Therapeutic potential of salicylamide derivatives for combating viral infections.

Since time immemorial human beings have constantly been fighting against viral infections. The ongoing and devastating coronavirus disease 2019 pandemic represents one of the most severe and most significant public health emergencies in human history, highlighting an urgent need to develop broad-spectrum antiviral agents. Salicylamide (2-hydroxybenzamide) derivatives, represented by niclosamide and nitazoxanide, inhibit the replication of a broad range of RNA and DNA viruses such as flavivirus, influenza A virus, and coronavirus. Moreover, nitazoxanide was effective in clinical trials against different viral infections including diarrhea caused by rotavirus and norovirus, uncomplicated influenza A and B, hepatitis B, and hepatitis C. In this review, we summarize the broad antiviral activities of salicylamide derivatives, the clinical progress, and the potential targets or mechanisms against different viral infections and highlight their therapeutic potential in combating the circulating and emerging viral infections in the future.

Use of wastewater metrics to track COVID-19 in the U.S.: a national time-series analysis over the first three quarters of 2022 (preprint)

Background: Widespread use of at-home COVID-19 tests hampers determination of community COVID-19 incidence. Using nationwide data available through the US National Wastewater Surveillance System, we examined the performance of two wastewater metrics in predicting high case and hospitalizations rates both before and after widespread use of at-home tests. Methods: We performed area under the receiver operating characteristic (ROC) curve analysis (AUC) for two wastewater metrics- viral concentration relative to the peak of January 2022 ('wastewater percentile') and 15-day percent change in SARS-CoV-2 ('percent change'). Dichotomized reported cases (equal to or greater than 200 or <200 cases per 100,000) and new hospitalizations (equal to or greater than 10 or <10 per 100,000) were our dependent variables, stratified by calendar quarter. Using logistic regression, we assessed the performance of combining wastewater metrics. Results: Among 268 counties across 22 states, wastewater percentile detected high reported case and hospitalizations rates in the first quarter of 2022 (AUC 0.95 and 0.86 respectively) whereas the percent change did not (AUC 0.54 and 0.49 respectively). A wastewater percentile of 51% maximized sensitivity (0.93) and specificity (0.82) for detecting high case rates. A model inclusive of both metrics performed no better than using wastewater percentile alone. The predictive capability of wastewater percentile declined over time (AUC 0.84 and 0.72 for cases for second and third quarters of 2022). Conclusion: Nationwide, county wastewater levels above 51% relative to the historic peak predicted high COVID rates and hospitalization in the first quarter of 2022, but performed less well in subsequent quarters. Decline over time in predictive performance of this metric likely reflects underreporting of cases, reduced testing, and possibly lower virulence of infection due to vaccines and treatments.

Spatiotemporal evolution of NO2 diffusion in Beijing in response to COVID-19 lockdown using complex network.

The COVID-19 pandemic and the corresponding lockdown measures have been confirmed to reduce the air pollution in major megacities worldwide. Especially at some monitoring hotspots, NO2 has been verified to show a significant decrease. However, the diffusion pattern of these hotspots in responding to COVID-19 is not clearly understood at present stage. Hence, we selected Beijing, a typical megacity with the strictest lockdown measures during COVID-19 period, as the studied city and attempted to discover the NO2 diffusion process through complex network method. The improved metrics derived from the topological structure of the network were adopted to describe the performance of diffusion. Primarily, we found evidences that COVID-19 had significant effects on the spatial diffusion distribution due to combined effect of changed human activities and meteorological conditions. Besides, to further quantify the impacts of disturbance caused by different lockdown measures, we discussed the evolutionary diffusion patterns from lockdown period to recovery period. The results displayed that the difference between normal operation and pandemic operation firstly increased at the cutoff of lockdown measures but then declined after the implement of recovery measures. The source areas had greater vulnerability and lower resilience than receptors areas. Furthermore, based on the conclusion that the diffusion pattern changed during different periods, we explored the key stations on the path of diffusion process to further gain information. These findings could provide references for comprehending spatiotemporal pattern on city scale, which might be help for high-resolution air pollution mapping and prediction.

