An AIEgen/graphene oxide nanocomposite (AIEgen@GO)‐based two‐stage “turn‐on” nucleic acid biosensor for rapid detection of SARS‐CoV‐2 viral sequence

The ongoing outbreak of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS‐CoV‐2) pandemic has posed significant challenges in early viral diagnosis. Hence, it is urgently desirable to develop a rapid, inexpensive, and sensitive method to aid point‐of‐care SARS‐CoV‐2 detection. In this work, we report a highly sequence‐specific biosensor based on nanocomposites with aggregation‐induced emission luminogens (AIEgen)‐labeled oligonucleotide probes on graphene oxide nanosheets (AIEgen@GO) for one step‐detection of SARS‐CoV‐2‐specific nucleic acid sequences (Orf1ab or N genes). A dual “turn‐on” mechanism based on AIEgen@GO was established for viral nucleic acids detection. Here, the first‐stage fluorescence recovery was due to dissociation of the AIEgen from GO surface in the presence of target viral nucleic acid, and the second‐stage enhancement of AIE‐based fluorescent signal was due to the formation of a nucleic acid duplex to restrict the intramolecular rotation of the AIEgen. Furthermore, the feasibility of our platform for diagnostic application was demonstrated by detecting SARS‐CoV‐2 virus plasmids containing both Orf1ab and N genes with rapid detection around 1 h and good sensitivity at pM level without amplification. Our platform shows great promise in assisting the initial rapid detection of the SARS‐CoV‐2 nucleic acid sequence before utilizing quantitative reverse transcription‐polymerase chain reaction for second confirmation. An AIEgen‐graphene oxide (GO) nanocomposite‐based assay is designed for rapid detection of SARS‐CoV‐2 nucleic acids. The sensing mechanism is based on two‐stage fluorescence signal recovery due to fluorescence resonance energy transfer (FRET) effect by detaching AIEgen from GO surface and restricted intramolecular rotation (RIR) effect by formation of nucleic acid duplexes.

Assessing Asymptomatic, Presymptomatic, and Symptomatic Transmission Risk of Severe Acute Respiratory Syndrome Coronavirus 2.

BACKGROUND: The relative contributions of asymptomatic, presymptomatic, and symptomatic transmission of severe acute respiratory syndrome coronavirus 2 have not been clearly measured, although control measures may differ in response to the risk of spread posed by different types of cases. METHODS: We collected detailed information on transmission events and symptom status based on laboratory-confirmed patient data and contact tracing data from 4 provinces and 1 municipality in China. We estimated the variation in risk of transmission over time and the severity of secondary infections by symptomatic status of the infector. RESULTS: There were 393 symptomatic index cases with 3136 close contacts and 185 asymptomatic index cases with 1078 close contacts included in the study. The secondary attack rates among close contacts of symptomatic and asymptomatic index cases were 4.1% (128 of 3136) and 1.1% (12 of 1078), respectively, corresponding to a higher transmission risk from symptomatic cases than from asymptomatic cases (odds ratio, 3.79; 95% confidence interval, 2.06-6.95). Approximately 25% (32 of 128) and 50% (6 of 12) of the infected close contacts were asymptomatic from symptomatic and asymptomatic index cases, respectively, while more than one third (38%) of the infections in the close contacts of symptomatic cases were attributable to exposure to the index cases before symptom onset. CONCLUSIONS: Asymptomatic and presymptomatic transmissions play an important role in spreading infection, although asymptomatic cases pose a lower risk of transmission than symptomatic cases. Early case detection and effective test-and-trace measures are important to reduce transmission.

Recent Advances in Two-Dimensional Transition Metal Dichalcogenide Nanocomposites Biosensors for Virus Detection before and during COVID-19 Outbreak

The deadly Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) outbreak has become one of the most challenging pandemics in the last century. Clinical diagnosis reports a high infection rate within a large population and a rapid mutation rate upon every individual infection. The polymerase chain reaction has been a powerful and gold standard molecular diagnostic technique over the past few decades and hence a promising tool to detect the SARS-CoV-2 nucleic acid sequences. However, it can be costly and involved in complicated processes with a high demand for on-site tests. This pandemic emphasizes the critical need for designing cost-effective and fast diagnosis strategies to prevent a potential viral source by ultrasensitive and selective biosensors. Two-dimensional (2D) transition metal dichalcogenide (TMD) nanocomposites have been developed with unique physical and chemical properties crucial for building up nucleic acid and protein biosensors. In this review, we cover various types of 2D TMD biosensors available for virus detection via the mechanisms of photoluminescence/optical, field-effect transistor, surface plasmon resonance, and electrochemical signals. We summarize the current state-of-the-art applications of 2D TMD nanocomposite systems for sensing proteins/nucleic acid from different types of lethal viruses. Finally, we identify and discuss the advantages and limitations of TMD-based nanocomposites biosensors for viral recognition.

