Analysis of Medication Utilization in Isolated Areas of Fever Clinics During the COVID-19 Epidemic Outbreak: A Multicenter Study in General Hospitals in China (preprint)

Objective: To analyze the demographic characteristics and patterns of medication use among patients in fever clinics (FCs) during the COVID-19 outbreak in China and provide information for COVID-19 treatment. Method: Various-grade general hospitals in China were selected, and patient information was extracted during the initial wave of the COVID-19 epidemic. Demographic characteristics were analyzed, including visit time, age, sampling morbidity rate, and disease distribution. Prescription information from the FC database was extracted to analyze drug use and the rationality of the medication. Result: Between September 1 and December 31, 2022, 41,445 patients received treatment at FCs in 11 included hospitals. After the relaxation of COVID-19 control measures, there was a rapid increase in the number of daily patient visits (peaking >1,000 people/day, with a growth rate of 158.8%). The highest sampling morbidity rate was observed among individuals over 85 years old (>100 person-times/million population), followed by children (60-94 person-times/million population). Respiratory system diseases (39,295 cases) were the most diagnosed, with respiratory system infections (21,201 cases) and fever (15,132 cases) the most common. The proportion and frequency of use of essential national drugs were 34.3% and 73.1%, respectively, while those for the drugs recommended in the national COVID-19 treatment guidelines were 6.1% and 43.2%, respectively. Ibuprofen, acetaminophen, and Lianhua Qingwen had the highest frequency of drug use. The most prescribed drugs by cost were immunoglobulin, azivudine, and cefoperazone sulbactam. The water-electrolyte balance regulator drugs, respiratory system drugs, anti-infective drugs, and traditional Chinese patent drugs were the most frequently used. In contrast, immunomodulators, anti-infectives, and Chinese patent drugs had the largest monetary amounts. There was a significant difference in medication rationality between different hospital grades (P<0.001), with tertiary teaching hospitals having the highest rate. Conclusion: Strict epidemic control measures and the role of FCs played a crucial role in controlling the spread of the COVID-19 epidemic. Patients treated in FCs predominantly suffered from respiratory diseases, with older patients and children identified as high-risk populations. Physicians often choose national guidelines, essential drugs, and traditional Chinese for COVID-19 treatment. Tertiary teaching hospitals played a crucial role during the epidemic outbreak.

Risk assessment, emergency treatment and management countermeasures of positive case of COVID-19

Objective: To provide a basis for the adjustment of subsequent prevention and control through the emergency treatment of a case of positive screening for new cornavirus pneumonia in a medical institution and the investigation and evaluation of close contacts.

Impaired T Cell Functions Along with Elevated Activated Tregs at the Early Stage of Asymptomatic SARS-CoV-2 Infection (preprint)

Background: Limited data are available on the T cell responses for the asymptomatic SARS-CoV-2 infection case. Methods: The first imported SARS-CoV-2 infection case in Wuhan was admitted in hospital for quarantine and observation. The T cell responses were followed up by flow cytometry analysis of the peripheral blood nonnuclear cells (PBMCs) at days 7, 13, 22, and 28 after admission. Findings: We found the first imported SARS-CoV-2 infection in Wuhan is an asymptomatic case. His T cell differentiation, proliferation and activation matched the classical kinetics of T cell responses induced by viral infection, but the activation maintained at a relatively low level. Function analysis indicated frequencies of IFN-γ producing CD4+ and CD8+ T cells were notably lower than that of the healthy controls (HC) at day 7, and then rebound gradually. But IFN-γ+ CD8+ T cells were detained at a significant lower level even at day 28, when the SARS-CoV-2 virus had already become undetectable for 3 weeks. Moreover, percentage of IL-17 producing CD4+ T cells was also detained constantly at a much lower level compared to HC. At day 7, although percentage of Tregs was in normal range, the frequency of activated Treg (aTreg) was remarkably as high as 4·4-fold of that in HC. Interpretation: The T cell activation in the asymptomatic SARS-CoV-2 infection experienced a significant suppression and presented impairment of Th1/Th17 and CD8+ T cell functions. Early elevation of the aTregs might play role in the activation and function of T cells in the asymptomatic SARS-CoV-2 infection. Funding Statement: None. Declaration of Interests: All authors declare no competing interests.Ethics Approval Statement: This study was reviewed and approved by the Medical Ethical Committee of Wuhan Jinyintan hospital (approval number KY-2020-47·01). Written informed consent was obtained from the patient and the healthy controls.

Broad phenotypic alterations and potential dysfunctions of lymphocytes in COVID-19 recovered individuals (preprint)

