Abstract Objective: To identify correlation of elevated LDH & CRP levels with the outcomes of COVID-19. Methodology: The cross-sectional retrospective study consisted of 200 COVID-19 patients who presented at a private clinical in Baghdad, Iraq. It was carried out from February 2021 to February 2022. Data included age, gender and clinical presentation. Blood samples were taken for high sensitivity CRP and LDH in the serum. Results: Out of 200 patients, 50 were critical and 150 severe according to clinical features. LDH and CRP showed a significant increase (p=0.000) in critical patients. This group involved admission to the respiratory intensive care unit requiring mechanical ventilation than in patients with severe COVID-19 (760.5±6.3 vs.

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has caused enormous issues worldwide and is the most infectious pandemic. This study included 50 subjects (evenly distributed between sexes) and their range of ages starting from 2 to 67 years. According to the study's result, the ages and genders of subjects include susceptibility to COVID-19. Males were found to be more infected than females, and the ages of 36 to 67 were more common than other age ranges. Also, BMI calculations revealed that male patients with COVID-19 have the highest percentage of obesity. The clinical parameter results have been found serum C‐reactive protein (CRP) as an essential indicator that changes significantly in infection with COVID‐19 an

The two-neutron halo-nuclei (¹⁷B, ¹¹Li, ⁸He) was investigated using a two-body nucleon density distribution (2BNDD) with two frequency shell model (TFSM). The structure of valence two-neutron of ¹⁷B  nucleus in a pure (1d_5/2) state and in a pure (1p_1/2) state for  ¹¹L and ⁸He nuclei. For our tested nucleus, an efficient (2BNDD's) operator for point nucleon system folded with two-body correlation operator's functions was used to investigate nuclear matter density distributions, root-mean square (rms) radii, and elastic electron scattering form factors. In the nucleon-nucleon forces the correlation took account of

In this study three reactive dyes (blue B, red R and yellow Y) in single , binary and ternary solution were adsorbed by activated carbon AC in equilibrium and kinetic experiments. Surface area, Bulk and real density, and porosity were carried out for the activated carbon.
Batch Experiments of pH (2.5-8.5) and initial concentration (5-100) mg/l were carried out for single solution for each dye. Experiments of adsorbent dosage effect (0.1-1)g per 100 ml were studied as a variable to evaluate uptake% and adsorption capacity for single dyes(5, 10) ppm, binary and ternary (10) ppm of mixture solutions solution of dyes. Langmuir, and Freundlich, models were used as Equilibrium isotherm models for single solution. Extended Langmuir and Freun

Overlapped have been prepared from epoxy resin material added to carbon Nanotube and percentages weight (0.1, 0.05, 0.01) % Studied the mechanical properties of the composite (bending, tensile an d hardness) has been found that the Flexural and tensile modulus of the composites were higher than the pure epoxy resin this may be due to the high mechanical strength of carbon nano tube (CNT). The hardness of the epoxy carbon Nanotube composites increased and the reason is due to increased overlap and stacking between the additives and material basis, which reduces the movement of polymer molecules leading to increased resistance to scratching material and cutting, will become more resistance to plastic deformation.

The study using Nonparametric methods for roubust to estimate a location and scatter it is depending  minimum covariance determinant of multivariate regression model , due to the presence of outliear values and increase the sample size and presence of more than after the model regression multivariate therefore be difficult to find a median location .       

It has been the use of genetic algorithm Fast – MCD – Nested Extension and compared with neural Network Back Propagation of multilayer in terms of accuracy of the results and speed in finding median location ,while the best sample to be determined by relying on less distance (Mahalanobis distance)has the stu

Lung cancer is one of the most serious and prevalent diseases, causing many deaths each year. Though CT scan images are mostly used in the diagnosis of cancer, the assessment of scans is an error-prone and time-consuming task. Machine learning and AI-based models can identify and classify types of lung cancer quite accurately, which helps in the early-stage detection of lung cancer that can increase the survival rate. In this paper, Convolutional Neural Network is used to classify Adenocarcinoma, squamous cell carcinoma and normal case CT scan images from the Chest CT Scan Images Dataset using different combinations of hidden layers and parameters in CNN models. The proposed model was trained on 1000 CT Scan Images of cancerous and non-c

Background : Diabetes mellitus, also known as blood sugar, is a series of metabolic disorders described by high blood glucose levels (hyperglycemia), low blood glucose (hypoglycemia), or both, resulting from defects in insulin production, insulin action, or both. Numerous studies have shown that interleukin (IL-6) acts on skeletal muscle cells , liver cells, and pancreas cells to influence glucose balance and metabolism, which directly or indirectly contributes to the development of diabetes. Research in this area is crucial because diabetes is recognized as a major risk factor for many diseases like Diabetic retinopathy, Diabetic nephropathy, Diabetic Neuropathy , heart disease and others.  Patients and methods : In this study, we