Imbalance and breakout in the post-epidemic era: Research into the spatial patterns of freight demand network in six provinces of central China.

This study aims to explore the freight demand network spatial patterns in six provinces of central China from the perspective of the spread of the epidemic and the freight imbalance and breakout. To achieve this purpose, the big data of "cart search" demand information provided by small and medium freight enterprises on the freight information platform are analyzed. 343,690 pieces of freight demand big data on the freight information platform and Python, ArcGIS, UCINET, and Gephi software are used. The results show that: (1) The choke-point of unbalanced freight demand network is Wuhan, and the secondary choke-points are Hefei and Zhengzhou. (2) In southern China, a chain reaction circle of freight imbalance is formed with Wuhan, Hefei, and Nanchang as the centers. In northern China, a chain reaction circle of freight imbalance is formed with Zhengzhou and Taiyuan as the centers. (3) The freight demand of the six provinces in central China exhibits typical characteristics of long tail distribution with large span and unbalanced distribution. (4) The import and export of freight in different cities vary greatly, and the distribution is unbalanced. This study indicates the imbalance difference, chain reaction, keys and hidden troubles posed by the freight demand network. From the perspectives of freight transfer breakout, freight balance breakout, freight strength breakout, and breakout of freight periphery cities, we propose solutions to breakouts in the freight market in six provinces of central China in the post-epidemic era.

Erratum to risk factors and predictors associated with the severity of COVID-19 in China: a systematic review, meta-analysis, and meta-regression.

[This corrects the article DOI: 10.21037/jtd-20-1743.].

Risk factors and predictors associated with the severity of COVID-19 in China: a systematic review, meta-analysis, and meta-regression.

Since December 2019, the pneumonia cases infected with 2019 novel coronavirus have appeared, posing a critical threat to global health. In this study, we performed a meta-analysis to discover the different clinical characteristics between severe and non-severe patients with COVID-19 to find the potential risk factors and predictors of this disease's severity, as well as to serve as a guidance for subsequent epidemic prevention and control work. PubMed, Cochrane Library, Medline, Embase and other databases were searched to collect studies on the difference of clinical characteristics of severe and non-severe patients. Meta-analysis was performed using RevMan 5.3 software, and the funnel plots could be made to evaluate the publication bias. P>0.05 means no statistical significance. Furthermore, a meta-regression analysis was performed by using Stata 15.0 to find the potential factors of the high degree of heterogeneity (I2>50%). Sixteen studies have been included, with 1,172 severe patients and 2,803 non-severe patients. Compared with non-severe patients, severe patients were more likely to have the symptoms of dyspnea, hemoptysis, and the complications of ARDS, shock, secondary infection, acute kidney injury, and acute cardiac injury. Interestingly, the former smokers were more prevalent in severe cases as compared to non-severe cases, but there was no difference between the two groups of 'current smokers'. Except for chronic liver disease and chronic kidney disease, the underlying comorbidities of hypertension, diabetes, cardiovascular disease, chronic obstructive pulmonary disease (COPD), malignancy, cerebrovascular disease, and HIV can make the disease worse. In terms of laboratory indicators, the decreased lymphocyte and platelet count, and the increased levels of white blood cell (WBC), D-dimer, creatine kinase, lactate dehydrogenase, procalcitonin, alanine aminotransferase, aspartate aminotransferase, and C-reactive protein were more prevalent in severe patients. Meta-regression analysis showed that patient age, gender, and proportion of severe cases did not significantly impact on the outcomes of any clinical indexes that showed high degree of heterogeneity in the meta-analysis. In conclusion, the severity of COVID-19 could be evaluated by, radiologic finding, some symptoms like dyspnea and hemoptysis, some laboratory indicators, and smoking history, especially the ex-smokers. Compared with non-severe patients, severe patients were more likely to have complications and comorbidities including hypertension, cardiovascular disease etc., which were the risk factors for the disease to be severer, but the chronic liver disease and chronic kidney disease were not associated the severity of COVID-19 in China.

