Objectives: The study aims at:
1- Measuring the level of lead in workers’ saliva and blood in the factory.
2- Studying the correlation between the saliva lead level and the infection that caused by microorganisms, isolation and
identification.
3-Studying the influence of high blood lead level on the total white blood cells.
Methodology: This study has been conducted for the period from March 15th, 2010 to May, 20th
, 2010. A total of (60)
saliva and blood samples were collected from workers in batteries industry factory in Baghdad and another (20) samples
were collected as a control group. Lead level had been measured in blood and saliva samples, then microorganisms were
isolated the from the saliva samples. Microbial growth was submitted from morphological and biochemical investigation
for identification of bacterial and fungi. On the other hand; the total white blood cells and differential count were measured.
Results: The results showed that the streptococcus mutans isolates were dominated species which are isolated from the
saliva samples with 35% and 22.5% of candida albicans. On the other hand, the lead level had been measured in blood and
saliva samples, the results showed that the occupation years affect on accumulation of the lead in the blood and saliva and
significant differences were found at (p≤0.01) in samples from worker at 15 occupation years. Additionally, the results of
this study revealed statistically non-significant in total white blood cells count comparison with controlling group.
Recommendations: The study recommends conducting laboratories tests for workers in factories that deal directly or
indirectly with lead and establishing health education seminars to demonstrate the dangers of lead how we deal with it.
Clinical keratoconus (KCN) detection is a challenging and time-consuming task. In the diagnosis process, ophthalmologists must revise demographic and clinical ophthalmic examinations. The latter include slit-lamb, corneal topographic maps, and Pentacam indices (PI). We propose an Ensemble of Deep Transfer Learning (EDTL) based on corneal topographic maps. We consider four pretrained networks, SqueezeNet (SqN), AlexNet (AN), ShuffleNet (SfN), and MobileNet-v2 (MN), and fine-tune them on a dataset of KCN and normal cases, each including four topographic maps. We also consider a PI classifier. Then, our EDTL method combines the output probabilities of each of the five classifiers to obtain a decision b
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