The roles of meteorological variables, demographic factors, and policy response measures in the variation of COVID-19 daily cases: Evidence from different climate zones (preprint)

It is widely considered that weather conditions affect the spread of COVID-19, but to date, the collective influence of demographic factors and government policy response measures have hardly been considered. The objective of this study is to utilize a machine learning method to assess the corresponding roles of meteorological variables, demographic factors, and government response measures in daily new cases of COVID-19 among multiple climate zones at city/county level. The overall model showed good performance with a validated R2 of 0.86, as satisfactory as individual climate zone models. Population density ranked the most important factor, followed by meteorological variables and response measures. Ultraviolet radiation and temperature dominated among meteorological factors, but the association with daily new cases seemed to be inconsistent among different climate zones. Implementing stricter response measures could help effectively contain the spread of COVID-19, but did so with a lagged effect, and the typical lockdown measures might not be applicable to all climate conditions. This study preliminarily analyzed the roles of certain factors in the transmission of COVID-19, and provided practical evidence for developing an early health warning system of global pandemics by leveraging big data technology and multiple sourced data fusion.

High Number and Specific Comorbidities Could Impact the Immune Response in COVID-19 Patients

Background Cellular immunodeficiency and comorbidities are common in COVID-19 patients. Aim The purpose of this study was to investigate comorbidities impacting on the cellular immunity in COVID-19 patients. Methods The research objects included 55 healthy controls and 718 COVID-19 patients who divided into the control group and the COVID-19 group, respectively. Those in the COVID-19 group were divided into subgroups on the basis of the number and types of comorbidities present. Lymphocyte itself and its subsets were compared between the control group and the COVID-19 group, the groups with comorbidities based on the different number and types of comorbidities, and the relationship between the lymphocyte counts and subsets with the number and types of comorbidities was investigated. Results Compared with the control group, the lymphocyte counts and T cell subsets were significantly increased in the groups with comorbidities, but both B and NK cell subsets were significantly decreased in the no comorbidity group and in most of the groups with comorbidities (all P<0.05). In the three comorbidities group, the lymphocyte counts and T cell subsets were all significantly decreased, but the CD56+ percentage was obviously increased (all P<0.05). The number of comorbidities was negatively correlated with the lymphocyte counts and the T and NK cell subsets. A negative correlation also existed between cancer and both the lymphocyte counts and the T cell subsets, between chronic hepatitis B and the lymphocyte counts, and between chronic kidney disease and the CD3+ counts. A positive correlation existed between nonalcoholic fatty liver disease (NAFLD) disease and both lymphocyte and CD3+ counts. The risk factors were number of comorbidities for the lymphocyte count, CD3+CD4+ and CD3+CD8+ percentages, NAFLD for the lymphocyte and CD3+ counts, cardiovascular diseases for CD3+CD4+ and CD3+CD8+ percentages, diabetes mellitus for the CD3+CD8+ percentage, and cancer for the CD3+ percentage, respectively. Conclusions High numbers of comorbidities and specific comorbidities could impact the immune response of COVID-19 patients. This study provides a reference for clinicians in the identification of suitable and timely immunotherapy for COVID-19 patients. Clinical Trial Registry https://www.chictr.org.cn/enindex.aspx, identifier ChiCTR2000034563.

Epidemiological Characteristics of Traumatic Spinal Cord Injuries in Multi-Center Retrospective Study in Northwest China, 2017-2020. (preprint)

Introduction: Traumatic spinal cord injuries (TSCI) are worldwide public health problems. There has been a lack of extensive multi-center study of TSCI epidemiology in Northwest China in pre- and post-pandemic period of COVID-19. Materials: and Methods: A multi-center retrospective study of 14 hospitals of Northwest China was conducted on patients with TSCI between 2017 and 2020. Variables assessed included patient demographics, etiology, segmental distribution, treatment, waiting time for treatment and outcomes. Results: : The number of patients with TSCI showed an increasing trend from 2017 to 2019 while there were fewer patients in 2020 than in 2019. The male-to-female ratio was 3.67:1 and the mean age was 48±14.9. The major cause of TSCI was high fall (38.8%), low fall (27.7%), traffic accidents (23.9%), sports (2.6%) and others (7.0%). The segmental distribution showed a bimodal pattern, peak segments were C6 and Tl1, L1(14.7%) was the most frequently injured segments. Incomplete injury (72.8%) occurred more often than complete injury (27.2%). ASIA scale of most patients did not change before and after treatment both in operational or conservative group. 975 patients from urban and 1646 patients from rural areas were conducted, most urban residents could rush to get treatment after injured immediately (<1 h), whereas most rural patients get treatment spend several hours since injured. The rough annual incidence from 2017-2020 are 112.4, 143.4,152.2 and 132.6 per million people calculated by the population-coverage-rate. Conclusion: The incidence of TSCI in Northwest China is high and growing. However, under the pandemic policy reasons, it has decreased in 2020. The promotion of online work may be an effective primary prevention measures for trauma. Also, due to the distance from the hospitals with proper conditions, rural patients need to spend long time to there, the timely treatment of them should be paid attention.

