Pulmonary fibrosis in critically ill patients with COVID-19: A multi-center retrospective cohort study in South Korea (preprint)

Background: Pulmonary fibrosis persists long after recovering from coronavirus disease 2019 (COVID-19) infection, thereby reducing quality of life and lung function. We aimed to evaluate the prevalence and risk factors for pulmonary fibrosis in patients with severe COVID-19 pneumonia requiring mechanical ventilation, a high-risk group for developing pulmonary fibrosis. Methods: Clinical data and chest computed tomography (CT) scans of patients with severe COVID-19 pneumonia requiring mechanical ventilation were retrospectively collected from nine hospitals in South Korea. Fibrotic-like changes on chest CT were visually assessed. Results: We included 125 patients with a mean age of 68.5 years, 60.8% men and 7.2% having underlying lung disease. Based on follow-up chest CT (the median interval: 38.0 days, interquartile range: 24.0–68.0 days), 94 (75.2%) patients exhibited fibrotic-like changes, with traction bronchiectasis and/or bronchiolectasis being the most common change (60.8%). Adjusted Cox regression analysis revealed as association between hemoglobin levels ≤9 g/dL and an increased risk of pulmonary fibrosis development (HR: 3.182, 95% Cl: 1.203–8.415, P=0.025). Among all patients, 17.6% died during hospitalization and 71.2% experienced complications, including intubation-related airway injury (12.8%), ventilator-associated pneumonia (44.8%), lung injury (11.2%), and hemodynamic disturbance (33.4%). In-hospital mortality (16.1% vs. 18.1%) and complications (67.7% vs. 72.3%) were similar between patients with and without fibrotic-like changes. Conclusion: Our study demonstrated that in patients with severe COVID-19 pneumonia requiring mechanical ventilation, chest CT revealed fibrotic-like changes in approximately three-fourths of patients. Low hemoglobin levels might be associated with pulmonary fibrosis in severe COVID-19 pneumonia.

Seesaw Effect Between COVID-19 and Influenza From 2020 to 2023 in World Health Organization Regions: Correlation Analysis.

BACKGROUND: Seasonal influenza activity showed a sharp decline in activity at the beginning of the emergence of COVID-19. Whether there is an epidemiological correlation between the dynamic of these 2 respiratory infectious diseases and their future trends needs to be explored. OBJECTIVE: We aimed to assess the correlation between COVID-19 and influenza activity and estimate later epidemiological trends. METHODS: We retrospectively described the dynamics of COVID-19 and influenza in 6 World Health Organization (WHO) regions from January 2020 to March 2023 and used the long short-term memory machine learning model to learn potential patterns in previously observed activity and predict trends for the following 16 weeks. Finally, we used Spearman correlation coefficients to assess the past and future epidemiological correlation between these 2 respiratory infectious diseases. RESULTS: With the emergence of the original strain of SARS-CoV-2 and other variants, influenza activity stayed below 10% for more than 1 year in the 6 WHO regions. Subsequently, it gradually rose as Delta activity dropped, but still peaked below Delta. During the Omicron pandemic and the following period, the activity of each disease increased as the other decreased, alternating in dominance more than once, with each alternation lasting for 3 to 4 months. Correlation analysis showed that COVID-19 and influenza activity presented a predominantly negative correlation, with coefficients above -0.3 in WHO regions, especially during the Omicron pandemic and the following estimated period. The diseases had a transient positive correlation in the European region of the WHO and the Western Pacific region of the WHO when multiple dominant strains created a mixed pandemic. CONCLUSIONS: Influenza activity and past seasonal epidemiological patterns were shaken by the COVID-19 pandemic. The activity of these diseases was moderately or greater than moderately inversely correlated, and they suppressed and competed with each other, showing a seesaw effect. In the postpandemic era, this seesaw trend may be more prominent, suggesting the possibility of using one disease as an early warning signal for the other when making future estimates and conducting optimized annual vaccine campaigns.

Thoracic Mobilization and Respiratory Muscle Endurance Training Improve Diaphragm Thickness and Respiratory Function in Patients with a History of COVID-19.

