Minia University (also spelled Minia) was established in Minia, in 1976 by virtue of Republican Decree No. (93), separating it from Assiut University. The campus is north of Minia. Its emblem is Nefertiti Bust.
Abstract Background Diabetes mellitus is a global epidemic leads to multiple serious health complications, including nephropathy. Diabetic nephropathy is a serious kidney-related complication of type 1 or 2 diabetes that is prevalent in almost 40% of the people with diabetes. We examined whether folic acid and melatonin can reduce progression of nephropathy in rats of type 1 diabetes mellitus by controlling the level of oxidative stress, glucose, lipids, and cytokines. Methods Forty-two male albino rats were distributed into six groups, (n = 7 per group). Five of the groups were induced with diabetes by a single intraperitoneal injection of freshly prepared streptozotocin at a dose of 50 mg/kg body weight. After the induction of diabetes, the rats were treated with folic acid (100 mg/kg) and melatonin (10 mg/kg) separately and in combination daily for 6 weeks, whereas, the other diabetic group was treated with glibenclamide (5 mg/kg). One of the diabetic groups served as a positive control. One-way ANOVA was used to compare those five subfields ability followed by LSD multiple comparisons. Results The data indicated that diabetes significantly altered the body weight, lipids and kidney function. Diabetic rats exhibited a significant increase in plasma levels of urea, uric acid, creatinine, sodium, tumor necrosis factor alpha (TNF-α), interleukin-6(IL-6), cholesterol, triglycerides, and low-density lipoprotein (LDL). In contrast, plasma total protein, potassium, high-density lipoprotein (HDL) and interleukin-10 (IL-10) decreased significantly in diabetic rats compared to the control rats. Moreover, levels of renal malondialdehyde (MDA) and nitric oxide (NO) were significantly increased while the levels of renal glutathione(GSH), superoxide dismutase(SOD), and catalase (CAT) were significantly decreased in diabetic rats comparison to those in the control rats. Hence, diabetic rats treated with folic acid and melatonin alone as well as in combination showed improvements with respect to the indices in addition to a significant recovery observed via histopathology when compared to the diabetic group. Conclusions These results revealed that treatment with folic acid in combination with melatonin in diabetic rats was more effective than treatment with either of folic acid or melatonin alone to alleviate the symptoms of diabetic nephropathy.
Skin cancer is one of the most dangerous types of cancer due to its immediate appearance and the possibility of rapid spread. It arises from uncontrollably growing cells, rapidly dividing cells in one area of the body, invading other bodily tissues, and spreading throughout the body. Early detection helps prevent cancer progress from reaching critical levels, reducing the risk of complications and the need for more aggressive treatment options. Convolutional neural networks (CNNs) revolutionize skin cancer diagnosis by extracting intricate features from images, enabling an accurate classification of lesions. Their role extends to early detection, providing a powerful tool for dermatologists to identify abnormalities in their nascent stages, ultimately improving patient outcomes. This study proposes a novel deep convolutional neural network (DCNN) approach to classifying skin cancer lesions. The proposed DCNN model is evaluated using two unbalanced datasets, namely HAM10000 and ISIC-2019. The DCNN model is compared with other transfer learning models, including VGG16, VGG19, DenseNet121, DenseNet201, and MobileNetV2. Its performance is assessed using four widely used evaluation metrics: accuracy, recall, precision, F1-score, specificity, and AUC. The experimental results demonstrate that the proposed DCNN model outperforms other deep learning (DL) models that utilized these datasets. The proposed DCNN model achieved the highest accuracy with the HAM10000 and ISIC-2019 datasets, reaching 98.5% and 97.1% , respectively. These experimental results show how competitive and successful the DCNN model is in overcoming the problems caused by class imbalance and raising skin cancer classification accuracy. Furthermore, the proposed model demonstrates superior performance, particularly excelling in terms of accuracy, compared to other recent studies that utilize the same datasets, which highlights the robustness and effectiveness of the proposed DCNN.