The Effects of Pulsed Electric Field On The Interaction Between SARS-Cov-2 And Human ACE2: Y505 Is A Key Target (preprint)

A novel coronavirus has rapidly spread to almost every country in the world, causing over 233 million confirmed cases of coronavirus disease 2019 (COVID-19) and over 209,761,242 deaths by late September 2021. Binding the receptor binding domain (RBD) to the host cell surface receptor protein, angiotensin converter enzyme (ACE2), is a key step in virus infection. In this study, we applied a pulsed electric field to the RBD/ACE2 complex based on molecular dynamics simulation and demonstrated that the electric field affects the structure and binding affinity of the complex. Additionally, residue Y505 is the crucial medium for the effects of electric field on the complex. Overall, these results may help apply an external electric field to virus suppression.

Mapping the host protein interactome of non-coding regions in SARS-CoV-2 genome (preprint)

A deep understanding of SARS-CoV-2-host interactions is crucial to the development of effective therapeutics. The role of non-coding regions of viral RNA (ncrRNAs) has not been scrutinized. We developed a method using MS2 affinity purification coupled with liquid chromatography-mass spectrometry (MAMS) to systematically map the interactome of SARS-CoV-2 ncrRNA in different human cell lines. Integration of the results defined the core and cell-type-specific ncrRNA-host protein interactomes. The majority of ncrRNA-binding proteins were involved in RNA biogenesis, protein translation, viral infection, and stress response. The 5′ UTR interactome is enriched with proteins in the snRNP family and is a target for the regulation of viral replication and transcription. The 3′ UTR interactome is enriched with proteins involved in the cytoplasmic RNP granule (stress granule) and translation regulation. We show that the ORF10 is likely to be a part of 3′ UTR. Intriguingly, the interactions between negative-sense ncrRNAs and host proteins, such as translation initiation factors and antiviral factors, suggest a pathological role of negative-sense ncrRNAs. Moreover, the cell-type-specific interactions between ncrRNAs and mitochondria may explain the differences of cell lines in viral susceptibility. Our study unveils a comprehensive landscape of the functional SARS-CoV-2 ncrRNA-host protein interactome, providing a new perspective on virus-host interactions and the design of future therapeutics.

Clinical characteristics and chest computed tomography findings of severe and critical coronavirus disease 2019 in Shanghai, China

Objective To sum up the clinical characteristics and chest computed tomography (CT) findings of severe and critical coronavirus disease 2019 (COVID-19) patients, and to explore the factors affecting the outcomes, so as to provide experience for the clinical diagnosis and treatment of severe and critical COVID-19. Methods The data of 25 severe and critical COVID-19 patients, who were treated in our hospital from Jan. 23, 2020 to Mar. 5, 2020, were collected. The clinical characteristics were retrospectively analyzed, and the clinical and laboratory indexes were compared between cured patients and uncured patients. The laboratory indicators of cured patients were further compared between the progressive and recovery stages. The chest CT findings of the patients were observed, and the lesion volume was quantified to assess the evolution of lung lesions using the CT image-based intelligent pneumonia lesion quantitative analysis software. Results There were 19 male and six female COVID-19 patients, and there were three deaths. The median age of 25 patients was 65 (63, 75) years old, and the body mass index (BMI) was 25.60 (23.51, 28.65) kg/m2. Twenty-two patients had a clear epidemiological history. Fever (22 cases) and cough (14 cases) were the most common first symptoms, and 18 patients had underlying diseases. Twelve patients were cured and discharged (median hospital stay was 25.5 d), and 13 patients were not cured, including three deaths and 10 cases with hospital stay>25 d with no remission. Compared with the uncured patients, the cured patients had significantly lower BMI, longer time from onset to progression to severe or critical illness, and higher CD4 +T lymphocyte counts (all P<0.05). Multivariate logistic regression analysis showed that high CD4 +T lymphocyte count was an independent protective factor for the cure and discharge of severe and critical COVID-19 patients (P=0.031). Compared with those in the progressive stage, the lymphocyte count and CD4 +T lymphocyte count of 12 cured patients were significantly higher in the progression stage, and the C-reactive protein (CRP) level, erythrocyte sedimentation rate (ESR) and procalcitonin level were significantly lower (all P<0.01). Twenty-one patients received chest CT examination in the progressive stage;and all of them had multiple ground-glass opacities and consolidation shadows of the multiple-lobe lateral band and the dorsal side of bilateral lungs, 20 cases had pleural thickening, 9 cases had a small amount of bilateral pleural effusion, and 8 cases had mediastinal lymphadenopathy. The 12 cured patients received CT examination during the recovery period, and their lesions were all improved to different extents;some patients had irregular fiber grid shadows and stripe shadows;and the pleural thickening and pleural effusion were reduced to different extents. The quantitative analysis curves showed that lesion volume in the 12 cured patients obviously increased in the progressive stage and reduced in the absorption stage, showing an inverted V shape;and lesion volume in the uncured patients (nine cases received CT examination for two or more times) showed a rapid increase in the progressive stage. Conclusion Most severe and critical COVID-19 patients in Shanghai are older, with higher BMI and underlying diseases. Low BMI, slow disease progression, and high CD4 +T lymphocyte count are beneficial to the improvement of COVID-19. The main findings of chest CT include multiple ground-glass opacities and consolidation shadows, mainly distributing in the lateral band and the dorsal side of lungs and mostly involving the pleura. The laboratory indexes, including the lymphocyte, CRP, CD4 +T lymphocyte, ESR and procalcitonin, and chest CT examination play an important role in the diagnosis, disease monitoring and prognosis assessment of COVID-19

