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Background: Iran was one of the first countries to become an epicenter of the coronavirus disease 2019 (COVID-19) epidemic. However, there is a dearth of data on the outcomes of COVID-19 and predictors of death in intensive care units (ICUs) in Iran. We collected extensive data from patients admitted to the ICUs of the one of the tertiary referral hospitals in Tehran, Iran, to investigate the predictors of ICU mortality.Methods: The study population included 290 COVID-19 patients who were consecutively admitted to the ICUs of the Sina hospital from May 5, 2021, to December 6, 2021, a period that included the peak of the epidemic of the delta (delta) variant. Demographic data, history of prior chronic diseases, laboratory data (including markers of inflammation), radiologic data, and medication data were collected. Results: Of the 290 patients admitted to the ICUs, 187 (64.5%) died and 103 (35.5%) survived. One hundred forty-one (141, 48.6%) were men, and the median age (10th percentile, 90th percentile) was 60 (41, 80). Using logistic regression models, older age, history of hypertension, high levels of inflammatory markers, low oxygen saturation, substantial lung involvement in computed tomography (CT) scans, and gravity of the disease as indicated by the WHO 8-point ordinal scale were primary predictors of mortality at ICU. The use of remdesivir and imatinib was associated with a statistically non-significant reduction in mortality. The use of tocilizumab had almost no effect on mortality.Conclusion: The findings are consistent with and add to the currently existing international literature. The findings may be used to predict risk of mortality from COVID-19 and provide some guidance on potential treatments
ABSTRACT
Acute kidney injury (AKI) is the second prevalent organ damage among COVID-19 infected individuals, which mainly affects those with critical diseases or underlying kidney disorders. Emerging data have suggested that AKI is associated with adverse outcomes, severe COVID-19 disease, and high mortality. However, the true nature and pathophysiology of COVID-19-associated kidney injury, and its effect on patients with underlying kidney diseases and transplant recipients, still remains controversial. Accordingly, this review study aimed primarily to describe the history of AKI in COVID-19 infected patients and to achieve a robust understanding of the latest findings on the mechanism of the injury. Secondly, this systematic and precise review of the literature concerning the aspects of AKI in infected patients with chronic kidney disease and transplant recipients provided a comprehensive report of mortality in these individuals. Finally, the present research suggested the possible protective measures that physicians can take to prevent, control, and treat this condition. Our study paves the way for future works with a more robust methodology to better understand COVID-19-related kidney injury. Copyright © 2022 The Author(s);Published by Society of Diabetic Nephropathy Prevention.
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Objective: The present study was conducted to compare mechanically ventilated patients with and without COVID-19 in terms of hemodynamic instability using cardiovascular indicators. Methods: This prospective cohort study assigned intubated and mechanically ventilated patients to two groups, i.e. with COVID-19 and without COVID-19. The hemodynamic parameters measured and compared between the two groups on the first day of ICU admission and the following four consecutive days using an ultrasonic cardiac output monitor (USCOM) included cardiac output (CO), systemic vascular resistance (SVR), stroke volume (SV), flow time corrected (FTc), minute distance (MD) and potential kinetic energy (PKE). Results: Forty-three patients (males: 62.7%) were assigned to the COVID-19 group and 40 (males: 64.1%) to the one without COVID-19. Insignificant differences were observed between the two groups at baseline in terms of the mean homodynamic variables measured using the USCOM (P>0.05). The mean CO increased (P=0.020), the mean SVR insignificantly changed (P=0.267), the mean MD increased (P=0.005) and PKE decreased (P=0.066) in the COVID-19 group during the five days of evaluation. In the same period, the mean CO insignificantly changed (P=0.937), the mean SVR increased (P=0.028) and changes in MD (P=0.808) and PKE (P=0.539) were insignificant in the group without COVID-19. The two groups were not significantly different in terms of the other homodynamic parameters during the follow-up (P>0.05). Conclusion: The five-day changes in the USCOM-measured homodynamic parameters were lower in the group without COVID-19 compared to in that with COVID-19. In the group without COVID-19, no statistically-significant differences were observed between the mean follow-up values of the variables, excluding SVR, and their baseline values.
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Coronavirus infectious disease 2019 (COVID-19) is confirmed to develop neurocognitive complications. In the present paper, we describe two patients with laboratory-confirmed COVID-19 and excessive daytime sleepiness. In the present study, we reported two laboratory-confirmed cases of COVID-19 with excessive daytime sleepiness. Patients had drowsiness and mild confusion on presentation. In both cases, CNS infections, including meningitis and encephalitis, were ruled out. Both patients’ symptoms remarkably improved following the therapeutic course indicating the direct effect of SARS-CoV2 in sleep modulating centers on the brain. COVID-19 should be considered in patients with excessive daytime sleepiness and drowsiness in the current outbreak.
