Impact of Divalent Metal Ions on Regulation of Trans-Cleavage Activity of CRISPR-Cas13a: A Combined Experimental and Computational Study.

CRISPR-LbuCas13a has emerged as a revolutionary tool for in vitro diagnosis. Similar to other Cas effectors, LbuCas13a requires Mg2+ to maintain its nuclease activity. However, the effect of other divalent metal ions on its trans-cleavage activity remains less explored. Herein, we addressed this issue by combining experimental and molecular dynamics simulation analysis. In vitro studies showed that both Mn2+ and Ca2+ could replace Mg2+ as cofactors of LbuCas13a. In contrast, Ni2+ , Zn2+ , Cu2+ , or Fe2+ inhibits the cis- and trans-cleavage activity, while Pb2+ does not affect it. Importantly, molecular dynamics simulations confirmed that calcium, magnesium, and manganese hydrated ions have a strong affinity to nucleotide bases, thus stabilizing the conformation of crRNA repeat region and enhancing the trans-cleavage activity. Finally, we showed that combination of Mg2+ and Mn2+ can further enhance the trans-cleavage activity to allow amplified RNA detection, revealing its potential advantage for in vitro diagnosis.

Harnessing the CRISPR-Cas13d System for Protein Detection via Dual-aptamer-based Transcription Amplification.

CRISPR-based biosensing technology has been emerging as a revolutionary diagnostics for many diseases-related biomarkers. In particular, RspCas13d, a newly identified RNA-guided Cas13d ribonuclease derived from Ruminococcus sp., has shown great promise for accurate and sensitive detection of RNA due to its RNA sequence-specific recognition and robust collateral trans-cleavage activity. However, its diagnostic utility is limited to detect nucleic-acid-related biomarkers. To address this limitation, we herein present a proof-of-concept demonstration of a target-responsive CRISPR-Cas13d sensing system for protein biomarkers. Such a system is rationally designed by integrating a Dual-Aptamer-based Transcription Amplification Strategy with CRISPR-Cas13d (DATAS-Cas13d), in which the protein binding initiates the in vitro RNA transcription followed by the activation of RspCas13d. Using a short fluorescent ssRNA as the signal reporter and cardiac troponin I (cTnI) as the model analyte, the DATAS-Cas13d system showed a wide linear range, low detection limit and high specificity for the detection of cTnI in buffer and human serum. Thanks to the facile integration of various bioreceptors into the DATAS-Cas13d system, the method could be adapted to detecting a broad range of clinically relevant protein biomarkers, and thus broaden the medical applications of Cas13d-based diagnostics.

COVID-19 and liver dysfunction in children: Current views and new hypotheses.

Coronavirus disease 2019 (COVID-19) poses an extremely serious global impact on public healthcare for individuals of all ages, including children. Increasing evidence has shown that liver abnormalities are commonly found in children with COVID-19, and age-related features in innate and adaptive response have been demonstrated. However, there are few reports and studies on COVID-19 related liver injury in children, and the data are scattered. So that many contradictions have arose. This situation is not only due to the serious ethical issues in studying pediatric patients with COVID-19, but also because of the short duration and wide coverage of the COVID-19 epidemic, the severity and complexity of clinical cases varied, as did the inclusion criteria for case reporting and patient outcomes. Therefore, we totaled the incidences, characteristics and pathomechanism of liver injury in children since the COVID-19 outbreak. The etiology of COVID-19-related liver injury is divided into three categories: (1) The direct mechanism involves severe acute respiratory syndrome coronavirus 2 binding to angiotensin-converting enzyme 2 in the liver or bile duct to exert direct toxicity; (2) the indirect mechanisms include an inflammatory immune response and hypoxia; and (3) COVID-19-related treatments, such as mechanical ventilation and antiviral drugs, may cause liver injury. In summary, this minireview provides fundamental insights into COVID-19 and liver dysfunction in children.

Therapeutic potency of compound RMY-205 for pulmonary fibrosis induced by SARS-CoV-2 nucleocapsid protein.