BackgroundLymphopenia is a typical symptom in the COVID-19 patients. While millions of patients are clinical recovered, little is known about the immune status of lymphocytes in these individuals. MethodsA clinical recovered cohort (CR) of 55 COVID-19 individuals (discharged from hospital 4 to 11 weeks), and 55 age and sex matched healthy donors cohort (HD) were recruited. Detailed analysis on phenotype of the lymphocytes in peripheral blood mononuclear cells (PBMCs) was performed by flow cytometry. FindingsCompared with cohort HD, the CD8+ T cells in cohort CR had higher Teff and Tem, but lower Tc1 (IFN-{gamma}+), Tc2 (IL-4+) and Tc17 (IL-17A+) frequencies. The CD4+ T cells of CR had decreased frequency, especially on the Tcm subset. Moreover, CD4+ T cells of CR expressed lower PD-1 and had lower frequencies of Th1 (IFN-{gamma}+), Th2 (IL-4+), Th17 (IL-17A+) as well as circulating Tfh (CXCR5+PD-1+). Accordingly, isotype-switched memory B cell (IgM-CD20hi) in CR had significantly lower proportion in B cells, though level of activation marker CD71 elevated. For CD3-HLA-DRlo lymphocytes of CR, besides levels of IFN-{gamma}, Granzyme B and T-bet were lower, the correlation between T-bet and IFN-{gamma} became irrelevant. In addition, taken into account of discharged days, all the lowered function associated phenotypes showed no recovery tendency within whole observation period. InterpretationThe CR COVID-19 individuals still showed remarkable phenotypic alterations in lymphocytes after clinical recovery 4 to 11 weeks. This suggests SARS-CoV-2 infection imprints profoundly on lymphocytes and results in long-lasting potential dysfunctions. FundingKunming Science and Technology Department (2020-1-N-037)

Impaired T cell functions along with elevated activated Tregs at the early stage of asymptomatic SARS-CoV-2 infection (preprint)

Background Limited data are available on the T cell responses for the asymptomatic SARS-CoV-2 infection case. Methods The first imported SARS-CoV-2 infection case in Wuhan was admitted in hospital for quarantine and observation. The T cell responses were followed up by flow cytometry analysis of the peripheral blood nonnuclear cells (PBMCs) at days 7, 13, 22, and 28 after admission. Findings We found the first imported SARS-CoV-2 infection in Wuhan is an asymptomatic case. His T cell differentiation, proliferation and activation matched the classical kinetics of T cell responses induced by viral infection, but the activation maintained at a relatively low level. Function analysis indicated frequencies of IFN-{gamma} producing CD4+ and CD8+ T cells were notably lower than that of the healthy controls (HC) at day 7, and then rebound gradually. But IFN-{gamma}+CD8+ T cells were detained at a significant lower level even at day 28, when the SARS-CoV-2 virus had already become undetectable for 3 weeks. Moreover, percentage of IL-17 producing CD4+ T cells was also detained constantly at a much lower level compared to HC. At day 7, although percentage of Tregs was in normal range, the frequency of activated Treg (aTreg) was remarkably as high as 4.4-fold of that in HC. Interpretation The T cell activation in the asymptomatic SARS-CoV-2 infection experienced a significant suppression and presented impairment of Th1/Th17 and CD8+ T cell functions. Early elevation of the aTregs might play role in the activation and function of T cells in the asymptomatic SARS-CoV-2 infection.

A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19) (preprint)

BackgroundThe outbreak of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2) has caused more than 2.5 million cases of Corona Virus Disease (COVID-19) in the world so far, with that number continuing to grow. To control the spread of the disease, screening large numbers of suspected cases for appropriate quarantine and treatment is a priority. Pathogenic laboratory testing is the gold standard but is time-consuming with significant false negative results. Therefore, alternative diagnostic methods are urgently needed to combat the disease. Based on COVID-19 radiographical changes in CT images, we hypothesized that Artificial Intelligences deep learning methods might be able to extract COVID-19s specific graphical features and provide a clinical diagnosis ahead of the pathogenic test, thus saving critical time for disease control. Methods and FindingsWe collected 1,065 CT images of pathogen-confirmed COVID-19 cases (325 images) along with those previously diagnosed with typical viral pneumonia (740 images). We modified the Inception transfer-learning model to establish the algorithm, followed by internal and external validation. The internal validation achieved a total accuracy of 89.5% with specificity of 0.88 and sensitivity of 0.87. The external testing dataset showed a total accuracy of 79.3% with specificity of 0.83 and sensitivity of 0.67. In addition, in 54 COVID-19 images that first two nucleic acid test results were negative, 46 were predicted as COVID-19 positive by the algorithm, with the accuracy of 85.2%. ConclusionThese results demonstrate the proof-of-principle for using artificial intelligence to extract radiological features for timely and accurate COVID-19 diagnosis. Author summaryTo control the spread of the COVID-19, screening large numbers of suspected cases for appropriate quarantine and treatment measures is a priority. Pathogenic laboratory testing is the gold standard but is time-consuming with significant false negative results. Therefore, alternative diagnostic methods are urgently needed to combat the disease. We hypothesized that Artificial Intelligences deep learning methods might be able to extract COVID-19s specific graphical features and provide a clinical diagnosis ahead of the pathogenic test, thus saving critical time. We collected 1,065 CT images of pathogen-confirmed COVID-19 cases along with those previously diagnosed with typical viral pneumonia. We modified the Inception transfer-learning model to establish the algorithm. The internal validation achieved a total accuracy of 89.5% with specificity of 0.88 and sensitivity of 0.87. The external testing dataset showed a total accuracy of 79.3% with specificity of 0.83 and sensitivity of 0.67. In addition, in 54 COVID-19 images that first two nucleic acid test results were negative, 46 were predicted as COVID-19 positive by the algorithm, with the accuracy of 85.2%. Our study represents the first study to apply artificial intelligence to CT images for effectively screening for COVID-19.