Effect of amubarvimab-romlusevimab for treatment of severe COVID-19 in intensive care units: a retrospective cohort study (preprint)

Amubarvimab-romlusevimab is used antiviral regimens currently recommended in China for the treatment of adult patients with mild or moderate SARS-CoV-2 infections who are at a high risk factor for progression to severe COVID-19, but its exact efficacy in patients with severe COVID-19 is not yet known. This is a single-center retrospective cohort study. A total of 121 patients in intensive care units(ICU) diagnosed with severe COVID-19 were evaluated.The amubarvimab-romlusevimab therapy can reduce the 14-day mortality（23.40% vs 41.89%, p=0.037）, 28-day mortality（29.79 % vs 51.35%，p=0.02）, and ICU mortality（29.79% vs 55.41%，p=0.006) of severe COVID-19. To reduce bias and make the two groups balanced and comparable, a 1:1 PSM was performed. In the matched population（n=47), there were no statistically significant differences between the mAbs (monoclonal antibody)group and the Non-antiviral group in 14-day, 28-day, and thromboembolic events in COVID-19 patients. The 40-day survival analysis shows that mAbs therapy can improve patient prognosis (HR=0.45, 95%CI=0.26-0.76, p=0.008). However, no significant intergroup difference in the 40-day cumulative viral conversion rate. In a univariate Cox regression analysis, The Amubarvimab - romlusevimab therapy( HR:0.464; CI:[0.252-0.853];p:0.013),CRP, PCT, PLT, Lactate, PT, PT-INR, and pt% level at admission were risk factors for clinical prognosis. After including the above covariates, Multifactorial COX regression shows that the Amubarvimab - romlusevimab therapy( HR:0.464; CI:[0.252-0.853];p:0.013), CRP, Lactate and PT-INR at admission are independent factors for mortality of severe COVID-19. Based on the current data, we conclude that amubarvimab-romlusevimab therapy is beneficial for patients with severe COVID-19.

Integration of human organoids single-cell transcriptomic profiles and human genetics repurposes critical cell type-specific drug targets for severe COVID-19 (preprint)

Human organoids recapitulate the cell type diversity and function of their primary organs holding tremendous potentials for basic and translational research. Advances in single-cell RNA sequencing (scRNA-seq) technology and genome-wide association study (GWAS) have accelerated the biological and therapeutic interpretation of trait-relevant cell types or states. Here, we constructed a computational framework to integrate atlas-level organoid scRNA-seq data, GWAS summary statistics, expression quantitative trait loci, and gene-drug interaction data for distinguishing critical cell populations and drug targets relevant to COVID-19 severity. We found that 39 cell types across eight kinds of organoids were significantly associated with COVID-19 outcomes. Notably, subset of lung mesenchymal stem cells (MSCs) increased proximity with fibroblasts predisposed to repair COVID-19-damaged lung tissue. Brain endothelial cell subset exhibited significant associations with severe COVID-19, and this cell subset showed a notable increase in cell-to-cell interactions with other brain cell types, including microglia. We repurposed 33 druggable genes, including IFNAR2, TYK2, and VIPR2, and their interacting drugs for COVID-19 in a cell-type-specific manner. Overall, our results showcase that host genetic determinants have cellular specific contribution to COVID-19 severity, and identification of cell type-specific drug targets may facilitate to develop effective therapeutics for treating severe COVID-19 and its complications.

Erratum to "Impact of delta variant and vaccination on SARS-CoV-2 secondary attack rate among household close contacts" [The Lancet Regional Health - Western Pacific 17 (2021) 100299].

[This corrects the article DOI: 10.1016/j.lanwpc.2021.100299.].

Severity of SARS-CoV-2 Omicron XBB subvariants in Singapore (preprint)

Several XBB subvariants such as XBB.1.5, XBB.1.9, XBB.1.16 and XBB.2.3 co-circulate in Singapore. Despite the different viral properties of XBB.1.16 as compared to other XBB subvariants, comparison on their severity is limited. In this study, we investigate the outcomes of hospitalisation and severe COVID-19 infection in individuals infected with different XBB subvariants, adjusted for potential confounders such as age and vaccination history. Overall, our preliminary analysis showed no difference in the severity of different XBB variants.

Impact of the COVID-19 pandemic on cardiac implantable electronic device implantation in China: Insights from 2 years of changing pandemic conditions.