Pulmonary fibrosis is a typical sequela of coronavirus disease 2019 (COVID-19), which is linked with a poor prognosis for COVID-19 patients. However, the underlying mechanism of pulmonary fibrosis induced by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is unclear. Here, we demonstrated that the nucleocapsid (N) protein of SARS-CoV-2 induced pulmonary fibrosis by activating pulmonary fibroblasts. N protein interacted with the transforming growth factor ß receptor I (TßRI), to disrupt the interaction of TßRI-FK506 Binding Protein12 (FKBP12), which led to activation of TßRI to phosphorylate Smad3 and boost expression of pro-fibrotic genes and secretion of cytokines to promote pulmonary fibrosis. Furthermore, we identified a compound, RMY-205, that bound to Smad3 to disrupt TßRI-induced Smad3 activation. The therapeutic potential of RMY-205 was strengthened in mouse models of N protein-induced pulmonary fibrosis. This study highlights a signaling pathway of pulmonary fibrosis induced by N protein and demonstrates a novel therapeutic strategy for treating pulmonary fibrosis by a compound targeting Smad3.

Highly Sensitive Poly-N-isopropylacrylamide Microgel-based Electrochemical Biosensor for the Detection of SARS-COV-2 Spike Protein.

Objective: Late 2019 witnessed the outbreak and widespread transmission of coronavirus disease 2019 (COVID-19), a new, highly contagious disease caused by novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Consequently, considerable attention has been paid to the development of new diagnostic tools for the early detection of SARS-CoV-2. Methods: In this study, a new poly-N-isopropylacrylamide microgel-based electrochemical sensor was explored to detect the SARS-CoV-2 spike protein (S protein) in human saliva. The microgel was composed of a copolymer of N-isopropylacrylamide and acrylic acid, and gold nanoparticles were encapsulated within the microgel through facile and economical fabrication. The electrochemical performance of the sensor was evaluated through differential pulse voltammetry. Results: Under optimal experimental conditions, the linear range of the sensor was 10 -13-10 -9 mg/mL, whereas the detection limit was 9.55 fg/mL. Furthermore, the S protein was instilled in artificial saliva as the infected human saliva model, and the sensing platform showed satisfactory detection capability. Conclusion: The sensing platform exhibited excellent specificity and sensitivity in detecting spike protein, indicating its potential application for the time-saving and inexpensive detection of SARS-CoV-2.

Case report of acute encephalitis following the AstraZeneca COVID-19 vaccine.

Coronavirus disease 2019 (COVID-19) vaccines have proven to be safe, effective and life-saving. However, little information is available on the neurological complications of COVID-19 vaccine. Here, we report a case who developed acute encephalomyelitis 1 week after being vaccinated with AstraZeneca COVID-19 vaccine (AZ vaccine). Autoimmune/inflammatory syndrome induced by adjuvants (ASIA) was also suspected. After intravenous dexamethasone and subcutaneous fondaparinux therapy, he returned to normal life without neurological sequelae. Four months later, he received Moderna COVID-19 vaccine without any sequelae.

A multitope SARS-CoV-2 vaccine provides long-lasting B cell and T cell immunity against Delta and Omicron variants.

BackgroundThe Delta and Omicron variants of SARS-CoV-2 are currently responsible for breakthrough infections due to waning immunity. We report phase I/II trial results of UB-612, a multitope subunit vaccine containing S1-RBD-sFc protein and rationally designed promiscuous peptides representing sarbecovirus conserved helper T cell and cytotoxic T lymphocyte epitopes on the nucleocapsid (N), membrane (M), and spike (S2) proteins.MethodWe conducted a phase I primary 2-dose (28 days apart) trial of 10, 30, or 100 µg UB-612 in 60 healthy young adults 20 to 55 years old, and 50 of them were boosted with 100 µg of UB-612 approximately 7 to 9 months after the second dose. A separate placebo-controlled and randomized phase II study was conducted with 2 doses of 100 µg of UB-612 (n = 3,875, 18-85 years old). We evaluated interim safety and immunogenicity of phase I until 14 days after the third (booster) dose and of phase II until 28 days after the second dose.ResultsNo vaccine-related serious adverse events were recorded. The most common solicited adverse events were injection site pain and fatigue, mostly mild and transient. In both trials, UB-612 elicited respective neutralizing antibody titers similar to a panel of human convalescent sera. The most striking findings were long-lasting virus-neutralizing antibodies and broad T cell immunity against SARS-CoV-2 variants of concern (VoCs), including Delta and Omicron, and a strong booster-recalled memory immunity with high cross-reactive neutralizing titers against the Delta and Omicron VoCs.ConclusionUB-612 has presented a favorable safety profile, potent booster effect against VoCs, and long-lasting B and broad T cell immunity that warrants further development for both primary immunization and heterologous boosting of other COVID-19 vaccines.Trial RegistrationClinicalTrials.gov: NCT04545749, NCT04773067, and NCT04967742.FundingUBI Asia, Vaxxinity Inc., and Taiwan Centers for Disease Control, Ministry of Health and Welfare.

