Understanding the epidemiological parameters that determine the transmission dynamics of COVID-19 is essential for public health intervention. Globally, a number of studies were conducted to estimate the average serial interval and incubation period of COVID-19. Combining findings of existing studies that estimate the average serial interval and incubation period of COVID-19 significantly improves the quality of evidence. Hence, this study aimed to determine the overall average serial interval and incubation period of COVID-19.
 
  We followed the PRISMA checklist to present this study. A comprehensive search strategy was carried out from international electronic databases (Google Scholar, PubMed, Science Direct, Web of Science, CINAHL, and Cochrane Library) by two experienced reviewers (MAA and DBK) authors between the 1st of June and the 31st of July 2020. All observational studies either reporting the serial interval or incubation period in persons diagnosed with COVID-19 were included in this study. Hetervely. In this study, the average serial interval of COVID-19 is shorter than the average incubation period, which suggests that substantial numbers of COVID-19 cases will be attributed to presymptomatic transmission.
 This systematic review and meta-analysis showed that the weighted pooled mean serial interval and incubation period of COVID-19 were 5.2, and 6.5 days, respectively. In this study, the average serial interval of COVID-19 is shorter than the average incubation period, which suggests that substantial numbers of COVID-19 cases will be attributed to presymptomatic transmission.
  Stillbirth is defined as fetal loss in pregnancy beyond 28 weeks by WHO. In this study, a machine-learning based method is proposed to predict stillbirth from livebirth and discriminate stillbirth before and during delivery and rank the features.
 
  A two-step stack ensemble classifier is proposed for classifying the instances into stillbirth and livebirth at the first step and then, classifying stillbirth before delivery from stillbirth during the labor at the second step.  https://www.selleckchem.com/products/CP-690550.html  The proposed SE has two consecutive layers including the same classifiers. The base classifiers in each layer are decision tree, Gradient boosting classifier, logistics regression, random forest and support vector machines which are trained independently and aggregated based on Vote boosting method. Moreover, a new feature ranking method is proposed in this study based on mean decrease accuracy, Gini Index and model coefficients to find high-ranked features.
 
  IMAN registry dataset is used in this study considering all births at or beyondows the significance of the proposed SE.
 
  Gestational age and fetal height are two most important features for discriminating livebirth from stillbirth. Moreover, hospital, province, delivery main cause, perinatal abnormality, miscarriage number and maternal age are the most important features for classifying stillbirth before and during delivery.
 Gestational age and fetal height are two most important features for discriminating livebirth from stillbirth. Moreover, hospital, province, delivery main cause, perinatal abnormality, miscarriage number and maternal age are the most important features for classifying stillbirth before and during delivery.
  Arsenic is a developmental neurotoxicant. It means that its neurotoxic effect could occur in offspring by maternal arsenic exposure. Our previous study showed that developmental arsenic exposure impaired social behavior and serotonergic system in C3H adult male mice. These effects might affect the next generation with no direct exposure to arsenic. This study aimed to detect the social behavior and related gene expression changes in F2 male mice born to gestationally arsenite-exposed F1 mice.
 
  Pregnant C3H/HeN mice (F0) were given free access to tap water (control mice) or tap water containing 85 ppm sodium arsenite from days 8 to 18 of gestation. Arsenite was not given to F1 or F2 mice. The F2 mice were generated by mating among control F1 males and females, and arsenite-F1 males and females at the age of 10 weeks. At 41 weeks and 74 weeks of age respectively, F2 males were used for the assessment of social behavior by a three-chamber social behavior apparatus. Histological features of the prefrontal cort significantly different. Interleukin-1β (IL-1β) mRNA expression was significantly increased only in 41-week-old arsenite-F2 mice.
 
  These findings suggest that maternal arsenic exposure affects social behavior in F2 male mice via serotonergic system in the prefrontal cortex. In this study, COX-2 were not increased although oxidative stress marker (HO-1) was increased significantly in arsnite-F2 male mice.
 These findings suggest that maternal arsenic exposure affects social behavior in F2 male mice via serotonergic system in the prefrontal cortex. In this study, COX-2 were not increased although oxidative stress marker (HO-1) was increased significantly in arsnite-F2 male mice.
  The novel coronavirus disease 2019 (COVID-19) was emergency turned into global public health after the first patients were detected in Wuhan, China, in December 2019. The disease rapidly expanded and led to an epidemic throughout China, followed by the rising number of cases worldwide. Given the high prevalence of COVID-19, rapid and accurate diagnostic methods are immediately needed to identify, isolate and treat the patients as soon as possible, decreasing mortality rates and the risk of public contamination by severe acute respiratory syndrome coronavirus 2(SARS-CoV-2).
 
  This case-control study was conducted in two hospitals in Alborz Province in Iran. All recruited cases in this study were symptomatic adults hospitalized as COVID-19 patients with compatible Computed tomographic (CT) scan findings and available rRT-PCR results. The patients were recruited in this study. The patients were categorized into positive and negative rRT-PCR groups and evaluated for symptoms, initial vital signs, comorbidity, cnegative rRT-PCR result, it is necessary to take a logical approach, and we recommended that the physician decides according to clinical manifestations, laboratory findings, and positive CT results.