The diagnosis of pandemic coronavirus pneumonia: A review of radiology examination and laboratory test

Immunohistochemical and Morphometric Analysis of Lung Tissue in Fatal COVID-19

The primary targets of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the lungs are type I pneumocytes, macrophages, and endothelial cells. We aimed to identify lung cells targeted by SARS-CoV-2 using viral nucleocapsid protein staining and morphometric features on patients with fatal COVID-19. We conducted a retrospective analysis of fifty-one autopsy cases of individuals who tested positive for SARS-CoV-2. Demographic and clinical information were collected from forensic reports, and lung tissue was examined for microscopic lesions and the presence of specific cell types. Half of the evaluated cohort were older than 71 years, and the majority were male (74.5%). In total, 24 patients presented diffuse alveolar damage (DAD), and 50.9% had comorbidities (56.9% obesity, 33.3% hypertension, 15.7% diabetes mellitus). Immunohistochemical analysis showed a similar pattern of infected macrophages, infected type I pneumocytes, and endothelial cells, regardless of the presence of DAD (p > 0.5). The immunohistochemical reactivity score (IRS) was predominantly moderate but without significant differences between patients with and without DAD (p = 0.633 IRS for type I pneumocytes, p = 0.773 IRS for macrophage, and p = 0.737 for IRS endothelium). The nucleus/cytoplasm ratio shows lower values in patients with DAD (median: 0.29 vs. 0.35), but the difference only reaches a tendency for statistical significance (p = 0.083). Our study confirms the presence of infected macrophages, type I pneumocytes, and endothelial cells with a similar pattern in patients with and without diffuse alveolar damage.

Impact of Artificial Intelligence (AI) Technology in Healthcare Sector: A Critical Evaluation of Both Sides of the Coin

The influence of artificial intelligence (AI) has drastically risen in recent years, especially in the field of medicine. Its influence has spread so greatly that it is determined to become a pillar in the future medical world. A comprehensive literature search related to AI in healthcare was performed in the PubMed database and retrieved the relevant information from suitable ones. AI excels in aspects such as rapid adaptation, high diagnostic accuracy, and data management that can help improve workforce productivity. With this potential in sight, the FDA has continuously approved more machine learning (ML) software to be used by medical workers and scientists. However, there are few controversies such as increased chances of data breaches, concern for clinical implementation, and potential healthcare dilemmas. In this article, the positive and negative aspects of AI implementation in healthcare are discussed, as well as recommended some potential solutions to the potential issues at hand.

Rapid, label-free and low-cost diagnostic kit for COVID-19 based on liquid crystals and machine learning

We report a label-free method for detection of the SARS-CoV-2 virus in nasopharyngeal swab samples without purification steps and multiplication of the target which simplifies and expedites the analysis process. The kit consists of a textile grid on which liquid crystals (LC) are deposited and the grid is placed in a crossed polarized microscopy. The swab samples are subsequently placed on the LCs. In the presence of a particular biomolecule, the direction of LCs changes locally based on the properties of the biomolecule and forms a particular pattern. As the swab samples are not perfectly purified, image processing and machine learning techniques are employed to detect the presence of specific molecules or quantify their concentrations in the medium. The method can differentiate negative and positive COVID-19 samples with an accuracy of 96% and also differentiate COVID-19 from influenza types A and B with an accuracy of 93%. The kit is portable, simple to manufacture, convenient to operate, cost effective, rapid and sensitive. The simplicity of the specimen processing, the speed of image acquisition, and fast diagnostic operations enable the deployment of the proposed technique for performing extensive on-spot screening of COVID-19 in public places.

Implementation of a COVID-19 screening tool in a southern Nigerian tertiary health facility

Screening for coronavirus disease 2019 (COVID-19) in emergency rooms of health facilities during outbreaks prevents nosocomial transmission. However, effective tools adapted for use in African countries are lacking. This study appraised an indigenous screening and triage tool for COVID-19 deployed at the medical emergency room of a Nigerian tertiary facility and determined the predictors of a positive molecular diagnostic test for COVID-19. A cross-sectional study of all patients seen between May and July 2020 at the Accident and Emergency of the University of Benin Teaching Hospital was conducted. Patients with any one of the inputs- presence of COVID-19 symptoms, history of international travel, age 60 years and above, presence of comorbidities and oxygen saturation < 94%- were stratified as high-risk and subjected to molecular testing for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Data was obtained from the screening record book patterned after a modified screening tool for COVID-19, deidentified and entered into IBM-SPSS version 25.0. Binary logistic regression was conducted to determine significant predictors of a positive SARS-CoV-2 test. The level of significance was set at p < 0.05. In total, 1,624 patients were screened. Mean age (standard deviation) was 53.9±18.0 years and 651 (40.1%) were 60 years and above. One or more symptoms of COVID-19 were present in 586 (36.1%) patients. Overall, 1,116 (68.7%) patients were designated high risk and tested for SARS-CoV-2, of which 359 (32.2%) were positive. Additional inputs, besides symptoms, increased COVID-19 detection by 108%. Predictors of a positive test were elderly age [AOR = 1.545 (1.127-2.116)], co-morbidity [AOR = 1.811 (1.296-2.530)] and oxygen saturation [AOR = 3.427 (2.595-4.528)]. This protocol using additional inputs such as oxygen saturation improved upon symptoms-based screening for COVID-19. Models incorporating identified predictors will be invaluable in resource limited settings.