Dynamic Allocation of Medical Resources During the Outbreak of Epidemics

During the outbreak of epidemics such as coronavirus disease (COVID-19), the local hospitals often withstand a sharp increase of patient influx, which renders the healthcare system on the verge of collapse. To alleviate the situation, the effective allocation of scarce medical resources during the pandemic plays a vital role. The essence of the healthcare system in time of emergency is to stay functional, and to be able to diagnose and hospitalize as many patients as possible. Fangcang shelter hospital, as a novel way to temporarily increase the capacity of the local healthcare system, is proven to be effective against the COVID-19 pandemic. To improve the performance of the healthcare system with Fangcang, many practical factors need to be taken into account, such as the patient deterioration during waiting to be admitted, the referral mechanism according to the severity of the patients, and the selective admission regulations. To address the high volatility and time-varying feature of the COVID-19, a multistage and multi-type medical service network model is established, and a dynamic allocation strategy of the medical resources at each stage is proposed based on a stochastic optimization problem, which is then solved via the fluid queueing approximation. Combined with the real data collected from Wuhan, it is revealed that the proposed algorithm could help with the allocation of medical resources during the outbreak of epidemics. Even with limited medical resources available, the method could still maintain a guaranteed service level while keeping the healthcare system operational. Furthermore, the simulation analysis validates that our method can effectively allocate medical resources at each stage, so as to stabilize the system performance and fulfill the medical demand for multiple types of patients. Note to Practitioners—To fend off the outbreak of epidemics, the lessons have to be learned from the past. The successful control of the spread of COVID-19 in Wuhan (China) is a classical example of applying modern medical practices and management tools. In the present article, the treating procedure of COVID-19 in Wuhan is modeled as a multistage decision problem, which includes the screening with nucleic acid testing, the further testing, the treatment of patients with mild/severe symptoms, or even critical patients. The introduction of Fangcang shelter hospital is crucial for winning the battle against COVID-19. The current study attempts to determine the timing of introducing the Fangcang shelter hospital during the outbreak of a major epidemic, and helps allocate the medical resources needed to contain the spread of the virus. It is discovered that the actual number of beds in the Fangcang shelter hospital is far more than what is necessary, and it would be better to have built the Fangcang some time in advance. In the meantime, the number of designated hospitals for COVID-19 is in line with the results obtained via the optimal staffing strategy proposed here, but it is also noticeable that these hospitals should be released of duty sooner to fight against not only COVID-19 but also other diseases in reality. [ FROM AUTHOR] Copyright of IEEE Transactions on Automation Science & Engineering is the property of IEEE 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.)

Synthesis and Biological Evaluation of Honokiol Derivatives Bearing 3-((5-phenyl-1,3,4-oxadiazol-2-yl)methyl)oxazol-2(3H)-ones as Potential Viral Entry Inhibitors against SARS-CoV-2.

The 2019 coronavirus disease (COVID-19) caused by SARS-CoV-2 virus infection has posed a serious danger to global health and the economy. However, SARS-CoV-2 medications that are specific and effective are still being developed. Honokiol is a bioactive component from Magnoliae officinalis Cortex with damp-drying effect. To develop new potent antiviral molecules, a series of novel honokiol analogues were synthesized by introducing various 3-((5-phenyl-1,3,4-oxadiazol-2-yl)methyl)oxazol-2(3H)-ones to its molecule. In a SARS-CoV-2 pseudovirus model, all honokiol derivatives were examined for their antiviral entry activities. As a result, 6a and 6p demonstrated antiviral entry effect with IC50 values of 29.23 and 9.82 µM, respectively. However, the parental honokiol had a very weak antiviral activity with an IC50 value more than 50 µM. A biolayer interfero-metry (BLI) binding assay and molecular docking study revealed that 6p binds to human ACE2 protein with higher binding affinity and lower binding energy than the parental honokiol. A competitive ELISA assay confirmed the inhibitory effect of 6p on SARS-CoV-2 spike RBD's binding with ACE2. Importantly, 6a and 6p (TC50 > 100 µM) also had higher biological safety for host cells than honokiol (TC50 of 48.23 µM). This research may contribute to the discovery of potential viral entrance inhibitors for the SARS-CoV-2 virus, although 6p's antiviral efficacy needs to be validated on SARS-CoV-2 viral strains in a biosafety level 3 facility.

