Discussion on the mechanism of Xiyanping injection in the treatment of human coronavirus infection based on network pharmacology and molecular docking

Objective: To explore the mechanism of Xiyanping injection in the treatment of human coronavirus infection based on network pharmacology and molecular docking method. Methods: The active components and targets of Xiyanping injection were screened by CNKI, SwissTarget Prediction and Targetnet. The Human Gene Database (Genecards), Online Human Mendelian Inheritance Database (OMIM) and Therapeutic Target Database (TTD) were searched to predict disease targets. Venny 2.1.0, Cytoscape 3.8.2 and STRING11.5 were used to construct "drug target-disease target Venn diagram", "drug-active ingredient-target-disease network" and "protein interaction network". The Database for Annotation, Visualization and Integrated Discovery (DAVID) and Bioinformatics, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) were used for the enrichment analysis and visualization. Finally, molecular docking was performed by AutoDock Vina and PyMOL. Results: The active ingredient of Xiyanping injection was andrographolide, andrographolide had 140 targets, 1 812 potential targets of human coronavirus infection, and 35 common targets of Xiyanping and human coronavirus infection;PPI network analysis and molecular docking showed that MAPK9, MAPK8, TYK2, CDKI and interleukin (IL)-6 among the 35 common targets might be the key targets of Xiyanping injection in the treatment of human coronavirus infection. Lactone was tightly bound;enrichment analysis showed that key targets were closely related to protein phosphorylation, cell signal transduction, and gene expression regulation, and key targets were NOD-like receptor signaling pathway, Toll-like receptor signaling pathway, FOXO signaling pathway, there was also an important link in the TNF signaling pathway. Conclusion: The active ingredient of Xiyanping injection was andmgrapholide, and its treatment of human coronavirus infection might affect NOD-like receptor signaling pathway, Toll-like receptor signaling pathway and FOXO signaling by inhibiting the activities of MAPK9, MAPK8, TYK2, CDK1 and IL-6. The activation of the pathway and the TNF signaling pathway regulates protein phosphorylation, cell signal transduction and gene expression, thereby exerting anti-inflammatory effects.

The effects of obesity and metabolic abnormalities on severe COVID-19-related outcomes after vaccination: A population-based study.

Breakthrough SARS-CoV-2 infections of vaccinated individuals are being reported globally, resulting in an increased risk of hospitalization and death among such patients. Therefore, it is crucial to identify the modifiable risk factors that may affect the protective efficacy of vaccine use against the development of severe COVID-19 and thus to initiate early medical interventions. Here, in population-based studies using the UK Biobank database and the 2021 National Health Interview Survey (NHIS), we analyzed 20,362 participants aged 50 years or older and 2,588 aged 18 years or older from both databases who tested positive for SARS-COV-2, of whom 33.1% and 67.7% received one or more doses of vaccine, respectively. In the UK Biobank, participants are followed from the vaccination date until October 18, 2021. We found that obesity and metabolic abnormalities (namely, hyperglycemia, hyperlipidemia, and hypertension) were modifiable factors for severe COVID-19 in vaccinated patients (all p < 0.05). When metabolic abnormalities were present, regardless of obesity, the risk of severe COVID-19 was higher than that of metabolically normal individuals (all p < 0.05). Moreover, pharmacological interventions targeting such abnormalities (namely, antihypertensive [adjusted hazard ratio (aHR) 0.64, 95% CI 0.48-0.86; p = 0.003], glucose-lowering [aHR 0.55, 95% CI 0.36-0.83; p = 0.004], and lipid-lowering treatments [aHR 0.50, 95% CI 0.37-0.68; p < 0.001]) were significantly associated with a reduced risk for this outcome. These results show that more proactive health management of patients with obesity and metabolic abnormalities is critical to reduce the incidence of severe COVID-19 after vaccination.

Assessment of respiratory support decision and the outcome of invasive mechanical ventilation in severe COVID-19 with ARDS.

