Background: Nicotine is the foremost chemical constituent responsible for addiction in tobacco products, in the non-ionized condition can be easily absorbed via epithelial tissue of the lung, the mouth, the nose  and across the skin

Objective:The study examines the harmful effect of   the nicotine which is an important component of cigarette in vitro.

Type of the study: Cross-sectional study.

Methods: Examines the harmful effect of   the nicotine which is an important component of cigarette in vitro by using two types of lung cancer cell lines (H460 TP53+/+, H441 TP53-/-).

Background BK polyomavirus is one of the common post-transplant viral infections, affecting ∼15% of renal transplantation recipients (RTR), leading to graft loss in more than half of cases. Objectives Study the rate of detection of BK virus (BKV) in RTRs in Pap-stained urine cytology specimens. Methods A single center study, urine samples were collected from 99 RTR patients, with 15 Living Donors (LD) and 15 patients with chronic kidney disease (CKD) were taken as controls. And urine cytology smears were Pap stained for detection of decoy cells (DCs). Results Out of the 99 RTRs, 27 (27.3%) patients were decoy positive, 8 out of these 27 patients had uncommon DCs, and 5 out of these 27 cytology positive patients (18.5%) had biopsy proven B

Drastic threat to the natural system is caused by the uncontrolled release of synthetic pollutants, including azo dyes. This study centered on the decolorization and biodegradation of water soluble azo dye reactive blue (RB) in a batch mode sequential anaerobic-aerobic processes. A local sewage treatment plant was the source where activated sludge was collected to be used as non-adapted mixed culture with both free and the alginate immobilized cells for RB biodegradation. Under anaerobic conditions, the free and immobilized mixed cells were proved to completely decolorize 10 mg/ L of RB within 20 and 30 h, respectively. Alginate- immobilized mixed cells, resulted in 88%, 87%, and 87% maximum COD removals with samples con

In recent years, with the rapid development of the current classification system in digital content identification, automatic classification of images has become the most challenging task in the field of computer vision. As can be seen, vision is quite challenging for a system to automatically understand and analyze images, as compared to the vision of humans. Some research papers have been done to address the issue in the low-level current classification system, but the output was restricted only to basic image features. However, similarly, the approaches fail to accurately classify images. For the results expected in this field, such as computer vision, this study proposes a deep learning approach that utilizes a deep learning algorithm.

The availability of different processing levels for satellite images makes it important to measure their suitability for classification tasks. This study investigates the impact of the Landsat data processing level on the accuracy of land cover classification using a support vector machine (SVM) classifier. The classification accuracy values of Landsat 8 (LS8) and Landsat 9 (LS9) data at different processing levels vary notably. For LS9, Collection 2 Level 2 (C2L2) achieved the highest accuracy of (86.55%) with the polynomial kernel of the SVM classifier, surpassing the Fast Line-of-Sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) at (85.31%) and Collection 2 Level 1 (C2L1) at (84.93%). The LS8 data exhibits similar behavior. Conv

Accurate emotion categorization is an important and challenging task in computer vision and image processing fields. Facial emotion recognition system implies three important stages: Prep-processing and face area allocation, feature extraction and classification. In this study a new system based on geometric features (distances and angles) set derived from the basic facial components such as eyes, eyebrows and mouth using analytical geometry calculations. For classification stage feed forward neural network classifier is used. For evaluation purpose the Standard database "JAFFE" have been used as test material; it holds face samples for seven basic emotions. The results of conducted tests indicate that the use of suggested distances, angles

In this work, satellite images for Razaza Lake and the surrounding area
district in Karbala province are classified for years 1990,1999 and
2014 using two software programming (MATLAB 7.12 and ERDAS
imagine 2014). Proposed unsupervised and supervised method of
classification using MATLAB software have been used; these are
mean value and Singular Value Decomposition respectively. While
unsupervised (K-Means) and supervised (Maximum likelihood
Classifier) method are utilized using ERDAS imagine, in order to get
most accurate results and then compare these results of each method
and calculate the changes that taken place in years 1999 and 2014;
comparing with 1990. The results from classification indicated that