Investigation on sleep-related cognition of Chinese health care workers during the first wave of COVID-19 pandemic.

Background: The COVID pandemic has brought tremendous negative effects on the mental health of health care workers, such as anxiety, depression, and sleep disorders. We conducted this study to evaluate the sleep-related cognition of Chinese health care workers (HCWs) during the first wave of COVID-19 pandemic and analyze its association with sleep quality, so as to provide scientific reference for improving sleep of HCWs. Patients and methods: A total of 404 HCWs from Yijishan Hospital of Wuhu City, China were enrolled in the study, selected by randomized cluster sampling in May 2020. We made a questionnaire to collect the general demographic information of the participants. The Pittsburgh Sleep Quality Index (PSQI) and a brief version of Dysfunctional Beliefs and Attitudes about Sleep Scale (DBAS-16) were used to measure sleep quality and sleep-related cognition, respectively. Results: The results showed that 312 HCWs (77.2%) had false beliefs and attitudes about sleep, while only 92 HCWs (22.8%) had correct beliefs about sleep. In addition, we found that those HCWs who were older, married, with a bachelor's degree or higher, nurses, more daily working hours (> 8 h) and monthly night shifts (≥ 5 times), had higher DBAS-16 scores (all p < 0.05). However, we did not find significant differences between men and women in DBAS-16 scores. According to the definition of PSQI, a total of 1/4 of the HCWs are poor sleepers and their DBAS-16 score was higher than good sleepers (t = 7.622, p < 0.001). In the end, we confirmed a positive correlation between sleep cognition and sleep quality (r = 0.392, p < 0.01). Conclusion: Our study revealed false beliefs and attitudes about sleep were prevalent among HCWs during the first wave of COVID-19 pandemic, and these false beliefs about sleep were closely correlated to sleep quality. We recommend fighting against these false beliefs about sleep.

Tracking of Mutational Signature of SARS-CoV-2 Omicron on Distinct Continents and Little Difference was Found.

The Omicron variant is currently ravaging the world, raising serious concern globally. Monitoring genomic variations and determining their influence on biological features are critical for tracing its ongoing transmission and facilitating effective measures. Based on large-scale sequences from different continents, this study found that: (i) The genetic diversity of Omicron is much lower than that of the Delta variant. Still, eight deletions (Del 1-8) and 1 insertion, as well as 130 SNPs, were detected on the Omicron genomes, with two deletions (Del 3 and 4) and 38 SNPs commonly detected on all continents and exhibiting high-occurring frequencies. (ii) Four groups of tightly linked SNPs (linkage I-IV) were detected, among which linkage I, containing 38 SNPs, with 6 located in the RBD, increased its occurring frequency remarkably over time. (iii) The third codons of the Omicron shouldered the most mutation pressures, while the second codons presented the least flexibility. (iv) Four major mutants with amino acid substitutions in the RBD were detected, and further structural analysis suggested that the substitutions did not alter the viral receptor binding ability greatly. It was inferred that though the Omicron genome harbored great changes in antigenicity and remarkable ability to evade immunity, it was immune-pressure selected. This study tracked mutational signatures of Omicron variant and the potential biological significance of the SNPs, and the linkages await further functional verification.

Encoding protein dynamic information in graph representation for functional residue identification

Summary Recent advances in protein function prediction exploit graph-based deep learning approaches to correlate the structural and topological features of proteins with their molecular functions. However, proteins in vivo are not static but dynamic molecules that alter conformation for functional purposes. Here, we apply normal mode analysis to native protein conformations and augment protein graphs by connecting edges between dynamically correlated residue pairs. In the multilabel function classification task, our method demonstrates a remarkable performance gain based on this dynamics-informed representation. The proposed graph neural network, ProDAR, increases the interpretability and generalizability of residue-level annotations and robustly reflects structural nuance in proteins. We elucidate the importance of dynamic information in graph representation by comparing class activation maps for hMTH1, nitrophorin, and SARS-CoV-2 receptor binding domain. Our model successfully learns the dynamic fingerprints of proteins and pinpoints the residues of functional impacts, with vast untapped potential for broad biotechnology and pharmaceutical applications.