Background: A substantial reduction in the number of cardiac implantable electronic device (CIED) implantation was reported in the early stages of the COVID-19 pandemic. None of the studies have yet explored changes in CIED implantation during the following pandemic. Objective: To explore changes in CIED implantation during the COVID-19 pandemic from 2020 to 2021. Methods: From 2019 to 2021, 177,263 patients undergone CIED implantation from 1,227 hospitals in China were included in the analysis. Generalized linear models measured the differences in CIED implantation in different periods. The relationship between changes in CIED implantation and COVID-19 cases was assessed by simple linear regression models. Results: Compared with the pre-COVID-19 period, the monthly CIED implantation decreased by 17.67% (95% CI: 16.62-18.72%, p < 0.001) in 2020. In 2021, the monthly number of CIED implantation increased by 15.60% (95% CI: 14.34-16.85%, p < 0.001) compared with 2020. For every 10-fold increase in the number of COVID-19 cases, the monthly number of pacemaker implantation decreased by 429 in 2021, while it decreased by 676 in 2020. The proportion of CIED implantation in secondary medical centers increased from 52.84% in 2019 to 56.77% in 2021 (p < 0.001). For every 10-fold increase in regional accumulated COVID-19 cases, the proportion of CIED implantation in secondary centers increased by 6.43% (95% CI: 0.47-12.39%, p = 0.036). Conclusion: The impact of the COVID-19 pandemic on the number of CIED implantation is diminishing in China. Improving the ability of secondary medical centers to undertake more operations may be a critical way to relieve the strain on healthcare resources during the epidemic.

Website analytics for government user behavior during COVID-19 pandemic

Purpose>The present study aims to clarify the following two research objectives: (1) the user behavior of government websites during the coronavirus disease (COVID-19) period and (2) how the government improved government's website design during the COVID-19 period.Design/methodology/approach>The authors used website analytics to examine usage patterns and behaviors of the government website via personal computer (PC) and mobile devices during the COVID-19 pandemic. In-depth interviews were conducted to understand the user experience of government website users and to gather users' opinions about how government websites should be redesigned.Findings>With the rising of the COIVID-19 pandemic, most studies expect that the use of government websites through a mobile device will grow astonishingly. The authors uncovered that the COVID-19 pandemic did not increase the use of government websites. Instead, severe declines in website usage were observed for all device users with the declines being more pronounced in mobile device users than in PC users. This is an admonitory caveat that reveals public health and pandemic prevention information announced on government websites cannot be effectively transmitted to the general public through official online platforms.Originality/value>The study highlights the gap in information behavior and usage patterns between PC and mobile device users when visiting government websites. Although mobile devices brought many new visitors, mobile devices are ineffective in retaining visitors and continuous long-term use. The results of localize experience is helpful in the improvement of government website evaluation worldwide.

In silico prediction of immune-escaping hot spots for future COVID-19 vaccine design (preprint)

The COVID-19 pandemic has had a widespread impact on a global scale, and the evolution of considerable dominants has already taken place. Some variants contained certain key mutations located on the receptor binding domain (RBD) of spike protein, such as E484K and N501Y. It is increasingly worrying that these variants could impair the efficacy of current vaccines or therapies. Therefore, how to design future vaccines to prevent the different variants remains urgent. In this work, we proposed an in silico approach, in which we combined binding free energy measured by computational mutagenesis of spike-antibody complexes and mutation frequency calculated from viral genome sequencing data, to estimate an immune-escaping score (IES) and predict immune-escaping hot spots. We identified 23 immune-escaping mutations on the RBD, nine of which occurred in omicron variants (R346K, K417N, N440K, L452Q, L452R, S477N, T478K, F490S, and N501Y), despite our dataset being curated before the omicron first appeared. The highest immune-escaping score (IES=1) was found for E484K, which agrees with recent studies stating that the mutation significantly reduced the efficacy of neutralization antibodies. Furthermore, our predicted binding free energy and IES show a high correlation with high-throughput deep mutational scanning (Pearson’s r = 0.70) and experimentally measured neutralization titers data (mean Pearson’s r = -0.80). In summary, our work provides valuable insights and will help design future COVID-19 vaccines.

