Impact and mechanism of the COVID-19 on the spatio-temporal patterns of Zhangjiajie tourism network attention

Based on the spatial-temporal perspective of geography, this paper quantitatively measures the impact of COVID-19 on the spatial-temporal pattern of tourism network attention in Zhangjiajie, and finally summarizes the influencing factors and mechanisms. The results show as follows. (1) From the perspective of time, the online attention of tourism in Zhangjiajie shows a trend of "decline to rebound, and to stability", which reflects the temporal mobility of the effect of COVID-19 on the tourism. (2) From the spatial dimension, the scale-order of attention to the Zhangjiajie' s tourism network is relatively stable, and the effect of COVID-19 on the tourism shows a trend of "distance decay" on the whole. (3) The adjustment of tourists' perception of tourism risk, destination familiarity and location, tourists' risk tolerance and authority restriction are the influencing factors of tourism net-work attention. These factors interact with each other to drive the spatio-temporal change of tourism network attention.

Potent NTD-Targeting Neutralizing Antibodies against SARS-CoV-2 Selected from a Synthetic Immune System.

The majority of neutralizing antibodies (NAbs) against SARS-CoV-2 recognize the receptor-binding domain (RBD) of the spike (S) protein. As an escaping strategy, the RBD of the virus is highly variable, evolving mutations to thwart a natural immune response or vaccination. Targeting non-RBD regions of the S protein thus provides a viable alternative to generating potential, robust NAbs. Using a pre-pandemic combinatorial antibody library of 1011, through an alternate negative and positive screening strategy, 11 non-RBD-targeting antibodies are identified. Amongst one NAb that binds specifically to the N-terminal domain of the S protein, SA3, shows mutually non-exclusive binding of the angiotensin-converting enzyme 2 receptor with the S protein. SA3 appears to be insensitive to the conformational change and to interact with both the "open" and "closed" configurations of the trimeric S protein. SA3 shows compatible neutralization as S-E6, an RBD-targeting NAb, against the wild type and variant of concern (VOC) B.1.351 (Beta) of the SARS-CoV-2 pseudo virus. More importantly, the combination of SA3 with S-E6 is synergistic and recovers from the 10-fold loss in neutralization efficacy against the VOC B.1.351 pseudo virus.

The effect of transpyloric enteral nutrition on inflammatory response and prognosis for patients with Corona Virus Disease-19 in intensive care unit: A STROBE compliant study.

To investigate the effect of transpyloric enteral nutrition (TEN) on NLRP1, inflammatory response and prognosis for patients with Corona Virus Disease-19 (COVID-19) in intensive care unit (ICU). The present prospective observational study included 29 cases of COVID-19 patients in ICU who admitted to our hospital during February 2020 to March 2020. All the patients were divided into gastrogavage groups (n = 16) and TEN group (n = 13) according to route of enteral nutrition. Serum levels of C-reactive protein (CRP), interleukin-1 ß (IL-1ß), interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α) and NLRP1 (NLR family pyrin domain containing 1) was detected by enzyme linked immunosorbent assay (ELISA). Serum levels of lymphocyte, albumin and hemoglobin was detected using an automatic biochemical analyzer. Patients' demographic and clinical characteristics were collected and analyzed. Kaplan-Meier (K-M) curve was conducted for survival analysis and receiver operating characteristic curve was used for the analysis of diagnostic value of biomarkers. All the patients were followed-up for 3 months. This study found that the survival group had higher rate of TEN therapies than the deceased. COVID-19 patients in ICU on TEN had lower APACHE II scores, frequency of feeding suspension and mortality, however, with higher content of albumin was found at 5th day. The incidence of nutritional intolerance including abdominal distension and gastric retention in patients on TEN was notably lower than those on gastrogavage. The serum levels of NLRP1, CRP, IL-1ß, IL-6 and TNF-α decreased in a time-dependent manner, but patients on TEN had lower levels of NLRP1, CRP and IL-1ß than patients on gastrogavage. A positive correlation was found among NLRP1 and inflammatory factors, and COVID-19 patients with lower NLRP1 had longer survival time. Serum NLRP1 also exhibited diagnostic value for the death of COVID-19 patients. TEN decreased inflammatory response and improved the prognosis for COVID-19 patients in ICU.

Patient's behavior of selection physician in online health communities: Based on an Elaboration likelihood model.