Background and Objectives: Common problems in people with COVID-19 include decreased respiratory strength and function. We investigated the effects of thoracic mobilization and respiratory muscle endurance training (TMRT) and lower limb ergometer (LE) training on diaphragm thickness and respiratory function in patients with a history of COVID-19. Materials and Methods: In total, 30 patients were randomly divided into a TMRT training group and an LE training group. The TMRT group performed thoracic mobilization and respiratory muscle endurance training for 30 min three times a week for 8 weeks. The LE group performed lower limb ergometer training for 30 min three times a week for 8 weeks. The participants' diaphragm thickness was measured via rehabilitative ultrasound image (RUSI) and a respiratory function test was conducted using a MicroQuark spirometer. These parameters were measured before the intervention and 8 weeks after the intervention. Results: There was a significant difference (p < 0.05) between the results obtained before and after training in both groups. Right diaphragm thickness at rest, diaphragm thickness during contraction, and respiratory function were significantly more improved in the TMRT group than in the LE group (p < 0.05). Conclusions: In this study, we confirmed the effects of TMRT training on diaphragm thickness and respiratory function in patients with a history of COVID-19.

Association between Covid-19 vaccination and incidence of type 1 diabetes in China: Evidence from 14.14 million registered residents between 2007 and 2021.

AIMS: Reports have suggested that COVID-19 vaccination may cause Type 1 diabetes (T1D), particularly fulminant T1D (FT1D). This study aimed to investigate the incidence of T1D in a general population of China, where>90% of the people have received three injections of inactivated SARS-Cov-2 vaccines in 2021. METHODS: A population-based registry of T1D was performed using data from the Beijing Municipal Health Commission Information Center. Annual incidence rates were calculated by age group and gender, and annual percentage changes were assessed using Joinpoint regression. RESULTS: The study included 14.14 million registered residents, and 7,697 people with newly diagnosed T1D were identified from 2007 to 2021. T1D incidence increased from 2.77 in 2007 to 3.84 per 100,000 persons in 2021. However, T1D incidence was stable from 2019 to 2021, and the incidence rate did not increase when people were vaccinated in January-December 2021. The incidence of FT1D did not increase from 2015 to 2021. CONCLUSIONS: The findings suggest that COVID-19 vaccination did not increase the onset of T1D or have a significant impact on T1D pathogenesis, at least not on a large scale.

Entangling of Peptide Nanofibers Reduces the Invasiveness of SARS-CoV-2.

The viral spike (S) protein on the surface of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) binds to angiotensin-converting enzyme 2 (ACE2) receptors on the host cells, facilitating its entry and infection. Here, functionalized nanofibers targeting the S protein with peptide sequences of IRQFFKK, WVHFYHK and NSGGSVH, which are screened from a high-throughput one-bead one-compound screening strategy, are designed and prepared. The flexible nanofibers support multiple binding sites and efficiently entangle SARS-CoV-2, forming a nanofibrous network that blocks the interaction between the S protein of SARS-CoV-2 and the ACE2 on host cells, and efficiently reduce the invasiveness of SARS-CoV-2. In summary, nanofibers entangling represents a smart nanomedicine for the prevention of SARS-CoV-2.

Fatigue as a Cause of Professional Dissatisfaction Among Chinese Nurses in Intensive Care Unit During COVID-19 Pandemic.

Aim: To clarify the mediating role of burnout and the moderating role of turnover intention in the association between fatigue and job satisfaction among Chinese nurses in intensive care units (ICU) during the COVID-19 pandemic. Methods: A cross-sectional survey of fifteen provinces in China was conducted, using an online questionnaire, from December 2020 to January 2021, during the COVID-19 pandemic. A total of 374 ICU nurses (effective response rate: 71.37%) provided sufficient responses. Sociodemographic factors, job demographic factors, fatigue, burnout, job satisfaction, and turnover intention were assessed using questionnaires. General linear modeling (GLM), hierarchical linear regression (HLR) analysis, and generalized additive modeling (GAM) were performed to examine all the considered research hypotheses. Results: Fatigue was found to be negatively and significantly associated with job satisfaction. Moreover, burnout played a partial mediating role and turnover intention played a moderating role in the relationship between fatigue and job satisfaction. Conclusion: Over time, a state of physical and mental exhaustion and work weariness among Chinese ICU nurses potentially results in job burnout and consequently promotes the level of job dissatisfaction. The results also found that turnover intention played a moderating role in the relationship between burnout and job satisfaction. Specific policies could be considered to eliminate nurses' fatigue and negative attitudes during times of public health emergencies.

