Problem: Cancer is regarded as one of the world's deadliest diseases. Machine learning and its new branch (deep learning) algorithms can facilitate the way of dealing with cancer, especially in the field of cancer prevention and detection. Traditional ways of analyzing cancer data have their limits, and cancer data is growing quickly. This makes it possible for deep learning to move forward with its powerful abilities to analyze and process cancer data. Aims: In the current study, a deep-learning medical support system for the prediction of lung cancer is presented. Methods: The study uses three different deep learning models (EfficientNetB3, ResNet50 and ResNet101) with the transfer learning concept. The three models are trained using a

Objective: Loranthus europaeus is a parasitic plant that lives on the branches of trees. The present study aimed to evaluate the anti-inflammatory effects of two different extracts on chronic inflammation induced by cotton pellets in rats. Methods: Loranthus seed was extracted by maceration with absolute methanol, in which dry Loranthus seeds were triturated in a mortar and macerated with 500 mL of methanol. After 24 h, the mixture was filtered, and the residue was re-extracted. The filtrates were combined and dried under vacuum. The mixture was mixed with 100 mL of distilled water and fractionated by petroleum ether and ethyl acetate using 70 mL × 3 times each. The organic fractions were dried, filtered, and evaporated to dryness.

This study was aimed to investigate the protective effect of vitamin D on L-arginine induced acute pancreatitis in rats. Twenty eight white Albino rats of both sexes were divided into 4 equal groups. Negative control rats (group I) intra-peritoneally injected with normal saline, positive control (group II) rats induced acute pancreatitis with L- arginine, group III rats treated with a single dose of vitamin D, and group IV treated with single dose of vitamin D prior to first dose of L- arginine. Serum amylase, lipase and cytokines such as tumour necrosis factor –alpha (TNF-α), interleukin-1 beta (IL1β); in addition, my eloperoxidase (MPO) enzyme activity in the pancreas were measured. In acute pancreatitis group (group II), there was a

The present study aimed to investigate the acetamiprid effects on biochemical aspects in albino mice. Thirty albino mice at the age of 6-8 weeks and average weight 25±5 g were divided into three groups each having ten (10) healthy mice. The first group was orally administrated with distilled water while the second and third groups were orally administrated with 50 mg/mL and 100 mg/mL respectively of acetamprid (0.1 mL) daily for one week. LD50 of acetamiprid was measured and found to be 200 mg/kg. The parameters of evaluations included liver function using Aspartate Aminotransferase (AST), Alanine Aminotransferase (ALT) and Alkaline Phosphatase (ALP). Lipid profile was anal

Introduction. The coexistence of non-small cell lung cancer (NSCLC) and chronic obstructive pulmonary disease (COPD) is commonly observed, primarily due to overlapping risk factors like smoking and environmental exposures. This dual diagnosis introduces complex clinical scenarios, often associated with worsened outcomes and heightened vulnerability to treatment-related side effects. Immune checkpoint inhibitors, especially atezolizumab, have emerged as pivotal agents in enhancing clinical outcomes for individuals diagnosed with NSCLC. Recent evidence suggests that atezolizumab remains effective and well-tolerated in NSCLC patients with coexisting COPD. This review evaluates the efficacy and safety of atezolizumab in NSCLC patients with coex

Objective: To evaluate knowledge towards smoking and its relationship with lung cancer among members of
Baghdad Nursing College.
Methodology: The study comprised 100 affiliates from the College of Nursing/ University of Baghdad that
included students, teaching staff and employees. All data was collected through a structured questionnaire
prepared by the National Cancer Research Center which were answered during a scientific symposium
organized by the center on lung Cancer Awareness in March 2016.The data were analyzed by using the SPSS,
version 22
Results: The age of the respondents ranged from (19-64 years); 76% were females and only 4% were smokers.
The results showed that the mean score for the level of knowled

These With recent developments in machine learning, data mining and computer vision, there is great potential for improvements in early detection of lung cancer using scans and data available. This paper details the methods and techniques used in our project, where the objective is to develop algorithms to determine whether a patient has or is likely to develop lung cancer using dataset images using data mining and machine learning for the classification and examination. We explore approaches to address the problem. Cancer is the most important cause of death globally. The disease diagnosis is a major process to treat the patients who are affected by cancer disease. The diagnosis process is more difficult comparatively known about t