Background: With the rapid development of "Internet + medicine" and the impact of the COVID-19 epidemic, online health communities have become an important way for patients to seek medical treatment. However, the mistrust between physicians and patients in online health communities has long existed and continues to impact the decision-making behavior of patients. The purpose of this article is to explore the influencing factors of patient decision-making in online health communities by identifying the relationship between physicians' online information and patients' selection behavior. Methods: In this study, we selected China's Good Doctor (www.haodf.com) as the source of data, scrapped 10,446 physician data from December 2020 to June 2021 to construct a logit model of online patients' selection behavior, and used regression analysis to test the hypotheses. Results: The number of types of services, number of scientific articles, and avatar in physicians' personal information all has a positive effect on patients' selection behavior, while the title and personal introduction hurt patients' selection behavior. Online word-of-mouth positively affected patients' selection behavior and disease risk had a moderating effect. Conclusion: Focusing on physician-presented information, this article organically combines the Elaboration likelihood model with trust source theory and online word-of-mouth from the perspective of the trusted party-physician, providing new ideas for the study of factors influencing patients' selection behavior in online health communities. The findings provide useful insights for patients, physicians, and community managers about the relationship between physician information and patients' selection behavior.

CapsCovNet: A Modified Capsule Network to Diagnose COVID-19 From Multimodal Medical Imaging.

Since the end of 2019, novel coronavirus disease (COVID-19) has brought about a plethora of unforeseen changes to the world as we know it. Despite our ceaseless fight against it, COVID-19 has claimed millions of lives, and the death toll exacerbated due to its extremely contagious and fast-spreading nature. To control the spread of this highly contagious disease, a rapid and accurate diagnosis can play a very crucial part. Motivated by this context, a parallelly concatenated convolutional block-based capsule network is proposed in this article as an efficient tool to diagnose the COVID-19 patients from multimodal medical images. Concatenation of deep convolutional blocks of different filter sizes allows us to integrate discriminative spatial features by simultaneously changing the receptive field and enhances the scalability of the model. Moreover, concatenation of capsule layers strengthens the model to learn more complex representation by presenting the information in a fine to coarser manner. The proposed model is evaluated on three benchmark datasets, in which two of them are chest radiograph datasets and the rest is an ultrasound imaging dataset. The architecture that we have proposed through extensive analysis and reasoning achieved outstanding performance in COVID-19 detection task, which signifies the potentiality of the proposed model.

Persistent Endothelial Dysfunction in Coronavirus Disease-2019 Survivors Late After Recovery.

BACKGROUND: Coronavirus disease 2019 (COVID-19) can result in an endothelial dysfunction in acute phase. However, information on the late vascular consequences of COVID-19 is limited. METHODS: Brachial artery flow-mediated dilation (FMD) examination were performed, and inflammatory biomarkers were assessed in 86 survivors of COVID-19 for 327 days (IQR 318-337 days) after recovery. Comparisons were made with 28 age-matched and sex-matched healthy controls and 30 risk factor-matched patients. RESULTS: Brachial artery FMD was significantly lower in the survivors of COVID-19 than in the healthy controls and risk factor-matched controls [median (IQR) 7.7 (5.1-10.7)% for healthy controls, 6.9 (5.5-9.4)% for risk factor-matched controls, and 3.5(2.2-4.6)% for COVID-19, respectively, p < 0.001]. The FMD was lower in 25 patients with elevated tumor necrosis factor (TNF)-α [2.7(1.2-3.9)] than in 61 patients without elevated TNF-α [3.8(2.6-5.3), p = 0.012]. Furthermore, FMD was inversely correlated with serum concentration of TNF-α (r = -0.237, p = 0.007). CONCLUSION: Survivors of COVID-19 have a reduced brachial artery FMD, which is inversely correlated with increased serum concentration of TNF-α. Prospective studies on the association of endothelial dysfunction with long-term cardiovascular outcomes, especially the early onset of atherosclerosis, are warranted in survivors of COVID-19.