An artificial intelligence system to predict the optimal timing for mechanical ventilation weaning for intensive care unit patients: A two-stage prediction approach

Background For the intensivists, accurate assessment of the ideal timing for successful weaning from the mechanical ventilation (MV) in the intensive care unit (ICU) is very challenging. Purpose Using artificial intelligence (AI) approach to build two-stage predictive models, namely, the try-weaning stage and weaning MV stage to determine the optimal timing of weaning from MV for ICU intubated patients, and implement into practice for assisting clinical decision making. Methods AI and machine learning (ML) technologies were used to establish the predictive models in the stages. Each stage comprised 11 prediction time points with 11 prediction models. Twenty-five features were used for the first-stage models while 20 features were used for the second-stage models. The optimal models for each time point were selected for further practical implementation in a digital dashboard style. Seven machine learning algorithms including Logistic Regression (LR), Random Forest (RF), Support Vector Machines (SVM), K Nearest Neighbor (KNN), lightGBM, XGBoost, and Multilayer Perception (MLP) were used. The electronic medical records of the intubated ICU patients of Chi Mei Medical Center (CMMC) from 2016 to 2019 were included for modeling. Models with the highest area under the receiver operating characteristic curve (AUC) were regarded as optimal models and used to develop the prediction system accordingly. Results A total of 5,873 cases were included in machine learning modeling for Stage 1 with the AUCs of optimal models ranging from 0.843 to 0.953. Further, 4,172 cases were included for Stage 2 with the AUCs of optimal models ranging from 0.889 to 0.944. A prediction system (dashboard) with the optimal models of the two stages was developed and deployed in the ICU setting. Respiratory care members expressed high recognition of the AI dashboard assisting ventilator weaning decisions. Also, the impact analysis of with- and without-AI assistance revealed that our AI models could shorten the patients’ intubation time by 21 hours, besides gaining the benefit of substantial consistency between these two decision-making strategies. Conclusion We noticed that the two-stage AI prediction models could effectively and precisely predict the optimal timing to wean intubated patients in the ICU from ventilator use. This could reduce patient discomfort, improve medical quality, and lower medical costs. This AI-assisted prediction system is beneficial for clinicians to cope with a high demand for ventilators during the COVID-19 pandemic.

Impact of the COVID-19 pandemic on cardiac implantable electronic device implantation in China: Insights from 2 years of changing pandemic conditions

Background A substantial reduction in the number of cardiac implantable electronic device (CIED) implantation was reported in the early stages of the COVID-19 pandemic. None of the studies have yet explored changes in CIED implantation during the following pandemic. Objective To explore changes in CIED implantation during the COVID-19 pandemic from 2020 to 2021. Methods From 2019 to 2021, 177,263 patients undergone CIED implantation from 1,227 hospitals in China were included in the analysis. Generalized linear models measured the differences in CIED implantation in different periods. The relationship between changes in CIED implantation and COVID-19 cases was assessed by simple linear regression models. Results Compared with the pre-COVID-19 period, the monthly CIED implantation decreased by 17.67% (95% CI: 16.62–18.72%, p < 0.001) in 2020. In 2021, the monthly number of CIED implantation increased by 15.60% (95% CI: 14.34–16.85%, p < 0.001) compared with 2020. For every 10-fold increase in the number of COVID-19 cases, the monthly number of pacemaker implantation decreased by 429 in 2021, while it decreased by 676 in 2020. The proportion of CIED implantation in secondary medical centers increased from 52.84% in 2019 to 56.77% in 2021 (p < 0.001). For every 10-fold increase in regional accumulated COVID-19 cases, the proportion of CIED implantation in secondary centers increased by 6.43% (95% CI: 0.47–12.39%, p = 0.036). Conclusion The impact of the COVID-19 pandemic on the number of CIED implantation is diminishing in China. Improving the ability of secondary medical centers to undertake more operations may be a critical way to relieve the strain on healthcare resources during the epidemic.

Clinical outcomes of ICU patients with COVID-19-associated pulmonary aspergillosis: a systematic review and meta-analysis