Background: The coronavirus disease 2019 (COVID-19) is an ongoing pandemic. Invasive mechanical ventilation (IMV) is essential for the management of COVID-19 with acute respiratory distress syndrome (ARDS). We aimed to assess the impact of compliance with a respiratory decision support system on the outcomes of patients with COVID-19-associated ARDS who required IMV. Methods: In this retrospective, single-center, case series study, patients with COVID-19-associated ARDS who required IMV at Zhongnan Hospital of Wuhan University, China, from January 8th, 2020, to March 24th, 2020, with the final follow-up date of April 20th, 2020, were included. Demographic, clinical, laboratory, imaging, and management information were collected and analyzed. Compliance with the respiratory support decision system was documented, and its relationship with 28-day mortality was evaluated. Results: The study included 46 COVID-19-associated ARDS patients who required IMV. The median age of the 46 patients was 68.5 years, and 31 were men. The partial pressure of arterial oxygen (PaO2)/fraction of inspired oxygen (FiO2) ratio at intensive care unit (ICU) admission was 104 mmHg. The median total length of IMV was 12.0 (interquartile range [IQR]: 6.0-27.3) days, and the median respiratory support decision score was 11.0 (IQR: 7.8-16.0). To 28 days after ICU admission, 18 (39.1%) patients died. Survivors had a significantly higher respiratory support decision score than non-survivors (15.0 [10.3-17.0] vs. 8.5 (6.0-10.3), P = 0.001). Using receiver operating characteristic (ROC) curve to assess the discrimination of respiratory support decision score to 28-day mortality, the area under the curve (AUC) was 0.796 (95% confidence interval [CI]: 0.657-0.934, P = 0.001) and the cut-off was 11.5 (sensitivity = 0.679, specificity = 0.889). Patients with a higher score (>11.5) were more likely to survive at 28 days after ICU admission (log-rank test, P < 0.001). Conclusions: For severe COVID-19-associated ARDS with IMV, following the respiratory support decision and assessing completion would improve the progress of ventilation. With a decision score of >11.5, the mortality at 28 days after ICU admission showed an obvious decrease.

Expanding role of CXCR2 and therapeutic potential of CXCR2 antagonists in inflammatory diseases and cancers.

C-X-C motif chemokine receptor 2 (CXCR2) is G protein-coupled receptor (GPCR) and plays important roles in various inflammatory diseases and cancers, including chronic obstructive pulmonary disease (COPD), atherosclerosis, asthma, and pancreatic cancer. Upregulation of CXCR2 is closely associated with the migration of neutrophils and monocytes. To date, many small-molecule CXCR2 antagonists have entered clinical trials, showing favorable safety and therapeutic effects. Hence, we provide an overview containing the discovery history, protein structure, signaling pathways, biological functions, structure-activity relationships and clinical significance of CXCR2 antagonists in inflammatory diseases and cancers. According to the latest development and recent clinical progress of CXCR2 small molecule antagonists, we speculated that CXCR2 can be used as a biomarker and a new target for diabetes and that CXCR2 antagonists may also attenuate lung injury in coronavirus disease 2019 (COVID-19).

Real-Time Monitoring of Infectious Disease Outbreaks with a Combination of Google Trends Search Results and the Moving Epidemic Method: A Respiratory Syncytial Virus Case Study.

The COVID-19 pandemic has disrupted the seasonal patterns of several infectious diseases. Understanding when and where an outbreak may occur is vital for public health planning and response. We usually rely on well-functioning surveillance systems to monitor epidemic outbreaks. However, not all countries have a well-functioning surveillance system in place, or at least not for the pathogen in question. We utilized Google Trends search results for RSV-related keywords to identify outbreaks. We evaluated the strength of the Pearson correlation coefficient between clinical surveillance data and online search data and applied the Moving Epidemic Method (MEM) to identify country-specific epidemic thresholds. Additionally, we established pseudo-RSV surveillance systems, enabling internal stakeholders to obtain insights on the speed and risk of any emerging RSV outbreaks in countries with imprecise disease surveillance systems but with Google Trends data. Strong correlations between RSV clinical surveillance data and Google Trends search results from several countries were observed. In monitoring an upcoming RSV outbreak with MEM, data collected from both systems yielded similar estimates of country-specific epidemic thresholds, starting time, and duration. We demonstrate in this study the potential of monitoring disease outbreaks in real time and complement classical disease surveillance systems by leveraging online search data.

Application of deep learning-based diagnostic systems in screening asymptomatic COVID-19 patients among oversea returnees.