Exploring the Relationship between the Spatial Distribution of Different Age Populations and Points of Interest (POI) in China

Population spatialization data is crucial to conducting scientific studies of coupled human–environment systems. Although significant progress has been made in population spatialization, the spatialization of different age populations is still weak. POI data with rich information have great potential to simulate the spatial distribution of different age populations, but the relationship between spatial distributions of POI and different age populations is still unclear, and whether it can be used as an auxiliary variable for the different age population spatialization remains to be explored. Therefore, this study collected and sorted out the number of different age populations and POIs in 2846 county-level administrative units of the Chinese mainland in 2010, divided the research data by region and city size, and explored the relationship between the different age populations and POIs. We found that there is a complex relationship between POI and different age populations. Firstly, there are positive, moderate-to-strong linear correlations between POI and population indicators. Secondly, POI has a different explanatory power for different age populations, and it has a higher explanatory power for the young and middle-aged population than the child and old population. Thirdly, the explanatory power of POI to different age populations is positively correlated with the urban economic development level. Finally, a small number of a certain kinds of POIs can be used to effectively simulate the spatial distributions of different age populations, which can improve the efficiency of obtaining spatialization data of different age populations and greatly save on costs. The study can provide data support for the precise spatialization of different age populations and inspire the spatialization of the other population attributes by POI in the future.

COVID-19 induces new-onset insulin resistance and lipid metabolic dysregulation via regulation of secreted metabolic factors.

Abnormal glucose and lipid metabolism in COVID-19 patients were recently reported with unclear mechanism. In this study, we retrospectively investigated a cohort of COVID-19 patients without pre-existing metabolic-related diseases, and found new-onset insulin resistance, hyperglycemia, and decreased HDL-C in these patients. Mechanistically, SARS-CoV-2 infection increased the expression of RE1-silencing transcription factor (REST), which modulated the expression of secreted metabolic factors including myeloperoxidase, apelin, and myostatin at the transcriptional level, resulting in the perturbation of glucose and lipid metabolism. Furthermore, several lipids, including (±)5-HETE, (±)12-HETE, propionic acid, and isobutyric acid were identified as the potential biomarkers of COVID-19-induced metabolic dysregulation, especially in insulin resistance. Taken together, our study revealed insulin resistance as the direct cause of hyperglycemia upon COVID-19, and further illustrated the underlying mechanisms, providing potential therapeutic targets for COVID-19-induced metabolic complications.

Nonlinear influence of winter meteorology and precursor on PM2.5 based on mathematical and numerical models: A COVID-19 and Winter Olympics case study

Air pollution during the COVID-19 epidemic in Beijing and its surrounding regions has received substantial attention. We collected observational data, including air pollutant concentrations and meteorological parameters, during January and February from 2018 to 2021. A statistical and a numerical model were applied to identify the formation of air pollution and the impact of emission reduction on air quality. Relative humidity, wind speed, SO2, NO2, and O3 had nonlinear effects on the PM2.5 concentration in Beijing, among which the effects of relative humidity, NO2, and O3 were prominent. During the 2020 epidemic period, high pollution concentrations were closely related to adverse meteorological conditions, with different parameters having different effects on the three pollution processes. In general, the unexpected reduction of anthropogenic emissions reduced the PM2.5 concentration, but led to an increase in the O3 concentration. Multi-scenario simulation results showed that anthropogenic emission reduction could reduce the average PM2.5 concentration after the Chinese Spring Festival, but improvement during days with heavy pollution was limited. Considering that O3 enhances the PM2.5 levels, to achieve the collaborative improvement of PM2.5 and O3 concentrations, further research should explore the collaborative emission reduction scheme with VOCs and NOx to achieve the collaborative improvement of PM2.5 and O3 concentrations. The conclusions of this study provide a basis for designing a plan that guarantees improved air quality for the 2022 Winter Olympics and other international major events in Beijing.

Efficient Identification of Anti-SARS-CoV-2 Compounds Using Chemical Structure- and Biological Activity-Based Modeling.

Identification of anti-SARS-CoV-2 compounds through traditional high-throughput screening (HTS) assays is limited by high costs and low hit rates. To address these challenges, we developed machine learning models to identify compounds acting via inhibition of the entry of SARS-CoV-2 into human host cells or the SARS-CoV-2 3-chymotrypsin-like (3CL) protease. The optimal classification models achieved good performance with area under the receiver operating characteristic curve (AUC-ROC) values of >0.78. Experimental validation showed that the best performing models increased the assay hit rate by 2.1-fold for viral entry inhibitors and 10.4-fold for 3CL protease inhibitors compared to those of the original drug repurposing screens. Twenty-two compounds showed potent (<5 µM) antiviral activities in a SARS-CoV-2 live virus assay. In conclusion, machine learning models can be developed and used as a complementary approach to HTS to expand compound screening capacities and improve the speed and efficiency of anti-SARS-CoV-2 drug discovery.

