Mechanisms of SARS-CoV-2 Infection-Induced Kidney Injury: A Literature Review.

The severe acute respiratory coronavirus 2 (SARS-CoV-2) has become a life-threatening pandemic. Clinical evidence suggests that kidney involvement is common and might lead to mild proteinuria and even advanced acute kidney injury (AKI). Moreover, AKI caused by coronavirus disease 2019 (COVID-19) has been reported in several countries and regions, resulting in high patient mortality. COVID-19-induced kidney injury is affected by several factors including direct kidney injury mediated by the combination of virus and angiotensin-converting enzyme 2, immune response dysregulation, cytokine storm driven by SARS-CoV-2 infection, organ interactions, hypercoagulable state, and endothelial dysfunction. In this review, we summarized the mechanism of AKI caused by SARS-CoV-2 infection through literature search and analysis.

Gender and Ethnic Disparities of Acute Kidney Injury in COVID-19 Infected Patients: A Literature Review.

Coronavirus disease 2019(COVID-19) has become a public health emergency of concern worldwide. COVID-19 is a new infectious disease arising from Coronavirus 2 (SARS-CoV-2). It has a strong transmission capacity and can cause severe and even fatal respiratory diseases. It can also affect other organs such as the heart, kidneys and digestive tract. Clinical evidence indicates that kidney injury is a common complication of COVID-19, and acute kidney injury (AKI) may even occur in severely ill patients. Data from China and the United States showed that male sex, Black race, the elderly, chronic kidney disease, diabetes, hypertension, cardiovascular disease, and higher body mass index are associated with COVID-19-induced AKI. In this review, we found gender and ethnic differences in the occurrence and development of AKI in patients with COVID-19 through literature search and analysis. By summarizing the mechanism of gender and ethnic differences in AKI among patients with COVID-19, we found that male and Black race have more progress to COVID-19-induced AKI than their counterparts.

Drug repurposing for COVID-19 using graph neural network and harmonizing multiple evidence.

Since the 2019 novel coronavirus disease (COVID-19) outbreak in 2019 and the pandemic continues for more than one year, a vast amount of drug research has been conducted and few of them got FDA approval. Our objective is to prioritize repurposable drugs using a pipeline that systematically integrates the interaction between COVID-19 and drugs, deep graph neural networks, and in vitro/population-based validations. We first collected all available drugs (n = 3635) related to COVID-19 patient treatment through CTDbase. We built a COVID-19 knowledge graph based on the interactions among virus baits, host genes, pathways, drugs, and phenotypes. A deep graph neural network approach was used to derive the candidate drug's representation based on the biological interactions. We prioritized the candidate drugs using clinical trial history, and then validated them with their genetic profiles, in vitro experimental efficacy, and population-based treatment effect. We highlight the top 22 drugs including Azithromycin, Atorvastatin, Aspirin, Acetaminophen, and Albuterol. We further pinpointed drug combinations that may synergistically target COVID-19. In summary, we demonstrated that the integration of extensive interactions, deep neural networks, and multiple evidence can facilitate the rapid identification of candidate drugs for COVID-19 treatment.

Particle composition, sources and evolution during the COVID-19 lockdown period in Chengdu, southwest China: Insights from single particle aerosol mass spectrometer data.

In order to investigate the effects of the Coronavirus Disease 2019 (COVID-19) lockdown on air quality in cities in southwest China, a single particle aerosol mass spectrometer (SPAMS) and other online equipments were used to measure the air pollution in Chengdu, one of the megacities in this area, before and during the lockdown period. It was found that the concentrations of fine particulate matter (PM2.5), nitric oxide (NO), nitrogen dioxide (NO2), sulfur dioxide (SO2) and carbon monoxide (CO) decreased by 38.6%, 77.5%, 47.0%, 35.1% and 14.1%, respectively, while the concentration of ozone (O3) increased by 57.5% from the time before to the time during lockdown. All particles collected during the study period could be divided into eight categories: biomass burning (BB), coal combustion (CC), vehicle emissions (VE), cooking emissions (CE), Dust, K-nitrate (K-NO3), K-sulfate (K-SO4) and K-sulfate-nitrate (K-SN) particles, and their contributions changed significantly after the beginning of lockdown. Compared to before lockdown, the contribution of VE particles experienced the largest reduction (by 14.9%), whereas the contributions of BB and CE particles increased by 7.0% and 7.3%, respectively, during the lockdown period. Regional transmission was critical for pollution formation before lockdown, whereas the pollution that occurred during the lockdown period was caused mainly by locally emitted particles (such as VE, CE and BB particles). Weighted potential source contribution function (WPSCF) analysis further verified and emphasized the difference in the contribution of regional transmission for pollution formation before and during lockdown. In addition, the potential source area and intensity of the particles emitted from different sources or formation mechanisms were quite different.

A meta-analysis of the global impact of the COVID-19 pandemic on stroke care & the Houston Experience.

