The pandemic of novel coronavirus disease (COVID-19) caused by the Severe Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) creates an immense menace to public health worldwide. Currently, the World Health Organization (WHO) has recognized the novel coronavirus as the main cause of global pandemic. Patients infected with this virus generally show fever, nausea, and respiratory illness, while some patients also manifest gastrointestinal symptoms such as abdominal pain, vomiting, and diarrhea. Traces of SARS-CoV-2 RNA have been found in gastrointestinal cells. Further angiotensin converting enzyme 2 (ACE2) the known receptor for the virus is extensively expressed in these cells. This implies that gastrointestinal tract can be infected and can also present them as a replication site for SARS-CoV-2, but since this infection may lead to multiple organ failure, therefore identification of another receptor is a plausible choice. This review aims to provide comprehensive information about probable receptors such as sialic acid and CD147 which may facilitate the virus entry. Several potential targets are mentioned which can be used as a therapeutic approach for COVID-19 and associated GI disorders. The gut microbiomes are responsible for high levels of interferon-gamma which causes hyper-inflammation and exacerbates the severity of the disease. Briefly, this article highlights the gut microbiome’s relation and provides potential diagnostic approaches like RDT and LC-MS for sensitive and specific identification of viral proteins. Altogether, this article reviews epidemiology, probable receptors and put forward the tentative ideas of the therapeutic targets and diagnostic methods for COVID-19 with gastrointestinal aspect of disease.Many patients with olfactory disorders were referred during the COVID-19 pandemic in 2020. The aim of this study was to detect outpatient cases with olfactory and taste disorders suspected to mild form of COVID-19 disease in Gorgan city in the north of Iran retrospectively. This study was performed on patients who had the complaints of olfactory disorders during 03/01/2020 to 04/01/2020. They also had the mild symptoms of upper respiratory tract infection. The control group included patients who had similar symptoms during this period but did not report olfactory or taste disturbances. Due to the limitations of serologic kits, this study was performed 2-3 months after the onset of symptoms. The number of patients and controls was 72 and 36 respectively. The range and the mean ± SD of patient’s age were 21-63 and 39.82 ± 9.82 years. In both groups, 44.44% were male and 55.56% were female. The time interval between the onset of symptoms and the serologic tests in both groups was 91.11 ± 16.20 days. In the cases and controls, the IgG titer was positive in 44.4% and 22.2% and the IgM titer was positive in 5.6% and 8.3% respectively. IgG antibody titers were higher in cases than in the control group (P = 0.024). There was no correlation among antibody titers and the severity of olfactory disturbances, the gender, and the age. The high COVID-19 IgG antibody titer in patients with olfactory disorder during the pandemic can probably be considered as a warning complaint of COVID-19 and may be used for isolation plans.The literature on China’s social media foreign propaganda mostly focuses on text-format contents in English, which may miss the real target and the tool for analysis. In this article, we traced 1256 Twitter accounts echoing China government’s #USAVirus propaganda before and after Twitter removed state-linked operations on June 12, 2020. The 3567 tweets with #USAVirus we collected, albeit many written in English, 74% of them attached with a lengthy simplified Chinese text-image. Distribution of the post-creation time fits the working-hour in China. Overall, 475 (37.8%) accounts we traced were later suspended after Twitter’s disclosure. Our dataset enables us to analyze why and why not Twitter suspends certain accounts. We apply the decision tree, random forest, and logit regression to explain the suspensions. All models suggest that the inclusion of a text-image is the most important predictor. The importance outweighs the number of followers, engagement, and the text content of the tweet. The prevalence of simplified Chinese text-images in the #USAVirus trend and their impact on Twitter account suspensions both evidence the importance of text-image in the study of state-led propaganda. Our result suggests the necessity of extracting and analyzing the content in the attached text-image.Since the birth of Christ, in these 2019 years, the man on earth has never experienced a survival challenge from any acellular protist compared to SARS-CoV-2. No specific drugs yet been approved. The host immunity is the only alternative to prevent and or reduce the infection and mortality rate as well. Here, a novel mechanism of melanin mediated host immunity is proposed having potent biotechnological prospects in health care management of COVID-19. Vitamin D is known to enhance the rate of melanin synthesis; and this may concurrently regulate the expression of furin expression. In silico analyses have revealed that the intermediates of melanin are capable of binding strongly with the active site of furin protease. On the other hand, furin expression is negatively regulated via 1-α-hydroxylase (CYP27B1), that belongs to vitamin-D pathway and controls cellular calcium levels. Here, we have envisaged the availability of biological melanin and elucidated the bio-medical potential. Thus, we propose a possible synergistic application of melanin and the enzyme CYP27B1 (regulates vitamin D biosynthesis) as a novel strategy to prevent viral entry through the inactivation of furin protease and aid in boosting our immunity at the cellular and humoral levels.In a survey of household cats and dogs of laboratory-confirmed COVID-19 patients, we found a high seroprevalence of SARS-CoV-2 antibodies, ranging from 21% to 53%, depending on the positivity criteria chosen. Seropositivity was significantly greater among pets from COVID-19+ households compared to those with owners of unknown status. Our results highlight the potential role of pets in the spread of the epidemic.