Imatinib and methazolamide ameliorate COVID-19-induced metabolic complications via elevating ACE2 enzymatic activity and inhibiting viral entry.

Coronavirus disease 2019 (COVID-19) represents a systemic disease that may cause severe metabolic complications in multiple tissues including liver, kidney, and cardiovascular system. However, the underlying mechanisms and optimal treatment remain elusive. Our study shows that impairment of ACE2 pathway is a key factor linking virus infection to its secondary metabolic sequelae. By using structure-based high-throughput virtual screening and connectivity map database, followed with experimental validations, we identify imatinib, methazolamide, and harpagoside as direct enzymatic activators of ACE2. Imatinib and methazolamide remarkably improve metabolic perturbations in vivo in an ACE2-dependent manner under the insulin-resistant state and SARS-CoV-2-infected state. Moreover, viral entry is directly inhibited by these three compounds due to allosteric inhibition of ACE2 binding to spike protein on SARS-CoV-2. Taken together, our study shows that enzymatic activation of ACE2 via imatinib, methazolamide, or harpagoside may be a conceptually new strategy to treat metabolic sequelae of COVID-19.

Clinical predictors of acute cardiac injury and normalization of troponin after hospital discharge from COVID-19.

BACKGROUND: Although acute cardiac injury (ACI) is a known COVID-19 complication, whether ACI acquired during COVID-19 recovers is unknown. This study investigated the incidence of persistent ACI and identified clinical predictors of ACI recovery in hospitalized patients with COVID-19 2.5 months post-discharge. METHODS: This retrospective study consisted of 10,696 hospitalized COVID-19 patients from March 11, 2020 to June 3, 2021. Demographics, comorbidities, and laboratory tests were collected at ACI onset, hospital discharge, and 2.5 months post-discharge. ACI was defined as serum troponin-T (TNT) level >99th-percentile upper reference limit (0.014ng/mL) during hospitalization, and recovery was defined as TNT below this threshold 2.5 months post-discharge. Four models were used to predict ACI recovery status. RESULTS: There were 4,248 (39.7%) COVID-19 patients with ACI, with most (93%) developed ACI on or within a day after admission. In-hospital mortality odds ratio of ACI patients was 4.45 [95%CI: 3.92, 5.05, p<0.001] compared to non-ACI patients. Of the 2,880 ACI survivors, 1,114 (38.7%) returned to our hospitals 2.5 months on average post-discharge, of which only 302 (44.9%) out of 673 patients recovered from ACI. There were no significant differences in demographics, race, ethnicity, major commodities, and length of hospital stay between groups. Prediction of ACI recovery post-discharge using the top predictors (troponin, creatinine, lymphocyte, sodium, lactate dehydrogenase, lymphocytes and hematocrit) at discharge yielded 63.73%-75.73% accuracy. INTERPRETATION: Persistent cardiac injury is common among COVID-19 survivors. Readily available patient data accurately predict ACI recovery post-discharge. Early identification of at-risk patients could help prevent long-term cardiovascular complications. FUNDING: None.

Interrelationship between 2019-nCov receptor DPP4 and diabetes mellitus targets based on protein interaction network.

Patients with diabetes are more likely to be infected with Coronavirus disease 2019 (COVID-19), and the risk of death is significantly higher than ordinary patients. Dipeptidyl peptidase-4 (DPP4) is one of the functional receptor of human coronavirus. Exploring the relationship between diabetes mellitus targets and DPP4 is particularly important for the management of patients with diabetes and COVID-19. We intend to study the protein interaction through the protein interaction network in order to find a new clue for the management of patients with diabetes with COVID-19. Diabetes mellitus targets were obtained from GeneCards database. Targets with a relevance score exceeding 20 were included, and DPP4 protein was added manually. The initial protein interaction network was obtained through String. The targets directly related to DPP4 were selected as the final analysis targets. Importing them into String again to obtain the protein interaction network. Module identification, gene ontology (GO) analysis and Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis were carried out respectively. The impact of DPP4 on the whole network was analyzed by scoring the module where it located. 43 DPP4-related proteins were finally selected from the diabetes mellitus targets and three functional modules were found by the cluster analysis. Module 1 was involved in insulin secretion and glucagon signaling pathway, module 2 and module 3 were involved in signaling receptor binding. The scoring results showed that LEP and apoB in module 1 were the highest, and the scores of INS, IL6 and ALB of cross module associated proteins of module 1 were the highest. DPP4 is widely associated with key proteins in diabetes mellitus. COVID-19 may affect DPP4 in patients with diabetes mellitus, leading to high mortality of diabetes mellitus combined with COVID-19. DPP4 inhibitors and IL-6 antagonists can be considered to reduce the effect of COVID-19 infection on patients with diabetes.

