The research aims to shed light on the internet and how to employ them and to take advantage of applications in scientific research among faculty members, from a survey the opinions of professors at the University of Alanbar and Almustansiriah, to see the reality of use of the Internet and ways to invest in the service of scientific research.
The follow descriptive analysis approach , which is appropriate to the nature of this study concerned with exploring the views on the uses of the Internet. The study reached the field the following results:
1- There are(60) % of respondents use the Internet on an ongoing basis to see new information and keep pace with scientific developments in the field of specialty and the average use of the

Background: Tumors of the oral cavity are under
estimated in general dental and medical practice,
some authors describe it as the forgetting disease,
others wondering if the attention paid to this disease
compared to its fatality (The 5-year survival rate is
about 50%) is enough for disease control? However;
this disease deserves a comprehensive assessment by
all dental and medical fields assumed to examine the
oral cavity regularly, especially otolaryngologist.
Objectives: To find out the sensitivity and specificity
of clinical examination in diagnosing oral tumors and
premalignant conditions by otolaryngologist.
Methods: Across sectional retrospective study was
conducted in the:
-study design:

An increasing number of emerging contaminants have been detected in surface waters, sediment, soil and ground water in different locations in the world, which is a new environmental challenges need an actual concern for international scientific and legislative communities. 

   The nonprescription and huge used pharmaceuticals ibuprofen and diclofenac sodium will be focused in this study. New adsorbent developed using cheap inorganic clay material (bentonite) and organic polymer polyureaformaldehyde (PUF), the combination of these two materials gave the surface more roughness with wide active site distribution. Batch adsorption experiment performed to each pharmaceutical individually to determine the optimum separat

A new spectrophotometric method for individual and simultaneous determination of cefixime and cephalexin depending on the first and second derivative mode techniques. The first and second derivative spectra of these compounds permitted individual and simultaneous determination of cefixime and cephalexin in concentration interval of (4– 24μg.ml-1 ) by measuring the amplitude of peak-to-base line, pea to peak at certain wavelengths and the area under peak at selected spectrum intervals. The methods showed reasonable precision and accuracy and have been applied to determine cefixime and cephalexin in two different pharmaceutical preparations.

The COVID-19 pandemic has necessitated new methods for controlling the spread of the virus, and machine learning (ML) holds promise in this regard. Our study aims to explore the latest ML algorithms utilized for COVID-19 prediction, with a focus on their potential to optimize decision-making and resource allocation during peak periods of the pandemic. Our review stands out from others as it concentrates primarily on ML methods for disease prediction.To conduct this scoping review, we performed a Google Scholar literature search using "COVID-19," "prediction," and "machine learning" as keywords, with a custom range from 2020 to 2022. Of the 99 articles that were screened for eligibility, we selected 20 for the final review.Our system

Background: techniques of image analysis have been used extensively to minimize interobserver variation of immunohistochemical scoring, yet; image acquisition procedures are often demanding, expensive and laborious. This study aims to assess the validity of image analysis to predict human observer’s score with a simplified image acquisition technique. Materials and methods: formalin fixed- paraffin embedded tissue sections for ameloblastomas and basal cell carcinomas were immunohistochemically stained with monoclonal antibodies to MMP-2 and MMP-9. The extent of antibody positivity was quantified using Imagej® based application on low power photomicrographs obtained with a conventional camera. Results of the software were employed

Machine Learning (ML) algorithms are increasingly being utilized in the medical field to manage and diagnose diseases, leading to improved patient treatment and disease management. Several recent studies have found that Covid-19 patients have a higher incidence of blood clots, and understanding the pathological pathways that lead to blood clot formation (thrombogenesis) is critical. Current methods of reporting thrombogenesis-related fluid dynamic metrics for patient-specific anatomies are based on computational fluid dynamics (CFD) analysis, which can take weeks to months for a single patient. In this paper, we propose a ML-based method for rapid thrombogenesis prediction in the carotid artery of Covid-19 patients. Our proposed system aims