OBJECTIVE: To systematically describe the outcomes of patients with COVID-19-associated pulmonary aspergillosis (CAPA). METHODS: All of the researches covering the clinical outcomes of CAPA were retrieved from databases such as ScienceDirect, PubMed, CNKI and MEDLINE (OVID) from Dec 31, 2019 to Dec 1, 2021. The literatures were screened out based on inclusion and exclusion criteria by 2 writers, the data were extracted, the quality of the literatures was evaluated, and meta-analysis was performed. RESULTS: Totally 14 cohort studies were included in this study, with 2 056 severe COVID-19 patients involved, including 338 CAPA patients and 1 718 non-CAPA patients. The incidence rate of CAPA was 16.4% among the ICU patients. As compared with the non-CAPA patients, the mortality rate of the CAPA patients was increased by 21% [risk difference (RD)]=0.21, 95% CI:0.15-0.27, (I-2=0%). No heterogeneity or publication bias was detected (t=1.98, P=0.069). Among the patients with underlying diseases, the patients with chronic obstructive pulmonary disease (COPD) were 2.37 times the risk of CAPA as high as the patients of the non-CAPA group (95% CI: 1.15-4.88, P=0.020). The creatinine level of the CAPA patients was higher than that of the non-CAPA patients (33.32 micro mol/L, 95% CI: 6.81-59.83, P=0.014). As compared with the non-CAPA patients, the patients who received renal replacement therapy were 2.33 times the risk of CAPA (95% CI: 1.43-3.80, P=0.001). CONCLUSION: 16.4% of the severe COVID-19 patients have CAPA, the mortality rate is high. COPD, serum creatinine and renal replacement therapy may remarkably increase the risk of CAPA, and it is suggested that a prospective screening of CAPA should be carried out for the severe COVID-19 patients.

Effects of Health Belief About COVID-19 on Knowledge Sharing: The Mediating Role of Self-Efficacy

While existing studies have explored factors that affect knowledge sharing among employees from different perspectives, there are still research gaps regarding whether health belief affects knowledge sharing among employees, specifically against the backdrop of the COVID-19 pandemic, and how such effects work. Thus, the purpose of this study is to determine the effect of bank employees’ health beliefs about COVID-19 on knowledge sharing mediated by their self-efficacy. From the perspective of social cognitive theory and the health belief model, this study investigates whether employees’ perception of susceptibility and severity of COVID-19 affects formal as well as informal knowledge sharing through knowledge sharing self-efficacy. A sample of 407 bank employees (200 women and 207 men) in China was used for the study. The formulated hypotheses were tested using structural equation modeling and bootstrapping. The results showed that employees’ perceived susceptibility to COVID-19 significantly undermines formal and informal knowledge sharing self-efficacy. However, there was no significant difference in the extent of its indirect effects on formal and informal knowledge sharing. Further, employees’ perceived severity of COVID-19 had no effect on knowledge sharing self-efficacy and on formal and informal knowledge sharing, which could have resulted from the COVID-19 outbreak in China.

Test-time Adaptation with Calibration of Medical Image Classification Nets for Label Distribution Shift (preprint)

Class distribution plays an important role in learning deep classifiers. When the proportion of each class in the test set differs from the training set, the performance of classification nets usually degrades. Such a label distribution shift problem is common in medical diagnosis since the prevalence of disease vary over location and time. In this paper, we propose the first method to tackle label shift for medical image classification, which effectively adapt the model learned from a single training label distribution to arbitrary unknown test label distribution. Our approach innovates distribution calibration to learn multiple representative classifiers, which are capable of handling different one-dominating-class distributions. When given a test image, the diverse classifiers are dynamically aggregated via the consistency-driven test-time adaptation, to deal with the unknown test label distribution. We validate our method on two important medical image classification tasks including liver fibrosis staging and COVID-19 severity prediction. Our experiments clearly show the decreased model performance under label shift. With our method, model performance significantly improves on all the test datasets with different label shifts for both medical image diagnosis tasks.

The Investigation of Key Factors in Polypropylene Extrusion Molding Production Quality

This study took food-grade polypropylene packaging products as the research project and discussed how to control the polypropylene extrusion sheet thickness and vacuum thermoforming quality and weight. The research objective was to find the key factors for reducing costs and energy consumption. The key aspects that may influence the polypropylene extrusion molding quality control were analyzed using literature and in-depth interviews with scholars and experts. These four main aspects are (1) key factors of polypropylene extrusion sheet production, (2) key factors of the extrusion line design, (3) key factors of polypropylene forming and mold manufacturing, and (4) key factors of mold and thermoforming line equipment design. These were revised and complemented by the scholar and expert group. There are 49 subitems for discussion. Thirteen scholars and experts were invited to use qualitative and quantitative research methods. A Delphi questionnaire survey team was organized to perform three Delphi questionnaire interviews. The statistical analyses of encoded data such as the mean (M), mode (Mo), and standard deviation (SD) of various survey options were calculated. Seeking a more cautious research theory and result, the K-S simple sample test was used to review the fitness and consistency of the scholars’ and experts’ opinions on key subitem factors. There are ten key factors in the production quality, including “A. Main screw pressure”, “B. Polymer temperature”, “C. T-die lips adjustment thickness”, “D. Cooling rolls pressing stability”, “E. Cooling rolls temperature stability”, “F. Extruder main screw geometric design”, “G. Heating controller is stable”, “H. Thermostatic control”, “I. Vacuum pressure”, and “J. Mold forming area design”. The key factors are not just applicable to classical polypropylene extrusion sheet and thermoforming production but also to related process of extrusion and thermoforming techniques in expanded polypropylene (EPP) sheets and polylactic acid (PLA). This study aims to provide a key technical reference for enterprises to improve quality to enhance the competitiveness of products, reduce production costs, and achieve sustainable development, energy savings, and carbon reductions.

