Background Cystic echinococcosis (CE), a worldwide zoonotic disease, is affected by various biological and environmental factors. We investigated dog/livestock populations, climatic and environmental factors influencing the distribution of human CE cases in Fars province, southwest Iran. Methods We mapped the addresses of 266 hospitalised CE patients (2004–2014) and studied the effects of different temperature models, mean annual rainfall and humidity, number of frosty days, slope, latitude, land covers, close proximity to nomads travel routes, livestock and dog densities on the occurrence of CE using geographical information systems approach. Data were analyzed by logistic regression. Results In the multivariate model predicting CE, living in an urban setting and densities of cattle and dogs were the most important CE predictors, sequentially. Dry (rained) farm, density of camel and sheep, close proximity to nomads travel routes, humidity, and slope also were considered as the determinants of CE distribution, when analyzed independently. Slope had a negative correlation with CE while temperature, frost days and latitude were not associated with CE. Conclusions In our study, an urban setting was the most important risk factor and likely due to a combination of the high density of key life cycle hosts, dogs and livestock, a large human susceptible population and the high number of abattoirs. Farmland and humidity were highly suggestive risk factors and these conditions support the increased survival of Echinococcus granulosus eggs in the soil. These findings support the development of strategies for control of disease. More research is needed test optimal interventions.

Background Corona Virus Disease 2019 (COVID-19) presentations range from those similar to the common flu to severe pneumonia resulting in hospitalization with significant morbidity and/or mortality. In this study, we made an attempt to develop a predictive scoring model to improve the early detection of high risk COVID-19 patients by analyzing the clinical features and laboratory data available on admission. Methods We retrospectively included 480 consecutive adult patients, aged 21–95, who were admitted to Faghihi Teaching Hospital. Clinical and laboratory features were collected from the medical records and analyzed using multiple logistic regression analysis. The final data analysis was utilized to develop a simple scoring model for the early prediction of mortality in COVID-19 patients. The score given to each associated factor was based on the coefficients of the regression analyses. Results A novel mortality risk score (COVID-19 BURDEN) was derived, incorporating risk factors identified in this cohort. CRP (> 73.1 mg/L), O2 saturation variation (greater than 90%, 84–90%, and less than 84%), increased PT (> 16.2 s), diastolic blood pressure (≤ 75 mmHg), BUN (> 23 mg/dL), and raised LDH (> 731 U/L) were the features constituting the scoring system. The patients are triaged to the groups of low- (score < 4) and high-risk (score ≥ 4) groups. The area under the curve, sensitivity, and specificity for predicting mortality in patients with a score of ≥ 4 were 0.831, 78.12%, and 70.95%, respectively. Conclusions Using this scoring system in COVID-19 patients, the patients with a higher risk of mortality can be identified which will help to reduce hospital care costs and improve its quality and outcome.

BACKGROUND The range of serum alanine aminotransferase (ALT) varies in different sub-populations or countries. Its population-specific cut-off points may provide a more effective screening tool for non-alcoholic fatty liver disease (NAFLD). Objectives To investigate the upper normal level (UNL) of ALT and its association with metabolic syndrome (MS) in a semi-urban population in southern Iran. METHODS The baseline data of Pars Cohort Study was used. A total of 9264 subjects aged 40-75 years were enrolled. UNL of ALT was estimated based on 95 percentile of ALT in participants who had body mass index (BMI) < 25. Multivariable logistic regression was applied and adjusted odds ratio (OR) and its 95% confidence interval (CI) were estimated. RESULTS 95 percentile of ALT was 41.71 U/L and 32.9 U/L in men and women, respectively. Abnormal waist circumference (OR: 1.72, 95%CI: 1.34, 2.21), triglyceride (OR: 1.63, 95%CI: 1.25, 2.13), fasting blood sugar (OR: 1.69, 95%CI: 1.32, 2.16), cholesterol level (OR: 1.06, 95%CI: 1.03, 1.09) and systolic blood pressure (OR: 1.08, 95%CI: 1.01, 1.16) were independently associated with ALT. CONCLUSION UNL of ALT in southern Iranian women is lower than the current recommended level, while these are almost the same for men. MS components are highly common in southern Iran and are associated with elevated serum ALT. Further studies are recommended to estimate the UNL of serum ALT among the Iranian population with NAFLD.

Background: Corona Virus Disease 2019 (COVID-19) presentation resembles common flu or can be more severe; it can result in hospitalization with significant morbidity and/or mortality. We made an attempt to develop a predictive model and a scoring system to improve the diagnostic efficiency for COVID-19 mortality via analysis of clinical features and laboratory data on admission. Methods: We retrospectively enrolled 480 consecutive adult patients, aged 21-95, who were admitted to Faghihi Teaching Hospital. Clinical and laboratory features were extracted from the medical records and analyzed using multiple logistic regression analysis. Results: A novel mortality risk score (COVID-19 BURDEN) was calculated, incorporating risk factors from this cohort. CRP (> 73.1 mg/L), O2 saturation variation (greater than 90%, 84-90%, and less than 84%), increased PT (>16.2s), diastolic blood pressure (≤75 mmHg), BUN (>23 mg/dL), and raised LDH (>731 U/L) are the features comprising the scoring system. The patients are triaged to the groups of low- (score <4) and high-risk (score ≥ 4) groups. The area under the curve, sensitivity, and specificity for predicting non-response to medical therapy with scores of ≥ 4 were 0.831, 78.12%, and 70.95%, respectively. Conclusion: Using this scoring system in COVID-19 patients, the severity of the disease will be determined in the early stages of the disease, which will help to reduce hospital care costs and improve its quality and outcome.

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