Aim of the work:the present study was carried out to investigate the possible ameliorating effects of three herbs: hops(H) 1.5g/kg diet , rosemary(R) 5g/kg diet and cat's claw (C C) 1.7g/kg diet on hepatic toxicity induced by 7,12-Dimethyl benz(A)antheracene , a hydrocarbon involves various negative consequence for human health and ecosystems conversation .Results:in this work,48 female rats at 50 day of age were divided into 8 groups; control ,hops(H), rosemary(R), cat ' s claw(CC),DMBA, DMBA+H,DMBA+R and DMBA+CC groups.Results:the results indicated that a single intraperitonial (i.p) dose of DMBA (30mg/Kg b.W) caused significant decrease in the percentage of body weight gain, but an increase in the hepatosomatic index. In addition , the results illustrated an increase in the liver malondialdehyde (MDA) contents and hydrogen peroxide levels (H2O2) accompanied by significant decrease in reduced glutathione (GSH) content and superoxide dismutase (SOD) and glutathione-S-transferase (GST) activities.The results, also reported significant decrease in serum total proteins, total albumin , globulin and liver total protein but serum total bilirubin was significantly elevated in the DMBA intoxicated group. Furthermore, aspartate aminotransaminase (ASAT), alanine aminotransaminase (ALAT) , γ-glutamyltransferase (GGT) and alkaline phosphatase (ALP) activities were significantly increased in serum but significantly decreased in the liver. On the other hand, intake of hops, rosemary and cat ' s claw minimize the disturbances observed in most of the tested parameter's resulted from DMBA administration and improve the liver functions mostly in the following order, rosemary ˃ hops ˃ cat's claw. Conclusion: it can be concluded that intake of such herbs (hops , rosemary, cat ' s claw)may be effective in reducing DMBA toxicity.

Background:Hearing loss during the first 3 years of life can hinder speech and language acquisition. Speech performance deteriorates rapidly with increased levels of background noise in cochlear implant users compared with normal-hearing (NH) listeners, especially when the noise is dynamic e.g., competing speaker or modulated noise. Studying CI users' susceptibility to noise remains a major challenge for researchers and is an important step toward improving CI users' performance in the adverse noisy conditions. Aim of the work:To evaluate speech perception in noise of a group of cochlear implanted (CI) children using different types of noise, at different signal to noise ratios (SNR) and explore the effect of age at surgery on speech understanding in noisy situations.Patient and Methods: Forty subjects divided into 2 groups were included in the present study. Group I: Ten normal hearing children (NH) with mean age of 95.5 months. Group II: Thirty CI users with mean age of 100.2 months. They were tested using the newly developed low-verbal sentences in noise test (LV-SIN) using white, multi-talker babble and story noise. Language and speech evaluation were done. Scoring was done by measuring the SNR 50 which is the level at which the child repeated 50% of the number of words per list.Results: Significant difference in LV-SIN test scores was obtained between NH children and CI users using the 3 types of noise. White noise showed the least challenging situation. Age at CI implantation was significantly correlated to the LV-SIN test scores.Conclusions: Children with CI need much higher signal to noise ratios (SNRs) than their NH peers and age at CI surgery highly affects their speech perception in noise.

Objective: Cochlear implant (CI) candidate selection is a lengthy, complicated process that entails subjective judgment on the interaction of multiple pre-operative variables. It is assumed that setting a scoring system for the process of CI candidate selection would help in precise and reliable decision making. This would also provide a tool that would help in providing a better quality of life for CI patients. Methods: Retrospective cohort study was held out in three post-CI rehabilitation centers. A total of 100 children records were analyzed with two statistical methods; conventional and Artificial Intelligence (AI) using Machine Learning. Language age deficit, phonological deficit, and social deficit were invented as new measures of CI performance; used to represent the developmental delay of those children in a single numeric value (in months). Results: Artificial Intelligence analysis surpassed conventional statistical methods for the prediction of the outcome measures of post-CI performance. This was clearly expressed using linear regression models. The AI classification model validation for predictive accuracy of language age deficit, phonological deficit, and social deficit were 56.66%, 88.11%, and 40.46% respectively. Conclusion: The production of a preliminary CI scoring model used for prediction of performance of patients was achieved. More data should be collected and fed to the software in order to improve its performance.