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Tania Dehesh,1 Paria Dehesh,2 Shahla Shojaei3 1Department of Biostatistics and Epidemiology, School of Public Health, Kerman University of Medical Sciences, Kerman, Iran; 2Department of Epidemiology, School of Public Health, Iran University of Medical Sciences, Tehran, Iran; 3College of Pharmacy, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Manitoba, CanadaCorrespondence: Paria Dehesh Email Paria_dehesh@yahoo.comPurpose: Depression and anxiety are common disorders in patients suffering from type 2 diabetes. These disorders can lead to premature morbidity, exacerbate disease complications, make patients suffer more, and increase health-care costs. As diabetes has increased worldwide recently, it is necessary to reduce the prevalence of factors that are associated with depression and anxiety in diabetes patients. This study aimed to assess the prevalence of anxiety and depression and to identify their associated factors, including metabolic components among people with type 2 diabetes.Patients and Methods: We performed a cross-sectional study in 1500 patients with type 2 diabetes in Kerman, in the southern part of Iran. The prevalence of depression and anxiety was estimated using the Beck Depression Inventory and the Hamilton Anxiety questionnaires, respectively. After calculating the proportions of depression and anxiety, univariate logistic regression was performed. Factors whose P-values were smaller than 0.2 in univariate logistic regression were included in multiple logistic regression for confounder adjustments. The analysis was performed using SPSS version 20.Results: The rates of depression and anxiety were 59% (95% CI: 54.48– 63.12) and 62% (95% CI: 59.51– 66.27), respectively. Factors found to be independently associated with anxiety were high FBS, high LDL-C, high TG, hypertension, complications, low physical activity. Factors found to be independently associated with depression were female gender, older age, high BMI, high FBS, high LDL-C, low HDL-C, high TG, high HbA1c, hypertension, and low physical activity. Complications were independently associated with anxiety but not with depression. Female gender, older age, high BMI, low HDL-C, and high HbA1c were independently associated with depression but not with anxiety.Conclusion: Current findings demonstrated that a large proportion of patients with type 2 diabetes suffer from depression and anxiety. This study also identified factors associated with these disorders. Controlling some metabolic variables will decrease the prevalence of these disorders and improves clinical remedy and quality of life in patients with type 2 diabetes.Keywords: anxiety, depression, type 2 diabetes, Hamilton questionnaire, Beck questionnaire

Cryptosporidium parvum is a microscopic parasite and furthermore, an identified agent that spends its life cycle in the mammalian gastrointestinal tract causing chronic and life-threatening diarrhoea in immunocompromised individuals. In this study, the GSE2077 series were selected from the NCBI site, which examined the contamination of the HCT-8 cell line with C. parvum in three treatment groups. Each of 24, 48, and 72 hours post-infection (PI) groups was compared with the mock, and the differentially expressed genes (DEGs) were identified during the analysis. For each comparison, the |logFC|= 2 and P values <0.05 were considered. The obtained values included: 24 hours=71 DEGs, 48 hours=82 DEGs, and 72 hours=55 DEGs. For the DEGs of each group, gene ontology diagrams were drawn separately using the Funrich3.1.3 software, including cellular components, biological processes, and molecular functions. The heat map diagrams were drawn with the R software and the heat map package. Also, the networks were plotted for each comparison in the Cytoscape software, and hub genes were obtained. Finally, the commonalities between the three treatment groups were identified using the FunRich software. Five common genes were revealed in all groups: RAD23B, DKK1, CXCL8, PHLDA1, and UGT1A3. [ABSTRACT FROM AUTHOR]

Scorpion stings pose a significant public health concern in Iran, resulting in approximately 45,000-50,000 cases and 19 deaths annually. The Khuzestan and Hormozgan provinces have the highest reported incidence rates, with an estimated 36,000 cases each year. This study focused on modeling the time series data of scorpion stings, specifically in Shoushtar City, from 2017 to 2022. Our objective was to investigate the presence of seasonality and long-term trends in the incidence of scorpion stings by utilizing advanced analytical techniques, such as the Autoregressive Integrated Moving Average (ARIMA) model. We applied the seasonal ARIMA model to fit a univariate time series of scorpion sting incidence. This study revealed a significant seasonal trend and an overall increase and decrease in scorpion sting cases during the study period. The best-fitting model for the available data was a seasonal ARIMA model in the form of ARIMA(0,0,1)(1,1,1)12. This model can forecast the frequency of scorpion sting cases in Southwestern Iran over the next two years. As a result, time series analysis can provide valuable insights into the patterns and trends of scorpion sting incidents, allowing for better planning and allocation of healthcare resources. By understanding the seasonal variations, proactive measures can be implemented to address the growing issue of scorpion stings in Iran effectively. [ABSTRACT FROM AUTHOR]

In addition to the waste of resources and economic losses, environmental damage by gas flaring is widespread and significant. Since flaring the associated gas gives no added value in exchange for its pollution and greenhouse gas (GHG) emissions, it could be identified as a top priority for mitigation. Iran is the third gas flaring country after Russia and Iraq among those facing this issue, and is responsible for 12.1% of the world's gas flaring. While the necessity of developing a method for the precise estimation of flaring GHG emissions is clear, especially for evaluating the result of countries' efforts to meet their nationally determined contribution target, there are huge uncertainties and discrepancies in the values of emission factors among various data sources due to the lack of actual measurements of the volume and diversity of the composition of flare gas. This study aimed to fill the gap in providing authentic data on Iran's gas flaring GHG and air pollutant emissions by developing a model based on satellite data on flare volumes, gas compositions, and combustion equations. Our results revealed that based on 2021 data on flaring volume, Iranian gas flares are emitting approximately 50 million metric tons of CO2 equivalent to the atmosphere annually, which could be reduced to 43 by only enhancing the flares' efficiency. It accounted for 5.5%–6% of the total GHG emissions of the country. Integr Environ Assess Manag 2023;19:735–748. © 2022 SETAC. Key Points: Iranian gas flares are emitting some 50 million metric tons of CO2 equivalent to the atmosphere annually, accounting for 5.5%–6% of the country's total GHG emissions.Slight increases in the flare's destruction efficiency can cause a huge decrease in its value of global warming potential.The gas flare's emission factors suggested by USEPA would be reliable only if a combustion efficiency of 98% and a destruction efficiency of 99% were considered, while emission factors suggested by Ecoinvent seem to be too optimistic to be true.By taking advantage of fair access to financial opportunities and international cooperation envisaged through the Convention (COP21), the Paris Agreement could be a historic opportunity for the complete elimination of gas flaring from Iran's upstream and downstream oil industries. [ABSTRACT FROM AUTHOR]