The epidemic of a novel coronavirus illness (COVID-19) becomes as a global threat. The aim of this study is first to find the best prediction models for daily confirmed cases in countries with high number of confirmed cases in the world and second to predict confirmed cases with these models in order to have more readiness in healthcare systems. This study was conducted based on daily confirmed cases of COVID-19 that were collected from the official website of Johns Hopkins University from January 22 ^th , 2020 to March 1 ^th , 2020. Auto Regressive Integrated Moving Average (ARIMA) model was used to predict the trend of confirmed cases. Stata version 12 and R version 3.6.2 were used. Parameters used for ARIMA were (2,1,0) for Mainland China, ARIMA(1,0,0) for South Korea, and ARIMA(3,1,0) for Thailand. Mainland China and Thailand were successful in haltering COVID-19 epidemic. Investigating their protocol in this control like quarantine should be in the first line of other countries’ program

Purpose: 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.

Background and aim: Dose-response modeling for radiotherapy-induced xerostomia in head and neck cancer (HN) patients is a promising frontier for personalized therapy. Feature extraction from diagnostic and therapeutic images (radiomics and dosiomics features) can be used for data-driven response modeling. The aim of this study is to develop xerostomia predictive models based on radiomics-dosiomics features. Methods: Data from the cancer imaging archive (TCIA) for 31 HN cancer patients were employed. For all patients, parotid CT radiomics features were extracted, utilizing Lasso regression for feature selection and multivariate modeling. The models were developed by selected features from pre-treatment (CT₁), mid-treatment (CT₂), post-treatment (CT₃), and delta features (ΔCT_2-1, ΔCT_3-1, ΔCT_3-2). We also considered dosiomics features extracted from the parotid dose distribution images (Dose model). Thus, combination models of radio-dosiomics (CT + dose & ΔCT + dose) were developed. Moreover, clinical, and dose-volume histogram (DVH) models were built. Nested 10-fold cross-validation was used to assess the predictive classification of patients into those with and without xerostomia, and the area under the receiver operative characteristic curve (AUC) was used to compare the predictive power of the models. The sensitivity and accuracy of models also were obtained. Results: In total, 59 parotids were assessed, and 13 models were developed. Our results showed three models with AUC of 0.89 as most predictive, namely ΔCT_2-1 + Dose (Sensitivity 0.99, Accuracy 0.94 & Specificity 0.86), CT₃ model (Sensitivity 0.96, Accuracy 0.94 & Specificity 0.86) and DVH (Sensitivity 0.93, Accuracy 0.89 & Specificity 0.84). These models were followed by Clinical (AUC 0.89, Sensitivity 0.81, Accuracy 0.97 & Specificity 0.89) and CT₂ & Dose (AUC 0.86, Sensitivity 0.97, Accuracy 0.87 & Specificity 0.82). The Dose model (developed by dosiomics features only) had AUC, Sensitivity, Specificity, and Accuracy of 0.72, 0.98, 0.33, and 0.79 respectively. Conclusion: Quantitative features extracted from diagnostic imaging during and after radiotherapy alone or in combination with dosiomics markers obtained from dose distribution images can be used for radiotherapy response modeling, opening up prospects for personalization of therapies towards improved therapeutic outcomes.

Purpose: The Bam earthquake was one of Iran’s worst natural disasters. As a reason of limited sample size, prevalence and risk factors of PTSD and depression were still unclear after a huge earthquake in Bam. Patients and Methods: A total of 1500 participants selected from the survivors by multistage sampling. Instruments included the demographic questionnaire, the PTSD Checklist-Civilian Version and the Beck Depression Inventory-2. Bivariable and multivariable logistic regressions were used to analyze the data. Results: The prevalence rates of PTSD and depressive symptoms were 38.7% (n = 581) and 40.1% (n=597), respectively. The most commonly occurring symptoms of PTSD were distress at reminders (73.7%), fear of recurrence (70.3%) and sleep disturbance (68.4%). Age (OR=1.5, 95% CI= [1.03, 1.74], P < 0.001) and being injured (OR=1.68, 95% CI= [1.09, 2.61], P=0.02) are positive risk factors of PTSD. The risk factors for depressive symptoms were old age (OR= 1.46, 95% CI= [1.02, 1.64], P < 0.001), female gender (OR=1.42, 95% CI=[1.14, 1.77], P=0.002), and death of work colleagues (OR=4.03, 95% CI= [1.54, 6.54], P=0.005). Conclusion: Professional and effective mental health services should design programs in order to aid the psychological wellbeing of the population focusing on older adults, females, those who lost work colleagues, and those who lost their family members.

Background: The purpose of this study was to identify latent classes of the severity of post-traumatic stress disorder (PTSD) among the survivors of the earthquake in Bam, south-eastern Iran, 17 years after the disaster. The most influential predictor variables of PTSD classes were also investigated. Methods: Eight hundred survivors of the Bam earthquake who were at least four years old in that disaster were selected by multistage sampling. The PTSD Checklist-Civilian Version was used. Latent class analysis was performed to identify subgroups of people with different PTSD symptom profiles. Latent class regression analysis was used to explore the influence of demographic and traumatic variables on each class membership. Results: We found three latent classes of PTSD, with the following profiles emerging: Low Symptom (56.6% of the participants), Moderate Symptom (23.5%), and Severe Symptom (19.9%). Old age [OR = 2.20, 95% CI = (1.46, 3.32)], physical injury [OR = 1.95, 95% CI = (1.24, 3.06)], being trapped under the rubble [OR = 1.81, 95% CI = (1.15, 2.86)], and death of a family member [OR = 1.86, 95% CI = (1.12, 3.09)] were positive risk factors for PTSD and increased the chance of being in classes having more severe symptoms. Having a high educational level was a negative risk factor [OR = 0.86, 95% CI = (0.67, 1.11)]. Conclusions: The severity of PTSD 17 years after the earthquake shows that natural disasters such as earthquakes have long-term consequences, and earthquake survivors must have psychological support and long term health care. After any catastrophic earthquake, governments should establish psychology and psychotherapy centers for earthquake victims, and these centers should support earthquake victims for a sufficiently long time.