Analyzing the impact of COVID-19 vaccination requirements on travelers’ selection of hotels using a fuzzy multi-criteria decision-making approach

In the later stages of the COVID-19 pandemic, hotels are taking various measures to balance pandemic prevention and business operations. Some hotels require travelers to be fully vaccinated prior to check-in, while others do not. In the latter type of hotels, fully vaccinated travelers may encounter others who are not vaccinated. All of these have created constraints for travelers to choose suitable hotel accommodation during this time. To address this issue, a fuzzy multi-criteria decision-making approach is proposed in this study to help traveler choose suitable hotel accommodation. In the proposed methodology, firstly, hotels are divided into two types considering their requirements for COVID-19 vaccination. Travelers are then asked to list the key factors to consider when choosing between these two types of hotels. To derive the priorities of these key factors, the proportionally calibrated fuzzy geometric mean (pcFGM) method is proposed. Subsequently, the fuzzy VIšekriterijumskoKOmpromisnoRangiranje (fuzzy VIKOR) method is applied to evaluate and compare the overall performances of different types of hotels for recommendations to travelers. The applicability of the proposed methodology is illustrated by a real case study. According to the experimental results, most hotels did not request travelers to be full vaccinated. Nevertheless, the hotels recommended to travelers covered both hotel types.

Identification, optimization, and biological evaluation of 3-O-ß-chacotriosyl ursolic acid derivatives as novel SARS-CoV-2 entry inhibitors by targeting the prefusion state of spike protein.

The COVID-19 pandemic generates a global threat to public health and continuously emerging SARS-CoV-2 variants bring a great challenge to the development of both vaccines and antiviral agents. In this study, we identified UA-18 and its optimized analog UA-30 via the hit-to-lead strategy as novel SARS-CoV-2 fusion inhibitors. The lead compound UA-30 showed potent antiviral activity against infectious SARS-CoV-2 (wuhan-HU-1 variant) in Vero-E6 cells and was also effective against infection of diverse pseudotyped SARS-CoV-2 variants with mutations in the S protein including the Omicron and Delta variants. More importantly, UA-30 might target the cavity between S1 and S2 subunits to stabilize the prefusion state of the SARS-CoV-2 S protein, thus leading to interfering with virus-cell membrane fusion. This study offers a set of novel SARS-CoV-2 fusion inhibitors against SARS-CoV-2 and its variants based on the 3-O-ß-chacotriosyl UA skeleton.

Leisure Agricultural Park Selection for Traveler Groups Amid the COVID-19 Pandemic

With the widespread vaccination against COVID-19, people began to resume regional tourism. Outdoor attractions, such as leisure agricultural parks, are particularly attractive because they are well ventilated and can prevent the spread of COVID-19. However, during the COVID-19 pandemic, the considerations around choosing a leisure agricultural park are different from usual, and will be affected by uncertainty. Therefore, this research proposes a fuzzy collaborative intelligence (FCI) approach to help select leisure agricultural parks suitable for traveler groups during the COVID-19 pandemic. The proposed FCI approach combines asymmetrically calibrated fuzzy geometric mean (acFGM), fuzzy weighted intersection (FWI), and fuzzy Vise Kriterijumska Optimizacija I Kompromisno Resenje (fuzzy VIKOR), which is a novel attempt in this field. The effectiveness of the proposed FCI approach has been verified by a case study in Taichung City, Taiwan. The results of the case study showed that during the COVID-19 pandemic, travelers (especially traveler groups) were very willing to go to leisure agricultural parks. In addition, the most important criterion for choosing a suitable leisure agricultural park was the ease of maintaining social distance, while the least important criterion was the distance from a leisure agricultural park. Further, the successful recommendation rate using the proposed methodology was as high as 90%.

