The traditionally use of indigenous medicinal plants in the treatment of burn, dermatrophytes and human infectious diseases and also still essential part of primary public health care. Antimicrobial activities of nine medicinal plants were determined in vitro through agar well diffusion method against pathogenic microorganism species of gastrointestinal tract. Medicinal plants extract of Cocculus pendulus, Malva neglecta, Rhazya stricta, Jaubertia aucheri, Corchorus depressus, Salvia bucharica, Microcephala lamellate, Berberis baluchistanica and Artemisa absinthium were found sensitive to Clostridium spp. The extracts of Malva neglecta, Jaubertia aucheri, Salvia bucharica and Berberis baluchistanica were observed sensitive to E. coli. Similarly the extracts of Malva neglecta, Jaubertia aucheri, Rhazya stricta, Corchorus depressus, and Artemisa absinthium were found sensitive to Salmonella spp. The extracts of Cocculus pendulus, Malva neglecta, Jaubertia aucheri, Corchorus depressus, Salvia bucharica, Microcephala lamellate, Berberis baluchistanica and Artemisa absinthium were sensitive to Shigella spp. The extracts of Cocculus pendulus, Jaubertia aucheri and Berberis baluchistanica were found sensitive to Klebsiella spp. The extracts of Cocculus pendulus, Rhazya stricta, Corchorus depressus, Microcephala lamellate and Artemisa absinthium were revealed resistance to E. coli. The extracts of Cocculus pendulus, Salvia bucharica, Microcephala lamellate and Berberis baluchistanica were revealed resistance to Salmonella spp. The extract of Rhazya stricta was revealed resistance to Shigella spp. The extracts of Malva neglecta, Rhazya stricta, Corchorus depressus, Salvia bucharica, Microcephala lamellate and Artemisa absinthium were revealed to resistance to Klebsiella spp. The extract of Jaubertia aucheri was highly sensitivity against E. coli, Salmonella spp, Shigella spp, Clostridium spp and Klebsiella spp.

Background: Hepatitis C infection is growing threat and major burden on public health, worldwide prevalence of hepatitis C is 3% (170 million infected people). Approximately 10 million people are infected in Pakistan and prevalence is expected to be higher in remote areas. The aim of this study is to assess knowledge, attitude and practice of adolescents towards hepatitis C in Quetta Pakistan. Methods: A cross sectional descriptive study carried out from February 2013 till April 2013. Sample size of this study was 456 and method of sampling was four stage cluster sampling. Samples were randomly selected from 12 schools in urban and semi-urban parts of district Quetta. Equal number of participants were selected (228 Male and 228 Female) from urban and semi-urban settings. Close ended questionnaire was designed, checked and used to access knowledge, attitude and practice of the participants towards hepatitis C infection Results: Response rate of this survey was 100 % and mean number of "Yes" answers to knowledge, attitude and practice questions were 51%, 46% and 42% respectively. The respondent's in urban setting has two times (1.92 odds) higher knowledge, positive attitude and practice than semi-urban participants. Furthermore the respondent's in higher educational level has two times (1.7 odds) better knowledge, positive attitude and practice than lower educational level participants. Furthermore this study indicates to reveal some evidence of stigmatization being exhibited by participants in both setting. Higher level of school education (group III) and older age (group III) respondents in both setting has positive attitudes toward hepatitis C than lower level of education (group I) and younger age (group I). In addition it is observed that adolescents by growing age and entering higher education group are getting more knowledge and positive attitude and practice about hepatitis C disease and it's a positive trend. Conclusions: This study observed that knowledge, positive attitudes and practices towards hepatitis C among adolescents was partial in both setting. There are some important gaps need to be strengthened especially in semiurban setting and female group of participants. The results of study indicate that there is lack of understanding about infection control and prevention of hepatitis C among study participants. Thus there is need of critical level of public awareness in district Quetta, especially among adolescents, to decrease burden of hepatitis C infection. Extensive health education campaigns about hepatitis C are beneficial for adolescents, particularly to residents of semi-urban and rural areas.

Impulse indicator saturation is a popular method for outlier detection in time series modeling, which outperforms the least trimmed squares (LTS), M-estimator, and MM-estimator. However, using the IIS method for outlier detection in cross-sectional analysis has remained unexplored. In this paper, we probe the feasibility of the IIS method for cross-sectional data. Meanwhile, we are interested in forecasting performance and covariate selection in the presence of outliers. IIS method uses Autometrics techniques to estimate the covariates and outlier as the number of covariates P > n observations. Besides Autometrics, regularization techniques are a well-known method for covariate selection and forecasting in high-dimensional analysis. However, the efficiency of regularization techniques for the IIS method has remained unexplored. For this purpose, we explore the efficiency of regularization techniques for out-of-sample forecast in the presence of outliers with 6 and 4 standard deviations (SD) and orthogonal covariates. The simulation results indicate that SCAD and MCP outperform in forecasting and covariate selection with 4 SD (20% and 5% outliers) compared to Autometrics. However, LASSO and AdaLASSO select more covariates than SCAD and MCP and possess higher RMSE. Overall, regularization techniques possess the least RMSE than Autometrics, as Autometrics possesses the least average gauge at the cost of the least average potency. We use COVID-19 cross-sectional data collected from 1 July 2021 to 30 September 2021 for real data analysis. The SCAD and MCP select CRP level, gender, and other comorbidities as an important predictor of hospital stay with the least out-of-sample RMSE of 7.45 and 7.50, respectively.