Background: Polycystic ovary syndrome (PCOS) is one of the most frequent endocrine illnesses affecting reproductive - age women. L-carnitine has important roles in oxidative stress, energy production and glucose metabolism. It affects insulin resistance as decreased plasma carnitine level has been well reported in type II diabetes mellitus. Hence, it means L-carnitine may reduce insulin resistance which is found in PCO disease. Objective: This study aims to measure the level of L-carnitine and insulin resistance in both obese and non- obese patients with PCOS. Patients and Methods: Sixty women within the reproductive age with PCOS (30 obese and 30 non- obese) were recruited from the Gynecology and Obstetrics Outpatient Clinic in Baghdad Teaching Hospital from June 2016 to June 2017. The data collected for each case included: Height, weight, waist circumference, blood pressure, obstetrical, medical, and medication history as well as ultrasound results. A physical examination was done to evaluate the clinical signs of hyperandrogenism. Biochemical measurements included fasting blood sugar, leutinizing hormone, follicular stimulating hormone, Testosterone and lipid profile were measured together with total L-carnitine (using L-Carnitine Assay Kit Sigma-Aldrich Co.). Insulin resistance was diagnosed according to National Cholesterol Education Program/Adult Treatment Panel III (NCEP/ATP III). PCOS is diagnosed according to the Rotterdam criteria. Results: This study revealed that insulin resistance (IR) was present in 51.7% of PCOS patients, which was higher in obese PCOS patients (73.3%) than in the non-obese (30%). Age of patients, serum cholesterol, LH, and FSH were not related to IR. High mean BMI, waist circumference, FBS and triglyceride were significantly associated with IR (p < 0.05), while low serum HDL and L-Carnitine were associated with IR (p < 0.05). The mean serum total L-carnitine in this study was 34.03μmol/L. Obese women had lower carnitine levels than non-obese women and low serum L-Carnitine was associated with IR. Serum triglyceride, FBS and testosterone were correlated negatively with serum L-carnitine (p < 0.05) and serum HDL correlated positively with serum L-carnitine (p value = 0.001). Conclusions: The mean value of serum total L-carnitine among the non-obese PCOS women was higher than among the obese ones. Low serum L-carnitine is associated with insulin resistance
In this study Candida speices was diagnosed in 26 swab samples from patients with denture stomatitis , investigates the antagonism activity of Lactobacillus was investigated against the yeast of Candida albicans in vitro.Results revealed that The inhibition effect of Lactic Acid Bacteria against C.albicans was examined in solid medium, L.plantarum gave higher inhibition average 11mm followed by L.acidophillus with average 9 mm and, L.fermentum , L.casei with averages 7 mm. Whereas the filtrates, the highest inhibition zone were 20 and 16 mm by L. plantarum and L.acidophillus, respectively.
Abstract
The aim of this work is to create a power control system for wind turbines based on fuzzy logic. Three power control loop was considered including: changing the pitch angle of the blade, changing the length of the blade and turning the nacelle. The stochastic law was given for changes and instant inaccurate assessment of wind conditions changes. Two different algorithms were used for fuzzy inference in the control loop, the Mamdani and Larsen algorithms. These two different algorithms are materialized and developed in this study in Matlab-Fuzzy logic toolbox which has been practically implemented using necessary intelligent control system in electrical engineerin
... Show MoreIn information security, fingerprint verification is one of the most common recent approaches for verifying human identity through a distinctive pattern. The verification process works by comparing a pair of fingerprint templates and identifying the similarity/matching among them. Several research studies have utilized different techniques for the matching process such as fuzzy vault and image filtering approaches. Yet, these approaches are still suffering from the imprecise articulation of the biometrics’ interesting patterns. The emergence of deep learning architectures such as the Convolutional Neural Network (CNN) has been extensively used for image processing and object detection tasks and showed an outstanding performance compare
... Show MoreThis study includes synthesis of some nitrogenous heterocyclic compounds linked to amino acid esters or heterocyclic amines that may have a potential activity as antimicrobial and/or cytotoxic. Quinolines are an important group of organic compounds that possess useful biological activity as antibacterial, antifungal and antitumor .8-Hydroxyquinoline (8-HQ) and numerous of its derivatives exhibit potent activities against fungi and bacteria which make them good candidates for the treatment of many parasitic and microbial infection diseases.
These pharmacological properties of quinolones aroused our interest in synthesizing several new compounds featuring heterocyclic rings of the quinoline derivatives linke
... Show MoreThis research study experimentally the effect of air flow rate on humidification process
parameters. Experimental data are obtained from air conditioning study unit T110D. Results obtained
from experimental test, calculations and psychometrics software are discussed. The effect of air flow rate
on steam humidification process parameters as a part of air-conditioning processes can be explained
according to obtained results. Results of the steam humidification processes (1,2) with and without
preheating with 5A and 7.5A shows decreasing in dry bulb temperature, humidity ratio, and heat add to
moist air with increasing air flow rate, but humidification load, and total energy of moist air increase with
increasing air flo
Data scarcity is a major challenge when training deep learning (DL) models. DL demands a large amount of data to achieve exceptional performance. Unfortunately, many applications have small or inadequate data to train DL frameworks. Usually, manual labeling is needed to provide labeled data, which typically involves human annotators with a vast background of knowledge. This annotation process is costly, time-consuming, and error-prone. Usually, every DL framework is fed by a significant amount of labeled data to automatically learn representations. Ultimately, a larger amount of data would generate a better DL model and its performance is also application dependent. This issue is the main barrier for