Assessing the impact of COVID-19 interventions on influenza-like illness in Beijing and Hong Kong: an observational and modeling study.

BACKGROUND: The impact of coronavirus diseases 2019 (COVID-19) related non-pharmaceutical interventions (NPIs) on influenza activity in the presence of other known seasonal driving factors is unclear, especially at the municipal scale. This study aimed to assess the impact of NPIs on outpatient influenza-like illness (ILI) consultations in Beijing and the Hong Kong Special Administrative Region (SAR) of China. METHODS: We descriptively analyzed the temporal characteristics of the weekly ILI counts, nine NPI indicators, mean temperature, relative humidity, and absolute humidity from 2011 to 2021. Generalized additive models (GAM) using data in 2011-2019 were established to predict the weekly ILI counts under a counterfactual scenario of no COVID-19 interventions in Beijing and the Hong Kong SAR in 2020-2021, respectively. GAM models were further built to evaluate the potential impact of each individual or combined NPIs on weekly ILI counts in the presence of other seasonal driving factors in the above settings in 2020-2021. RESULTS: The weekly ILI counts in Beijing and the Hong Kong SAR fluctuated across years and months in 2011-2019, with an obvious winter-spring seasonality in Beijing. During the 2020-2021 season, the observed weekly ILI counts in both Beijing and the Hong Kong SAR were much lower than those of the past 9 flu seasons, with a 47.5% [95% confidence interval (CI): 42.3%, 52.2%) and 60.0% (95% CI: 58.6%, 61.1%) reduction, respectively. The observed numbers for these two cities also accounted for only 40.2% (95% CI: 35.4%, 45.3%) and 58.0% (95% CI: 54.1%, 61.5%) of the GAM model estimates in the absence of COVID-19 NPIs, respectively. Our study revealed that, "Cancelling public events" and "Restrictions on internal travel" measures played an important role in the reduction of ILI in Beijing, while the "restrictions on international travel" was statistically most associated with ILI reductions in the Hong Kong SAR. CONCLUSIONS: Our study suggests that COVID-19 NPIs had been reducing outpatient ILI consultations in the presence of other seasonal driving factors in Beijing and the Hong Kong SAR from 2020 to 2021. In cities with varying local circumstances, some NPIs with appropriate stringency may be tailored to reduce the burden of ILI caused by severe influenza strains or other respiratory infections in future.

An approach combining deep learning and molecule docking for drug discovery of cathepsin L.

OBJECTIVES: Cathepsin L (CTSL) is a promising therapeutic target for metabolic disorders and COVID-19. However, there are still no clinically available CTSL inhibitors. Our objective is to develop an approach for the discovery of potential reversible covalent CTSL inhibitors. METHODS: The authors combined Chemprop, a deep learning-based strategy, and the Schrödinger CovDock algorithm to identify potential CTSL inhibitors. First, they used Chemprop to train a deep learning model capable of predicting whether a molecule would inhibit the activity of CTSL and performed predictions on ZINC20 in-stock librarie (~9.2 million molecules). Then, they selected the top-200 predicted molecules and performed the Schrödinger covalent docking algorithm to explore the binding patterns to CTSL (PDB: 5MQY). The authors then calculated the binding energies using Prime MM/GBSA and examined the stability between the best two molecules and CTSL using 100ns molecular dynamics simulations. RESULTS: The authors found five molecules that showed better docking results than the well-known cathepsin inhibitor odanacatib. Notably, two of these molecules, ZINC-35287427 and ZINC-1857528743, showed better docking results with CTSL compared to other cathepsins. CONCLUSION: Our approach enables drug discovery from large-scale databases with little computational consumption, which will save the cost and time required for drug discovery.