INTRODUCTION: Our study aimed to investigate the performance of deep learning (DL)-based diagnostic systems in alerting against COVID-19, especially among asymptomatic individuals coming from overseas, and to analyze the features of identified asymptomatic patients in detail. METHODOLOGY: DL diagnostic systems were deployed to assist in the screening of COVID-19, including the pneumonia system and pulmonary nodules system. 1,917 overseas returnees who underwent CT examination and rRT-PCR tests were enrolled. DL pneumonia system promptly alerted clinicians to suspected COVID-19 after CT examinations while the performance was evaluated with rRT-PCR results as the reference. The radiological features of asymptomatic COVID-19 cases were described according to the Nomenclature of the Fleischner Society. RESULTS: Fifty-three cases were confirmed as COVID-19 patients by rRT-PCR tests, including 5 asymptomatic cases. DL pneumonia system correctly alerted 50 cases as suspected COVID-19 with a sensitivity of 0.9434 and specificity of 0.9592 (within 2 minutes per case); while the pulmonary nodules system alerted 2 of the 3 missed asymptomatic cases. Additionally, five asymptomatic patients presented different characteristics such as elevated creatine kinase level and prolonged prothrombin time, as well as atypical radiological features. CONCLUSIONS: DL diagnostic systems are promising complementary approaches for prompt screening of imported COVID-19 patients, even the imported asymptomatic cases. Unique clinical and radiological characteristics of asymptomatic cases might be of great value in screening as well. ADVANCES IN KNOWLEDGE: DL-based systems are practical, efficient, and reliable to assist radiologists in screening COVID-19 patients. Differential features of asymptomatic patients might be useful to clinicians in the frontline to differentiate asymptomatic cases.

An original deep learning model using limited data for COVID-19 discrimination: A multicenter study.

OBJECTIVES: Artificial intelligence (AI) has been proved to be a highly efficient tool for COVID-19 diagnosis, but the large data size and heavy label force required for algorithm development and the poor generalizability of AI algorithms, to some extent, limit the application of AI technology in clinical practice. The aim of this study is to develop an AI algorithm with high robustness using limited chest CT data for COVID-19 discrimination. METHODS: A three dimensional algorithm that combined multi-instance learning with the LSTM architecture (3DMTM) was developed for differentiating COVID-19 from community acquired pneumonia (CAP) while logistic regression (LR), k-nearest neighbor (KNN), support vector machine (SVM), and a three dimensional convolutional neural network set for comparison. Totally, 515 patients with or without COVID-19 between December 2019 and March 2020 from five different hospitals were recruited and divided into relatively large (150 COVID-19 and 183 CAP cases) and relatively small datasets (17 COVID-19 and 35 CAP cases) for either training or validation and another independent dataset (37 COVID-19 and 93 CAP cases) for external test. Area under the receiver operating characteristic curve (AUC), sensitivity, specificity, precision, accuracy, F1 score, and G-mean were utilized for performance evaluation. RESULTS: In the external test cohort, the relatively large data-based 3DMTM-LD achieved an AUC of 0.956 (95% confidence interval, 95% CI, 0.929∼0.982) with 86.2% and 98.0% for its sensitivity and specificity. 3DMTM-SD got an AUC of 0.937 (95% CI, 0.909∼0.965), while the AUC of 3DCM-SD decreased dramatically to 0.714 (95% CI, 0.649∼0.780) with training data reduction. KNN-MMSD, LR-MMSD, SVM-MMSD, and 3DCM-MMSD benefited significantly from the inclusion of clinical information while models trained with relatively large dataset got slight performance improvement in COVID-19 discrimination. 3DMTM, trained with either CT or multi-modal data, presented comparably excellent performance in COVID-19 discrimination. CONCLUSIONS: The 3DMTM algorithm presented excellent robustness for COVID-19 discrimination with limited CT data. 3DMTM based on CT data performed comparably in COVID-19 discrimination with that trained with multi-modal information. Clinical information could improve the performance of KNN, LR, SVM, and 3DCM in COVID-19 discrimination, especially in the scenario with limited data for training.

T-Cell Repertoire Characteristics of Asymptomatic and Re-Detectable Positive COVID-19 Patients.

The prevention of the COVID-19 pandemic is highly complicated by the prevalence of asymptomatic and recurrent infection. Many previous immunological studies have focused on symptomatic and convalescent patients, while the immune responses in asymptomatic patients and re-detectable positive cases remain unclear. Here we comprehensively analyzed the peripheral T-cell receptor (TCR) repertoire of 54 COVID-19 patients in different courses, including asymptomatic, symptomatic, convalescent, and re-detectable positive cases. We identified a set of V-J gene combinations characterizing the upward immune responses through asymptomatic and symptomatic courses. Furthermore, some of these V-J combinations could be awakened in the re-detectable positive cases, which may help predict the risk of recurrent infection. Therefore, TCR repertoire examination has the potential to strengthen the clinical surveillance and the immunotherapy development for COVID-19.