Does Citizen Engagement With Government Social Media Accounts Differ During the Different Stages of Public Health Crises? An Empirical Examination of the COVID-19 Pandemic.

Background: The COVID-19 pandemic has created one of the greatest challenges to humankind, developing long-lasting socio-economic impacts on our health and wellbeing, employment, and global economy. Citizen engagement with government social media accounts has proven crucial for the effective communication and management of public health crisis. Although much research has explored the societal impact of the pandemic, extant literature has failed to create a systematic and dynamic model that examines the formation mechanism of citizen engagement with government social media accounts at the different stages of the COVID-19 pandemic. This study fills this gap by employing the Heuristic-Systematic Model and investigating the effects of the heuristic clues including social media capital, information richness, language features, dialogic loop, and the systematic clue including content types, on citizen engagement with government social media across three different stages of the pandemic, employing the moderating role of emotional valence. Methods: The proposed model is validated by scraping 16,710 posts from 22 provincial and municipal government micro-blog accounts in the Hubei province, China. Results: Results show that the positive effects of social media capital on citizen engagement were observed at all stages. However, the effects of information richness, language features, dialogic loop, and content types, and the moderating effect of emotional valence, varied across the different pandemic development stages. Conclusions: The findings provide suggestions for the further effective use of government social media, and better cope with crises. Government agencies should pay attention to the content and form of information shared, using technical means to analyze the information needs of citizens at different stages of public health emergencies, understanding the content most concerned by citizens, and formulating the content type of posts.

Red blood cell distribution width: A severity indicator in patients with COVID-19.

Red blood cell distribution width (RDW) was frequently assessed in COVID-19 infection and reported to be associated with adverse outcomes. However, there was no consensus regarding the optimal cutoff value for RDW. Records of 98 patients with COVID-19 from the First People's Hospital of Jingzhou were reviewed. They were divided into two groups according to the cutoff value for RDW on admission by receiver operator characteristic curve analysis: ≤11.5% (n = 50) and >11.5% (n = 48). The association of RDW with the severity and outcomes of COVID-19 was analyzed. The receiver operating characteristic curve indicated that the RDW was a good discrimination factor for identifying COVID-19 severity (area under the curve = 0.728, 95% CI: 0.626-0.830, p < 0.001). Patients with RDW > 11.5% more frequently suffered from critical COVID-19 than those with RDW ≤ 11.5% (62.5% vs. 26.0%, p < 0.001). Multivariate logistic regression analysis showed RDW to be an independent predictor for critical illness due to COVID-19 (OR = 2.40, 95% CI: 1.27-4.55, p = 0.007). A similar result was obtained when we included RDW > 11.5% into another model instead of RDW as a continuous variable (OR = 5.41, 95% CI: 1.53-19.10, p = 0.009). RDW, as an inexpensive and routinely measured parameter, showed promise as a predictor for critical illness in patients with COVID-19 infection. RDW > 11.5% could be the optimal cutoff to discriminate critical COVID-19 infection.

Doctor recommendation on healthcare consultation platforms: an integrated framework of knowledge graph and deep learning

Purpose>Recommending suitable doctors to patients on healthcare consultation platforms is important to both the patients and the platforms. Although doctor recommendation methods have been proposed, they failed to explain recommendations and address the data sparsity problem, i.e. most patients on the platforms are new and provide little information except disease descriptions. This research aims to develop an interpretable doctor recommendation method based on knowledge graph and interpretable deep learning techniques to fill the research gaps.Design/methodology/approach>This research proposes an advanced doctor recommendation method that leverages a health knowledge graph to overcome the data sparsity problem and uses deep learning techniques to generate accurate and interpretable recommendations. The proposed method extracts interactive features from the knowledge graph to indicate implicit interactions between patients and doctors and identifies individual features that signal the doctors' service quality. Then, the authors feed the features into a deep neural network with layer-wise relevance propagation to generate readily usable and interpretable recommendation results.Findings>The proposed method produces more accurate recommendations than diverse baseline methods and can provide interpretations for the recommendations.Originality/value>This study proposes a novel doctor recommendation method. Experimental results demonstrate the effectiveness and robustness of the method in generating accurate and interpretable recommendations. The research provides a practical solution and some managerial implications to online platforms that confront information overload and transparency issues.

