[Changes in Secondary Inorganic Ions in PM2.5 at Different Pollution Stages Before and After COVID-19 Control].

To investigate the change characteristics of secondary inorganic ions in PM2.5 at different pollution stages before and after COVID-19, the online monitoring of winter meteorological and atmospheric pollutant concentrations in Zhengzhou from December 15, 2019 to February 15, 2020 was conducted using a high-resolution (1 h) online instrument. This study analyzed the causes of the haze process of COVID-19, the diurnal variation characteristics of air pollutants, and the distribution characteristics of air pollutants at different stages of haze.The results showed that Zhengzhou was mainly controlled by the high-pressure ridge during the haze process, and the weather situation was stable, which was conducive to the accumulation of air pollutants. SNA was the main component of water-soluble ions, accounting for more than 90%. Home isolation measures during COVID-19 had different impacts on the distribution characteristics of air pollutants in different haze stages. After COVID-19, the concentration of PM2.5 in the clean, occurrence, and dissipation stages increased compared with that before COVID-19 but significantly decreased in the development stage. The home isolation policy significantly reduced the high value of PM2.5. The concentrations of NO2, SO2, NH3, and CO were the highest in the haze development stage, showing a trend of first increasing and then decreasing. The concentration of O3 was lowest in the pre-COVID-19 development stage but highest in the post-COVID-19 development stage. The linear correlation between[NH4+]/[SO42-] and[NO3-]/[SO42-] at different time periods before and after COVID-19 was strong, indicating that the home isolation policy of COVID-19 did not change the generation mode of NO3-, and the corresponding reaction was always the main generation mode of NO3-. The correlation between[excess-NH4+] and[NO3-] was high in different periods before COVID-19, and NO3- generation was related to the increase in NH3 or NH4+ in the process of PM2.5 pollution in Zhengzhou.

How Primary Care Physicians Can Recognize and Treat Long COVID.

This Medical News article discusses challenges to treatment of post­COVID-19 condition in primary care.

Circulating pediatric respiratory pathogens in Taiwan during 2020: Dynamic change under low COVID-19 incidence.

BACKGROUND: In Taiwan, there were only 799 confirmed COVID-19 cases in 2020. The unique backdrop amidst a pandemic and promotion of nonpharmaceutical interventions generated some distinct changes in the epidemiology of common respiratory pathogens. In this study, we aimed to investigate the dynamic changes in respiratory pathogens in children during 2020. METHODS: We performed a retrospective cohort study at a tertiary hospital in southern Taiwan during 2020. Patients aged 0-18 years who visited the pediatric emergency department were enrolled. Children who presented with clinical symptoms (fever or respiratory illness) and received nasopharyngeal swabs for multiplex polymerase chain reaction (PCR) were included in our analysis. We also compared respiratory syncytial virus (RSV) trends from previous years by PCR and lateral flow immunochromatographic assays from 2017 to 2020. RESULTS: A total of 120 children were tested. The overall detection rate was 55%. With strengthened restrictions, the detection rate dropped from 70% to 30%. However, non-enveloped viruses (rhinovirus/enterovirus and adenovirus) were in constant circulation. Upon easing prevention measures, the detection rate remained above 60%, and an outbreak of an enveloped virus (RSV and parainfluenza virus) was noted. Compared with 2017-2019, the cyclical RSV epidemic was delayed, with a large surge in late 2020. CONCLUSIONS: We observed a constant circulation of non-enveloped viruses when strict nonpharmaceutical interventions were employed and a delayed surge of enveloped viruses during the easing of restrictions. Continuous surveillance and monitoring of the evolutionary dynamics of respiratory viruses is important, while easing restrictions requires balanced judgment.