Background The clinical implication of the increased serum progesterone level on the day of HCG administration in assisted reproduction treatment (ART) is still controversial. The current study aimed to compare the predictive value of serum progesterone on day of HCG administration / metaphase II oocyte (P/MII) ratio on IVF/ ICSI outcome to serum progesterone (P) level alone and the ratio of serum progesterone/estradiol level (P/E2) ratio in prediction of pregnancy rates after ART. Material & methods Two hundred patients admitted to the IVF/ICSI program at Minia IVF center in Egypt in the period from October 2016 to May 2018 were included in this study. Serum Progesterone (P) and Estradiol (E2) levels were estimated on the day of HCG administration. The ratio between serum P and the number of MII oocytes (P/MII ratio) was calculated and the predictive values of the three parameters (P, P/E2 ratio and P/MII ratio) in prediction of cycle outcomes were measured. Results P/ MII oocyte ratio was significantly lower in patients who attained clinical pregnancy (n = 97) as compared with those who couldn't whilst there was no significant difference in P and P/E2 ratio between the two groups. Using a cut off value of 0.125, the sensitivity and specificity of progesterone/ MII ratio in prediction of no pregnancy in IVF/ICSI were 75.7 and 77.1% respectively with the area under The Receiver operating curve (ROC-AUC) = 0.808. The respective values of the ROC-AUC for the P and P/E2 ratio were 0.651 and 0.712 with sensitivity and specificity of 71.2 and 73.5%for P level and of 72.5 and 75.3% for P/E2 ratio. Implantation or clinical pregnancy rates were significantly different between patients with high and low P/MII ratio irrespective of day of embryo transfer (day 3 or 5). Conclusions In patients with normal ovarian response, serum progesterone on day of HCG / MII oocyte ratio can be a useful predictor of pregnancy outcomes and in deciding on freezing of all embryos for later transfer instead of high progesterone level alone.
To mitigate the drinking water crisis, it is imperative to enhance the water productivity of solar stills, particularly in arid regions. This study presents the first application of eggshell halves filled with either white sand grains or processed eggshell powder as cost-effective natural energy storage and corrugation materials to improve the production of hemispherical solar stills. To investigate the proposed techniques, three identical hemispherical solar still were developed and evaluated under identical weather conditions. The configurations included: a conventional still with egg-shaped metal basin coated with nanoparticles (HSD-SET), a modified still with eggshell halves filled with white sand grains (MHSD-SET&ES_SG), and a modified still with eggshell halves filled with eggshell powder (MHSD-SET&ES_ESP). Additionally, the microstructure and morphological properties of eggshells were examined using scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDS) to ascertain their chemical composition. The findings showed a significant enhancement in water productivity, with increases of up to 51.94 % (9.64 kgm- 2day- 1) and 85.64 % (11.87 kgm- 2day- 1), for the MHSDSET&ES_SG and MHSD-SET&ES_ESP configurations, respectively, compared to the conventional HSD-SET (6.35 kgm- 2day- 1). It is noted that the productivity improvement for the MHSD-SET&ES_ESP was 22.18 % higher than that of MHSD-SET&ES_SG. Furthermore, the adoption of these modified configurations led to reductions in productivity cost by 48.81 % and 79.77 % and shortened the payback time by 32.85 % and 40.92 % respectively. Finally, the annual CO2 emissions were reduced by 2.83 and 3.33 tons for the MHSD-SET&ES_SG and MHSDSET&ES_ESP configurations, respectively.
Electrolytic water hydrogen production is an effective method for achieving the absorption of excess renewable energy and peak shaving and valley filling in the power system. However, when facing large-scale and longdistance hydrogen transportation needs, existing hydrogen transportation strategies struggle to transport hydrogen economically and flexibly from hydrogen production plants (HPPs) to various hydrogen users. To this end, this paper proposes a distributionally robust optimization (DRO) scheduling model for the electric-hydrogen integrated energy system (EHIES) based pipeline-road collaborative hydrogen transportation (PRCHT). Firstly, by analyzing the transportation mechanism of hydrogen-blended pipelines and combining the relationship between the pipeline's storage and the gas pressure at both ends, this work constructs a quasi-dynamic transportation model for natural gas hydrogen blending with a variable hydrogen blending ratio. Next, by employing the improved McCormick technique and piecewise linearization method, the quasi-dynamic model is transformed into a mixed-integer linear programming (MILP) model. Furthermore, by integrating the trailer-based hydrogen transportation model, a PRCHT model is developed. Finally, considering the high uncertainty in wind power output, a DRO scheduling model for the integrated electricity-hydrogen energy system based on Wasserstein distance is proposed. The DRO model is then transformed into a MILP problem using the conditional value-at-risk (CVaR) approximation method. The simulation results demonstrate that the proposed scheduling model reduces the total system cost by 19.43 % compared to the constant hydrogen blending ratio benchmark, while preventing 22.68 % of potential hydrogen load shedding relative to the natural-gas-pipeline-exclusive transport model. Meanwhile, the employed algorithm improves computational efficiency and achieves a robust optimization of the scheduling decisions by balancing system robustness and economic performance.
