Dr Mohammed Mahmoud Youssif

BACKGROUND AND PURPOSE: Given the recent rise in Cannabis legalisation, accessibility to Cannabis and consumption have increased during pregnancy. Therefore, there could be unintended developmental consequences. The endocannabinoid system plays a key role in fetal development and later-life energy homeostasis. We explored the long-term effects of maternal voluntary Cannabis consumption on the metabolic outcomes of a high-fat diet (HFD) in adult offspring.

EXPERIMENTAL APPROACH: Pregnant mice voluntarily consumed Cannabis extract equivalent to 5 mg kg day Δ9-tetrahydrocannabinol (THC) from gestational day 1.5 until postnatal day (PD) 10. Pregnancy and pup outcomes and active maternal behaviour were recorded. Male and female offspring (PD49) were placed on a 12-week HFD or control diet; their weight gain, adiposity, glucose tolerance, insulin sensitivity, circulating hormones and pancreatic structure were measured.

KEY RESULTS: Perinatal Cannabis exposure (PCE) pup weight was initially reduced but restored by PD16. PCE did not influence weight gain or metabolic characteristics of male mice on a HFD. PCE female but not male offspring on a HFD had reduced accumulation of adipose tissue and lower insulin, leptin and resistin independent of body weight. PCE females on control diet also showed altered basal insulin sensitivity likely because of increased glucagon levels in parallel with reduced islets of Langerhans size and enhanced gene expression of cannabinoid 2 receptors in white adipose tissue.

CONCLUSION AND IMPLICATIONS: PCE adversely affected glycaemic control in female offspring on control diet while it mitigated HFD-induced metabolic dysfunction. This raises concerns about the long-term effects of PCE on the metabolic health of offspring.

Effective path planning in flood emergency scenarios is critical for ensuring timely evacuation while minimizing safety risks. Traditional path-planning algorithms often prioritize shortest or most cost-efficient routes, potentially ignoring safety considerations. This study introduces an improved behavior-based A-star (A*) algorithm for evacuation scenarios, implemented in a cellular automata (CA) environment to address inefficiencies and dynamic flood challenges. The key innovation is optimizing the heuristic function using depth sensitivity perception (DSP), which prioritizes safer paths based on real-time water depth assessments. Experiments across diverse flood scenarios demonstrate that the optimized A* significantly outperforms traditional A* and Dijkstra’s algorithms, reducing explored nodes by 90.06% and 93.13%, lowering safety risks, and shortening computational times by 87.65% and 88.06%, respectively. These results validate the efficacy of depth-sensitive heuristics in enhancing evacuation pathfinding within complex flood environments.

In the context of sustainable development, electric vehicles (EVs) and renewable-based distributed generation (RDG) integration into the distribution networks (DNs) introduce various merits. They involve lowering harmful emissions, employing various types of energy sources, and encouraging the dependence of renewable energy. However, the two most challenging issues posing a grave danger to the technical operation of the DN would be the inappropriate integration of RDGs and EVs. Consequently, in order to guarantee safe operation, distribution system operator (DSO) is responsible for precisely identifying two crucial terms, distributed generation hosting capacity (DG-HC) and electric vehicles hosting capacity (EV-HC). Despite an increase in the amount of research on HC approaches, there is still a remarkable research gap in the discussion of models that effectively combine demand response (DR), smart inverters (SI) Volt/VAR control, DG-HC, and EV-HC targets simultaneously. This study offers a hierarchical bi-level optimization HC framework depends on both dynamic tariff-based DR and SI Volt/VAR control. In the lower layer, Participating customers’ load curves and EV aggregators’ charging demands are optimally adjusted, on a forecast basis, based on the proposed dynamic tariff. Nonetheless, the suggested multi-objective function—which includes DG-HC, EV-HC maximization, and loss minimization—is optimized by the DSO at the upper layer based on all of these optimal load curves. To further support the proposed objective function, DR is mixed with the optimal Volt/VAR controlling offered by SIs. In addition, the role of grid-connected EVs (GCEVs) on DG-HC increase is revealed with both uncoordinated and coordinated charging schemes. To check the proposed approach robustness, three types of loads are considered. Through comparison with three other optimization approaches, the effectiveness of the equilibrium optimizer (EO) is demonstrated when it is employed to solve the proposed optimization scheme. The suggested planning approach is applied on both the IEEE 33-bus test system, and a real DN with 59 buses in Cairo, Egypt. Several significant conclusions are validated by the obtained results. First, the DG-HC assessment differs significantly depending on whether the EV charging demand is considered or not. Considering the EV integration in IEEE 33-bus, the mean value of the optimal DG-HC increased by more than 133% during the day. Secondly, the implementation of the proposed dynamic-pricing DR program in the IEEE 33-bus DN significantly improved both the DG-HC and EV-HC, named as the combined DG-EV-HC, with improvements of around 34% and 27% for DG-HC and EV-HC respectively. Finally, in IEEE 33-bus, the combined DG-EV hosting capacity was improved by approximately 49.2% regarding DG-HC and 61.2% regarding EV-HC, using the proposed synergistic DR-Volt/VAR control enhancement technique.

