Hemophagocytic lymphohistiocytosis: a rare and life-threatening diagnosis

Hemophagocytic lymphohistiocytosis (HLH) is a rare and life-threatening syndrome of excessive activation of immune system. It frequently affects infants from birth to 18 months of age, but is also observed in children and adults of all ages. HLH can occur as a familial or sporadic disorder, and it is triggered by a variety of events, Infection being the most common trigger both in familial and in sporadic cases. Prompt treatment is very critical in cases of HLH, but the greatest barrier is often delay in diagnosis due to the rarity of this syndrome, variable clinical presentation, and lack of specificity of the clinical and laboratory findings. The key clinical features of HLH are high persistent fever, hepatosplenomegaly, blood cytopenia, elevated aminotransferase and ferritin levels, and coagulopathy. A diagnosis of HLH is mostly under-recognized, and is associated with high mortality, especially in adults; thus, prompt diagnosis and treatment are essential. We here present a rare case of HLH in an adult which was non-familial and infection being the trigger causing secondary hemophagocytic lymphohistiocytosis

Monitoring vegetation degradation using remote sensing and machine learning over India – a multi-sensor, multi-temporal and multi-scale approach

Vegetation cover degradation is often a complex phenomenon, exhibiting strong correlation with climatic variation and anthropogenic actions. Conservation of biodiversity is important because millions of people are directly and indirectly dependent on vegetation (forest and crop) and its associated secondary products. United Nations Sustainable Development Goals (SDGs) propose to quantify the proportion of vegetation as a proportion of total land area of all countries. Satellite images form as one of the main sources of accurate information to capture the fine seasonal changes so that long-term vegetation degradation can be assessed accurately. In the present study, Multi-Sensor, Multi-Temporal and Multi-Scale (MMM) approach was used to estimate vulnerability of vegetation degradation. Open source Cloud computing system Google Earth Engine (GEE) was used to systematically monitor vegetation degradation and evaluate the potential of multiple satellite data with variable spatial resolutions. Hotspots were demarcated using machine learning techniques to identify the greening and the browning effect of vegetation using coarse resolution Normalized Difference Vegetation Index (NDVI) of MODIS. Rainfall datasets of Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) for the period 2000–2022 were also used to find rainfall anomaly in the region. Furthermore, hotspot areas were identified using high-resolution datasets in major vegetation degradation areas based on long-term vegetation and rainfall analysis to understand and verify the cause of change whether anthropogenic or climatic in nature. This study is important for several State/Central Government user departments, Universities, and NGOs to lay out managerial plans for the protection of vegetation/forests in India

PREPARATION AND CHARACTERIZATION OF METFORMIN LOADED STEARIC ACID COUPLED F127 NANOPARTICLES

Objective: The objective of this study was to prepare and evaluate metformin nanoparticles (MN) using stearic acid-coupled F127 (SAF127) copolymer and polyvinyl alcohol by emulsion solvent evaporation technique.Method: Metformin is the first-line drug for the treatment of type II diabetes mellitus belongs to Biopharmaceutical Classification System Class III. The prepared MN was characterized for particle size, polydispersity index (PDI), zeta potential, drug entrapment, percentage yield, in vitro drug release, and stability studies. The compatibility studies were performed by Fourier transform infrared (FTIR) and differential scanning calorimetry (DSC). The crystallographic and surface properties were studied by X-ray diffractometry and scanning electron microscopy, respectively.Results: The mean particle diameter of prepared nanoparticles ranged from 207.8 to 977.64 nm, PDI value ranged from 0.146 to 0.694, and zeta potential ranged from −20.5 to −6.97 mV. The drug entrapment efficiency of these nanoparticles varies between 18.81 to 69.01 %. The drug to SAF127 copolymer (10/30 w/w) ratio (MN3) showed optimum results. The MN3 had spherical morphology with semi-amorphous nature. The results of FTIR and DSC analysis showed that there was no significant interaction between drug and excipients. The prepared polymeric nanoparticles were stable at 5±3°C up to 3 months. In vitro release of drug from MN3 was 20.52% in the first 1 h and remaining drug was released up to 30 h.Conclusion: The results of this study confirmed the sustained drug release profile of metformin loaded SAF127 copolymer nanoparticles. These nanoparticles can be best stored up to 3 months