National surveillance of antimicrobial susceptibilities to dalbavancin, telavancin, tedizolid, eravacycline, omadacycline and other comparator antibiotics and serotype distribution of invasive Streptococcus pneumoniae isolates in adults: results from the Surveillance of Multicenter Antimicrobial Resistance in Taiwan (SMART) programme in 2017-2020.

OBJECTIVES: The aim of this study was to investigate the trends in serotypes and in vitro antimicrobial susceptibility of Streptococcus pneumoniae causing adult invasive pneumococcal disease (IPD) to dalbavancin, telavancin, tedizolid, eravacycline, omadacycline and other comparator antibiotics from 2017-2020 following implementation of the 13-valent pneumococcal conjugate vaccine (PCV-13) and during the COVID-19 (coronavirus disease 2019) pandemic. METHODS: During the study period, 237 S. pneumoniae isolates were collected from non-duplicate patients, covering 15.0% of IPD cases in Taiwan. Antimicrobial susceptibility testing was performed using a Sensititre® system. A latex agglutination method (ImmuLex™ Pneumotest Kit) was used to determine serotypes. RESULTS: Susceptibility rates were high for vancomycin (100%), teicoplanin (100%) and linezolid (100%), followed by ceftaroline (non-meningitis) (98.3%), moxifloxacin (94.9%) and quinupristin/dalfopristin (89.9%). MIC50 and MIC90 values of dalbavancin, telavancin, tedizolid, eravacycline and omadacycline were generally low. Non-vaccine serotype 23A was the leading cause of IPD across the adult age range. Isolates of serotype 15B were slightly fewer than those of PCV-13 serotypes in patients aged ≥65 years. The overall case fatality rate was 15.2% (36/237) but was especially high for non-PCV-13 serotype 15B (21.4%; 3/14). Vaccine coverage was 44.7% for PCV-13 and 49.4% for the 23-valent pneumococcal polysaccharide vaccine (PPSV-23), but was 57% for both PCV-13 and PPSV-23. CONCLUSION: The incidence of IPD was stationary after PCV-13 introduction and only dramatically decreased in the COVID-19 pandemic in 2020. The MIC50 and MIC90 values of dalbavancin, telavancin, tedizolid, eravacycline, omadacycline were generally low for S. pneumoniae causing adult IPD.

Nationwide surveillance of antimicrobial resistance in invasive isolates of Streptococcus pneumoniae in Taiwan from 2017 to 2019.

BACKGROUND/PURPOSE: Streptococcus pneumoniae causes pneumonia and other invasive diseases, and is a leading cause of mortality in the elderly population. The present study aimed to provide current antimicrobial resistance and epidemiological profiles of S. pneumoniae infections in Taiwan. METHODS: A total of 252 nonduplicate S. pneumoniae isolates were collected from patients admitted to 16 hospitals in Taiwan between January 2017 and December 2019, and were analyzed. The minimum inhibitory concentration of antibiotics was determined using the Vitek 2 automated system for antimicrobial susceptibility testing. Furthermore, epidemiological profiles of S. pneumoniae infections were analyzed. RESULTS: Among the strains analyzed, 88% were recognized as invasive pneumococcal strains. According to the Clinical and Laboratory Standards Institute criteria for non-meningitis, the prevalence of penicillin-non-susceptible S. pneumoniae demonstrated a declining trend from 43.6% in 2017 to 17.2% in 2019. However, the rate of penicillin-non-susceptible S. pneumoniae was 85.7% based on the criteria for meningitis. Furthermore, the prevalence of ceftriaxone-non-susceptible S. pneumoniae was 62.7% based on the criteria for meningitis. Isolates demonstrated higher susceptibility toward doripenem and ertapenem than toward meropenem and imipenem. An increased rate of non-susceptibility toward levofloxacin was observed in southern Taiwan (15.1%) and elderly patients (≥65 years; 11.4%). Most isolates were susceptible to vancomycin and linezolid. CONCLUSION: Empirical treatment with ceftriaxone monotherapy for pneumococcal meningitis should be carefully monitored owing to its high non-susceptibility rate. The susceptibility rates of most isolates to penicillin (used for treating non-meningitis pneumococcal diseases), carbapenems (ertapenem and doripenem), respiratory quinolones (moxifloxacin and levofloxacin), vancomycin, and linezolid suggested the potential of these antibiotics in treating pneumococcal diseases in Taiwan.