Analysis of Continuous Mutation and Evolution on Circulating SARS-CoV-2

Monitoring the mutation and evolution of the virus is important for tracing its ongoing transmission and facilitating effective vaccine development. A total of 342 complete genomic sequences of SARS-CoV-2 were analyzed in this study. Compared to the reference genome reported in December 2019, 465 mutations were found, among which, 347 occurred in only 1 sequence, while 26 occurred in more than 5 sequences. For these 26 further identified as SNPs, 14 were closely linked and were grouped into 5 profiles. Phylogenetic analysis revealed the sequences formed 2 major groups. Most of the sequences in late period (March and April) constituted the Cluster II, while the sequences before March in this study and the reported S/L and A/B/C types in previous studies were all in Cluster I. The distributions of some mutations were specific geographically or temporally, the potential effect of which on the transmission and pathogenicity of SARS-CoV-2 deserves further evaluation and monitoring. Two mutations were found in the receptor-binding domain (RBD) but outside the receptor-binding motif (RBM), indicating that mutations may only have marginal biological effects but merit further attention. The observed novel sequence divergence is of great significance to the study of the transmission, pathogenicity, and development of an effective vaccine for SARS-CoV-2.

DuDoTrans: Dual-Domain Transformer Provides More Attention for Sinogram Restoration in Sparse-View CT Reconstruction (preprint)

While Computed Tomography (CT) reconstruction from X-ray sinograms is necessary for clinical diagnosis, iodine radiation in the imaging process induces irreversible injury, thereby driving researchers to study sparse-view CT reconstruction, that is, recovering a high-quality CT image from a sparse set of sinogram views. Iterative models are proposed to alleviate the appeared artifacts in sparse-view CT images, but the computation cost is too expensive. Then deep-learning-based methods have gained prevalence due to the excellent performances and lower computation. However, these methods ignore the mismatch between the CNN's \textbf{local} feature extraction capability and the sinogram's \textbf{global} characteristics. To overcome the problem, we propose \textbf{Du}al-\textbf{Do}main \textbf{Trans}former (\textbf{DuDoTrans}) to simultaneously restore informative sinograms via the long-range dependency modeling capability of Transformer and reconstruct CT image with both the enhanced and raw sinograms. With such a novel design, reconstruction performance on the NIH-AAPM dataset and COVID-19 dataset experimentally confirms the effectiveness and generalizability of DuDoTrans with fewer involved parameters. Extensive experiments also demonstrate its robustness with different noise-level scenarios for sparse-view CT reconstruction. The code and models are publicly available at https://github.com/DuDoTrans/CODE

Biological Significance of the Genomic Variation and Structural Dynamics of SARS-CoV-2 B.1.617.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants have been emerging and circulating globally since the start of the COVID-19 pandemic, of which B.1.617 lineage that was first reported in India at the end of 2020, soon became predominant. Tracing genomic variations and understanding their impact on the viral properties are the foundations for the vaccine and drug development and for the mitigation measures to be taken or lifted. In this study, 1,051 near-complete genomes and 1,559 spike (S) sequences belonging to the B.1.617 were analyzed. A genome-wide spread of single nucleotide polymorphisms (SNPs) was identified. Of the high frequency mutations identified, 61% (11/18) involved structural proteins, despite two third of the viral genome encoding nonstructural proteins. There were 22 positive selection sites, mostly distributed across the S protein, of which 16 were led by non-C to U transition and should be of a special attention. Haplotype network revealed that a large number of daughter haplotypes were continually derived throughout the pandemic, of which H177, H181 H219 and H286 from the ancestor haplotype H176 of B.1.617.2 were widely prevalent. Besides the well known substitutions of L452R, P681R and deletions of E156 and F157, as well as the potential biological significance, structural analysis in this study still indicated that new amino acid changes in B.1.617, such as E484Q and N501Y, had reshaped the viral bonding network, and increasingly sequenced N501Y mutant with a potential enhanced binding ability was detected in many other countries in the follow-up monitoring. Although we can't conclude the properties of all the mutants including N501Y thoroughly, it merits focusing on their spread epidemically and biologically.