Development and testing of a multi-lingual Natural Language Processing-based deep learning system in 10 languages for COVID-19 pandemic crisis: A multi-center study

Purpose The COVID-19 pandemic has drastically disrupted global healthcare systems. With the higher demand for healthcare and misinformation related to COVID-19, there is a need to explore alternative models to improve communication. Artificial Intelligence (AI) and Natural Language Processing (NLP) have emerged as promising solutions to improve healthcare delivery. Chatbots could fill a pivotal role in the dissemination and easy accessibility of accurate information in a pandemic. In this study, we developed a multi-lingual NLP-based AI chatbot, DR-COVID, which responds accurately to open-ended, COVID-19 related questions. This was used to facilitate pandemic education and healthcare delivery. Methods First, we developed DR-COVID with an ensemble NLP model on the Telegram platform (https://t.me/drcovid_nlp_chatbot). Second, we evaluated various performance metrics. Third, we evaluated multi-lingual text-to-text translation to Chinese, Malay, Tamil, Filipino, Thai, Japanese, French, Spanish, and Portuguese. We utilized 2,728 training questions and 821 test questions in English. Primary outcome measurements were (A) overall and top 3 accuracies;(B) Area Under the Curve (AUC), precision, recall, and F1 score. Overall accuracy referred to a correct response for the top answer, whereas top 3 accuracy referred to an appropriate response for any one answer amongst the top 3 answers. AUC and its relevant matrices were obtained from the Receiver Operation Characteristics (ROC) curve. Secondary outcomes were (A) multi-lingual accuracy;(B) comparison to enterprise-grade chatbot systems. The sharing of training and testing datasets on an open-source platform will also contribute to existing data. Results Our NLP model, utilizing the ensemble architecture, achieved overall and top 3 accuracies of 0.838 [95% confidence interval (CI): 0.826–0.851] and 0.922 [95% CI: 0.913–0.932] respectively. For overall and top 3 results, AUC scores of 0.917 [95% CI: 0.911–0.925] and 0.960 [95% CI: 0.955–0.964] were achieved respectively. We achieved multi-linguicism with nine non-English languages, with Portuguese performing the best overall at 0.900. Lastly, DR-COVID generated answers more accurately and quickly than other chatbots, within 1.12–2.15 s across three devices tested. Conclusion DR-COVID is a clinically effective NLP-based conversational AI chatbot, and a promising solution for healthcare delivery in the pandemic era.

Development and testing of a multi-lingual Natural Language Processing-based deep learning system in 10 languages for COVID-19 pandemic crisis: A multi-center study.

Purpose: The COVID-19 pandemic has drastically disrupted global healthcare systems. With the higher demand for healthcare and misinformation related to COVID-19, there is a need to explore alternative models to improve communication. Artificial Intelligence (AI) and Natural Language Processing (NLP) have emerged as promising solutions to improve healthcare delivery. Chatbots could fill a pivotal role in the dissemination and easy accessibility of accurate information in a pandemic. In this study, we developed a multi-lingual NLP-based AI chatbot, DR-COVID, which responds accurately to open-ended, COVID-19 related questions. This was used to facilitate pandemic education and healthcare delivery. Methods: First, we developed DR-COVID with an ensemble NLP model on the Telegram platform (https://t.me/drcovid_nlp_chatbot). Second, we evaluated various performance metrics. Third, we evaluated multi-lingual text-to-text translation to Chinese, Malay, Tamil, Filipino, Thai, Japanese, French, Spanish, and Portuguese. We utilized 2,728 training questions and 821 test questions in English. Primary outcome measurements were (A) overall and top 3 accuracies; (B) Area Under the Curve (AUC), precision, recall, and F1 score. Overall accuracy referred to a correct response for the top answer, whereas top 3 accuracy referred to an appropriate response for any one answer amongst the top 3 answers. AUC and its relevant matrices were obtained from the Receiver Operation Characteristics (ROC) curve. Secondary outcomes were (A) multi-lingual accuracy; (B) comparison to enterprise-grade chatbot systems. The sharing of training and testing datasets on an open-source platform will also contribute to existing data. Results: Our NLP model, utilizing the ensemble architecture, achieved overall and top 3 accuracies of 0.838 [95% confidence interval (CI): 0.826-0.851] and 0.922 [95% CI: 0.913-0.932] respectively. For overall and top 3 results, AUC scores of 0.917 [95% CI: 0.911-0.925] and 0.960 [95% CI: 0.955-0.964] were achieved respectively. We achieved multi-linguicism with nine non-English languages, with Portuguese performing the best overall at 0.900. Lastly, DR-COVID generated answers more accurately and quickly than other chatbots, within 1.12-2.15 s across three devices tested. Conclusion: DR-COVID is a clinically effective NLP-based conversational AI chatbot, and a promising solution for healthcare delivery in the pandemic era.

