Objective: To assess the impact of anxiety and stress during pregnancy upon neonatal outcome Methodology: A descriptive purposive study was used to assess the impact of anxiety and stress during pregnancy upon neonatal outcome. The study was conducted from (22nd \ September \ 2020 to 15th \ February \ 2021). A non-probability sample (purposive sample) was selected from 100 women. Data were collected through an interview with the mother in the counseling clinic, during the third trimester of pregnancy, as well as after childbirth in the labour wards to assess the outcome of pregnancy. Data were analyzed through descriptive statistics (frequency and percentages). Results: The most important thing observed in this study was the n

Background: Preeclampsia occurs in 3-5% of
pregnancies and is a major cause (12-20 %) of
maternal mortality in developed countries. It is the
leading cause of preterm birth and intra-uterine
growth restrictions (IUGR).
Objective: The study was designed to determine and
demonstrate the ultra structural changes of
endothelial cells in placenta of women suffering from
hypertensive disease.
Patients & Methods: Placental samples were
obtained from two groups of pregnant women
groups (preeclamptic and normal pregnant women).
The specimens were fixed in 2.5% gluteraldehyde
and preceded for electron microscopic examination.
Results: Placenta of women with preeclampsia has
shown marked degenerative

vitrofertilization/intracytoplasmic sperm injection-embryo transfer (IVF/ICSI-ET) for infertile patients. Insulin like growth factor 1 (IGF1), Dehydroepiandrosterone sulfate (DHEAS) and Total Testosterone (TT) may have a role on follicle growth, oocyte quality, embryo quality and subsequent pregnancy in patients undergoing IVF cycles. To explore the possibility of using DHEAS, TT and IGF1 as predictive indicator for successful pregnancy in patients undergoing (IVF/ICSI-ET). A prospective study was performed enrolling (12) non pregnant control group,(7) pregnant control group,(18) non-pregnant PCOS group and (12) pregnant PCOS group. The collection of blood and follicular fluid (FF) samples was done at the day of oocyte aspiration. Electroch

The study aimed to assess the expression of CD49d and CD26 in newly diagnosed CLL patients and find their correlation with clinical Binet stage, and other clinical parameters. This study was conducted on 51 newly diagnosed CLL patients based on lymphocyte count > 5×109/L and immunophenotyping. The expression of CD49d, and CD26 were investigated using eight-color flow cytometer. The expression of CD49d and CD26 were detected in 56.9 %, 68.8 % of CLL patients, respectively. The correlation between CD49d expression and CD26 expression was statistically significant (p < 0.001) with high concordance rate between them. The positive expression of both CD49d and CD26 had statistically significant association with clinical Binet staging (p < 0.001,

This review discusses the gingival biotypes, their characteristics, analysis based on the measurement of the dentopapillary complex. Also discuss their response to inflammation, surgery, and ridge healing after tooth extraction, their influence in the behavior of the peri-implant tissue

Aim of the study is to find any correlation between obesity (insulin resistance) and type I diabetes in children. Obesity and diabetes mellitus are the common health problems, and obesity is common cause of the insulin resistance. The results revealed marked increased in glucose, insulin, HbAlc and insulin resistance in obese diabetic type I patients comparing to control group they were obese and non-obese found to be within normal values for glucose, insulin, FIbAlc , and insulin resistance.

<span lang="EN-US">Diabetes is one of the deadliest diseases in the world that can lead to stroke, blindness, organ failure, and amputation of lower limbs. Researches state that diabetes can be controlled if it is detected at an early stage. Scientists are becoming more interested in classification algorithms in diagnosing diseases. In this study, we have analyzed the performance of five classification algorithms namely naïve Bayes, support vector machine, multi layer perceptron artificial neural network, decision tree, and random forest using diabetes dataset that contains the information of 2000 female patients. Various metrics were applied in evaluating the performance of the classifiers such as precision, area under the c