OBJECTIVE: To review the global impact of the COVID-19 pandemic on stroke care-metrics and report data from a health system in Houston. METHODS: We performed a meta-analysis of the published literature reporting stroke admissions, intracerebral hemorrhage (ICH) cases, number of thrombolysis (tPA) and thrombectomy (MT) cases, and time metrics (door to needle, DTN; and door to groin time, DTG) during the pandemic compared to prepandemic period. Within our hospital system, between January-June 2019 and January-June 2020, we compared the proportion of stroke admissions and door to tPA and MT times. RESULTS: A total of 32,640 stroke admissions from 29 studies were assessed. Compared to prepandemic period, the mean ratio of stroke admissions during the pandemic was 70.78% [95% CI, 65.02%, 76.54%], ICH cases was 83.10% [95% CI, 71.01%, 95.17%], tPA cases was 81.74% [95% CI, 72.33%, 91.16%], and MT cases was 88.63% [95% CI, 74.12%, 103.13%], whereas DTN time was 104.48% [95% CI, 95.52%, 113.44%] and DTG was 104.30% [95% CI, 81.99%, 126.61%]. In Houston, a total of 4808 cases were assessed. There was an initial drop of ~30% in cases at the pandemic onset. Compared to 2019, there was a significant reduction in mild strokes (NIHSS 1-5) [N (%), 891 (43) vs 635 (40), P = 0.02]. There were similar mean (SD) (mins) DTN [44 (17) vs 42 (17), P = 0.14] but significantly prolonged DTG times [94 (15) vs 85 (20), P = 0.005] in 2020. INTERPRETATION: The COVID-19 pandemic led to a global reduction in stroke admissions and treatment interventions and prolonged treatment time metrics.

Impact of SARS-CoV-2 on Male Reproductive Health: A Review of the Literature on Male Reproductive Involvement in COVID-19.

Coronavirus Disease 2019 (COVID-19) has created a global pandemic. Global epidemiological results show that elderly men are susceptible to infection of COVID-19. The difference in the number of cases reported by gender increases progressively in favor of male subjects up to the age group ≥60-69 (66.6%) and ≥70-79 (66.1%). Through literature search and analysis, we also found that men are more susceptible to SARS-CoV-2 infection than women. In addition, men with COVID-19 have a higher mortality rate than women. Male represents 73% of deaths in China, 59% in South Korea, and 61.8% in the United States. Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is the pathogen of COVID-19, which is transmitted through respiratory droplets, direct and indirect contact. Genomic analysis has shown that SARS-CoV-2 is 79% identical to SARS-CoV, and both use angiotensin-converting enzyme 2 (ACE2) as the receptor for invading cells. In addition, Transmembrane serine protease 2 (TMPRSS2) can enhance ACE2-mediated virus entry. However, SARS-CoV-2 has a high affinity with human ACE2, and its consequences are more serious than other coronaviruses. ACE2 acts as a "gate" for viruses to invade cells and is closely related to the clinical manifestations of COVID-19. Studies have found that ACE2 and TMPRSS2 are expressed in the testis and male reproductive tract and are regulated by testosterone. Mature spermatozoon even has all the machinery required to bind SARS-CoV-2, and these considerations raise the possibility that spermatozoa could act as potential vectors of this highly infectious disease. This review summarizes the gender differences in the pathogenesis and clinical manifestations of COVID-19 and proposes the possible mechanism of orchitis caused by SARS-CoV-2 and the potential transmission route of the virus. In the context of the pandemic, these data will improve the understanding of the poor clinical outcomes in male patients with COVID-19 and the design of new strategies to prevent and treat SARS-CoV-2 infection.

Drug Repurposing for COVID-19 using Graph Neural Network with Genetic, Mechanistic, and Epidemiological Validation.

Amid the pandemic of 2019 novel coronavirus disease (COVID-19) infected by SARS-CoV-2, a vast amount of drug research for prevention and treatment has been quickly conducted, but these efforts have been unsuccessful thus far. Our objective is to prioritize repurposable drugs using a drug repurposing pipeline that systematically integrates multiple SARS-CoV-2 and drug interactions, deep graph neural networks, and in-vitro/population-based validations. We first collected all the available drugs (n= 3,635) involved in COVID-19 patient treatment through CTDbase. We built a SARS-CoV-2 knowledge graph based on the interactions among virus baits, host genes, pathways, drugs, and phenotypes. A deep graph neural network approach was used to derive the candidate representation based on the biological interactions. We prioritized the candidate drugs using clinical trial history, and then validated them with their genetic profiles, in vitro experimental efficacy, and electronic health records. We highlight the top 22 drugs including Azithromycin, Atorvastatin, Aspirin, Acetaminophen, and Albuterol. We further pinpointed drug combinations that may synergistically target COVID-19. In summary, we demonstrated that the integration of extensive interactions, deep neural networks, and rigorous validation can facilitate the rapid identification of candidate drugs for COVID-19 treatment.