Heterogeneity analysis of COVID - 19 clinical phenotype in Wuhan (preprint)

BackgroundSince the outbreak of coronavirus disease 2019 (COVID-19), some studies reported the clinical characteristics of COVID-19 patients in hospital. However, these studies did not investigate the clinical symptoms heterogeneity of COVID-19 patients in the outpatient. This study aimed to describe the heterogeneity of clinical characteristics of outpatient COVID-19 patients.MethodsCOVID-19 patients visiting the respiratory outpatient department of our hospital from January 1st to February 28st 2020 were retrospectively analyzed. Based on the complaints, the patients were classified into four groups including group A (patients without symptoms), group B (patients with fever), group C (patients with respiratory symptoms but without fever), and group D (patients with extra-respiratory symptoms but without fever). The difference of clinical characteristics, basic diseases, laboratory examination of outpatient, characteristics of chest CT imaging among all the groups were analyzed and compared.ResultsA total of 309 COVID-19 patients were included with 126 men and 183 women. The common symptoms included fatigue (59.87%, 95% CI: 54.17-65.38%), loss of appetite (51.13%, 95% CI: 45.41-56.83%), fever (50.81%, 95% CI: 45.09-56.51%), muscle soreness (41.42%, 95% CI:35.88-47.14%), and dry cough (35.28%, 95% CI:29.95-40.89%). The percentages of group A to group D were 2.91%, 50.81%, 18.12%, and 28.16%, respectively. The most common symptoms in Group D included fatigue, loss of appetite, muscle soreness. ConclusionThe heterogeneity of clinical symptoms for COVID-19 patients in the outpatient is significant. We should pay attention to patients without symptoms or those with only extra-respiratory symptoms, who are prone to missed diagnosis.

Analysis of risk factors of severe COVID-19 patients (preprint)

Objective: To explore relevant risk factors for severity of patients diagnosed with novel coronavirus pneumonia (COVID-19). Methods: The clinical data of 292 patients with COVID-19 admitted to Hubei Provincial Hospital of Integrated Chinese & Western Medicine from January 1, 2020 to February 29, 2020 were analyzed retrospectively. Patients were divided into mild or severe group according to the Guidance for Corona Virus Disease 2019 (7th version) released by the Chinese National Health Committee. The clinical data were collected at the time of admission, including demographics, clinical characteristics, laboratory tests, imaging characteristics and outcomes of treatments. We applied univariable and multivariable logistic regression methods to explore the risk factors associated with severity of the disease.Results: The median age of patients in the severe group ((68.19±12.51) years) was significantly older than mild group ((54.14 ± 13.62) years). The male sex was more predominant in severe group (63.45%) than that of mild group (38.1%). There were more smokers (8.97% vs 1.36%) and drinkers (4.14% vs 0%) in severe group than that of mild group. Patients in the severe group had more underlying diseases. Hypertension（48.97% vs 23.81%），coronary heart disease (22.07% vs 1.36%, P<0.0001) , chronic obstructive pulmonary disease (6.21% vs 1.36%), malignant tumor (7.59% vs 2.04%) and chronic kidney disease (3.45% vs 0%) were more frequent in severe group than in mild group. The dyspnea, chest tightness and dry cough were more common in severe group (43.45%, 66.9% and 66.21%) than in mild group (23.13%, 44.22% and 53.74%). Abnormality of chest radiography were more frequent in the severe group, there were more ground glass opacities, consolidation of lung and white lung in the severe cases (88.97%, 44.07% and 46.21%) than in mild cases (78.91%, 19.05% and 2.04%). Patients in the severe group were more likely to receive methylprednisolone, oxygen therapy and mechanical ventilation. Lasso algorithm showed that age, C-reactive protein (CRP), creatine kinase (CK) and α-hydroxybutyrate dehydrogenase (α-HBDB) were independent risk factors for severe COVID-19, but the count of CD4+T lymphocyte was the protective factor. Conclusion: This retrospective study of 292 COVID-19 patients revealed that age, CRP, CK, α-HBDB and the count of CD4+T lymphocyte were independent risk factors for severity of COVID-19. Identifying patients with risk factors at an early stage of the disease are helpful for outcome prediction and clinical management.