Anxiety, depression and post-traumatic stress disorder and related factors among Chinese population during the COVID-19 pandemic: A cross-sectional study.

The sudden outbreak of coronavirus disease 2019 (COVID-19) has deep and wide negative mental impacts on the public, and studies on the impact of COVID-19 on social and mental well-being are necessary. This study aimed to evaluate mental distress, including anxiety, depression, and post-traumatic stress disorder (PTSD), and its related risk factors in Chinese adults in the early stages of the COVID-19 pandemic. This study used a large-scale cross-sectional design. A total of 2067 adult participants completed the online survey via REDcap from 1st to 15th of March 2020 during the COVID-19 outbreak in China. Anxiety, depression, PTSD, and related risk factors, including self-efficacy, coping style, and social support, were measured using valid and reliable instruments. The data were analyzed using multiple linear regression. We found that 201 (9.7%) participants reported moderate-to-severe anxiety, 669 (33.8%) reported depression, and 368 (17.8%) reported symptoms of PTSD. Self-efficacy, coping style, and social support significantly affected anxiety, depression, and PTSD symptoms. Participants' sociodemographic characteristics, COVID-19 pandemic-related factors, low self-efficacy, low social support, and negative coping were predictors of mental distress during the COVID-19 pandemic. Our study will help healthcare professionals carry out early predictions and identification of high-risk groups and provide appropriate interventions to target groups during public health emergencies that plague the world.

Lipid metabolism of plasma-derived small extracellular in COVID-19 convalescent patients (preprint)

The coronavirus disease 2019 (COVID-19), which affects multiple organs, is causing an unprecedented global public health crisis. Most COVID-19 patients recover gradually upon appropriate interventions. Viruses were reported to utilize the small extracellular vesicles (sEVs) to escape the attack from the host’s immune system. This study aimed to examine the lipid profile of plasma small extracellular vesicles of recovered COVID-19 patients (RCs). Plasma sEVs were separated from 83 RCs 3 months after discharge without underlying diseases, including 18 recovered asymptomatic patients (RAs), 32 recovered moderate patients (RMs), and 33 recovered severe and critical patients (RSs), and 19 healthy controls (HCs) by Total Exosome Isolation. Lipids were extracted from sEVs and then subjected to targeted liquid chromatography-mass spectrometry. Size, concentration, and distribution of plasma-derived sEVs from RAs, RMs, RSs, and HCs did not differ in RCs and HCs as validated by transmission electron microscopy, nanoparticle tracking analysis, and immunoblot analysis. Fifteen subclasses of 508 lipids were detected in plasma sEVs from HCs, RAs, RMs, and RSs, such as phosphatidylcholines (PCs) and diacylglycerols (DAGs), etc. Total lipid intensity displayed downregulation in RCs compared with HCs. The relative abundance of DAGs gradually dropped, whereas PCs, lysophosphatidylcholines, and sphingomyelins were higher in RCs relative to HCs, especially RSs. 88 lipids out of 241 were significantly different and a conspicuous increase in lipid profiles of RCs was revealed with disease status. The lipids alternations were found to be significantly correlated with the clinical indices in RCs and HCs, suggesting that the impact of COVID-19 on lipid metabolism lingered for a long time. The lipid abnormalities bore an intimate link with glycerophospholipid metabolism and glycosylphosphatidylinositol anchor biosynthesis. Furthermore, the lipidomic analysis showed that RCs were at higher risk of developing diabetes and sustaining hepatic impairment. The abnormality of immunomodulation in RCs might still exist. The study may offer new insights into the mechanism of organ dysfunction and help identify novel therapeutic targets in the RCs.