Estimation of Broadleaf Tree Canopy Height of Wolong Nature Reserve Based on InSAR and Machine Learning Methods

Tree height is an important parameter for calculating forest carbon sink and assessing forest carbon cycle. In order to obtain forest tree height over a large area both efficiently and at a low cost, this study proposed an Interferometric Synthetic Aperture Radar (InSAR) combined with a machine learning method to estimate the tree canopy height. The forest height in the study area was obtained using Unmanned Aerial Vehicle (UAV) photogrammetry, which was considered to be the true canopy height. Two machine learning methods (Random Forest, Multi-layer perceptron) were used to establish the relationship between phase center height calculated by InSAR DEM differential interference method and coherent amplitude method with true canopy height. The topographic factor, backward scattering coefficient and coherence coefficient were introduced into the relationship model. It was found that the accuracy of tree height estimation using random forest and two InSAR methods can reach 0.95 and 0.94. The root-mean-square error was 1.76 m, 1.86 m, respectively. The accuracy of tree height estimation using multi-layer perceptron and two InSAR methods was 0.25 and 0.2. The root-mean-square error was 3.96 m and 4.13 m. The results indicated that the combination of InSAR and machine learning can estimate canopy height efficiently and at a low cost. Moreover, the integrated learning algorithm random forest demonstrated better stability and higher accuracy than the single learning algorithm multi-layer perceptron.

Effect of the Nintendo Ring Fit Adventure Exergame on Running Completion Time and Psychological Factors Among University Students Engaging in Distance Learning During the COVID-19 Pandemic: Randomized Controlled Trial.

BACKGROUND: The COVID-19 outbreak has not only changed the lifestyles of people globally but has also resulted in other challenges, such as the requirement of self-isolation and distance learning. Moreover, people are unable to venture out to exercise, leading to reduced movement, and therefore, the demand for exercise at home has increased. OBJECTIVE: We intended to investigate the relationships between a Nintendo Ring Fit Adventure (RFA) intervention and improvements in running time, cardiac force index (CFI), sleep quality (Chinese version of the Pittsburgh Sleep Quality Index score), and mood disorders (5-item Brief Symptom Rating Scale score). METHODS: This was a randomized prospective study and included 80 students who were required to complete a 1600-meter outdoor run before and after the intervention, the completion times of which were recorded in seconds. They were also required to fill out a lifestyle questionnaire. During the study, 40 participants (16 males and 24 females, with an average age of 23.75 years) were assigned to the RFA group and were required to exercise for 30 minutes 3 times per week (in the adventure mode) over 4 weeks. The exercise intensity was set according to the instructions given by the virtual coach during the first game. The remaining 40 participants (30 males and 10 females, with an average age of 22.65 years) were assigned to the control group and maintained their regular habits during the study period. RESULTS: The study was completed by 80 participants aged 20 to 36 years (mean 23.20, SD 2.96 years). The results showed that the running time in the RFA group was significantly reduced. After 4 weeks of physical training, it took females in the RFA group 19.79 seconds (P=.03) and males 22.56 seconds (P=.03) less than the baseline to complete the 1600-meter run. In contrast, there were no significant differences in the performance of the control group in the run before and after the fourth week of intervention. In terms of mood disorders, the average score of the RFA group increased from 1.81 to 3.31 for males (difference=1.50, P=.04) and from 3.17 to 4.54 for females (difference=1.38, P=.06). In addition, no significant differences between the RFA and control groups were observed for the CFI peak acceleration (CFIPA)_walk, CFIPA_run, or sleep quality. CONCLUSIONS: RFA could either maintain or improve an individual's physical fitness, thereby providing a good solution for people involved in distance learning or those who have not exercised for an extended period. TRIAL REGISTRATION: ClinicalTrials.gov NCT05227040; https://clinicaltrials.gov/ct2/show/NCT05227040.