Randomized, Double-Blinded, Placebo-Controlled Phase 2 Trial of an Inactivated Severe Acute Respiratory Syndrome Coronavirus 2 Vaccine in Healthy Adults.

BACKGROUND: We evaluated an inactivated severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccine for immunogenicity and safety in adults aged 18-59 years. METHODS: In this randomized, double-blinded, controlled trial, healthy adults received a medium dose (MD) or a high dose (HD) of the vaccine at an interval of either 14 days or 28 days. Neutralizing antibody (NAb) and anti-S and anti-N antibodies were detected at different times, and adverse reactions were monitored for 28 days after full immunization. RESULTS: A total of 742 adults were enrolled in the immunogenicity and safety analysis. Among subjects in the 0, 14 procedure, the seroconversion rates of NAb in MD and HD groups were 89% and 96% with geometric mean titers (GMTs) of 23 and 30, respectively, at day 14 and 92% and 96% with GMTs of 19 and 21, respectively, at day 28 after immunization. Anti-S antibodies had GMTs of 1883 and 2370 in the MD group and 2295 and 2432 in the HD group. Anti-N antibodies had GMTs of 387 and 434 in the MD group and 342 and 380 in the HD group. Among subjects in the 0, 28 procedure, seroconversion rates for NAb at both doses were both 95% with GMTs of 19 at day 28 after immunization. Anti-S antibodies had GMTs of 937 and 929 for the MD and HD groups, and anti-N antibodies had GMTs of 570 and 494 for the MD and HD groups, respectively. No serious adverse events were observed during the study period. CONCLUSIONS: Adults vaccinated with inactivated SARS-CoV-2 vaccine had NAb as well as anti-S/N antibody and had a low rate of adverse reactions. CLINICAL TRIALS REGISTRATION: NCT04412538.

Research on Sustainable Management Strategies for the Machine Tool Industry during the COVID-19 Pandemic in Taiwan

The machine tool industry is an economically important industry in Taiwan. However, due to the limited natural resources in Taiwan, many of the raw materials required for production must be imported. In 2020, COVID-19, the most serious infectious disease in modern times, broke out across the globe. This has had a great impact on the economic and industrial development of various countries and indirectly affected the development of the machine tool industry. The machine tool industry generally is facing shocks and crises. Therefore, this research article mainly discusses a sustainable operation strategy for the machine tool industry during the COVID-19 epidemic period in Taiwan. Firstly, through the literature on dynamic capability theory (DCT) and expert interviews, the relevant dimensions and criteria are summarized. Then, the fuzzy Delphi method (FDM) and the analytic network process (ANP) are integrated to confirm the relevant dimensions and criteria and to sort the criteria. The five dimensions, in order, are integration ability, learning ability, quality improvement, environmental adaptation, and marketing ability. The ten criteria are diversified learning and innovation ability, integration of multiple sources of knowledge, the ability to learn across departments, the ability to adapt to the external environment, marketing strategy ability, organizational learning ability, integration of resources, improved management efficiency, market research ability, and backward integration. Finally, we put forward business strategies for the ranking results and provide relevant research and industry references.

Research Progress in Chinese Herbal Medicines for Treatment of Sepsis: Pharmacological Action, Phytochemistry, and Pharmacokinetics.

Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection; the pathophysiology of sepsis is complex. The incidence of sepsis is steadily increasing, with worldwide mortality ranging between 30% and 50%. Current treatment approaches mainly rely on the timely and appropriate administration of antimicrobials and supportive therapies, but the search for pharmacotherapies modulating the host response has been unsuccessful. Chinese herbal medicines, i.e., Chinese patent medicines, Chinese herbal prescriptions, and single Chinese herbs, play an important role in the treatment of sepsis through multicomponent, multipathway, and multitargeting abilities and have been officially recommended for the management of COVID-19. Chinese herbal medicines have therapeutic actions promising for the treatment of sepsis; basic scientific research on these medicines is increasing. However, the material bases of most Chinese herbal medicines and their underlying mechanisms of action have not yet been fully elucidated. This review summarizes the current studies of Chinese herbal medicines used for the treatment of sepsis in terms of clinical efficacy and safety, pharmacological activity, phytochemistry, bioactive constituents, mechanisms of action, and pharmacokinetics, to provide an important foundation for clarifying the pathogenesis of sepsis and developing novel antisepsis drugs based on Chinese herbal medicines.