Search

Imaging tools are potentially able to provide valuable data regarding the development of an efficient vaccine against viral diseases. Tracking immune cells in vivo by imaging modalities can help us understand the intrinsic behaviors of immune cells in response to vaccine components. Imaging patterns at the vaccination site and draining lymph nodes might provide useful information about the vaccine potency. Besides, serial lung CT imaging has been purposed to evaluate vaccine efficiency regarding its protection against typical lung lesions of viral pneumonias. On the other hand, vaccination causes various confusing radiologic patterns that pose diagnostic challenges for clinicians and pitfalls for reading radiologists. This manuscript reviews potential applications of imaging modalities in the process of vaccine development and also goes over some of the imaging findings/pitfalls following vaccination., Highlights • Imaging tools could track the immune cell dynamics in vivo, which holds valuable research attention in the vaccination field. • Serial CT imaging might be a great indicator of vaccine efficiency in research setting. • Vaccination causes various confusing radiologic patterns that pose diagnostic challenges for clinicians and radiologists.

Background Ambient fine particulate matter (PM2.5) is associated with a wide range of acute and chronic health effects, including increased risk of respiratory infection. However, evidence specifically related to novel coronavirus disease (COVID-19) is limited. Methods COVID-19 case counts for 111 Canadian health regions were obtained from the COVID-19 Canada Open Data portal. Annual PM2.5 data for 2000–2016 were estimated from a national exposure surface based on remote sensing, chemical transport modelling and ground observations, and minimum and maximum temperature data for 2000–2015 were based on a national interpolated surface derived from thin-plate smoothing splines. Population counts and sociodemographic data by health region were obtained from the 2016 census, and health data (self-rated health and prevalence of smoking, obesity, and selected chronic diseases) by health region, were obtained from the Canadian Community Health Survey. Data on total number of COVID-19 tests and changes in mobility comparing post-vs. pre-introduction of social distancing measures were available by province. Data were analyzed using negative binomial regression models. Results After controlling for province, temperature, demographic and health characteristics and days since peak incidence by health region, long-term PM2.5 exposure exhibited a positive association with COVID-19 incidence (incidence rate ratio 1.07, 95% confidence interval 0.97–1.18 per μg/m3). This association was larger in magnitude and statistically significant in analyses excluding provinces that reported cases only for aggregated health regions, excluding health regions with less than median population density, and restricted to the most highly affected provinces (Quebec and Ontario). Conclusions We observed a positive association between COVID-19 incidence and long-term PM2.5 exposure in Canadian health regions. The association was larger in magnitude and statistically significant in more highly affected health regions and those with potentially less exposure measurement error. While our results generate hypotheses for further testing, they should be interpreted with caution and require further examination using study designs less prone to bias., Highlights • Evidence linking air pollution to novel coronavirus infections is limited. • Analyzed association of PM2.5 and COVID-19 incidence in 111 Canadian health regions. • Controlled for province, weather, demographic and health factors, days since peak. • Long-term PM2.5 exposure exhibited a positive association with COVID-19 incidence.

Highlights • Blockchain may be a trusted ally of healthcare institutions across the globe. • The main features of blockchain that make it useful in the healthcare field are immutability, decentralization, and traceability. • Blockchain may play a crucial role in mitigating the COVID-19 challenges. • Blockchain applications are still immature in the fight against COVID-19., Background: As public health strategists and policymakers explore different approaches to lessen the devastating effects of novel coronavirus disease (COVID-19), blockchain technology has emerged as a resource that can be utilized in numerous ways. Many blockchain technologies have been proposed or implemented during the COVID-19 pandemic; however, to the best of our knowledge, no comprehensive reviews have been conducted to uncover and summarise the main feature of these technologies. Objective: This study aims to explore proposed or implemented blockchain technologies used to mitigate the COVID-19 challenges as reported in the literature. Methods: We conducted a scoping review in line with guidelines of PRISMA Extension for Scoping Reviews (PRISMA-ScR). To identify relevant studies, we searched 11 bibliographic databases (e.g., EMBASE and MEDLINE) and conducted backward and forward reference list checking of the included studies and relevant reviews. The study selection and data extraction were conducted by 2 reviewers independently. Data extracted from the included studies was narratively summarised and described. Results: 19 of 225 retrieved studies met eligibility criteria in this review. The included studies reported 10 used cases of blockchain to mitigate COVID-19 challenges; the most prominent use cases were contact tracing and immunity passports. While the blockchain technology was developed in 10 studies, its use was proposed in the remaining 9 studies. The public blockchain technology was the most commonly utilized type in the included studies. All together, 8 different consensus mechanisms were used in the included studies. Out of 10 studies that identified the used platform, 9 studies used Ethereum to run the blockchain. Solidity was the most prominent programming language used in developing blockchain technology in the included studies. The transaction cost was reported in only 4 of the included studies and varied between USD 10−10 and USD 5. The expected latency and expected scalability were not identified in the included studies. Conclusion: Blockchain technologies are expected to play an integral role in the fight against the COVID-19 pandemic. Many possible applications of blockchain were found in this review; however, most of them are not mature enough to reveal their expected impact in the fight against COVID-19. We encourage governments, health authorities, and policymakers to consider all blockchain applications suggested in the current review to combat COVID-19 challenges. There is a pressing need to empirically examine how effective blockchain technologies are in mitigating COVID-19 challenges. Further studies are required to assess the performance of blockchain technologies’ fight against COVID-19 in terms of transaction cost, scalability, and/or latency when using different consensus algorithms, platforms, and access types.