Generalized SEIR Epidemic Model for COVID-19 in a Multipatch Environment

In this paper, an n -patch SEIR epidemic model for the coronavirus disease 2019 (COVID-19) is presented. It is shown that there is unique disease-free equilibrium for this model. Then, the dynamic behavior is studied by the basic reproduction number. The transmission of COVID-19 is fitted based on actual data. The influence of quarantined rate and population migration rate on the spread of COVID-19 is also discussed by simulation. [ FROM AUTHOR] Copyright of Discrete Dynamics in Nature & Society is the property of Hindawi Limited and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

A high throughput screening assay for inhibitors of SARS-CoV-2 pseudotyped particle entry.

Effective small molecule therapies to combat the SARS-CoV-2 infection are still lacking as the COVID-19 pandemic continues globally. High throughput screening assays are needed for lead discovery and optimization of small molecule SARS-CoV-2 inhibitors. In this work, we have applied viral pseudotyping to establish a cell-based SARS-CoV-2 entry assay. Here, the pseudotyped particles (PP) contain SARS-CoV-2 spike in a membrane enveloping both the murine leukemia virus (MLV) gag-pol polyprotein and luciferase reporter RNA. Upon addition of PP to HEK293-ACE2 cells, the SARS-CoV-2 spike protein binds to the ACE2 receptor on the cell surface, resulting in priming by host proteases to trigger endocytosis of these particles, and membrane fusion between the particle envelope and the cell membrane. The internalized luciferase reporter gene is then expressed in cells, resulting in a luminescent readout as a surrogate for spike-mediated entry into cells. This SARS-CoV-2 PP entry assay can be executed in a biosafety level 2 containment lab for high throughput screening. From a collection of 5,158 approved drugs and drug candidates, our screening efforts identified 7 active compounds that inhibited the SARS-CoV-2-S PP entry. Of these seven, six compounds were active against live replicating SARS-CoV-2 virus in a cytopathic effect assay. Our results demonstrated the utility of this assay in the discovery and development of SARS-CoV-2 entry inhibitors as well as the mechanistic study of anti-SARS-CoV-2 compounds. Additionally, particles pseudotyped with spike proteins from SARS-CoV-2 B.1.1.7 and B.1.351 variants were prepared and used to evaluate the therapeutic effects of viral entry inhibitors.

Nanomaterials as Promising Theranostic Tools in Nanomedicine and Their Applications in Clinical Disease Diagnosis and Treatment.

In recent decades, with the rapid development of nanotechnology, nanomaterials have been widely used in the medical field, showing great potential due to their unique physical and chemical properties including minimal size and functionalized surface characteristics. Nanomaterials such as metal nanoparticles and polymeric nanoparticles have been extensively studied in the diagnosis and treatment of diseases that seriously threaten human life and health, and are regarded to significantly improve the disadvantages of traditional diagnosis and treatment platforms, such as poor effectiveness, low sensitivity, weak security and low economy. In this review, we report and discuss the development and application of nanomaterials in the diagnosis and treatment of diseases based mainly on published research in the last five years. We first briefly introduce the improvement of several nanomaterials in imaging diagnosis and genomic sequencing. We then focus on the application of nanomaterials in the treatment of diseases, and select three diseases that people are most concerned about and that do the most harm: tumor, COVID-19 and cardiovascular diseases. First, we introduce the characteristics of nanoparticles according to the excellent effect of nanoparticles as delivery carriers of anti-tumor drugs. We then review the application of various nanoparticles in tumor therapy according to the classification of nanoparticles, and emphasize the importance of functionalization of nanomaterials. Second, COVID-19 has been the hottest issue in the health field in the past two years, and nanomaterials have also appeared in the relevant treatment. We enumerate the application of nanomaterials in various stages of viral pathogenesis according to the molecular mechanism of the complete pathway of viral infection, pathogenesis and transmission, and predict the application prospect of nanomaterials in the treatment of COVID-19. Third, aiming at the most important causes of human death, we focus on atherosclerosis, aneurysms and myocardial infarction, three of the most common and most harmful cardiovascular diseases, and prove that nanomaterials could be involved in a variety of therapeutic approaches and significantly improve the therapeutic effect in cardiovascular diseases. Therefore, we believe nanotechnology will become more widely involved in the diagnosis and treatment of diseases in the future, potentially helping to overcome bottlenecks under existing medical methods.