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Hospital-related infections have been widely reported during the Covid-19 outbreak that exposes healthcare professionals to at greater risk of infection. This cross-sectional study was designed to evaluate the frequency of absenteeism in hospital staff during the first and second months of the Covid-19 pandemic, from 26 February until 19 April 2020. Occupational data and sickness absenteeism characteristics were collected from the records of the nursing management department. 304 (17.8%) had sick leave due to coronavirus or corona-like symptoms. Nurses and then nursing aid workers constituted the categories with more frequency of sick leave. The more median days of sick leave were observed among supervisors and midwives. About 80% of participants had typical symptoms of Coronavirus. In conclusion, the median days of sick leave due to coronavirus were not as high as we thought. This could be due to human resource shortage and insufficient knowledge regarding return-to-work guidelines in the early phase of this pandemic.
ABSTRACT
At the beginning of 2020, a new type of Coronavirus (Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2)) dismayed the world and led to public health emergencies. This virus has caused a remarkable percentage of morbidity and mortality. Also, the lack of an effective treatment to fight this virus is another concern that should be given attention. Herbal medicines and purified natural products have been reported for their antiviral activity against SARS-CoV-2. In this study, molecular docking of effective compounds in the extracts and essential oils of Zingiber officinale, Glycyrrhiza glabra Sambucus nigra, Panax ginseng Ocimum basilicum, and Origanum vulgare was carried out to investigate their binding to the X-ray structure of the ACE2 binding domain of SARSCoV-2. The Glide docking program was utilized for molecular docking with standard precision (SP) and extra precision (XP). Finally, 7 compounds-mainly belong to Panax ginseng-showed a higher docking score than some known antiviral compounds. Floralginsenoside B, which is extracted from Panax ginseng, indicated a strong binding affinity (-8.618 kcal/mol) to the crucial residues of the receptor binding domain of SARS-CoV-2 comparing to Doravirine (-7.2 kcal/mol), Hetacillin (-7.1 kcal/mol), Ketoprofen (-7.0 kcal/mol), and Mefloquine (-7.0 kcal/mol) reported in previous articles. Based on the excellent binding affinities of these herbal compounds, we concluded that these phytochemicals could be promising candidates for fighting against the COVID-19 pandemic. © 2021 by the authors.
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Today, the use of computer networks is evident in all walks of life, including mobile phones, vehicles, personal computers, the workplace and more. Creating and operating networks between each of the above-mentioned devices require good infrastructure to be able to communicate between these devices. To solve this problem, there were networks called ad-hoc networks, which were able to network with each other without the need for specific infrastructure and only through direct and wireless communication between the equipment. But how to make the right connections in these types of networks, as well as high-speed communication for devices, are also challenges for researchers. In this study, we try to implement the communication between these devices by the DSR routing algorithm and, using the Q learning technique of the reinforced learning algorithm, we enhance it to respond to QOS in Ad-Hoc mobile networks compared to previous solutions. In addition to what has been improved in this article. In the following articles, we will try to improve the discussion of communication security using this method and also apply this method to routing VANETs networks. © 2020 © 2020 Ehsan Moqimi, Amir Najafi and Mojtaba Ajami. This open access article is distributed under a Creative Commons Attribution (CC-BY) 3.0 license.
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In this paper, we study and investigate the spread of the coronavirus disease 2019 (COVID-19) in Iraq and Egypt by using compartmental, logistic regression, and Gaussian models. We developed a generalized SEIR model for the spread of COVID-19, taking into account mildly and symptomatically infected individuals. The logistic and Gaussian models were utilized to forecast and predict the numbers of confirmed cases in both countries. We estimated the parameters that best fit the incidence data. The results provide discouraging forecasts for Iraq from 22 February to 8 October 2020 and for Egypt from 15 February to 8 October 2020. To provide a forecast of the spread of COVID-19 in Iraq, we present various simulation scenarios for the expected peak and its timing using Gaussian and logistic regression models, where the predicted cases showed a reasonable agreement with the officially reported cases. We apply our compartmental model with a time-periodic transmission rate to predict the possible start of the second wave of the COVID-19 epidemic in Egypt and the possible control measures. Our sensitivity analyses of the basic reproduction number allow us to conclude that the most effective way to prevent COVID-19 cases is by decreasing the transmission rate. The findings of this study could therefore assist Iraqi and Egyptian officials to intervene with the appropriate safety measures to cope with the increase of COVID-19 cases. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.