted in the most effective protection of the foundation. The best arrangement, (P = t = 0.2 L and Z = >0.6–0.8 L) reduced the maximum scour depth by up to 30.4% and 32.8% for the abutment with smaller and larger widths, respectively.

Climate variability, mainly the annual air temperature and precipitation, have received great attention worldwide. The magnitude of this climate variability changes with variation in location. Rajasthan comes under the arid and semi-arid zone of India in which monsoon is a principal element of water resource. Due to erratic and scanty rainfall in this zone, agriculture is totally dependent on the monsoon. The objective of the present study is to assess the meteorological drought characteristics using Drought Indices Calculator DrinC from the historical rainfall records of the Barmer District of Rajasthan State by employing the criterion of percentage departure (D%), rainfall anomaly index (RAI) and standardized precipitation index (SPI). Trend analysis of seasonal and extreme annual monthly rainfall was carried out for the Barmer District of Rajasthan State using the data period between 1901 and 2002 at the 5% level of significance. Sen's slope estimator was also applied to identify the trend. Temporal analysis is useful to predict and identify the possible drought severity and its duration in the study region. It also helps in understanding its effect on groundwater recharge and increasing the risk of water shortage. Trend analysis of rainfall over 102 years shows an increasing trend in pre-monsoon, post-monsoon, southwest monsoon and annual rainfall and a decreasing trend in winter rainfall. Through this study, policy makers and local administrators will be benefitted, which will help them in taking proactive drought relief decisions in the drought-hit regions. HIGHLIGHTS To identify possible drought severity and duration in the study region.; It will also provide an intermediate and long-term assessment of meteorological droughts.; The study also deals with identification of drought patterns, and changes with the 102 years of the time span.; Trend analysis is carried out using Mann–Kendall test to quantify the rainfall pattern.; Magnitude of trend is carried out using Sen's slope.;

Abstract Introduction This study assessed the association between early initiation of eslicarbazepine acetate (ESL) as first-line therapy (1L cohort) or as first adjunctive regimen to either levetiracetam (LEV) or lamotrigine (LTG) (add-on cohort), and healthcare resource utilization (HCRU) and charges among adults with treated focal seizures (FS). Methods This retrospective, longitudinal cohort analysis used Symphony Health’s Integrated Dataverse (IDV®) claims data to identify patients aged ≥ 18 years with a diagnosis of FS who had a new prescription for ESL between April 2015 and June 2018. Baseline was the 90-day period immediately prior to the date of the first-dispensed claim for ESL (index date) with a follow-up of 1–4 consecutive 90-day periods. Linear regression models were estimated to assess changes in HCRU and charge outcomes. Results There were 274 and 153 patients who received ESL in the 1L cohort and add-on cohort, respectively. The 1L cohort experienced significant reductions from baseline during follow-up in all-cause inpatient (IP; P

Abstract Introduction The aim of this analysis was to assess the relationship between formulary restrictions and antiepileptic drug (AED) dispensation in patients with focal seizure (FS). Study Design A retrospective cohort analysis was conducted using data from Symphony Health’s Integrated Dataverse® (1 April 2015–30 June 2018). Methods This study included two patient populations: the overall patient population (N = 54,097) and a pediatric population (

Abstract Background Adverse Drug Reactions (ADRs) are of great public health concern. FDA-approved drug labeling summarizes ADRs of a drug product mainly in three sections, i.e., Boxed Warning (BW), Warnings and Precautions (WP), and Adverse Reactions (AR), where the severity of ADRs are intended to decrease in the order of BW > WP > AR. Several reported studies have extracted ADRs from labeling documents, but most, if not all, did not discriminate the severity of the ADRs by the different labeling sections. Such a practice could overstate or underestimate the impact of certain ADRs to the public health. In this study, we applied the Medical Dictionary for Regulatory Activities (MedDRA) to drug labeling and systematically analyzed and compared the ADRs from the three labeling sections with a specific emphasis on analyzing serious ADRs presented in BW, which is of most drug safety concern. Results This study investigated New Drug Application (NDA) labeling documents for 1164 single-ingredient drugs using Oracle Text search to extract MedDRA terms. We found that only a small portion of MedDRA Preferred Terms (PTs), 3819 out of 21,920 or 17.42%, were observed in a whole set of documents. In detail, 466/3819 (12.0%) PTs were in BW, 2023/3819 (53.0%) were in WP, and 2961/3819 (77.5%) were in AR sections. We also found a higher overlap of top 20 occurring BW PTs with WP sections compared to AR sections. Within the MedDRA System Organ Class levels, serious ADRs (sADRs) from BW were prevalent in Nervous System disorders and Vascular disorders. A Hierarchical Cluster Analysis (HCA) revealed that drugs within the same therapeutic category shared the same ADR patterns in BW (e.g., nervous system drug class is highly associated with drug abuse terms such as dependence, substance abuse, and respiratory depression). Conclusions This study demonstrated that combining MedDRA standard terminologies with data mining techniques facilitated computer-aided ADR analysis of drug labeling. We also highlighted the importance of labeling sections that differ in seriousness and application in drug safety. Using sADRs primarily related to BW sections, we illustrated a prototype approach for computer-aided ADR monitoring and studies which can be applied to other public health documents.