Prolonged SARS-CoV-2 Viral Shedding in Patients with COVID-19 was Associated with Delayed Initiation of Arbidol Treatment and Consulting Doctor Later: A Retrospective Cohort Study.

OBJECTIVE: To study data about SARS-CoV-2 virus shedding and clarify the risk factors for prolonged virus shedding. METHODS: Data were retrospectively collected from adults hospitalized with laboratory-confirmed coronavirus disease-19 (COVID-19) in Wuhan Union Hospital. We compared clinical features among patients with prolonged (a positive SARS-CoV-2 RNA on day 23 after illness onset) and short virus shedding and evaluated risk factors associated with prolonged virus shedding by multivariate regression analysis. RESULTS: Among 238 patients, the median age was 55.5 years, 57.1% were female, 92.9% (221/238) were administered with arbidol, 58.4% (139/238) were given arbidol in combination with interferon. The median duration of SARS-CoV-2 virus shedding was 23 days (IQR, 17.8-30 days) with a longest one of 51 days. The patients with prolonged virus shedding had higher value of D-dimer (P=0.002), IL-6 (P<0.001), CRP (P=0.005) and more lobes lung lesion (P=0.014) on admission, as well as older age (P=0.017) and more patients with hypertension (P=0.044) than in those the virus shedding less than 23 days. Multivariate regression analysis revealed that prolonged viral shedding was significantly associated with initiation arbidol >8 days after symptom onset [OR: 2.447, 95% CI (1.351-4.431)], ≥3 days from onset of symptoms to first medical visitation [OR: 1.880, 95% CI (1.035-3.416)], illness onset before Jan. 31, 2020 [OR: 3.289, 95% CI (1.474-7.337)]. Arbidol in combination with interferon was also significantly associated with shorter virus shedding [OR: 0.363, 95% CI (0.191-0.690)]. CONCLUSION: Duration of SARS-CoV-2 virus shedding was long. Early initiation of arbidol and arbidol in combination with interferon as well as consulting doctor timely after illness onset were helpful for SARS-CoV-2 clearance.

Multi-omic profiling of plasma reveals molecular alterations in children with COVID-19.

Rationale: Children usually develop less severe symptoms responding to Coronavirus Disease 2019 (COVID-19) than adults. However, little is known about the molecular alterations and pathogenesis of COVID-19 in children. Methods: We conducted plasma proteomic and metabolomic profilings of the blood samples of a cohort containing 18 COVID-19-children with mild symptoms and 12 healthy children, which were enrolled from hospital admissions and outpatients, respectively. Statistical analyses were performed to identify molecules specifically altered in COVID-19-children. We also developed a machine learning-based pipeline named inference of biomolecular combinations with minimal bias (iBM) to prioritize proteins and metabolites strongly altered in COVID-19-children, and experimentally validated the predictions. Results: By comparing to the multi-omic data in adults, we identified 44 proteins and 249 metabolites differentially altered in COVID-19-children against healthy children or COVID-19-adults. Further analyses demonstrated that both deteriorative immune response/inflammation processes and protective antioxidant or anti-inflammatory processes were markedly induced in COVID-19-children. Using iBM, we prioritized two combinations that contained 5 proteins and 5 metabolites, respectively, each exhibiting a total area under curve (AUC) value of 100% to accurately distinguish COVID-19-children from healthy children or COVID-19-adults. Further experiments validated that all the 5 proteins were up-regulated upon coronavirus infection. Interestingly, we found that the prioritized metabolites inhibited the expression of pro-inflammatory factors, and two of them, methylmalonic acid (MMA) and mannitol, also suppressed coronaviral replication, implying a protective role of these metabolites in COVID-19-children. Conclusion: The finding of a strong antagonism of deteriorative and protective effects provided new insights on the mechanism and pathogenesis of COVID-19 in children that mostly underwent mild symptoms. The identified metabolites strongly altered in COVID-19-children could serve as potential therapeutic agents of COVID-19.

ACEI/ARB use and risk of infection or severity or mortality of COVID-19: a systematic review and meta-analysis. (Special Issue: Pharmacoepidemiology and pathogenetics of 2019-nCoV.)