Sex Differences in Psychological Status and Fatigue of Frontline Staff After the COVID-19 Outbreak in China: A Cross-Sectional Study.

Background: The coronavirus disease 2019 (COVID-19) is spreading globally, and it is significant to pay attention to the mental health of frontline staff in this pandemic. This study is aimed to explore the sex difference among the frontline staff in demographics, characteristics of mental state, and the potential relationship between them. Method: A total of 2,614 Chinese frontline staff were recruited. The Self-Rating Anxiety Scale (SAS) and the Patient Health Questionnaire-9 (PHQ-9) were used for assessing the mental status of frontline staff, and the Fatigue Self-Assessment Scale (FSAS) was used for detecting fatigue. Result: The prevalence rate of anxiety for female frontline staff is higher than that of male (P = 0.003), and the prevalence rate of depression is similar between them (P = 0.091). After comparing the risk factors of unhealthy mental state between different sexes, it is found that family income below 100,000 [depression: odds ratio (OR) 1.37; 95% CI, 1.08-1.73; anxiety: OR 1.99; 95% CI, 1.44-2.75], unsupported of family (depression: OR 10.94; 95% CI, 1.39-85.79; anxiety: OR 11.92; 95% CI, 3.80-37.36), and marriage (depression: OR 1.67; 95% CI, 1.15-2.43) are risk factors for male, and total fatigue (depression: OR 2.96; 95% CI, 1.46-6.02) is risk factor for female. Conclusions: This study found that depression and anxiety are widespread among the frontline staff of COVID-19, and anxiety showed a higher prevalence rate among female frontline staff. From the sex difference in risk factors, the focus of psychological interventions may differ between genders. Men with low family income, unsupported by family or marriage, and women with a high score of total fatigue required particular attention to their psychological status.

Investigation on sleep quality in Chinese medical staff during the COVID-19 pandemic.

The aims of this study were to evaluate the situation of sleep quality among Chinese medical staff during the coronavirus disease 2019 (COVID-19) pandemic. We used the Pittsburgh Sleep Quality Index (PSQI) to assess sleep quality among medical staff from 8 May 2020 to 22 May 2020 in People's Republic of China. A total of 101 (25%) participants were poor sleepers, while 303 (75%) were good sleepers, as defined by PSQI. The PSQI scores were higher in high age, married, master/doctor, nurse, and frequent night shifts groups when compared with those in middle and low age, unmarried and divorced/widowed, bachelor or below, clinician and other job, low frequency night shifts, respectively (all p < 0.01). Multiple linear regression analysis showed that higher PSQI score was positively associated with higher educational background, age, and more frequent night shifts (all p < 0.001). Sleep quality of medical staff should be improved, especially for high age, married, master/doctor, nurse, and frequent night shifts groups.

Predictive Value of Prognostic Nutritional Index on COVID-19 Severity.

Background: The prognostic nutritional index (PNI) has been described as a simple risk-stratified tool for several diseases. We explored the predictive role of the PNI on coronavirus disease 2019 (COVID-19) severity. Methods: A total of 101 patients with COVID-19 were included in this retrospective study from January 2020 to March 2020. They were divided into two groups according to COVID-19 severity: non-critical (n = 56) and critical (n = 45). The PNI was calculated upon hospital admission: 10 × serum albumin (g/dL) + 0.005 × total lymphocyte count (/mm3). Critical COVID-19 was defined as having one of the following features: respiratory failure necessitating mechanical ventilation; shock; organ dysfunction necessitating admission to the intensive care unit (ICU). The correlation between the PNI with COVID-19 severity was analyzed. Results: The PNI was significantly lower in critically ill than that in non-critically ill patients (P < 0.001). The receiver operating characteristic curve indicated that the PNI was a good discrimination factor for identifying COVID-19 severity (P < 0.001). Multivariate logistic regression analysis showed the PNI to be an independent risk factor for critical illness due to COVID-19 (P = 0.002). Conclusions: The PNI is a valuable biomarker that could be used to discriminate COVID-19 severity.

