Hanan Hassan Fouad

The consumption of non-steroidal anti-inflammatory drugs (NSAIDs) have increased lately around the world, as they are considered essential and popular drugs for effective reduction of pain and inflammation. They have analgesic, antipyretic, and anti-inflammatory activities; also, it was reported recently that they protect against various critical disorders like heart attacks and cancer. However, oral use of NSAIDs may cause several pulmonary, gastrointestinal, hepatic, cardiovascular, cerebral, and renal complications. Therefore, topical NSAIDs were recommended as a substitute to oral NSAIDs for the treatment of inflammation and pain. Still, the skin permeation of NSAIDs is considered a challenge, as the skin have an effective barrier function. Therefore, this review investigates various advanced vesicular nanocarriers and their applications through the skin, to augment the topical delivery of NSAIDs through stratum corneum over the conventional systems, enhance their effectiveness, and reduce the unwanted side effects. These innovative systems can manage bioavailability, solubility, stability, safety, and efficacy issues present in conventional systems. © 2023, The Author(s).

Patients with hyperthyroidism are commonly diagnosed with mood disorders. Naringin, (4',5,7-trihydrocyflavanone-7--rhamnoglucoside), a natural bioflavonoid, has many neurobehavioral activities including anxiolytic and antidepressant properties. The role of Wingless (Wnt) signaling in psychiatric disorders is considered substantial but debatable. Recently, regulation of Wnt signaling by naringin has been reported in different disorders. Therefore, the present study aimed to investigate the possible role of Wnt/GSK-3β/β-catenin signaling in hyperthyroidism-induced mood disturbances and explore the therapeutic effects of naringin. Hyperthyroidism was induced in rats by intraperitoneal injection of 0.3 mg/kg levothyroxine for 2 weeks. Naringin was orally administered to rats with hyperthyroidism at a dose of 50 or 100 mg/kg for 2 weeks. Hyperthyroidism induced mood alterations as revealed by behavioral tests and histopathological changes including marked necrosis and vacuolation of neurons in the hippocampus and cerebellum. Intriguingly, hyperthyroidism activated Wnt/p-GSK-3β/β-catenin/DICER1/miR-124 signaling pathway in the hippocampus along with an elevation in serotonin, dopamine, and noradrenaline contents and a reduction in brain-derived neurotrophic factor (BDNF) content. Additionally, hyperthyroidism induced upregulation of cyclin D-1 expression, malondialdehyde (MDA) elevation, and glutathione (GSH) reduction. Naringin treatment alleviated behavioral and histopathological alterations and reversed hyperthyroidism-induced biochemical changes. In conclusion, this study revealed, for the first time, that hyperthyroidism could affect mental status by stimulating Wnt/p-GSK-3β/β-catenin signaling in the hippocampus. The observed beneficial effects of naringin could be attributed to increasing hippocampal BDNF, controlling the expression of Wnt/p-GSK-3β/β-catenin signaling as well as its antioxidant properties.

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Recent climate change (CC) scenarios from the Coupled Model Intercomparison Project Phase 6 (CMIP6) have just been released in coarse resolution. Deep learning (DL) based on statistical downscaling has recently been used, but more research is needed, particularly in arid regions, because little is known about their suitability for extrapolating future CC scenarios. Here we analyzed this issue by downscaling maximum, and minimum temperature over the Egyptian domain based on one General Circulation Model (GCM) as CanESM5 and two shared socioeconomic pathways (SSPs) as SSP4.5 and SSP8.5 from CMIP6 using Convolutional Neural Network (CNN) herein after called CNNSD. The downscaled maximum and minimum temperatures based CNNSD was able to reproduce the observed climate over historical and future periods at a finer resolution (0.1°), reducing the biases exhibited by the original scenario. To the best of our knowledge, this is the first time CNN has been used to downscale CMIP6 scenarios, particularly in arid regions. The downscaled analysis showed that maximum and minimum temperatures are expected to rise by 4.8 °C and 4.0 °C, respectively, in the future (2015–2100), compared to the historical period, under the moderate scenario (SSP4.5). Meanwhile, under the Fossil-fueled Development scenario (SSP8.5), these values will rise by 6.3 °C and 4.2 °C, respectively as analyzed by the CNNSD. The developed approach could be used not only in Egypt but also in other developing countries, which are especially vulnerable to climate change and has a scarcity of related research. The established downscaled approach's supply can be used to provide climate services, as a driver for impact studies and adaptation decisions, and as information for policy development. More research is needed, however, to include multi-GCMs to quantify the uncertainties between GCMs and SSPs, improving the outputs for use in climate change impacts and adaptations for food and nutrition security.

Rainfall erosivity is a key factor that influences soil erosion by water. Rain-gauge measurements are commonly used to estimate rainfall erosivity. However, long-term gauge records with sub-hourly resolutions are lacking in large parts of the world. Satellite observations provide spatially continuous estimates of rainfall, but they are subject to biases that affect estimates of rainfall erosivity. We employed a novel approach to map global rainfall erosivity based on a high-temporal-resolution (30-min), long-term (2001–2020) satellite-based precipitation product—the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM-IMERG)—and mean annual rainfall erosivity from the Global Rainfall Erosivity Database (GloREDa) stations (n = 3286). We used a residual-based merging scheme to integrate GPM-IMERG-based rainfall erosivity with GloREDa using Geographically Weighted Regression (GWR). The accuracy of the GWR-based merging scheme was evaluated with a 10-fold cross-validation against GloREDa stations. Based on GPM-IMERG-only, the global mean annual rainfall erosivity was estimated to be 1173 MJ mm ha−1 h−1 yr−1 with a standard deviation of 1736 MJ mm ha−1 h−1 yr−1. The mean value estimated via GPM-IMERG merged with GloREDa was 2020 MJ mm ha−1 h−1 yr−1 with a standard deviation of 3415 MJ mm ha−1 h−1 yr−1. Overall, GPM-IMERG-only estimates underestimated rainfall erosivity. The underestimations were greatest in areas of high rainfall erosivity. The accuracy of rainfall erosivity estimates from GPM-IMERG merged with GloREDa substantially improved (Nash-Sutcliffe efficiency = 0.83, percent bias = −2.4%, and root mean square error = 1122 MJ mm ha−1 h−1 yr−1) compared to estimates by GPM-IMERG-only (Nash-Sutcliffe efficiency = 0.51, percent bias = 27.8%, and root mean square error = 1730 MJ mm ha−1 h−1 yr−1). Improving satellite-based global rainfall erosivity estimates through integrating with gauge data is relevant as it can contribute to enhancing soil erosion modeling and, in turn, support land degradation neutrality programs.