Epidemiological characteristics of coronavirus disease 2019 (COVID-19) patients in IRAN: A single center study.

BackgroundAn outbreak of COVID-19 in Iran has spread throughout the country. Identifying the epidemiological characteristics of this disease will help to make appropriate decisions and thus control the epidemic. The aim of this study was characterization of the epidemiological features of COVID-19 in Iran.MethodsIn this retrospective study, data related to the epidemiological characteristics of COVID-19 patients admitted to Baqiyatallah Hospital in Tehran, Iran, from 19 February 2020 to 15 April 2020 have been analyzed and reported. Patient characteristics including age, gender and underlying diseases were investigated. Data were collected through patient records. Sex ratio, Case Fatality Rate (CFR) and daily trend of cases were also determined. A multiple logistic regression analysis was also performed to assess affecting factors on mortality.ResultsFrom February 19, 2020 to April 15, 2020, 12870 patients referred to the hospital emergency department, of which 2968 were hospitalized with COVID-19 diagnosis. The majority of cases were in the age group of 50 to 60 years of old. The male-to-female ratio was 1.93:1. A total of 239 deaths occurred among all cases for an overall CFR of 1.85% based on the total number of patients (both outpatient and inpatient) and 8.06% among hospitalized patients. Out of all patients 10.89% had comorbidity. Diabetes, chronic respiratory diseases, hypertension, cardiovascular diseases, chronic Kidney diseases and cancer were the most common comorbidities with 3.81, 2.02 , 1.99 , 1.25, 0.60 and 0.57 %, respectively. Male gender (OR=1.45, 95% CI: 1.08-1.96), older age (OR=1.05, 95% CI: 1.04-1.06) and having underlying diseases (OR=1.53, 95% CI: 1.04-2.24) were significantly associated with mortality.ConclusionsThe results of this study showed that Male gender, older age and having comorbidities were significantly associated with the risk of death among COVID-19 patients. It is important to pay special attention to male elderly patients with underlying diseases

Optimization of process parameters for trimethoprim and sulfamethoxazole removal by magnetite-chitosan nanoparticles using Box–Behnken design

Abstract The contamination of the aquatic environment with antibiotics is among the major and developing problems worldwide. The present study investigates the potential of adsorbent magnetite-chitosan nanoparticles (Fe3O4/CS NPs) for removing trimethoprim (TMP) and sulfamethoxazole (SMX). For this purpose, Fe3O4/CS NPs were synthesized by the co-precipitation method, and the adsorbent characteristics were investigated using XRD, SEM, TEM, pHzpc, FTIR, and VSM. The effect of independent variables (pH, sonication time, adsorbent amount, and analyte concentration) on removal performance was modeled and evaluated by Box–Behnken design (BBD). The SEM image of the Fe3O4/CS adsorbent showed that the adsorbent had a rough and irregular surface. The size of Fe3O4/CS crystals was about 70 nm. XRD analysis confirmed the purity and absence of impurities in the adsorbent. TEM image analysis showed that the adsorbent had a porous structure, and the particle size was in the range of nanometers. In VSM, the saturation magnetization of Fe3O4/CS adsorbent was 25 emu g−1 and the magnet could easily separate the adsorbent from the solution. The results revealed that the optimum condition was achieved at a concentration of 22 mg L−1, a sonication time of 15 min, an adsorbent amount of 0.13 g/100 mL, and a pH of 6. Among different solvents (i.e., ethanol, acetone, nitric acid, and acetonitrile), significant desorption of TMP and SMX was achieved using ethanol. Also, results confirmed that Fe3O4/CS NPs can be used for up to six adsorption/desorption cycles. In addition, applying the Fe3O4/CS NPs on real water samples revealed that Fe3O4/CS NPs could remove TMP and SMX in the 91.23–95.95% range with RSD (n = 3) < 4. Overall, the Fe3O4/CS NPs exhibit great potential for removing TMP and SMX antibiotics from real water samples

Symptoms of anxiety and depression: A comparison among patients with different chronic conditions

Background: Although patients with chronic diseases are at high-risk for symptoms of anxiety and depression, few studies have compared patients with different chronic conditions in this regard. This study aimed to compare patients with different chronic medical conditions in terms of anxiety and depression symptoms after controlling for the effects of socio-demographic and clinical data. Methods: This cross-sectional study enrolled 2234 adults, either healthy (n = 362) or patients with chronic medical conditions (n = 1872). Participants were recruited from the outpatient clinic of Baqiyatallah Hospital, Tehran, Iran. Patients had one of the following five medical conditions: coronary artery disease (n = 675), renal transplantation (n = 383), chronic hemodialysis (n = 68), rheumatoid conditions (rheumatoid arthritis, osteoarthritis, systemic lupus erythematosus and ankylosing spondylitis) (n = 666) and viral hepatitis (n = 80). Independent factors included socio-demographic data, pain disability, and somatic comorbidities (Ifudu index). Outcomes included symptoms of anxiety and depression through Hospital Anxiety and Depression Scale (HADS). Two multinomial regression models were used to determine the predictors of anxiety and depression symptoms. Results: After controlling the effect of age, sex, educational level, comorbidities, disability and pain, rheumatoid arthritis and hepatitis were predictors of higher anxiety symptoms, while coronary artery disease and chronic hemodialysis were predictors of depression symptoms. Conclusions: Although all chronic conditions may require psychological consideration; be that as it may, different chronic diseases are dissimilar in terms of their mental health need. Anxiety for rheumatoid arthritis and hepatitis as well as depression for coronary artery disease and chronic hemodialysis is more important

Epidemiological characteristics of coronavirus disease 2019 (COVID-19) patients in IRAN: A single center study.

BackgroundAn outbreak of COVID-19 in Iran has spread throughout the country. Identifying the epidemiological characteristics of this disease will help to make appropriate decisions and thus control the epidemic. The aim of this study was characterization of the epidemiological features of COVID-19 in Iran.MethodsIn this retrospective study, data related to the epidemiological characteristics of COVID-19 patients admitted to Baqiyatallah Hospital in Tehran, Iran, from 19 February 2020 to 15 April 2020 have been analyzed and reported. Patient characteristics including age, gender and underlying diseases were investigated. Data were collected through patient records. Sex ratio, Case Fatality Rate (CFR) and daily trend of cases were also determined. A multiple logistic regression analysis was also performed to assess affecting factors on mortality.ResultsFrom February 19, 2020 to April 15, 2020, 12870 patients referred to the hospital emergency department, of which 2968 were hospitalized with COVID-19 diagnosis. The majority of cases were in the age group of 50 to 60 years of old. The male-to-female ratio was 1.93:1. A total of 239 deaths occurred among all cases for an overall CFR of 1.85% based on the total number of patients (both outpatient and inpatient) and 8.06% among hospitalized patients. Out of all patients 10.89% had comorbidity. Diabetes, chronic respiratory diseases, hypertension, cardiovascular diseases, chronic Kidney diseases and cancer were the most common comorbidities with 3.81, 2.02 , 1.99 , 1.25, 0.60 and 0.57 %, respectively. Male gender (OR=1.45, 95% CI: 1.08-1.96), older age (OR=1.05, 95% CI: 1.04-1.06) and having underlying diseases (OR=1.53, 95% CI: 1.04-2.24) were significantly associated with mortality.ConclusionsThe results of this study showed that Male gender, older age and having comorbidities were significantly associated with the risk of death among COVID-19 patients. It is important to pay special attention to male elderly patients with underlying diseases