The effects of angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) on the risk of COVID-19 infection and disease progression are yet to be investigated. The relationship between ACEI/ARB use and COVID-19 infection was systematically reviewed. To identify relevant studies that met predetermined inclusion criteria, unrestricted searches of the PubMed, Embase, and Cochrane Library databases were conducted. The search strategy included clinical date published until May 9, 2020. Twelve articles involving more than 19,000 COVID-19 cases were included. To estimate overall risk, random-effects models were adopted. Our results showed that ACEI/ARB exposure was not associated with a higher risk of COVID-19 infection (OR = 0.99;95% CI, 0-1.04;P = 0.672). Among those with COVID-19 infection, ACEI/ARB exposure was also not associated with a higher risk of having severe infection (OR = 0.98;95% CI, 0.87-1.09;P = 0.69) or mortality (OR = 0.73, 95%CI, 0.5-1.07;P = 0.111). However, ACEI/ARB exposure was associated with a lower risk of mortality compared to those on non-ACEI/ARB antihypertensive drugs (OR = 0.48, 95% CI, 0.29-0.81;P = 0.006). In conclusion, current evidence did not confirm the concern that ACEI/ARB exposure is harmful in patientswith COVID-19 infection. This study supports the current guidelines that discourage discontinuation of ACEIs or ARBs in COVID-19 patients and the setting of the COVID-19 pandemic.

Corticosteroids Treatment of Patients with Coronavirus Disease 2019: A Propensity Score Matching Study.

The role of corticosteroids in the treatment of Coronavirus disease 2019 (COVID-19) is controversial. In the present study, we evaluated the effects of adjuvant corticosteroids treatment on the outcome of patients with COVID-19 (n=966), using Propensity Score Matching to adjust for potential differences between the corticosteroids group (n=289) and the non-corticosteroids group (n=677). Analysis of data without adjusting differences in baseline characteristics indicated that the proportion of mechanical ventilation and the mortality was higher in the corticosteroids treatment group in total or severe/critical patients. The duration of viral shedding was longer in the non-corticosteroids treatment group in total or general/mild patients. After adjusting the difference between the corticosteroids and non-corticosteroids treatment group, the analysis revealed that the use of corticosteroids had no effect on the duration of viral shedding, in-hospital mortality or 28-day mortality.

Case report: Nutrition therapy and side-effects monitoring in critically ill coronavirus disease 2019 patients.

An outbreak of pneumonia proved to be infected by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), named Coronavirus Disease 2019 (COVID-19) by World Health Organization (WHO), has rapidly and widely spread to the whole world, affecting thousands of people. COVID-19 patients have poor gastrointestinal function and microecological disorders, which lead to the frequent occurrence of aspiration pneumonia, gastric retention, and diarrhea. In the meanwhile, it takes a certain period of time for nutrition therapy to reach the patient's physiological amount. Refeeding syndrome and hypoglycemia may occur during this period, causing the high risk of death in critical patients. Therefore, we reported the nutrition therapy and side-effects monitoring as well as the adjustment of the nutrition therapy of 2 critical COVID-19 patients, thus provide clinical evidence for nutrition therapy and prevention of the side effects.

Novel risk scoring system for predicting acute respiratory distress syndrome among hospitalized patients with coronavirus disease 2019 in Wuhan, China.

BACKGROUND: The mortality rate from acute respiratory distress syndrome (ARDS) is high among hospitalized patients with coronavirus disease 2019 (COVID-19). Hence, risk evaluation tools are required to immediately identify high-risk patients upon admission for early intervention. METHODS: A cohort of 220 consecutive patients with COVID-19 were included in this study. To analyze the risk factors of ARDS, data obtained from approximately 70% of the participants were randomly selected and used as training dataset to establish a logistic regression model. Meanwhile, data obtained from the remaining 30% of the participants were used as test dataset to validate the effect of the model. RESULTS: Lactate dehydrogenase, blood urea nitrogen, D-dimer, procalcitonin, and ferritin levels were included in the risk score system and were assigned a score of 25, 15, 34, 20, and 24, respectively. The cutoff value for the total score was > 35, with a sensitivity of 100.00% and specificity of 81.20%. The area under the receiver operating characteristic curve and the Hosmer-Lemeshow test were 0.967 (95% confidence interval [CI]: 0.925-0.989) and 0.437(P Value = 0.437). The model had excellent discrimination and calibration during internal validation. CONCLUSIONS: The novel risk score may be a valuable risk evaluation tool for screening patients with COVID-19 who are at high risk of ARDS.

