Background: Congenital heart disease is one of the most common developmental anomalies in children. These patients commonly have poor oral health that increase caries risk. Dental management of children with congenital heart disease requires special attention, because of their heightened susceptibility to infectious endocarditis. The aims of this study were to assess the severity of dental caries of primary and permanent teeth and treatment needs in relation to nutritional indicator (Body Mass Index) among children with congenital heart disease. Materials and Methods: In this case-control study, case group consisted of 399 patients aged between 6-12 years old with congenital heart disease were examined for dental status in Ibn Al-Bitar spec

In this study, an efficient photocatalyst for dissociation of water was prepared and studied. The chromium oxide (Cr₂O₃) with Titanium dioxide (TiO₂) nanofibers (Cr₂O₃-TNFs) nanocomposite with (chitosan extract) were synthesized using ecologically friendly methods such as ultrasonic and hydrothermal techniques; such TiO₂ exhibits nanofibers (TNFs) shape struct

In this research, Artificial Neural Networks (ANNs) technique was applied in an attempt to predict the water levels and some of the water quality parameters at Tigris River in Wasit Government for five different sites. These predictions are useful in the planning, management, evaluation of the water resources in the area. Spatial data along a river system or area at different locations in a catchment area usually have missing measurements, hence an accurate prediction. model to fill these missing values is essential.
The selected sites for water quality data prediction were Sewera, Numania , Kut u/s, Kut d/s, Garaf observation sites. In these five sites models were built for prediction of the water level and water quality parameters.

In our article, three iterative methods are performed to solve the nonlinear differential equations that represent the straight and radial fins affected by thermal conductivity. The iterative methods are the Daftardar-Jafari method namely (DJM), Temimi-Ansari method namely (TAM) and Banach contraction method namely (BCM) to get the approximate solutions. For comparison purposes, the numerical solutions were further achieved by using the fourth Runge-Kutta (RK4) method, Euler method and previous analytical methods that available in the literature. Moreover, the convergence of the proposed methods was discussed and proved. In addition, the maximum error remainder values are also evaluated which indicates that the propo

Placental calcification is an aging process that is increasingly linked to adverse pregnancy outcomes when it occurs prematurely. Vitamin D has immunomodulatory properties and may play a role in placental calcification. This study explored the association between maternal vitamin D, inflammatory cytokines, and placental calcification. A total of 46 pregnant women aged 16–40 years were included in this study. They were divided into four groups: Group 1 (control group; n = 10, without placental calcification), Group 2 (n = 12, Grade 1 placental calcification), Group 3 (n = 12, Grade 2 placental calcification), and Group 4 (n = 12, Grade 3 placental calcification). Serum vitamin D (25(OH)D) and cytokine (TNF-α,

Background: A case-control study design revealeda relationship between the present of fluoride, and the reduction of dental caries and the increase prevalence and severity of dental fluorosis .The aim of this study was to assess the prevalence and severity of dental caries in relation to dental fluorosis among school children in Al-Muthana'a Governorate. Materials and methods: It was conducted among primary school students aged 12 years old, the age was taken according to the criteria of World Health Organization (1997) (1).The number of students was selected in each sector of control group according to number of schools in that sector .Sectors of control group which depend on water of river as source of drinking water. Case group which inc

Genome sequencing has significantly improved the understanding of HIV and AIDS through accurate data on viral transmission, evolution and anti-therapeutic processes. Deep learning algorithms, like the Fined-Tuned Gradient Descent Fused Multi-Kernal Convolutional Neural Network (FGD-MCNN), can predict strain behaviour and evaluate complex patterns. Using genotypic-phenotypic data obtained from the Stanford University HIV Drug Resistance Database, the FGD-MCNN created three files covering various antiretroviral medications for HIV predictions and drug resistance. These files include PIs, NRTIs and NNRTIs. FGD-MCNNs classify genetic sequences as vulnerable or resistant to antiretroviral drugs by analyzing chromosomal information and id