Site-Specific and Stable Conjugation of the SARS-CoV-2 Receptor-Binding Domain to Liposomes in the Absence of Any Other Adjuvants Elicits Potent Neutralizing Antibodies in BALB/c Mice.

Understanding immune responses toward viral infection will be useful for potential therapeutic intervention and offer insights into the design of prophylactic vaccines. The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of the COVID-19 pandemic. To understand the complex immune responses toward SARS-CoV-2 infection, here we developed a method to express and purify the recombinant and engineered viral receptor-binding domain (RBD) to more than 95% purity. We could encapsulate RNA molecules into the interior of a virion-sized liposome. We conjugated the purified RBD proteins onto the surface of the liposome in an orientation-specific manner with defined spatial densities. Both the encapsulation of RNAs and the chemical conjugation of the RBD protein on liposome surfaces were stable under physiologically relevant conditions. In contrast to soluble RBD proteins, a single injection of RBD-conjugated liposomes alone, in the absence of any other adjuvants, elicited RBD-specific B cell responses in BALB/c mice, and the resulting animal sera could potently neutralize HIV-1 pseudovirions that displayed the SARS-CoV-2 spike proteins. These results validate these supramolecular structures as a novel and effective tool to mimic the structure of enveloped viruses, the use of which will allow systematic dissection of the complex B cell responses to SARS-CoV-2 infection.

Weight Gain Associated with COVID-19 Lockdown in Children and Adolescents: A Systematic Review and Meta-Analysis.

BACKGROUND: Lockdown is an effective nonpharmaceutical intervention to reduce coronavirus disease 2019 (COVID-19) transmission, but it restricts daily activity. We aimed to investigate the impact of lockdown on pediatric body weight and body mass index (BMI). METHODS: The systematic review and meta-analysis were conducted following the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) statement. Four online databases (EMBASE, Medline, the Cochrane Library and CINAHL) were searched. RESULTS: The pooled results showed that lockdown was associated with significant body weight gain (MD 2.67, 95% CI 2.12-3.23; p < 0.00001). The BMI of children with comorbidities or obesity did not change significantly. The BMI of general population was significantly higher during lockdown than before the pandemic (MD 0.94, 95% CI 0.32-1.56; p = 0.003). However, heterogeneity was high (I2 = 84%). Among changes in weight classification, increases in the rates of obesity (OR 1.23, 95% CI 1.10-1.37; p = 0.0002) and overweight (OR 1.17, 95% CI 1.06-1.29; p = 0.001) were reported. CONCLUSIONS: Our meta-analysis showed significant increases in body weight and BMI during lockdown among school-age children and adolescents. The prevalence of obesity and overweight also increased. The COVID-19 pandemic worsened the burden of childhood obesity.

Declined eGFR Associated With Poor Prognosis in COVID-19 Patients in Wuhan, China: A Retrospective Cohort Study (preprint)