Human ACE2-Functionalized Gold "Virus-Trap" Nanostructures for Accurate Capture of SARS-CoV-2 and Single-Virus SERS Detection.

The current COVID-19 pandemic urges the extremely sensitive and prompt detection of SARS-CoV-2 virus. Here, we present a Human Angiotensin-converting-enzyme 2 (ACE2)-functionalized gold "virus traps" nanostructure as an extremely sensitive SERS biosensor, to selectively capture and rapidly detect S-protein expressed coronavirus, such as the current SARS-CoV-2 in the contaminated water, down to the single-virus level. Such a SERS sensor features extraordinary 106-fold virus enrichment originating from high-affinity of ACE2 with S protein as well as "virus-traps" composed of oblique gold nanoneedles, and 109-fold enhancement of Raman signals originating from multi-component SERS effects. Furthermore, the identification standard of virus signals is established by machine-learning and identification techniques, resulting in an especially low detection limit of 80 copies mL-1 for the simulated contaminated water by SARS-CoV-2 virus with complex circumstance as short as 5 min, which is of great significance for achieving real-time monitoring and early warning of coronavirus. Moreover, here-developed method can be used to establish the identification standard for future unknown coronavirus, and immediately enable extremely sensitive and rapid detection of novel virus. Supplementary Information: The online version contains supplementary material available at 10.1007/s40820-021-00620-8.

Risk factors analysis of COVID-19 patients with ARDS and prediction based on machine learning.

COVID-19 is a newly emerging infectious disease, which is generally susceptible to human beings and has caused huge losses to people's health. Acute respiratory distress syndrome (ARDS) is one of the common clinical manifestations of severe COVID-19 and it is also responsible for the current shortage of ventilators worldwide. This study aims to analyze the clinical characteristics of COVID-19 ARDS patients and establish a diagnostic system based on artificial intelligence (AI) method to predict the probability of ARDS in COVID-19 patients. We collected clinical data of 659 COVID-19 patients from 11 regions in China. The clinical characteristics of the ARDS group and no-ARDS group of COVID-19 patients were elaborately compared and both traditional machine learning algorithms and deep learning-based method were used to build the prediction models. Results indicated that the median age of ARDS patients was 56.5 years old, which was significantly older than those with non-ARDS by 7.5 years. Male and patients with BMI > 25 were more likely to develop ARDS. The clinical features of ARDS patients included cough (80.3%), polypnea (59.2%), lung consolidation (53.9%), secondary bacterial infection (30.3%), and comorbidities such as hypertension (48.7%). Abnormal biochemical indicators such as lymphocyte count, CK, NLR, AST, LDH, and CRP were all strongly related to the aggravation of ARDS. Furthermore, through various AI methods for modeling and prediction effect evaluation based on the above risk factors, decision tree achieved the best AUC, accuracy, sensitivity and specificity in identifying the mild patients who were easy to develop ARDS, which undoubtedly helped to deliver proper care and optimize use of limited resources.

Short-term high-dose gavage of hydroxychloroquine changes gut microbiota but not the intestinal integrity and immunological responses in mice.