Hybrid In Silico Approach Reveals Novel Inhibitors of Multiple SARS-CoV-2 Variants.

The National Center for Advancing Translational Sciences (NCATS) has been actively generating SARS-CoV-2 high-throughput screening data and disseminates it through the OpenData Portal (https://opendata.ncats.nih.gov/covid19/). Here, we provide a hybrid approach that utilizes NCATS screening data from the SARS-CoV-2 cytopathic effect reduction assay to build predictive models, using both machine learning and pharmacophore-based modeling. Optimized models were used to perform two iterative rounds of virtual screening to predict small molecules active against SARS-CoV-2. Experimental testing with live virus provided 100 (∼16% of predicted hits) active compounds (efficacy > 30%, IC50 ≤ 15 µM). Systematic clustering analysis of active compounds revealed three promising chemotypes which have not been previously identified as inhibitors of SARS-CoV-2 infection. Further investigation resulted in the identification of allosteric binders to host receptor angiotensin-converting enzyme 2; these compounds were then shown to inhibit the entry of pseudoparticles bearing spike protein of wild-type SARS-CoV-2, as well as South African B.1.351 and UK B.1.1.7 variants.

Viral Proteases as Targets for Coronavirus Disease 2019 Drug Development.

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), continues to be a global threat since its emergence. Although several COVID-19 vaccines have become available, the prospective timeframe for achieving effective levels of vaccination across global populations remains uncertain. Moreover, the emergence of SARS-CoV-2 variants presents continuing potential challenges for future vaccination planning. Therefore, development of effective antiviral therapies continues to be an urgent unmet need for COVID-19. Successful antiviral regimens for the treatment of human immunodeficiency virus and hepatitis C virus infections have established viral proteases as validated targets for antiviral drug development. In this context, we review protease targets in drug development, currently available antiviral protease inhibitors, and therapeutic development efforts on SARS-CoV-2 main protease and papain-like protease. SIGNIFICANCE STATEMENT: Coronavirus disease 2019 (COVID-19) continues to be a global threat since its emergence. The development of effective antiviral therapeutics for COVID-19 remains an urgent and long-term need. Because viral proteases are validated drug targets, specific severe acute respiratory syndrome coronavirus 2 protease inhibitors are critical therapeutics to be developed for treatment of COVID-19.

Efficacy of Thymosin Alpha 1 in the Treatment of COVID-19: A Multicenter Cohort Study.

Background: Thymosin alpha 1 (Tα1) is widely used to treat patients with COVID-19 in China; however, its efficacy remains unclear. This study aimed to explore the efficacy of Tα1 as a COVID-19 therapy. Methods: We performed a multicenter cohort study in five tertiary hospitals in the Hubei province of China between December 2019 and March 2020. The patient non-recovery rate was used as the primary outcome. Results: All crude outcomes, including non-recovery rate (65/306 vs. 290/1,976, p = 0.003), in-hospital mortality rate (62/306 vs. 271/1,976, p = 0.003), intubation rate (31/306 vs. 106/1,976, p = 0.001), acute respiratory distress syndrome (ARDS) incidence (104/306 vs. 499/1,976, p = 0.001), acute kidney injury (AKI) incidence (26/306 vs. 66/1,976, p < 0.001), and length of intensive care unit (ICU) stay (14.9 ± 12.7 vs. 8.7 ± 8.2 days, p < 0.001), were significantly higher in the Tα1 treatment group. After adjusting for confounding factors, Tα1 use was found to be significantly associated with a higher non-recovery rate than non-Tα1 use (OR 1.5, 95% CI 1.1-2.1, p = 0.028). An increased risk of non-recovery rate associated with Tα1 use was observed in the patient subgroups with maximum sequential organ failure assessment (SOFA) scores ≥2 (OR 2.0, 95%CI 1.4-2.9, p = 0.024), a record of ICU admission (OR 5.4, 95%CI 2.1-14.0, p < 0.001), and lower PaO2/FiO2 values (OR 1.9, 95%CI 1.1-3.4, p = 0.046). Furthermore, later initiation of Tα1 use was associated with a higher non-recovery rate. Conclusion: Tα1 use in COVID-19 patients was associated with an increased non-recovery rate, especially in those with greater disease severity.