Background: Increasing evidence revealed that kidney was one of the targets of SARS-CoV-2. However, the incidences of kidney abnormalities were significantly different, from 0·5 to 75·4% in coronavirus disease 2019 (COVID-19) patients. The association of kidney injury with prognosis remain controversial.Methods: In this retrospective cohort study, laboratory confirmed COVID-19 in patients with severe type were enrolled. Demographic, clinical, and laboratory data were collected. Association of estimated glomerular filtration rate (eGFR) with 28-days mortality was analyzed.Findings: The total 28-days mortality of hospitalization was 22·3% (79/354). Non-survivors had a significantly declined eGFR levels than survivors (75·95 [IQR: 47·22,92·84] ml/min/1·73m2 vs. 96·43 [IQR: 84·11,108·47] ml/min/1·73m2, P <0·0001). The 28-days mortality in declined eGFR group (<90 ml/min/1·73m2) was significantly higher than that in normal eGFR group (38·5% vs. 10·7%, P <0·0001). Multivariate logistic regression revealed that the independent risk factors of 28-days outcome included lower eGFR (OR: 3·97, 95%CI: 1·42-11·11), elevated WBC (OR: 7·08, 95%CI: 3·15-15·90), lymphopenia (OR: 2·58, 95%CI: 1·21-5·49), and IL-6 (OR: 7·90, 95%CI: 2·19-28·49). Kaplan-Meier analysis indicated the survival disadvantage in patients with declined eGFR. ROC curve showed the eGFR cut-off value for predicting 28-days death was 82·2 μmol/L, with the sensitivity of 76·7%, and specialty of 66·3%.Interpretation: Declined eGFR was associated with poor prognosis, and could be used an independent risk factor of 28-days mortality in COVID-19 patients. Early detection, and surveillance for eGFR may benefit to identify patients with high-risk of progression.Funding Information: Program of Key Talents of Medical Science in Jiangsu Province, Suzhou science and technology development plan.Declaration of Interests: The authors have declared that no conflict of interest exists.Ethics Approval Statement: This study was approved by the institutional review boards at the First Affiliated Hospital of Soochow University and Wuhan Tongji Hospital. As COVID-19 is an emerging infectious disease, the written informed consent was exempted.

Declined eGFR Associated with Poor Prognosis in COVID-19 Patients in Wuhan, China: A Retrospective Cohort Study (preprint)

Background: Increasing evidence revealed that kidney was one of the targets of SARS-CoV-2. However, the incidences of kidney abnormalities were significantly different, from 0.5 to 75.4% in coronavirus disease 2019 (COVID-19) patients. The association of kidney injury with prognosis remain controversial. Methods: : In this retrospective cohort study, laboratory confirmedCOVID-19inpatients with severe type were enrolled. Demographic, clinicaland laboratory data were collected. Association of estimated glomerular fifiltration rate (eGFR)with 28-days mortality was analyzed. Results: : The total 28-days mortality of hospitalizationwas 22.3% (79/354). Non-survivors had a significantly declined eGFR levels than survivors (75.95 [IQR: 47.22,92.84] ml/min/1.73m 2 vs. 96.43 [IQR: 84.11,108.47] ml/min/1.73m 2 , P <0.001). The 28-days mortality in declined eGFR group (<90 ml/min/1.73m 2 ) was significantly higher than that in normal eGFR group (38.5% vs. 10.7%, P <0.001). Multivariate logistic regression revealed that the independent risk factors of 28-days outcome included lower eGFR (OR: 3.97, 95%CI: 1.42-11.11), elevated WBC (OR: 7.08, 95%CI: 3.15-15.90), lymphopenia (OR: 2.58, 95%CI: 1.21-5.49)andIL-6 (OR: 7.90, 95%CI: 2.19-28.49). Kaplan-Meier analysis indicated the survival disadvantage in patients with declined eGFR. ROC curve showed the eGFR cut-off value for predicting 28-days death was 82.2 μmol/L, with the sensitivity of 76.7% and speciality of 66.3%. Conclusion: Declined eGFR was associated with poor prognosis and could be used an independent risk factor of 28-days mortality in COVID-19 patients. Early detection and surveillance for eGFR may benefit to identify patients with high-risk ofprogression.

