بحوث سكوبس — محمد حيدر حمد

This Study was conducted out at the Ministry of Agriculture's Poultry Research Station/Animal Resources Department/Agricultural Research Center. To see how body weight (BW) and leptin hormone (LEP) levels in breeder blood affect fertility and hatchability. 140 Iraqi local laying chickens (120 females + 20 males) aged 28 weeks were used in the study. Following the numbering of females, the birds were grown in individual cages and dispersed sequentially on cages. The experiment was divided into three periods, each lasting 28 days, during which the breeder's live body weight was recorded and divided into two categories (greater than 1.5 kg and less than 1.5 kg), and blood samples were collected at the end of each period to determine the concentration of leptin hormone in the breeders' blood. For comparison between mothers' performance, hormone concentration is separated into three groups: high, medium, and low. The percentage of fertile eggs (FE), the percentage of hatched chicks from total eggs (HAT), the percentage of hatched chicks from fertile eggs (HAF), and the percentage of mortality (MO) all showed a significant increase (p<0.05), and a linear relationship was discovered between the studied traits and hormone concentration levels. Leptin arrived at the best predictive values that reflect reality by computing regression and correlation coefficients and using a hypothetical technique in estimating prediction results. This study concludes that body weight and leptin levels have unknown impacts on hatching fertility rates. © (2024), (University of Baghdad, College of Agriculture). All Rights Reserved.

Oxysterols are cholesterol metabolites generated in the liver and other peripheral tissues as a mechanism of removing excess cholesterol. Oxysterols have a wide range of biological functions, including the regulation of sphingolipid metabolism, platelet aggregation, and apoptosis. However, it has been found that metabolites derived from cholesterol play essential functions in cancer development and immunological suppression. In this regard, research indicates that 27-hydroxycholesterol (27-HC) might act as an estrogen, promoting the growth of estrogen receptor (ER) positive breast cancer cells. The capacity of cholesterol to dynamically modulate signaling molecules inside the membrane and particular metabolites serving as signaling molecules are two possible contributory processes. 27-HC is a significant metabolite produced mainly through the CYP27A1 (Cytochrome P450 27A1) enzyme. 27-HC maintains cholesterol balance biologically by promoting cholesterol efflux via the liver X receptor (LXR) and suppressing de novo cholesterol production through the Insulin-induced Genes (INSIGs). It has been demonstrated that 27-HC is able to function as a selective ER regulator. Moreover, enhanced 27-HC production is in favor of the growth of end-stage malignancies in the brain, thyroid organs, and colon, as shown in breast cancer, probably due to pro-survival and pro-inflammatory signaling induced by unbalanced levels of oxysterols. However, the actual role of 27-HC in cancer promotion and progression remains debatable, and many studies are warranted to be performed to unravel the precise function of these molecules. This review article will summarize the latest evidence on the deleterious or beneficial functions of 27-HC in various types of cancer, such as breast cancer, prostate cancer, colon cancer, gastric cancer, ovarian cancer, endometrial cancer, lung cancer, melanoma, glioblastoma, thyroid cancer, adrenocortical cancer, and hepatocellular carcinoma. © 2022 Elsevier B.V.

We developed a simulation methodology on the basis of machine learning techniques for simulation of pharmaceutical solubility in a supercritical solvent, i.e., CO2 with the perspective of nanodrug production. The X variables considered in this simulation work included pressure and temperature of the system, whereas the response (Y) was considered to be drug solubility in the solvent. The model drug considered in this work is Fenoprofen in which its solubility was modeled at different temperature and pressure to assess its suitability for supercritical processing in nanomedicine preparation via supercritical green technology. The solubility dataset of this work includes 32 data points with two input parameters (temperature and pressure) and one output (solubility in mole fraction) which was assumed to be Y in the computations. Models based on machine learning, including CNN, DNN, and GRNN, were selected as the basis for modeling and analysis performed in this study. Also, the hyper-parameters of these methods have been fine-tuned with the help of an algorithm called Bat algorithm, which comes from nature inspiration. The optimized models with the R2 criterion all have a score higher than 0.99, which shows the significant impact of the Bat algorithm in improving the accuracy of the employed models. Also, based on the calculated RMSE, DNN, CNN, and GRNN have error rates of 7.57E-05, 7.25E-05, and 3.94E-05, respectively. Indeed, GRNN model was finally selected as the research primary model for the solubility prediction, and the max error was reduced to 8.47E-05 using this method. © 2022 Elsevier B.V.