Drug repurposing approach to combating coronavirus: Potential drugs and drug targets.

In the past two decades, three highly pathogenic human coronaviruses severe acute respiratory syndrome coronavirus (SARS-CoV), Middle East respiratory syndrome coronavirus, and, recently, SARS-CoV-2, have caused pandemics of severe acute respiratory diseases with alarming morbidity and mortality. Due to the lack of specific anti-CoV therapies, the ongoing pandemic of coronavirus disease 2019 (COVID-19) poses a great challenge to clinical management and highlights an urgent need for effective interventions. Drug repurposing is a rapid and feasible strategy to identify effective drugs for combating this deadly infection. In this review, we summarize the therapeutic CoV targets, focus on the existing small molecule drugs that have the potential to be repurposed for existing and emerging CoV infections of the future, and discuss the clinical progress of developing small molecule drugs for COVID-19.

Open resource of clinical data from patients with pneumonia for the prediction of COVID-19 outcomes via deep learning.

Data from patients with coronavirus disease 2019 (COVID-19) are essential for guiding clinical decision making, for furthering the understanding of this viral disease, and for diagnostic modelling. Here, we describe an open resource containing data from 1,521 patients with pneumonia (including COVID-19 pneumonia) consisting of chest computed tomography (CT) images, 130 clinical features (from a range of biochemical and cellular analyses of blood and urine samples) and laboratory-confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) clinical status. We show the utility of the database for prediction of COVID-19 morbidity and mortality outcomes using a deep learning algorithm trained with data from 1,170 patients and 19,685 manually labelled CT slices. In an independent validation cohort of 351 patients, the algorithm discriminated between negative, mild and severe cases with areas under the receiver operating characteristic curve of 0.944, 0.860 and 0.884, respectively. The open database may have further uses in the diagnosis and management of patients with COVID-19.

Plasma Proteomics Identify Biomarkers and Pathogenesis of COVID-19.

The coronavirus disease 2019 (COVID-19) pandemic is a global public health crisis. However, little is known about the pathogenesis and biomarkers of COVID-19. Here, we profiled host responses to COVID-19 by performing plasma proteomics of a cohort of COVID-19 patients, including non-survivors and survivors recovered from mild or severe symptoms, and uncovered numerous COVID-19-associated alterations of plasma proteins. We developed a machine-learning-based pipeline to identify 11 proteins as biomarkers and a set of biomarker combinations, which were validated by an independent cohort and accurately distinguished and predicted COVID-19 outcomes. Some of the biomarkers were further validated by enzyme-linked immunosorbent assay (ELISA) using a larger cohort. These markedly altered proteins, including the biomarkers, mediate pathophysiological pathways, such as immune or inflammatory responses, platelet degranulation and coagulation, and metabolism, that likely contribute to the pathogenesis. Our findings provide valuable knowledge about COVID-19 biomarkers and shed light on the pathogenesis and potential therapeutic targets of COVID-19.

Humanistic care and psychological counseling on psychological disorders in medical students after COVID-19 outbreak: A protocol of systematic review.

BACKGROUND: The objective of this study is to investigate the effects of humanistic care and psychological counseling (HCPC) on psychological disorders (PD) in medical students after coronavirus disease 2019 (COVID-19) outbreak. METHODS: We will search randomized controlled trials or case-controlled studies of HCPC on PD in medical students after COVID-19 outbreak in the following electronic databases: PUBMED/MEDLINE, EMBASE, Cochrane Library, CINAHL, AMED, WANGFANG, and CNKI. The time is restricted from the construction of each database to the present. All process of study selection, data collection, and study quality evaluation will be carried out by two independent authors. Any different opinions will be solved by a third author through discussion. We will employ RevMan 5.3 software to conduct statistical analysis. RESULTS: This study will provide a better understanding of HCPC on PD in medical students after COVID-19 outbreak. CONCLUSIONS: This study may offer strong evidence for clinical practice to treat PD in medical students after COVID-19 outbreak. STUDY REGISTRATION: CRD42020193199.