Computed tomography (CT) is used for initial diagnosis and therapy monitoring of patients with coronavirus disease 2019 (COVID-19). As patients of all ages are affected, radiation dose is a concern. While follow-up CT examinations lead to high cumulative radiation doses, the ALARA principle states that the applied dose should be as low as possible while maintaining adequate image quality. The aim of this study was to evaluate parameter settings for two commonly used CT scanners to ensure sufficient image quality/diagnostic confidence at a submillisievert dose.

We retrospectively analyzed 36 proven COVID-19 cases examined on two different scanners. Image quality was evaluated objectively as signal-to-noise ratio (SNR)/contrast-to-noise ratio (CNR) measurement and subjectively by two experienced, independent readers using 3-point Likert scales. CT dose index volume (CTDIvol) and dose-length product (DLP) were extracted from dose reports, and effective dose was calculated.

With the tested parameter settings we achieved effective doses below 1 mSv (median 0.5 mSv, IQR 0.2 mSv, range 0.3-0.9 mSv) in all 36 patients. Thirty-four patients had typical COVID-19 findings. Both readers were confident regarding the typical COVID-19 CT-characteristics in all cases (3 ± 0). Objective image quality parameters were SNR

17.0 ± 5.9, CNR

7.5 ± 5.0, and CNR

15.3 ± 6.1.

With the tested parameters, we achieved applied doses in the submillisievert range, on two different CT scanners without sacrificing diagnostic confidence regarding COVID-19 findings.

With the tested parameters, we achieved applied doses in the submillisievert range, on two different CT scanners without sacrificing diagnostic confidence regarding COVID-19 findings.With over 575,000 deaths and about 13.3 million cases globally, the COVID-19 pandemic has had a terrible impact globally during the 6 months since cases were first detected in China. Conscious of the many challenges presented in settings with abundance of resources and with robust health systems, where mortality has been significant and transmission difficult to control, there was a logical concern to see how the virus could impact African countries, and their fragile and weak health systems. Such an anticipated „tsunami”, with potentially devastating consequences, seems however to not have yet arrived, and African countries, albeit witnessing an increasing degree of autochthonous transmission, seem to this day relatively unaffected by the pandemic. In this article we review the current situation of the pandemic in the African continent, trying to understand the determinants of its slow progress.Using online data for prices and real-time debit card transaction data on changes in expenditures for Switzerland allows us to track inflation on a daily basis. While the daily price index fluctuates around the official price index in normal times, it drops immediately after the lockdown related to the COVID19 pandemic. Official statistics reflect this drop only with a lag, specifically because data collection takes time and is impeded by lockdown conditions. Such daily real-time information can be useful to gauge the relative importance of demand and supply shocks and thus inform policymakers who need to determine appropriate policy measures.Since December 2019, the coronavirus disease (COVID-19) outbreak has caused many death cases and affected all sectors of human life. With gradual progression of time, COVID-19 was declared by the world health organization (WHO) as an outbreak, which has imposed a heavy burden on almost all countries, especially ones with weaker health systems and ones with slow responses. In the field of healthcare, deep learning has been implemented in many applications, e.g., diabetic retinopathy detection, lung nodule classification, fetal localization, and thyroid diagnosis. Numerous sources of medical images (e.g., X-ray, CT, and MRI) make deep learning a great technique to combat the COVID-19 outbreak. Motivated by this fact, a large number of research works have been proposed and developed for the initial months of 2020. In this paper, we first focus on summarizing the state-of-the-art research works related to deep learning applications for COVID-19 medical image processing. Then, we provide an overview of deep learning and its applications to healthcare found in the last decade. Next, three use cases in China, Korea, and Canada are also presented to show deep learning applications for COVID-19 medical image processing. Finally, we discuss several challenges and issues related to deep learning implementations for COVID-19 medical image processing, which are expected to drive further studies in controlling the outbreak and controlling the crisis, which results in smart healthy cities.

This case is shared to reiterate and confirm the principles of ensuring the safety of the surgical team caring for COVID-19-confirmed patients, thus, preventing the spread of infection within the hospital.

A 54-year-old male, COVID-19-confirmed patient complaining of abdominal pain since two days prior was transferred to our hospital. Perforated appendicitis with a periappendiceal abscess was diagnosed by computed tomography. Laparoscopic appendectomy was performed in a negative-pressure operating room. The surgical team wore enhanced personal protective equipment. Electrocautery was not used during surgery and no other special instruments were applied to reduce aerosol generation. No special instruments or filters were used for the removal of intra-abdominal gas. The operation was completed successfully and no immediate surgical complications occurred. The patient advanced to a normal diet on the 4th postoperative day. The patient was treated with antibiotics for bacteremia and antiviral therapy for underlying pneumonia in the setting of COVID-19 with most symptoms dissipating by the 7th postoperative day.