Spatial sensitivity analysis of COVID-19 infections concerning the satellite-based four air pollutants levels.

The novel coronavirus (COVID-19), first reported in late December 2019, has affected the lives of many people throughout the world. Significant studies have been conducted on this pandemic, some of which have addressed understanding the relationship between different air pollutants and confirmed cases. In this study, the effects of four air pollutants (carbon monoxide, nitrogen dioxide, ozone, and sulfur dioxide) were assessed from February 19 to March 22, 2020 to explore how they can affect COVID-19 contagion in Iran. The mean concentrations of air pollutants were extracted from Sentinel 5P data. The COVID-19 confirmed case densities of two provinces, Semnan and Qom, were more than all other provinces. The effect of pollutants on the confirmed case densities was analyzed using multiple linear regression in order to estimate the impact coefficients for individual provinces. The impact coefficients determine the level of each pollutant's contribution to the density of total confirmed cases. Carbon monoxide, nitrogen dioxide, sulfur dioxide, and ozone had both considerable negative and positive correlations with the density of confirmed COVID-19 cases, although sulfur dioxide was correlated more negatively than positively. In Semnan, a high hot spot province, nitrogen dioxide had the most significant effect on the density of confirmed cases among all pollutants, while the effect of carbon monoxide was greater in Qom. The results indicated that even short-term exposure to higher concentrations of the pollutants could lead to an increased risk of COVID-19 outbreaks, which should be considered in adopting adequate and appropriate control policies to manage the disease.

Reprint of "A new approach to analyze data from EEG-based concealed face recognition system".

The purpose of this study is to extend a feature set with non-linear features to improve classification rate of guilty and innocent subjects. Non-linear features can provide extra information about phase space. The Event-Related Potential (ERP) signals were recorded from 49 subjects who participated in concealed face recognition test. For feature extraction, at first, several morphological characteristics, frequency bands, and wavelet coefficients (we call them basic-features) are extracted from each single-trial ERP. Recurrence Quantification Analysis (RQA) measures are then computed as non-linear features from each single-trial. We apply Genetic Algorithm (GA) to select the best feature set and this feature set is used for classification of data using Linear Discriminant Analysis (LDA) classifier. Next, we use a new approach to improve classification results based on introducing an adaptive-threshold. Results indicate that our method is able to correctly detect 91.83% of subjects (45 correct detection of 49 subjects) using combination of basic and non-linear features, that is higher than 87.75% for basic and 79.59% for non-linear features. This shows that combination of non-linear and basic- features could improve classification rate.

A new approach to analyze data from EEG-based concealed face recognition system.

The purpose of this study is to extend a feature set with non-linear features to improve classification rate of guilty and innocent subjects. Non-linear features can provide extra information about phase space. The Event-Related Potential (ERP) signals were recorded from 49 subjects who participated in concealed face recognition test. For feature extraction, at first, several morphological characteristics, frequency bands, and wavelet coefficients (we call them basic-features) are extracted from each single-trial ERP. Recurrence Quantification Analysis (RQA) measures are then computed as non-linear features from each single-trial. We apply Genetic Algorithm (GA) to select the best feature set and this feature set is used for classification of data using Linear Discriminant Analysis (LDA) classifier. Next, we use a new approach to improve classification results based on introducing an adaptive-threshold. Results indicate that our method is able to correctly detect 91.83% of subjects (45 correct detection of 49 subjects) using combination of basic and non-linear features, that is higher than 87.75% for basic and 79.59% for non-linear features. This shows that combination of non-linear and basic- features could improve classification rate.

Cholestasis induced antinociception and decreased gene expression of MOR1 in rat brain.

We examined antinociception and gene expression of mu-opioid receptor 1 (MOR1) in some brain areas of cholestatic rats, 21 days after common bile duct ligation (BDL). Cholestasis was induced in male Wistar rats during laparotomy and common BDL. Pain behavior was assessed on days 7, 14 or 21 of BDL using a hotplate test in control, sham and cholestatic groups. On day 21 of BDL, other groups of rats were sacrificed, whole brains were extracted, and the hypothalamus, prefrontal cortex (PFC), hippocampus and striatum in control, sham and cholestatic rats were dissected. We used a semi-quantitative reverse transcription-polymerase chain reaction (RT-PCR) method for evaluating MOR1 gene expression. The results revealed that cholestatic rats showed significant antinociception on days 14 and 21 of ligation with the most significant effect on day 21, which was prevented by naloxone (1 mg/kg). On the other hand, the expression of MOR1 gene compared to the sham group was decreased by 42% in the hypothalamus, 41% in the PFC, and 67% in the hippocampus after 21 days of BDL, while no significant change in its expression in the striatum was observed. It can be concluded that a change in endogenous opioid levels and its subsequent influence on the gene expression of MOR in some areas of the rat brain may underlie the altered nociception and other possible pathological changes such as pruritus after induction of cholestasis.

Comparison of the RTS and ISS scores on prediction of survival chances in multiple trauma patients.

PURPOSE OF THE STUDY: Trauma represents the third cause of death after cardio vascular disease and tumors. Also in Iran, road accidents are one of the leading causes of death. Rapid evaluation of trauma severity and prediction of prognosis and mortality rate and probability of survival and rapid treatment of patients is necessary. One of the useful instruments for this is ISS and RTS scoring systems. This study evaluated 70 multi trauma patients in Fatemi trauma center affiliated to Ardabil University of medical science. MATERIALS AND METHODS: This study was prospective study populations were 70 trauma patients admitted in Fatemi trauma center. During the II month, and patients data was collected by clinical evaluating of patients and follow up them and arranged as a questionnaire then related findings were evaluated by SPSS software. RESULTS: The average age of patients was 37.6±23.5 years and minimum and maximum age was 1 and 85 years. The most common involved group was 10-19 years (13 men and 1 woman). 81.4% of patients (57 cases were male) and 18.6% were female (13 cases). The most common causes of trauma was car accident with 64.2% frequency (43 cases) and then motorcycle accident with 16.4% frequency (11 cases) and all injured patient due to motorcycle accident compose the age group less than 40 years old. Also car accident had the highest frequency in both gender. Other causes of trauma were fall down with 13.5% frequency (9 cases) and under debris 5.9% (4 cases). Also from 70 studied patients, 67 cases (95.7%) had blunt trauma and 3 cases (4.3%) had penetrating trauma. The most penetrating trauma occurs in ages less than 50 years and was in the range of 30-50 years. The average RTS and ISS was 10.67±1.45 and 18.11±8.64, high and low scores of ISS existed in all age groups but a low score of RTS was highest in the children age group. The average length of ICU stay was 12.14±11.11 days. Overall mortality was 15.7 (11 cases). In this study, by the ISS increasing, the mortality rate also increased. But there is no relation between the mortality rate and RTS ratio. CONCLUSION: The ISS scoring system performed better than the RTS in predicting of mortality and probability of survival and the length of ICU stay and had high accuracy and can predict patients' outcome better by ISS measuring.