Optimal parameterization of COVID-19 epidemic models

ABSTRACT At the time of writing, coronavirus disease 2019 (COVID-19) is seriously threatening human lives and health throughout the world. Many epidemic models have been developed to provide references for decision-making by governments and the World Health Organization. To capture and understand the characteristics of the epidemic trend, parameter optimization algorithms are needed to obtain model parameters. In this study, the authors propose using the Levenberg–Marquardt algorithm (LMA) to identify epidemic models. This algorithm combines the advantage of the Gauss–Newton method and gradient descent method and has improved the stability of parameters. The authors selected four countries with relatively high numbers of confirmed cases to verify the advantages of the Levenberg–Marquardt algorithm over the traditional epidemiological model method. The results show that the Statistical-SIR (Statistical-Susceptible–Infected–Recovered) model using LMA can fit the actual curve of the epidemic well, while the epidemic simulation of the traditional model evolves too fast and the peak value is too high to reflect the real situation. 摘要 现如今, 新冠肺炎(COVID-19)严重威胁着世界各国人民的生命健康.许多流行病学模型已经被用于为政策制定者和世界卫生组织提供决策参考.为了更加深刻的理解疫情趋势的变化特征, 许多参数优化算法被用于反演模型参数.本文提议使用结合了高斯-牛顿法和梯度下降法的Levenberg–Marquardt(LMA)算法来优化模型参数.使用四个病例数相对较多的国家来验证这一算法的优势:相较于传统流行病学模型模拟曲线过早过快的到达峰值, 应用LMA的Statistical-SIR(Statistical-Susceptible–Infected–Recovered)模型可以更好地拟合实际疫情曲线.

Improvement of the Global Prediction System of the COVID-19 Pandemic based on the ensemble empirical mode decomposition and autoregressive–moving-average model in a hybrid approach

ABSTRACT In 2020, the COVID-19 pandemic spreads rapidly around the world. To accurately predict the number of daily new cases in each country, Lanzhou University has established the Global Prediction System of the COVID-19 Pandemic (GPCP). In this article, the authors use the ensemble empirical mode decomposition (EEMD) model and autoregressive–moving-average (ARMA) model to improve the prediction results of GPCP. In addition, the authors also conduct direct predictions for those countries with a small number of confirmed cases or are in the early stage of the disease, whose development trends of the pandemic do not fully comply with the law of infectious diseases and cannot be predicted by the GPCP model. Judging from the results, the absolute values of the relative errors of predictions in countries such as Cuba have been reduced significantly and their prediction trends are closer to the real situations through the method mentioned above to revise the prediction results out of GPCP. For countries such as El Salvador with a small number of cases, the absolute values of the relative errors of prediction become smaller. Therefore, this article concludes that this method is more effective for improving prediction results and direct prediction. 摘要 2020年, 新型冠状病毒肺炎(COVID-19)在世界范围内迅速传播.为准确预测各国每日新增发病人数, 兰州大学开发了COVID-19流行病全球预测系统(GPCP).在本文的研究中, 我们使用集合经验模态分解(EEMD)模型和自回归-移动平均(ARMA)模型对GPCP的预测结果进行改进, 并对发病人数较少或处于发病初期, 不完全符合传染病规律, GPCP模型无法预测的国家进行直接预测.从结果来看, 使用该方法修正预测结果, 古巴等国家预测误差均大幅下降, 且预测趋势更接近真实情况.对于萨尔瓦多等发病人数较少的国家直接进行预测, 相对误差较小, 预测结果较为准确.该方法对于改进预测结果和直接预测均较为有效.

Virtual care to increase military medical centre capacity in the primary health care setting: A prospective self-controlled pilot study of symptoms collection and telemedicine.