Patients with autosomal dominant polycystic kidney disease (ADPKD) and tuberous sclerosis complex (TSC) are born with normal or near-normal kidneys that later develop cysts and prematurely lose function. Both renal cystic diseases appear to be mediated, at least in part, by disease-promoting extracellular vesicles (EVs) that induce genetically intact cells to participate in the renal disease process. We used centrifugation and size exclusion chromatography to isolate the EVs for study. We characterized the EVs using tunable resistive pulse sensing, dynamic light scattering, transmission electron microscopy, and Western blot analysis. We performed EV trafficking studies using a dye approach in both tissue culture and in vivo studies. We have previously reported that loss of the Tsc2 gene significantly increased EV production and here demonstrate that the loss of the Pkd1 gene also significantly increases EV production. Using a cell culture system, we also show that loss of either the Tsc2 or Pkd1 gene results in EVs that exhibit an enhanced uptake by renal epithelial cells and a prolonged half-life. Loss of the primary cilia significantly reduces EV production in renal collecting duct cells. Cells that have a disrupted Pkd1 gene produce EVs that have altered kinetics and a prolonged half-life, possibly impacting the duration of the EV cargo effect on the recipient cell. These results demonstrate the interplay between primary cilia and EVs and support a role for EVs in polycystic kidney disease pathogenesis.

Objective Multiple clinical trials fail to identify clinically measurable health benefits of daily multivitamin and multimineral (MVM) consumption in the general adult population. Understanding the determinants of widespread use of MVMs may guide efforts to better educate the public about effective nutritional practices. The objective of this study was to compare self-reported and clinically measurable health outcomes among MVM users and non-users in a large, nationally representative adult civilian non-institutionalised population in the USA surveyed on the use of complementary health practices.Design Cross-sectional analysis of the effect of MVM consumption on self-reported overall health and clinically measurable health outcomes.Participants Adult MVM users and non-users from the 2012 National Health Interview Survey (n=21 603).Primary and secondary outcome measures Five psychological, physical, and functional health outcomes: (1) self-rated health status, (2) needing help with routine needs, (3) history of 10 chronic diseases, (4) presence of 19 health conditions in the past 12 months, and (5) Kessler 6-Item (K6) Psychological Distress Scale to measure non-specific psychological distress in the past month.Results Among 4933 adult MVM users and 16 670 adult non-users, MVM users self-reported 30% better overall health than non-users (adjusted OR 1.31; 95% CI 1.17 to 1.46; false discovery rate adjusted p

Abstract Introduction Chronic hepatitis C (CHC) infection is associated with extrahepatic manifestations (EHMs) which can affect renal, cardiovascular and other comorbidities. The effect of CHC treatment with short-duration regimens on these EHMs is not well defined. Hence, we examined longitudinal estimated glomerular filtration rate (eGFR), triglycerides and glucose values to assess the impact of short-duration CHC therapy on renal, cardiovascular and metabolic diseases, respectively. Methods We conducted analyses of all patients without cirrhosis treated with glecaprevir and pibrentasvir (G/P) for 8 weeks in two phase 3 clinical trials. In addition, one phase 3 trial was carried out to explore the effects of treatment on renal EHMs in patients with advanced renal impairment at baseline. As a sensitivity analysis, we included all CHC patients treated with G/P for 8 or 12 weeks enrolled across five phase 3 trials. Adjusting for baseline demographics and clinical properties via mixed regression models enabled evaluation of changes in EHMs through end of treatment. Results G/P treatment for 8 weeks resulted in statistically significant declines in triglycerides (− 28.6 mg/dl) and glucose (− 11.2 mg/dl), while there was no statistically significant decline in eGFR. Biomarker improvements were greatest among patients with elevated triglycerides and elevated glucose at baseline. Similar effects were observed across all patients treated with G/P for 8 or 12 weeks. Conclusion Short-duration treatment with G/P resulted in stable renal function and improvements in cardiovascular and metabolic EHM markers, especially in patients with severe EHMs at baseline. Funding AbbVie Inc.