Comparison of Liver Biomarkers in 288 COVID-19 Patients: A Mono-Centric Study in the Early Phase of Pandemic.

Background and Aims: Recent reports have indicated that hepatic dysfunction occurred in a proportion of patients with coronavirus disease 2019 (COVID-19). We aimed to compare and describe the liver biomarkers in different subtypes of COVID-19 patients. Methods: This study enrolled 288 COVID-19 patients in Huangshi Hospital of Traditional Chinese Medicine. All patients were divided into ordinary, severe, and critical groups according to the Diagnosis and Treatment Protocol for Novel Coronavirus Pneumonia (Trial Version 7). Demographic, clinical characteristics and liver biomarkers were compared among the three groups. Results: During hospitalization, AST, TBiL, and ALP levels in ordinary and severe patients fluctuated within the normal range with a rising trend in critical patients except AST. ALT and GGT levels fluctuated within the normal range showing an upward trend, while LDH levels in the critical group exceeded the normal range. Prealbumin showed an upward trend, especially in the severe group. At discharge, AST and LDH levels in ordinary and severe groups were lower than their baselines but increased in the critical group. In contrast to albumin, TBiL levels were increased in ordinary and critical groups while decreased in the severe group. The stratified analysis revealed factors affecting liver function in critical cases included highest temperature ≥38.0°C, age ≥60 and symptom of hypoxemia. Conclusions: COVID-19 can cause severe hepatic dysfunction in critical patients, requiring early monitoring and intervention. LDH, ALP, GGT, TBiL, prealbumin, and albumin may be helpful for evaluating and predicting disease prognosis due to their correlation with disease severity in COVID-19.

Effects of four types of integrated Chinese and Western medicines for the treatment of COVID-19 in China: a network meta-analysis

SUMMARY OBJECTIVE Various integrated Chinese and Western medicines might be beneficial for the treatment of Coronavirus disease 2019 (COVID-19). This study aims to evaluate the efficacy of lung computed tomography (CT) of four integrated Chinese and Western medicines in the treatment of COVID-19 using network meta-analysis (NMA). METHODS Multiple databases were consulted to find randomized controlled trials of four different types of integrated Chinese and Western medicines for the treatment of COVID-19. NMA was conducted on the data using Stata (13.0) software. The odds ratio (OR) was calculated. The studies included in this paper were divided into a control group (Western medicine) and an observation group (one of four integrated Chinese and Western medicines). RESULTS 5 eligible publications were identified. A total of 598 cases were included in the study, and the results showed that the four types of integrated Chinese and Western medicines (symptomatic and supportive care with Qingfei Touxie Fuzheng, Lianhua Qingke, and Xuebijing) were significantly superior (P &lt;0.05) to symptomatic and supportive care alone, except for symptomatic and supportive care with Lianhua Qingwen. The combination of symptomatic and supportive care with Lianhua Qingke had the highest probability of being the most clinically efficacious intervention, with a surface under the cumulative ranking (SUCRA) curve of 85.7. CONCLUSIONS A combination of symptomatic and supportive care with Lianhua Qingke is the best option among the four integrated Chinese and Western medicines considered for the treatment of COVID-19. RESUMO OBJETIVO Vários medicamentos chineses e ocidentais integrados podem ser benéficos para o tratamento da COVID-19. O objetivo deste estudo é avaliar a eficácia da tomografia computadorizada (TC) de pulmão de quatro medicamentos chineses e ocidentais integrados para o tratamento da COVID-19 usando uma meta-análise em rede (NMA). MÉTODOS Vários bancos de dados foram consultados para encontrar ensaios clínicos randomizados de quatro tipos diferentes de medicamentos chineses e ocidentais integrados para o tratamento da COVID-19. A NMA foi realizada nos dados usando o software Stata (13.0). O odds ratio (OR) foi calculado. Os estudos incluídos neste artigo foram divididos em um grupo de controle (medicina ocidental) e um grupo de observação (um dos quatro medicamentos chineses e ocidentais integrados). RESULTADOS 5 publicações elegíveis foram identificadas. Um total de 598 casos foram incluídos no estudo, e os resultados mostraram que os quatro tipos de medicamentos chineses e ocidentais integrados (tratamento sintomático e de suporte com Qingfei Touxie Fuzheng, Lianhua Qingke e Xuebijing) foram significativamente superiores (P &lt;0,05) a somente cuidados sintomáticos e de suporte, exceto cuidados sintomáticos e de suporte com Lianhua Qingwen. A combinação de cuidados sintomáticos e de suporte com Lianhua Qingke teve a maior probabilidade de ser a intervenção clinicamente mais eficaz, com uma superfície abaixo da curva de classificação cumulativa (SUCRA) de 85,7. CONCLUSÕES Uma combinação de tratamento sintomático e de suporte com Lianhua Qingke é a melhor opção entre os quatro medicamentos integrados chineses e ocidentais considerados para o tratamento de COVID-19.