Chronic pancreatitis (CP), a progressive inflammatory disease characterized by pancreatic tissue destruction and fibrosis, is considered a challenging health burden due to insufficiencies of current management procedures. Autophagy impairment has emerged as a major triggering event in pancreatitis, raising interest in exploring the potential of targeting autophagy as a possible interventional strategy. This study aimed to evaluate the possible ameliorative effect of two autophagy modulators, simvastatin and eugenol, on CP-related perturbations in an arginine-induced rat model. Repeated L-arginine administration (5 g/kg divided into 2 doses with a 1 hr interval, given intraperitoneally every 3 day for a total of 10 times) provoked CP features, demonstrated by acinar damage, oxidative stress, inflammation, and fibrosis. Arginine-triggered pancreatitis was accompanied by hampered pancreatic autophagic flux, evidenced by overexpression of pancreatic p62 and LC3-Ⅱ and downregulation of pancreatic AMPK and LAMP-1 mRNA expression. Treatment with simvastatin (20 mg/kg, intraperitoneally 24 hr, before each arginine dose) and eugenol (50 mg/kg/day orally for 30 days) achieved significant anti-oxidative, anti-inflammatory, and anti-fibrotic effects, and reversed the arginine-instigated autophagic blockade, with superior ameliorative effects attained by eugenol. Altogether, simvastatin and eugenol provide a promising interventional approach for CP, at least partly, by restoring the impaired autophagic flux associated with CP.

This study investigated the efficacy of a locally formulated nano-silver particle (Ag-NPs) treatment for controlling Eimeria spp. in chickens. Various concentrations of Ag-NPs were evaluated for their ability to inhibit the sporulation of five Eimeria species oocysts in vitro, with LC and LC values determined. The most effective concentrations (20 μg/mL and 25 μg/mL) were administered to experimentally infected chickens for five consecutive days through their feed, either as a normal diet or supplemented with a Vitamin A, E, C, and selenium complex (VSeC). Results showed a significant reduction in the number of oocysts shed, lower lesion scores, and improved weight gain in chickens treated with Ag-NPs, with effects comparable to the reference drug Amprolium. Ag-NPs at 25 μg/mL exhibited the highest efficacy, though mild silver toxicity was observed. Supplementation with VSeC enhanced all parameters and mitigated the mild toxicity associated with the high-dose Ag-NP treatment. Further analysis of redox parameters (malondialdehyde, catalase, glutathione peroxidase, and superoxide dismutase) and liver (alanine aminotransferase, aspartate aminotransferase) and kidney function (serum urea, creatinine) revealed slight elevations in these markers, which were negligible in the VSeC-supplemented groups. In conclusion, Ag-NPs at doses of 20-25 μg/mL for five days were effective in controlling Eimeria infection in chickens. The inclusion of VSeC in the feed provided protective effects, alleviating potential toxicity and enhancing overall treatment outcomes.

Chlorphenoxamine hydrochloride is a chemical that has attracted interest because of its notable anti-histaminic and anti-cholinergic characteristics. Moreover, it has been recognized as a highly efficient tool in the fight against several lethal viral diseases, such as Severe Acute Respiratory Syndrome Coronavirus. In this study, five efficient and straightforward univariate spectrophotometric approaches are proposed for accurately measuring the quantities of Caffeine and Chlorphenoxamine HCl in bulk forms and combined pharmaceutical formulations. Notably, these methods based on an advanced approach using the factorized response spectrum and not require any initial processing.They have been classified into three spectrophotometric platform windows. The study of Window I focuses on absorption spectra of substances in their original states(zero-order). It includes, absorbance resolution method (AR), extended absorbance difference method (EAD), and factorized zero order method (FZM). Window II focuses on the factorized derivative method (FDM), while Window III focuses on factorized ratio difference method (FRM). These approaches successfully measured concentration of Caffeine and Chlorphenoxamine HCl within a range of 3-35.0 and 3-45.0 μg/mL, respectively. The factorized response spectrum's exclusivity stems from its capacity to fully separate the mentioned components in mixture and recover the pure spectra. Validation of the suggested approaches has been conducted according to guidelines established by International Council for Harmonization, which demonstrated acceptable levels of accuracy and precision. The scope of this work has been expanded to include verification of content uniformity of dosage units according to recommendations outlined in United States Pharmacopoeia. Greenness profile of the proposed approaches has been properly assessed using state-of-the-art software metrics, in comparison to the reported one. Finally, the proposed methods demonstrated strong compliance with the recently established principles of white field of analytical chemistry.

Breast cancer (BC) remains one of the most prevalent malignancies affecting women worldwide, necessitating ongoing research to improve treatment outcomes and minimize adverse effects associated with chemotherapy. This article explores the role of genetic variations, particularly single nucleotide polymorphisms (SNPs), in influencing the efficacy and toxicity of chemotherapeutic agents used in BC treatment. It highlights the impact of polymorphisms in drug metabolism and transport genes, such as UDP-glucuronosyltransferase 1A1 (UGT1A1), carbonyl reductase 1 (CBR1), and ATP-binding cassette multidrug transporter (ABCB1) on the risk of adverse effects, including cardiotoxicity and hematological toxicities. By identifying specific SNPs associated with drug response and toxicity, this research underscores the potential for personalized medicine approaches to optimize treatment regimens, enhance therapeutic efficacy, and minimize side effects in BC patients. The findings advocate for the integration of genetic screening in clinical practice to improve patient outcomes and tailor chemotherapy based on individual genetic profiles.