Differential Antibody Response to Inactivated COVID-19 Vaccines in Healthy Subjects.

The appearance and magnitude of the immune response and the related factors correlated with SARS-CoV-2 vaccination need to be defined. Here, we enrolled a prospective cohort of 52 participants who received two doses of inactivated vaccines (BBIBP-CorV). Their serial plasma samples (n = 260) over 2 months were collected at five timepoints. We measured antibody responses (NAb, S-IgG and S-IgM) and routine blood parameter. NAb seroconversion occurred in 90.7% of vaccinated individuals and four typical NAb kinetic curves were observed. All of the participants who seroconverted after the first dose were females and had relatively high prevaccine estradiol levels. Moreover, those without seroconversion tended to have lower lymphocyte counts and higher serum SAA levels than those who experienced seroconversion. The NAb titers in young vaccine recipients had a significantly higher peak than those in elderly recipients. S-IgG and S-IgM dynamics were accompanied by similar trends in NAb. Here, we gained insight into the dynamic changes in NAbs and preliminarily explored the prevaccine blood parameters related to the kinetic subclasses, providing a reference for vaccination strategies.

Transcriptome Analysis of Peripheral Blood Mononuclear Cells Reveals Distinct Immune Response in Asymptomatic and Re-Detectable Positive COVID-19 Patients.

The existence of asymptomatic and re-detectable positive coronavirus disease 2019 (COVID-19) patients presents the disease control challenges of COVID-19. Most studies on immune responses in COVID-19 have focused on moderately or severely symptomatic patients; however, little is known about the immune response in asymptomatic and re-detectable positive (RP) patients. Here we performed a comprehensive analysis of the transcriptomic profiles of peripheral blood mononuclear cells (PBMCs) from 48 COVID-19 patients which included 8 asymptomatic, 13 symptomatic, 15 recovered and 12 RP patients. The weighted gene co-expression network analysis (WGCNA) identified six co-expression modules, of which the turquoise module was positively correlated with the asymptomatic, symptomatic, and recovered COVID-19 patients. The red module positively correlated with symptomatic patients only and the blue and brown modules positively correlated with the RP patients. The analysis by single sample gene set enrichment analysis (ssGSEA) revealed a lower level of IFN response and complement activation in the asymptomatic patients compared with the symptomatic, indicating a weaker immune response of the PBMCs in the asymptomatic patients. In addition, gene set enrichment analysis (GSEA) analysis showed the enrichment of TNFα/NF-κB and influenza infection in the RP patients compared with the recovered patients, indicating a hyper-inflammatory immune response in the PBMC of RP patients. Thus our findings could extend our understanding of host immune response during the progression of COVID-19 disease and assist clinical management and the immunotherapy development for COVID-19.

Comprehensive Profiling of Inflammatory Factors Revealed That Growth Differentiation Factor-15 Is an Indicator of Disease Severity in COVID-19 Patients.

To systematically explore potential biomarkers which can predict disease severity in COVID-19 patients and prevent the occurrence or development of severe COVID-19, the levels of 440 factors were analyzed in patients categorized according to COVID-19 disease severity; including asymptomatic, mild, moderate, severe, convalescent and healthy control groups. Factor candidates were validated by ELISA and functional relevance was uncovered by bioinformatics analysis. To identify potential biomarkers of occurrence or development of COVID-19, patient sera from three different severity groups (moderate, severe, and critical) at three time points (admission, remission, and discharge) and the expression levels of candidate biomarkers were measured. Eleven differential factors associated with disease severity were pinpointed from 440 factors across 111 patients of differing disease severity. The dynamic changes of GDF15 reflect the progression of the disease, while the other differential factors include TRAIL R1, IGFBP-1, IGFBP-4, VCAM-1, sFRP-3, FABP2, Transferrin, GDF15, IL-1F7, IL-5Rα, and CD200. Elevation of white blood cell count, neutrophil count, neutrophil-lymphocyte ratio (NLR), Alanine aminotransferase and Aspartate aminotransferase, low lymphocyte and eosinophil counts in the severe group were associated with the severity of COVID-19. GDF15 levels were observed to be associated with the severity of COVID-19 and the dynamic change of GDF15 levels was closely associated with the COVID-19 disease progression. Therefore, GDF15 might serve as an indicator of disease severity in COVID-19 patients.