GIS integrated RUSLE model-based soil loss estimation and watershed prioritization for land and water conservation aspects

Land degradation has become one of the major threats throughout the globe, affecting about 2.6 billion people in more than 100 countries. The highest rate of land degradation is in Asia, followed by Africa and Europe. Climate change coupled with anthropogenic activities have accelerated the rate of land degradation in developing nations. In India, land degradation has affected about 105.48 million hectares. Thus, modeling and mapping soil loss, and assessing the vulnerability threat of the active erosional processes in a region are the major challenges from the land and water conservation aspects. The present study attempted rigorous modeling to estimate soil loss from the Banas Basin of Rajasthan state, India, using GIS-integrated Revised Universal Soil Loss Equation (RUSLE) equation. Priority ranking was computed for different watersheds in terms of the degree of soil loss from their catchments, so that appropriate conservation measures can be implemented. The total area of Banas basin (68,207.82 km2) was systematically separated into 25 watersheds ranging in area from 113.0 to 7626.8 km2. Rainfall dataset of Indian Meteorological Department for 30 years (1990–2020), FAO based Soil map for soil characterization, ALOS PALSAR digital elevation model for topographic assessment, and Sentinal-2 based land use and land cover map were integrated for modeling and mapping soil erosion/loss risk assessment. The total annual soil loss in the Banas basin was recorded as 21,766,048.8 tons. The areas under very low (0–1 t ha-1 year-1), low (1–5 t ha-1 year-1), medium (5–10 t ha-1 year-1), high (10–50 t ha-1 year-1) and extreme (&gt;50 t ha-1 year-1) soil loss categories were recorded as 24.2, 66.8, 7.3, 0.9, and 0.7%, respectively, whereas the respective average annual soil loss values were obtained as 0.8, 3.0, 6.0, 23.1, and 52.0 t ha-1 year-1. The average annual soil loss among different watersheds was recorded in the range of 1.1–84.9 t ha-1 year-1, being highest (84.9 t ha-1 year-1) in WS18, followed by WS10 (38.4 t ha-1 year-1), SW25 (34.7 t ha-1 year-1) and WS23 (17.9 t ha-1 year-1), whereas it was lowest for WS8 (1.1 t ha-1 year-1). Thus, WS18 obtained the highest/top priority rank in terms of the average annual soil loss (84.9 t ha-1 year-1) to be considered as the first priority for land and water conservation planning and implementation. The quantitative results of this study would be useful for implementation of land and water conservation measures in the problematic areas of the Banas basin for controlling soil loss through water erosion.Validerad;2023;Nivå 2;2023-04-13 (hanlid);</p

Amphiphilic, lauric acid-coupled pluronic-based nano-micellar system for efficient glipizide delivery

Glipizide; an insulin secretagogue belonging to the sulfonylurea class, is a widely used antidiabetic drug for managing type 2 diabetes. However, the need for life-long administration and repeated doses poses challenges in maintaining optimal blood glucose levels. In this regard, orally active sustained-release nano-formulations can be a better alternative to traditional antidiabetic formulations. The present study explored an innovative approach by formulating orally active sustained-release nano-micelles using the amphiphilic lauric acid-conjugated-F127 (LAF127) block copolymer. LAF127 block copolymer was synthesized through esterification and thoroughly characterized before being employed to develop glipizide-loaded nano-micelles (GNM) via the thin-film hydration technique. The optimized formulation exhibited mean particle size of 341.40 ± 3.21 nm and depicted homogeneous particle size distribution with a polydispersity index (PDI) < 0.2. The formulation revealed a surface charge of −17.11 ± 6.23 mV. The in vitro release studies of glipizide from developed formulation depicted a sustained release profile. Drug loaded micelles exhibited a substantial reduction in blood glucose levels in diabetic rats for a duration of up to 24 h. Notably, neither the blank nano-micelles of LAF127 nor the drug loaded micelles manifested any indications of toxicity in healthy rats. This study provides an insight on suitability of synthesized LAF127 block copolymer for development of effective oral drug delivery systems for anti-diabetic activity without any significant adverse effects