Anxiety, depression and post-traumatic stress disorder and related factors among Chinese population during the COVID-19 pandemic: A cross-sectional study

The sudden outbreak of coronavirus disease 2019 (COVID-19) has deep and wide negative mental impacts on the public, and studies on the impact of COVID-19 on social and mental well-being are necessary. This study aimed to evaluate mental distress, including anxiety, depression, and post-traumatic stress disorder (PTSD), and its related risk factors in Chinese adults in the early stages of the COVID-19 pandemic. This study used a large-scale cross-sectional design. A total of 2067 adult participants completed the online survey via REDcap from 1st to 15th of March 2020 during the COVID-19 outbreak in China. Anxiety, depression, PTSD, and related risk factors, including self-efficacy, coping style, and social support, were measured using valid and reliable instruments. The data were analyzed using multiple linear regression. We found that 201 (9.7%) participants reported moderate-to-severe anxiety, 669 (33.8%) reported depression, and 368 (17.8%) reported symptoms of PTSD. Self-efficacy, coping style, and social support significantly affected anxiety, depression, and PTSD symptoms. Participants' sociodemographic characteristics, COVID-19 pandemic-related factors, low self-efficacy, low social support, and negative coping were predictors of mental distress during the COVID-19 pandemic. Our study will help healthcare professionals carry out early predictions and identification of high-risk groups and provide appropriate interventions to target groups during public health emergencies that plague the world.

New-onset COVID-19-related diabetes: an early indicator of multi-organ injury and mortally of SARS-CoV-2 infection.

Objective: The pandemic of 2019 coronavirus (SARS-CoV-2) disease (COVID-19) has imposed a severe public health burden worldwide. Most patients with COVID-19 were mild. Severe patients progressed rapidly to critical condition including acute respiratory distress syndrome (ARDS), multi-organ failure and even death. This study aims to find early multi-organ injury indicators and blood glucose for predicting mortality of COVID-19. Methods: Fasting blood glucose (FBG) ≥7.0 mmol/L for two times during hospitalization and without a history of diabetes were defined as new-onset COVID-19-related diabetes (CRD). Indicators of injuries for multiple organs, including the lung, heart, kidney and liver, and glucose homeostasis were specifically analyzed for predicting death. Results: A total of 120 patients with a severity equal to or greater than Moderate were hospitalized. After excluding patients with history of diabetes, chronic heart, kidney, and liver disease, 69 patients were included in the final analysis. Of the 69 patients, 23 were Moderate, 20 were Severe, and 26 were Critical (including 16 deceased patients). Univariable analysis indicated that CRD, lactate dehydrogenase (LDH), hydroxybutyrate dehydrogenase (HBDH), creatine kinase (CK) and creatinine (Cr) were associated with death. Multivariable analysis indicated that CRD was an independent predictor for death (HR = 3.75, 95% CI 1.26-11.15). Abnormal glucose homeostasis or CRD occurred earlier than other indicators for predicting poor outcomes. Indicators of multiple organ injury were in parallel with the expression patterns of ACE2 (the SARS-CoV-2 receptor) in different organs including pancreatic islet. Conclusions: New-onset COVID-19-related diabetes is an early indicator of multi-organ injury and predictor for poor outcomes and death in COVID-19 patients. As it is easy to perform for clinical practices and self-monitoring, glucose testing will be helpful for predicting poor outcomes to facilitate appropriate intensive care.

Seroprevalence and dynamics of anti-SARS-CoV-2 antibodies: a longitudinal study based on patients with underlying diseases in Wuhan.