Sustaining a national surgical training programme during the COVID-19 pandemic.

The COVID-19 pandemic has disrupted all segments of daily life, with the healthcare sector being at the forefront of this upheaval. Unprecedented efforts have been taken worldwide to curb this ongoing global catastrophe that has already resulted in many fatalities. One of the areas that has received little attention amid this turmoil is the disruption to trainee education, particularly in specialties that involve acquisition of procedural skills. Hand surgery in Singapore is a standalone combined programme that relies heavily on dedicated cross-hospital rotations, an extensive didactic curriculum and supervised hands-on training of increasing complexity. All aspects of this training programme have been affected because of the cancellation of elective surgical procedures, suspension of cross-hospital rotations, redeployment of residents, and an unsustainable duty roster. There is a real concern that trainees will not be able to meet their training requirements and suffer serious issues like burnout and depression. The long-term impact of suspending training indefinitely is a severe disruption of essential medical services. This article examines the impact of a global pandemic on trainee education in a demanding surgical speciality. We have outlined strategies to maintain trainee competencies based on the following considerations: 1) the safety and wellbeing of trainees is paramount; 2) resource utilization must be thoroughly rationalized; 3) technology and innovative learning methods must supplant traditional teaching methods; and 4) the changes implemented must be sustainable. We hope that these lessons will be valuable to other training programs struggling to deliver quality education to their trainees, even as we work together to battle this global catastrophe.

Analysis of dynamic disturbance in blood coagulation function of patients with Coronavirus Disease 2019: A retrospective observational study.