False-Negative Results of Real-Time Reverse-Transcriptase Polymerase Chain Reaction for Severe Acute Respiratory Syndrome Coronavirus 2: Role of Deep-Learning-Based CT Diagnosis and Insights from Two Cases.

The epidemic of 2019 novel coronavirus, later named as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is still gradually spreading worldwide. The nucleic acid test or genetic sequencing serves as the gold standard method for confirmation of infection, yet several recent studies have reported false-negative results of real-time reverse-transcriptase polymerase chain reaction (rRT-PCR). Here, we report two representative false-negative cases and discuss the supplementary role of clinical data with rRT-PCR, including laboratory examination results and computed tomography features. Coinfection with SARS-COV-2 and other viruses has been discussed as well.

A deep learning integrated radiomics model for identification of coronavirus disease 2019 using computed tomography.

Since its first outbreak, Coronavirus Disease 2019 (COVID-19) has been rapidly spreading worldwide and caused a global pandemic. Rapid and early detection is essential to contain COVID-19. Here, we first developed a deep learning (DL) integrated radiomics model for end-to-end identification of COVID-19 using CT scans and then validated its clinical feasibility. We retrospectively collected CT images of 386 patients (129 with COVID-19 and 257 with other community-acquired pneumonia) from three medical centers to train and externally validate the developed models. A pre-trained DL algorithm was utilized to automatically segment infected lesions (ROIs) on CT images which were used for feature extraction. Five feature selection methods and four machine learning algorithms were utilized to develop radiomics models. Trained with features selected by L1 regularized logistic regression, classifier multi-layer perceptron (MLP) demonstrated the optimal performance with AUC of 0.922 (95% CI 0.856-0.988) and 0.959 (95% CI 0.910-1.000), the same sensitivity of 0.879, and specificity of 0.900 and 0.887 on internal and external testing datasets, which was equivalent to the senior radiologist in a reader study. Additionally, diagnostic time of DL-MLP was more efficient than radiologists (38 s vs 5.15 min). With an adequate performance for identifying COVID-19, DL-MLP may help in screening of suspected cases.

Corticosteroid therapy in critically ill patients with COVID-19: a multicenter, retrospective study.

BACKGROUND: Corticoid therapy has been recommended in the treatment of critically ill patients with COVID-19, yet its efficacy is currently still under evaluation. We investigated the effect of corticosteroid treatment on 90-day mortality and SARS-CoV-2 RNA clearance in severe patients with COVID-19. METHODS: 294 critically ill patients with COVID-19 were recruited between December 30, 2019 and February 19, 2020. Logistic regression, Cox proportional-hazards model and marginal structural modeling (MSM) were applied to evaluate the associations between corticosteroid use and corresponding outcome variables. RESULTS: Out of the 294 critically ill patients affected by COVID-19, 183 (62.2%) received corticosteroids, with methylprednisolone as the most frequently administered corticosteroid (175 accounting for 96%). Of those treated with corticosteroids, 69.4% received corticosteroid prior to ICU admission. When adjustments and subgroup analysis were not performed, no significant associations between corticosteroids use and 90-day mortality or SARS-CoV-2 RNA clearance were found. However, when stratified analysis based on corticosteroid initiation time was performed, there was a significant correlation between corticosteroid use (≤ 3 day after ICU admission) and 90-day mortality (logistic regression adjusted for baseline: OR 4.49, 95% CI 1.17-17.25, p = 0.025; Cox adjusted for baseline and time varying variables: HR 3.89, 95% CI 1.94-7.82, p < 0.001; MSM adjusted for baseline and time-dependent variants: OR 2.32, 95% CI 1.16-4.65, p = 0.017). No association was found between corticosteroid use and SARS-CoV-2 RNA clearance even after stratification by initiation time of corticosteroids and adjustments for confounding factors (corticosteroids use ≤ 3 days initiation vs no corticosteroids use) using MSM were performed. CONCLUSIONS: Early initiation of corticosteroid use (≤ 3 days after ICU admission) was associated with an increased 90-day mortality. Early use of methylprednisolone in the ICU is therefore not recommended in patients with severe COVID-19.

Prediction of COVID-19 Patients at High Risk of Progression to Severe Disease.