Accurately Differentiating COVID-19, Other Viral Infection, and Healthy Individuals Using Multimodal Features via Late Fusion Learning (preprint)

Effectively identifying COVID-19 patients using non-PCR clinical data is critical for the optimal clinical outcomes. Currently, there is a lack of comprehensive understanding of various biomedical features and appropriate technical approaches to accurately detecting COVID-19 patients. In this study, we recruited 214 confirmed COVID-19 patients in non-severe (NS) and 148 in severe (S) clinical type, 198 non-infected healthy (H) participants and 129 non-COVID viral pneumonia (V) patients. The participants' clinical information (23 features), lab testing results (10 features), and thoracic CT scans upon admission were acquired as three input feature modalities. To enable late fusion of multimodality data, we developed a deep learning model to extract a 10-feature high-level representation of the CT scans. Exploratory analyses showed substantial differences of all features among the four classes. Three machine learning models (k-nearest neighbor kNN, random forest RF, and support vector machine SVM) were developed based on the 43 features combined from all three modalities to differentiate four classes (NS, S, V, and H) at once. All three models had high accuracy to differentiate the overall four classes (95.4%-97.7%) and each individual class (90.6%-99.9%). Multimodal features provided substantial performance gain from using any single feature modality. Compared to existing binary classification benchmarks often focusing on single feature modality, this study provided a novel and effective breakthrough for clinical applications. Findings and the analytical workflow can be used as clinical decision support for current COVID-19 and other clinical applications with high-dimensional multimodal biomedical features.

Baidu Jieduan granule, traditional Chinese medicine, in the treatment of moderate coronavirus disease-2019 (COVID-19): study protocol for an open-label randomized controlled clinical trial (preprint)

Background: Currently, coronavirus disease-2019 (COVID-19) is continuously and rapidly circulating, resulting in serious and extensive impact on human health. Due to the absence of antiviral medicine for COVID-19 thus far, it is desperately need to develop the effective medicine. Traditional Chinese medicine (TCM) has been widely applied in the treatment of epidemic diseases in China, hoping to produce clinical efficacy and decrease the use of antibiotics and glucocorticoid. The aim of this study is to evaluate the efficacy and safety of Baidu Jieduan granule in curing COVID-19. Methods/design: This multicenter, open-label randomized controlled trial is conducted 300 cases with COVID-19. The patients will be randomly (1:1) divided into treatment group or control group. All cases will receive standard therapy at the same time. The experiment group will receive Baidu Jieduan granule treatment twice a day for 14 days. The outcomes are assessed at baseline and at 3, 5, 7, 14 days after treatment initiation. The primary outcome is the rate of symptom (fever, fatigue, and coughing) recovery. Adverse events (AEs) will be monitored throughout the trial.Discussion: The study will provide a high-quality clinical evidence to support the efficacy and safety of Baidu Jieduan granule in treatment of moderate COVID-19, and also enrich the theory and practice of TCM in treating COVID-19. Trial registration: Chinese Clinical Trial Registry, ChiCTR2000029869. Registered on 15 February 2020

Patient-derived mutations impact pathogenicity of SARS-CoV-2 (preprint)

The sudden outbreak of the severe acute respiratory syndrome-coronavirus (SARS-CoV-2) has spread globally with more than 1,300,000 patients diagnosed and a death toll of 70,000. Current genomic survey data suggest that single nucleotide variants (SNVs) are abundant. However, no mutation has been directly linked with functional changes in viral pathogenicity. Here we report functional characterizations of 11 patient-derived viral isolates, all of which have at least one mutation. Importantly, these viral isolates show significant variation in cytopathic effects and viral load, up to 270-fold differences, when infecting Vero-E6 cells. We observed intrapersonal variation and 6 different mutations in the spike glycoprotein (S protein), including 2 different SNVs that led to the same missense mutation. Therefore, we provide direct evidence that the SARS-CoV-2 has acquired mutations capable of substantially changing its pathogenicity.