Coronavirus Disease 2019 (COVID-19) has became a major problem affecting global health security.To assess the differences and dynamic changes of blood coagulation function in COVID-19 patients with different severity.A total of 261 COVID-19 patients from January 24 to March 25, 2020 in Huangshi, Hubei Province were enrolled.We designed a retrospective observational study. Clinical information, including age, blood routine and blood coagulation function, were collected. According to the Diagnosis and Treatment Guidelines for COVID-19 (seventh version) that issued by the National Health Committee of the People's Republic of China, patients were divided into 3 subgroups: 186 ordinary, 45 severe and 30 critical ones. We compared the differences in blood coagulation factors among groups.Average age in critical group (71.47 ±â€Š11.48 years) was the oldest of 3 subgroups. At admission, statistically differences could be observed among ordinary, severe and critical patients in D-dimer (0.18 ±â€Š0.33, 0.63 ±â€Š1.13 and 1.16 ±â€Š1.58 mg/L), fibrinogen/fibrin degradation products (FDP) (3.11 ±â€Š5.30, 9.82 ±â€Š23.91 and 21.94 ±â€Š40.98 µg/ml), platelet [(169 ±â€Š62.85), (188 ±â€Š71.56) and (117 ±â€Š38.31) × 10/L)] and lymphocyte count [(1.18 ±â€Š0.46), (0.82 ±â€Š0.35) and (0.75 ±â€Š0.39) × 10/L)], respectively (P < .05). During hospitalization, the peak values of coagulation and valley values of blood routine were monitored. There were significant differences among ordinary, severe and critical patients in D-dimer (0.26 ±â€Š0.46, 1.39 ±â€Š1.51 and 2.89 ±â€Š1.68 mg/L), FDP (3.29 ±â€Š5.52, 23.68 ±â€Š39.07 and 56.11 ±â€Š49.94 µg/ml), platelet [(164 ±â€Š55.53), (171 ±â€Š69.96) and (84 ±â€Š57.80) × 10/L)] and lymphocyte count [(1.10 ±â€Š0.46), (0.65 ±â€Š0.35) and (0.55 ±â€Š0.31) × 10/L)], respectively (P < .001). D-dimer and FDP in the course of disease in severe/critical groups showed a first upward and then downward trend.We concluded that coagulation function indexes such as D-dimer and FDP could be served as markers to estimate COVID-19 patients condition. Close monitoring of coagulation function may be helpful for early diagnosis of severe patients and guidance of treatments.

Analysis of Continuous Mutation and Evolution on Circulating SARS-CoV-2.

Monitoring the mutation and evolution of the virus is important for tracing its ongoing transmission and facilitating effective vaccine development. A total of 342 complete genomic sequences of SARS-CoV-2 were analyzed in this study. Compared to the reference genome reported in December 2019, 465 mutations were found, among which, 347 occurred in only 1 sequence, while 26 occurred in more than 5 sequences. For these 26 further identified as SNPs, 14 were closely linked and were grouped into 5 profiles. Phylogenetic analysis revealed the sequences formed 2 major groups. Most of the sequences in late period (March and April) constituted the Cluster II, while the sequences before March in this study and the reported S/L and A/B/C types in previous studies were all in Cluster I. The distributions of some mutations were specific geographically or temporally, the potential effect of which on the transmission and pathogenicity of SARS-CoV-2 deserves further evaluation and monitoring. Two mutations were found in the receptor-binding domain (RBD) but outside the receptor-binding motif (RBM), indicating that mutations may only have marginal biological effects but merit further attention. The observed novel sequence divergence is of great significance to the study of the transmission, pathogenicity, and development of an effective vaccine for SARS-CoV-2.

Effects of four types of integrated Chinese and Western medicines for the treatment of COVID-19 in China: a network meta-analysis.

OBJECTIVE Various integrated Chinese and Western medicines might be beneficial for the treatment of Coronavirus disease 2019 (COVID-19). This study aims to evaluate the efficacy of lung computed tomography (CT) of four integrated Chinese and Western medicines in the treatment of COVID-19 using network meta-analysis (NMA). METHODS Multiple databases were consulted to find randomized controlled trials of four different types of integrated Chinese and Western medicines for the treatment of COVID-19. NMA was conducted on the data using Stata (13.0) software. The odds ratio (OR) was calculated. The studies included in this paper were divided into a control group (Western medicine) and an observation group (one of four integrated Chinese and Western medicines). RESULTS 5 eligible publications were identified. A total of 598 cases were included in the study, and the results showed that the four types of integrated Chinese and Western medicines (symptomatic and supportive care with Qingfei Touxie Fuzheng, Lianhua Qingke, and Xuebijing) were significantly superior (P < 0.05) to symptomatic and supportive care alone, except for symptomatic and supportive care with Lianhua Qingwen. The combination of symptomatic and supportive care with Lianhua Qingke had the highest probability of being the most clinically efficacious intervention, with a surface under the cumulative ranking (SUCRA) curve of 85.7. CONCLUSIONS A combination of symptomatic and supportive care with Lianhua Qingke is the best option among the four integrated Chinese and Western medicines considered for the treatment of COVID-19.