Objectives The assessment of illness severity at admission can contribute to decreased mortality in patients with the coronavirus disease (COVID-19). This study was conducted to evaluate the effectiveness of the Sequential Organ Failure Assessment (SOFA) and Quick Sequential Organ Failure Assessment (qSOFA) scoring systems at admission for the prediction of mortality risk in COVID-19 patients. Methods We included 140 critically ill COVID-19 patients. Data on demographics, clinical characteristics, and laboratory findings at admission were used to calculate SOFA and qSOFA against the in-hospital outcomes (survival or death) that were ascertained from the medical records. The predictive accuracy of both scoring systems was evaluated by the receiver operating characteristic (ROC) curve analysis. Results The area under the ROC curve for SOFA in predicting mortality was 0.890 (95% CI: 0.826–0.955), which was higher than that of qSOFA (0.742, 95% CI 0.657–0.816). An optimal cutoff of ≥3 for SOFA had sensitivity, specificity, positive predictive value, and negative predictive value of 90.00%, 83.18%, 50.00%, and 97.80%, respectively. Conclusions This novel report indicates that SOFA could function as an effective adjunctive risk-stratification tool at admission for critical COVID-19 patients. The performance of qSOFA is accepted but inferior to that of SOFA.

Background Since novel coronavirus disease (COVID-19) emerged, various clinical features of COVID-19 have been reported. Methods We conducted a systematic review of published studies reporting the clinical features of COVID-19. Two investigators independently searched PubMed (December 2019–February 2020) for eligible articles. A meta-analysis was performed to measure the frequencies of clinical outcomes and symptoms of COVID-19. A stratified analysis was conducted according to the timeline of outbreak and exposure histories: Group I, most patients were exposed to the Hunan seafood wholesale market and lived in Wuhan, Hubei province; Group II, patients lived in Hubei province but were not directly exposed to the market; and Group III, patients lived outside Hubei. Results Thirteen studies, all from China, were eligible. The estimated mortality rate among all studies was 2.12%, but that in Group I was 8.66%. The incidence of acute respiratory distress syndrome in Group I was 20.00%. Both fever and cough were major symptoms, and their frequencies were higher in Group I than in Groups II and III, while the frequency of diarrhea in Group I was lower than that in Group III. The estimated frequency of dyspnea in Group I was 37.18%, while those in Groups II and III were 16.95% and 7.03%, respectively. Conclusions The trends in the clinical features of COVID-19 changed from December 2019 to February 2020. During this observation period, as the infection continued to spread, the clinical conditions for majority of patients became less severe with the changes in the route of transmission.

Background Mexico declared the first case of novel coronavirus disease (COVID-19) in February 2020. At the time we write this article, our country is facing a community spread phase, expecting a rapid increase in the number of cases and fatalities. The Fray Antonio Alcalde Civil Hospital of Guadalajara has been declared a non-COVID-19 hospital with the mission of providing care to patients already registered and also those transferred from neurosurgical departments of neighboring centers, which have been converted into COVID-19 only hospitals. Methods An organized response regarding personnel, surgical case selection, operating room behavior, and facility reorganization were designed to prevent an internal coronavirus outbreak in the neurosurgery department at the Fray Antonio Alcalde Civil Hospital of Guadalajara. Results Distancing actions by the staff and residents, including ward case discussions, neurosurgery rounds, and classes, will be carried out virtually. We classified neurosurgical patients into 4 groups depending on whether their condition demands care in 0–6 hours, 6–48 hours, 48 hours to 14 days, and >14 days. Subsequently, a questionnaire with epidemiologic, radiologic, clinical, and serologic criteria will be applied to determine the risk of COVID-19 infection to define to which area they are going to be transferred according to the different risk zones in our facility. Conclusions Despite not being a COVID-19 center, we consider all patients at the neurosurgical ward and staff members as asymptomatic carriers or infected in the preclinical period. Specific measures must be taken to ensure the safety and care of neurosurgical patients and medical staff during the community spread phase.