BACKGROUND: Assessing the humoral immunity of patients with underlying diseases after being infected with SARS-CoV-2 is essential for adopting effective prevention and control strategies. The purpose of this study is to analyze the seroprevalence of people with underlying diseases and the dynamic change features of anti-SARS-CoV-2 antibodies. METHODS: We selected 100 communities in Wuhan using the probability-proportional-to-size sampling method. From these 100 communities, we randomly selected households according to a list provided by the local government. Individuals who have lived in Wuhan for at least 14 days since December 2019 and were ≥ 40 years old were included. From April 9-13, 2020, community staff invited all selected individuals to the community healthcare center in batches by going door-to-door or telephone. All participants completed a standardized electronic questionnaire simultaneously. Finally, 5 ml of venous blood was collected from all participants. Blood samples were tested for the presence of pan-immunoglobulins, IgM, IgA, and IgG antibodies against SARS-CoV-2 nucleocapsid protein and neutralising antibodies were assessed. During the period June 11-13, 2020 and October 9-December 5, 2020, all family members of a positive family and matched negative families were followed up twice. RESULTS: The seroprevalence of anti-SARS-CoV-2 antibodies in people with underlying diseases was 6.30% (95% CI [5.09-7.52]), and that of people without underlying diseases was 6.12% (95% CI [5.33-6.91]). A total of 313 people were positive for total antibodies at baseline, of which 97 had underlying disease. At the first follow-up, a total of 212 people were positive for total antibodies, of which 66 had underlying disease. At the second follow-up, a total of 238 people were positive for total antibodies, of which 68 had underlying disease. A total of 219 participants had three consecutive serum samples with positive total antibodies at baseline. The IgG titers decreased significantly with or without underlying diseases (P < 0.05) within the 9 months at least, while the neutralizing antibody titer remained stable. The titer of asymptomatic patients was lower than that of symptomatic patients (baseline, P = 0.032, second follow-up, P = 0.018) in the underlying diseases group. CONCLUSION: Our research focused on the serological changes of people with and without underlying diseases in a state of single natural infection. Regardless of the underlying diseases, the IgG titer decreased significantly over time, while there was no significant difference in the decline rate of IgG between with and without underlying diseases. Moreover, the neutralizing antibody titer remained relatively stable within the 9 months at least.

Theory of planned behavior explains males' and females' intention to receive COVID-19 vaccines differently.

Uptake of COVID-19 vaccines is an important measure to curb the transmission of the coronavirus. Before the vaccines were available, numerous studies found that people had a moderate-to-high intention to receive the vaccines. Several studies have also used the theory of planned behavior (TPB) to predict people's COVID-19 vaccination intention with three elements (i.e. attitudes, subjective norms, and perceived behavioral control) . However, the vaccination rate falters after the vaccines became available, and there were few updated data documenting people's vaccination intention and how well TPB can explain their intention. In addition, studies investigating other outcomes found that the predictive utilities of TPB often varied across gender, but such gender differences received little consideration in the literature of COVID-19 vaccination intention. To help fill these gaps, we examined the associations between TPB elements and people's intention to receive COVID-19 vaccines and the moderation of gender in the context of vaccination campaign. Participants were 405 Chinese citizens. They reported on the three TPB elements and intention to receive vaccines in the coming months. Descriptive results showed that participants' vaccination intention was moderate. Results of path analysis showed that subjective norms and perceived behavioral control were positively related to vaccination intention for the whole sample. Furthermore, results of multigroup path analysis showed that attitudes were only related to males', while subjective norms were only related to females', intention. These findings enhance the utility of TPB in explaining people's COVID-19 vaccination intention and inform gender-specific strategies to boost males' and females' vaccination intention.

Potential drug discovery for COVID-19 treatment targeting Cathepsin L using a deep learning-based strategy.

Cathepsin L (CTSL), a cysteine protease that can cleave and activate the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein, could be a promising therapeutic target for coronavirus disease 2019 (COVID-19). However, there is still no clinically available CTSL inhibitor that can be used. Here, we applied Chemprop, a newly trained directed-message passing deep neural network approach, to identify small molecules and FDA-approved drugs that can block CTSL activity to expand the discovery of CTSL inhibitors for drug development and repurposing for COVID-19. We found 5 molecules (Mg-132, Z-FA-FMK, leupeptin hemisulfate, Mg-101 and calpeptin) that were able to significantly inhibit the activity of CTSL in the nanomolar range and inhibit the infection of both pseudotype and live SARS-CoV-2. Notably, we discovered that daptomycin, an FDA-approved antibiotic, has a prominent CTSL inhibitory effect and can inhibit SARS-CoV-2 pseudovirus infection. Further, molecular docking calculation showed stable and robust binding of these compounds with CTSL. In conclusion, this study suggested for the first time that Chemprop is ideally suited to predict additional inhibitors of enzymes and revealed the noteworthy strategy for screening novel molecules and drugs for the treatment of COVID-19 and other diseases with unmet needs.