Simulation of Salinity Effects on Past, Present, and Future Soil Organic Carbon Stocks

Soil organic carbon (SOC) models are used to predict changes in SOC stocks and carbon dioxide (CO₂) emissions from soils, and have been successfully validated for non-saline soils. However, SOC models have not been developed to simulate SOC turnover in saline soils. Due to the large extent of salt-affected areas in the world, it is important to correctly predict SOC dynamics in salt-affected soils. To close this knowledge gap, we modified the Rothamsted Carbon Model (RothC) to simulate SOC turnover in salt-affected soils, using data from non-salt-affected and salt-affected soils in two agricultural regions in India (120 soils) and in Australia (160 soils). Recently we developed a decomposition rate modifier based on an incubation study of a subset of these soils. In the present study, we introduce a new method to estimate the past losses of SOC due to salinity and show how salinity affects future SOC stocks on a regional scale. Because salinity decreases decomposition rates, simulations using the decomposition rate modifier for salinity suggest an accumulation of SOC. However, if the plant inputs are also adjusted to reflect reduced plant growth under saline conditions, the simulations show a significant loss of soil carbon in the past due to salinization, with a higher average loss of SOC in Australian soils (55 t C ha^–1) than in Indian soils (31 t C ha^–1). There was a significant negative correlation (<i>p</i> < 0.05) between SOC loss and osmotic potential. Simulations of future SOC stocks with the decomposition rate modifier and the plant input modifier indicate a greater decrease in SOC in saline than in non-saline soils under future climate. The simulations of past losses of SOC due to salinity were repeated using either measured charcoal-C or the inert organic matter predicted by the Falloon et al. equation to determine how much deviation from the Falloon et al. equation affects the amount of plant inputs generated by the model for the soils used in this study. Both sets of results suggest that saline soils have lost carbon and will continue to lose carbon under future climate. This demonstrates the importance of both reduced decomposition and reduced plant input in simulations of future changes in SOC stocks in saline soils

Simulation of salinity effects on past, present, and future soil organic carbon stocks

Soil organic carbon (SOC) models are used to predict changes in SOC stocks and carbon dioxide (CO(2)) emissions from soils, and have been successfully validated for non-saline soils. However, SOC models have not been developed to simulate SOC turnover in saline soils. Due to the large extent of salt-affected areas in the world, it is important to correctly predict SOC dynamics in salt-affected soils. To close this knowledge gap, we modified the Rothamsted Carbon Model (RothC) to simulate SOC turnover in salt-affected soils, using data from non-salt-affected and salt-affected soils in two agricultural regions in India (120 soils) and in Australia (160 soils). Recently we developed a decomposition rate modifier based on an incubation study of a subset of these soils. In the present study, we introduce a new method to estimate the past losses of SOC due to salinity and show how salinity affects future SOC stocks on a regional scale. Because salinity decreases decomposition rates, simulations using the decomposition rate modifier for salinity suggest an accumulation of SOC. However, if the plant inputs are also adjusted to reflect reduced plant growth under saline conditions, the simulations show a significant loss of soil carbon in the past due to salinization, with a higher average loss of SOC in Australian soils (55 t C ha(-1)) than in Indian soils (31 t C ha(-1)). There was a significant negative correlation (p < 0.05) between SOC loss and osmotic potential. Simulations of future SOC stocks with the decomposition rate modifier and the plant input modifier indicate a greater decrease in SOC in saline than in non-saline soils under future climate. The simulations of past losses of SOC due to salinity were repeated using either measured charcoal-C or the inert organic matter predicted by the Falloon et al. equation to determine how much deviation from the Falloon et al. equation affects the amount of plant inputs generated by the model for the soils used in this study. Both sets of results suggest that saline soils have lost carbon and will continue to lose carbon under future climate. This demonstrates the importance of both reduced decomposition and reduced plant input in simulations of future changes in SOC stocks in saline soils.Raj Setia, Pete Smith, Petra Marschner, Pia Gottschalk, Jeff Baldock, Vipan Verma, Deepika Setia and Jo Smit