INTRODUCTION: The Singapore Armed Forces (SAF) recognises the potential benefits and looks to harnessing telemedicine for primary health care services. In this prospective self-controlled pilot study, we aimed to evaluate the safety, efficiency and user satisfaction outcomes of virtual care (VC) at a military medical centre. METHODS: Out of 320 patients seen during the study period, 28 were enrolled in this study and underwent on-premises VC, comprising digital symptoms collection and telemedicine in addition to the usual in-person physician consultation. Safety outcomes were measured based on the diagnostic concordance between physicians. Efficiency was measured based on consultation times, and user satisfaction was evaluated using a standard questionnaire. RESULTS: There was a higher caseload of both upper respiratory infections and dermatological conditions in our population, in which telemedicine performed well. In terms of safety, telemedicine achieved a mean diagnostic concordance of 92.8% compared to in-person consultations. In terms of efficiency, consultation times were 26.2% - or 2 minutes and 15 seconds - shorter on average with telemedicine (p = 0.0488). User satisfaction was favourable, with 85.5% of patients satisfied with the VC experience. DISCUSSION: This study has been invaluable in showing that on-premises telemedicine is a safe, efficient and effective means to extend and increase our surge capacity for primary health care. Our results have given us reasonable confidence to explore a larger-scale implementation in our network of military medical centres in the future.

Prognostic Value of Serum Creatinine in Severe Covid-19 Patients in Wuhan, China: A Retrospective Cohort Study (preprint)

Objectives: Although the respiratory and immune systems are the major targets of SARS-CoV-2, increasing evidence revealed that kidney injury was not rare in coronavirus disease 2019 (COVID-19). However, the incidences of kidney abnormalities were significantly different, from 0.5 to 75.4% in several reports. The association of kidney injury with prognosis remain controversial.Methods:In this retrospective single center cohort study, laboratory confirmedCOVID-19inpatients with severe type were enrolled. Demographic, clinicaland laboratory data were collected. Association ofserum creatinine (SCr)with 28-days mortality in severe COVID-19 patients was analyzed.Results:18.79% (48/304) patients died during the first 28-days of hospitalization.Non-survivors had a significantly higher SCr levels than survivors (109.27μmol/L vs. 69.99μmol/L, P <0.001). The 28-days mortality in high SCr group (>76μmol/L) was significantly higher than that in low SCr group (31.7% vs. 7.5%, P <0.001). Multivariate logistic regression revealed that the independent risk factors of 28-days outcome included age(OR: 2.95, 95%CI: 1.08-8.05), WBC (OR: 6.09, 95%CI: 2.27-6.39), lymphopenia (OR: 3.49, 95%CI: 1.55-7.92), IL-6 (OR: 4.44, 95%CI: 1.64-11.99) and SCr (OR: 2.69, 95%CI: 1.18-6.11). Kaplan-Meier analysis demonstrated the survival disadvantage in patients with high SCr levels (>76μmol/L). ROC curve showed the SCr cut-off value for predicting 28-days death was 77.5 μmol/L, with the sensitivity of 68.8% and speciality of 74.1%.Conclusion: SCr was associated with poor prognosis and might be an independent risk factor for in-hospital death. The cut-off value of SCr for prognosis prediction was 77.5 μmol/L, with the sensitivity of 68.8% and speciality of 74.1%.

Clinical Features and Lymphocyte Subsets in Recovered Covid-19 Patients With Prolonged Viral Rna Shedding Duration (preprint)

Background:The 2019 novel coronavirus disease (COVID-19) spread in many countries.Data about viral shedding duration, particularly the prolonged ones of the pathogen SARS-Coronavirus-2 (SARS-CoV-2) is scarce. The longest viral RNA sheddingduration reported previously was 37 days. Herein, we report the clinical and immunologic features ofrecovered COVID-19cases with a medium viral RNA shedding duration of 44 days. Cases presentation: Nine laboratory-confirmed COVID-19 cases from Wuhan with viral RNA shedding duration more than 30 days were included in our study,5 of them were moderate.Althoughinflammatory markers were significantlyhigher, the medium duration in severepatients was similar to that in moderate patients (44.5days vs. 43.6days). Severepatients showed higher NK cells levels, although the T cells and B cells were lower as compared with moderate patients. Contrary to previous reports in influenza, prolonged viralshedding time did not cause poor prognosis in this study.Conclusions: There could be characteristic immunological dysfunction in COVID-19 patients with prolonged viral shedding durationand interestingly, prolonged viral shedding duration seemed not to be related with poor prognosis.