In order to develop a novel scoring model for the prediction of coronavirus disease-19 (COVID-19) patients at high risk of severe disease, we retrospectively studied 419 patients from five hospitals in Shanghai, Hubei, and Jiangsu Provinces from January 22 to March 30, 2020. Multivariate Cox regression and orthogonal projections to latent structures discriminant analysis (OPLS-DA) were both used to identify high-risk factors for disease severity in COVID-19 patients. The prediction model was developed based on four high-risk factors. Multivariate analysis showed that comorbidity [hazard ratio (HR) 3.17, 95% confidence interval (CI) 1.96-5.11], albumin (ALB) level (HR 3.67, 95% CI 1.91-7.02), C-reactive protein (CRP) level (HR 3.16, 95% CI 1.68-5.96), and age ≥60 years (HR 2.31, 95% CI 1.43-3.73) were independent risk factors for disease severity in COVID-19 patients. OPLS-DA identified that the top five influencing parameters for COVID-19 severity were CRP, ALB, age ≥60 years, comorbidity, and lactate dehydrogenase (LDH) level. When incorporating the above four factors, the nomogram had a good concordance index of 0.86 (95% CI 0.83-0.89) and had an optimal agreement between the predictive nomogram and the actual observation with a slope of 0.95 (R2 = 0.89) in the 7-day prediction and 0.96 (R2 = 0.92) in the 14-day prediction after 1,000 bootstrap sampling. The area under the receiver operating characteristic curve of the COVID-19-American Association for Clinical Chemistry (AACC) model was 0.85 (95% CI 0.81-0.90). According to the probability of severity, the model divided the patients into three groups: low risk, intermediate risk, and high risk. The COVID-19-AACC model is an effective method for clinicians to screen patients at high risk of severe disease.

Epidemiological and clinical characteristics of 70 cases of coronavirus disease and concomitant hepatitis B virus infection: A multicentre descriptive study.

The interaction between existing chronic liver diseases caused by hepatitis B virus (HBV) infection and COVID-19 has not been studied. We analysed 70 COVID-19 cases combined with HBV infection (CHI) to determine the epidemiological, clinical characteristics, treatment and outcome. We investigated clinical presentation, imaging and laboratory parameters of COVID-19 patients of seven hospitals from Jan 20 to March 20, 2020. Multivariate analysis was used to analyse risk factors for progression of patients with COVID-19 combined with HBV infection. Compared with COVID-19 without HBV infection (WHI) group, patients with dual infection had a higher proportion of severe/critically ill disease (32.86% vs. 15.27%, P = .000), higher levels of alanine aminotransferase (ALT), aspartate transaminase (AST) and activated partial thromboplastin (APTT) [50(28-69)vs 21(14-30), P = .000; 40(25-54) vs 23(18-30), P = .000; 34.0(27.2-38.7) vs 37.2(31.1-41.4), P = .031]. The utilization rates of Arbidol and immunoglobulin were significantly higher than those in the co-infected group [48.57% vs. 35.64%, P < .05; 21.43% vs. 8.18%, P < .001], while the utilization rate of chloroquine phosphate was lower (1.43% vs 14.00%, P < .05) in the co-infected patients group. Age and c-reactive protein (CRP) level were independent risk factors for recovery of patients with COVID-19 combined with HBV infection. The original characteristics of COVID-19 cases combined with HBV infection were higher rate of liver injury, coagulation disorders, severe/critical tendency and increased susceptibility. The elderly and patients with higher level of CRP were more likely to experience a severe outcome of COVID-19.

New thoughts on the diagnosis and treatment of patients with diabetes mellitus in relation to coronavirus disease.

The coronavirus disease 2019 (COVID-19) outbreak that occurred in late 2019 has posed a huge threat to the health of all humans, especially for individuals who already have diabetes mellitus (DM). DM is one of the most serious diseases that affect human health, with high morbidity and rates of complications. Medical scientists worldwide have been working to control blood sugar levels and the complications associated with sugar level alterations, with an aim to reduce the adverse consequences of acute and chronic complications caused by DM. Patients with DM face great challenges during the pandemic owing to not only changes in the allocation of medical resources but also their abnormal autoimmune status, which reduces their resistance to infections. This increases the difficulty in treatment and the risk of mortality. This review presents, from an epidemiological viewpoint, information on the susceptibility of patients with DM to COVID-19 and the related treatment plans and strategies used in this population.