Sensitivity of SARS-CoV-2 Detection With Nasopharyngeal Swabs

Background: SARS-CoV-2-infected subjects have been proven contagious in the symptomatic, pre-symptomatic and asymptomatic phase. The identification of these patients is crucial in order to prevent virus circulation. No reliable data on the sensitivity of nasopharyngeal swabs (NPS) are available because of the lack of a shared reference standard to identify SARS-CoV-2 infected patients. The aim of our study was to collect data on patients with a known diagnosis of COVID-19 who underwent serial testing to assess NPS sensitivity. Methods: The study was a multi-center, observational, retrospective clinical study with consecutive enrollment. We enrolled patients who met all of the following inclusion criteria: clinical recovery, documented SARS-CoV-2 infection (≥1 positive rRT-PCR result) and ≥1 positive NPS among the first two follow-up swabs. A positive NPS not preceded by a negative nasopharyngeal swab collected 24-48 h earlier was considered a true positive. A negative NPS followed by a positive NPS collected 24-48 h later was regarded as a false negative. The primary outcome was to define sensitivity of SARS-CoV-2 detection with NPS. Results: Three hundred and ninety three NPS were evaluated in 233 patients; the sensitivity was 77% (95% CI, 73 to 81%). Sensitivity of the first follow-up NPS (n = 233) was 79% (95% CI, 73 to 84%) with no significant variations over time. We found no statistically significant differences in the sensitivity of the first follow-up NPS according to time since symptom onset, age, sex, number of comorbidities, and onset symptoms. Conclusions: NPS utility in the diagnostic algorithm of COVID-19 should be reconsidered.

Patents Related to Pathogenic Human Coronaviruses

BACKGROUND

Coronaviruses have caused outbreaks of respiratory disease since the beginning of the 21st century, representing a significant threat to public health. Together, the severe acute respiratory syndrome coronavirus (SARS-CoV), the respiratory syndrome coronavirus (MERS-CoV), and, more recently, the novel coronavirus (SARS-CoV-2) have caused a large number of deaths around the world. Thus, investments in research and the development of strategies aimed at diagnosing, treating, and preventing these infections are urgently needed.

OBJECTIVE

The objective of this study was to analyze the patents that address pathogenic coronaviruses in Google Patents databases in the last year (2019-2020).

METHODS

The search strategy was carried out in April 2020, based on the keywords "SARS", "SARS-CoV", "MERS", "MERS-CoV", "SARS-CoV-2" and "COVID-19. Out of the patents examined, 25 were selected for a short description in this study.

RESULTS

A total of 191 patents were analyzed, 149 of which were related to SARS-CoV, and 29 and 12 were related to MERS-CoV and SARS- CoV2, respectively. The patents addressed the issues of diagnosis, therapeutic agents, prevention and control, along with other applications.

CONCLUSION

Several promising strategies have been documented in intellectual property databases favoring the need for further studies on the pathogenesis and optimization of the diagnosis and therapeutic treatment for these emerging infections.

Safety considerations during return to work in the context of stable COVID-19 epidemic control: an analysis of health screening results of all returned staff from a hospital

In March 2020, China had periodically controlled the coronavirus disease-19 (COVID-19) epidemic. We reported the results of health screening for COVID-19 among returned staff of a hospital and conducted a summary analysis to provide valuable experience for curbing the COVID-19 epidemic and rebound. In total, 4729 returned staff from Zhongnan Hospital of Wuhan University, Wuhan, China were examined for COVID-19, and the basic information, radiology and laboratory test results were obtained and systematically analysed. Among the 4729 employees, medical staff (62.93%) and rear-service personnel (30.73%) were the majority. The results of the first physical examination showed that 4557 (96.36%) were normal, 172 (3.64%) had abnormal radiological or laboratory test results. After reexamination and evaluation, four were at high risk (asymptomatic infections) and were scheduled to transfer to a designated hospital, and three were at low risk (infectivity could not be determined) and were scheduled for home isolation observation. Close contacts were tracked and managed by the Center for Disease Control and Prevention (CDC) in China. Asymptomatic infections are a major risk factor for returning to work. Extensive health screening combined with multiple detection methods helps to identify asymptomatic infections early, which is an important guarantee in the process of returning to work.