Novel cleavage sites identified in SARS-CoV-2 spike protein reveal mechanism for cathepsin L-facilitated viral infection and treatment strategies.

The spike (S) protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an important target for vaccine and drug development. However, the rapid emergence of variant strains with mutated S proteins has rendered many treatments ineffective. Cleavage of the S protein by host proteases is essential for viral infection. Here, we discovered that the S protein contains two previously unidentified Cathepsin L (CTSL) cleavage sites (CS-1 and CS-2). Both sites are highly conserved among all known SARS-CoV-2 variants. Our structural studies revealed that CTSL cleavage promoted S to adopt receptor-binding domain (RBD) "up" activated conformations, facilitating receptor-binding and membrane fusion. We confirmed that CTSL cleavage is essential during infection of all emerged SARS-CoV-2 variants (including the recently emerged Omicron variant) by pseudovirus (PsV) infection experiment. Furthermore, we found CTSL-specific inhibitors not only blocked infection of PsV/live virus in cells but also reduced live virus infection of ex vivo lung tissues of both human donors and human ACE2-transgenic mice. Finally, we showed that two CTSL-specific inhibitors exhibited excellent In vivo effects to prevent live virus infection in human ACE2-transgenic mice. Our work demonstrated that inhibition of CTSL cleavage of SARS-CoV-2 S protein is a promising approach for the development of future mutation-resistant therapy.

Association of the Cholinergic Anti-Inflammatory Pathway Activity with Proinflammatory Factors and Prognosis of Patients with Acute Respiratory Distress Syndrome

Objective The cholinergic anti-inflammatory pathway (CAP) has been shown to modulate cytokine release by activating alpha-7 nicotinic acetylcholine receptors (α7nAChR) in monocytes/macrophages. However, their association with proinflammatory factors and prognosis in patients with acute respiratory distress syndrome (ARDS) has not been clarified. Here, we explored the correlation between CAP activity, proinflammatory factors, and the prognosis of ARDS patients. Methods The data of patients with ARDS (n = 65;underwent treatment) and healthy individuals (the control group;n = 65;underwent routine physical examination) at the Chongqing People's Hospital were investigated. Based on the survival status, ARDS patients were divided into a death ARDS group (n = 22) and a survival ARDS group (n = 43), and based on the diagnostic criteria of ARDS, the patients were also divided into a severe ARDS group (n = 30) and a mild-to-moderate ARDS group (n = 35). The levels of acetylcholine (ACh), acetylcholinesterase (AChE), and α7nAChR mRNA in peripheral blood monocytes were assessed. The levels of TNF-α and IL-6 in peripheral serum and peripheral monocytes were detected by ELISA and Western blot tests. The association between α7nAChR and inflammatory factors and prognosis was analyzed. The receiver-operating characteristic (ROC) curve was used to evaluate the reliability of CAP-related factors in predicting the survival status of ARDS patients. Results Compared with the control group, the levels of ACh, AChE, and α7nAChR mRNA of the ARDS group were significantly decreased. And, the ACh, AChE, and α7nAChR mRNA levels in the death/severe ARDS group were significantly lower than in the survival/mild-to-moderate ARDS group. However, the levels of TNF-α and IL-6 were significantly higher in the severe/death ARDS group. Furthermore, we observed that CAP-related factors were negatively correlated with the levels of IL-6 and TNF-α in peripheral serum in the ARDS group. The ROC curve showed that CAP-related factors were reliable markers for predicting the survival status of ARDS patients. Conclusion The related factors of the cholinergic anti-inflammatory pathway were significantly decreased in patients with ARDS, suggesting the ACh, AChE, and α7nAChR levels as potential indicators to evaluate the severity and prognosis status of ARDS patients.