AIMS: Hydroxychloroquine (HCQ), a widely used antimalarial drug, is proposed to treat coronavirus disease 2019 (COVID-19). However, no report is currently available regarding the direct effects of HCQ on gut microbiota, which is associated with the outcomes of elderly patients with COVID-19. Here, we first investigated the effects of HCQ on intestinal microecology in mice. MAIN METHODS: Fifteen female C57BL/6J mice were randomly divided into two groups: HCQ group (n = 10) and control group (n = 5). Mice in the HCQ group were administered with HCQ at dose of 100 mg/kg by gavage daily for 14 days. The feces of mice were collected before and on the 7th and 14th days after HCQ challenge, and then analyzed by 16S rRNA amplicon sequencing. At the end of the experiment, the hematology, serum biochemistry and cytokines were determined, respectively. The mRNA expression of tight junction proteins in colonic tissues were also studied by RT-PCR. KEY FINDINGS: HCQ challenge had no effects on the counts of white blood cells, the levels of serum cytokines, and the gene expression of tight junction proteins in colon. HCQ also did not increase the content of serum d-lactate in mice. Notably, HCQ significantly decreased the diversity of gut microbiota, increased the relative abundance of phylum Bacteroidetes whereas decreased that of Firmicutes. SIGNIFICANCE: Short-term high dose HCQ challenge changes gut microbiota but not the intestinal integrity and immunological responses in mice. Special attention should be paid to the effects of HCQ on intestinal microecology in future clinical use.

Risk Factors Analysis of COVID-19 Patients with ARDS and Prediction Based on Machine Learning (preprint)

COVID-19 is a newly emerging infectious disease, which is generally susceptible to human beings and has caused huge losses to people's health. Acute respiratory distress syndrome (ARDS) is one of the common clinical manifestations of severe COVID-19 and it is also responsible for the current shortage of ventilators worldwide. This study aims to analyze the clinical characteristics of COVID-19 ARDS patients and establish a diagnostic system based on artificial intelligence (AI) method to predict the probability of ARDS in COVID-19 patients. We collected clinical data of 659 COVID-19 patients from 11 regions in China. The clinical characteristics of the ARDS group and no-ARDS group of COVID-19 patients were elaborately compared and both traditional machine learning algorithms and deep learning-based method were used to build the prediction models. Results indicated that the median age of ARDS patients was 56.5 years old, which was significantly older than those with non-ARDS by 7.5 years. Male and patients with BMI>25 were more likely to develop ARDS. The clinical features of ARDS patients included cough (80.3%), polypnea (59.2%), lung consolidation (53.9%), secondary bacterial infection (30.3%), and comorbidities such as hypertension (48.7%). Abnormal biochemical indicators such as lymphocyte count, CK, NLR, AST, LDH, and CRP were all strongly related to the aggravation of ARDS. Furthermore, through various AI methods for modeling and prediction effect evaluation based on the above risk factors, decision tree achieved the best AUC, accuracy, sensitivity and specificity in identifying the mild patients who were easy to develop ARDS, which undoubtedly helped to deliver proper care and optimize use of limited resources.

A deep learning approach to characterize 2019 coronavirus disease (COVID-19) pneumonia in chest CT images.

OBJECTIVES: To utilize a deep learning model for automatic detection of abnormalities in chest CT images from COVID-19 patients and compare its quantitative determination performance with radiological residents. METHODS: A deep learning algorithm consisted of lesion detection, segmentation, and location was trained and validated in 14,435 participants with chest CT images and definite pathogen diagnosis. The algorithm was tested in a non-overlapping dataset of 96 confirmed COVID-19 patients in three hospitals across China during the outbreak. Quantitative detection performance of the model was compared with three radiological residents with two experienced radiologists' reading reports as reference standard by assessing the accuracy, sensitivity, specificity, and F1 score. RESULTS: Of 96 patients, 88 had pneumonia lesions on CT images and 8 had no abnormities on CT images. For per-patient basis, the algorithm showed superior sensitivity of 1.00 (95% confidence interval (CI) 0.95, 1.00) and F1 score of 0.97 in detecting lesions from CT images of COVID-19 pneumonia patients. While for per-lung lobe basis, the algorithm achieved a sensitivity of 0.96 (95% CI 0.94, 0.98) and a slightly inferior F1 score of 0.86. The median volume of lesions calculated by algorithm was 40.10 cm3. An average running speed of 20.3 s ± 5.8 per case demonstrated the algorithm was much faster than the residents in assessing CT images (all p < 0.017). The deep learning algorithm can also assist radiologists make quicker diagnosis (all p < 0.0001) with superior diagnostic performance. CONCLUSIONS: The algorithm showed excellent performance in detecting COVID-19 pneumonia on chest CT images compared with resident radiologists. KEY POINTS: • The higher sensitivity of deep learning model in detecting COVID-19 pneumonia were found compared with radiological residents on a per-lobe and per-patient basis. • The deep learning model improves diagnosis efficiency by shortening processing time. • The deep learning model can automatically calculate the volume of the lesions and whole lung.