Evaluating SARS-CoV-2 antibody reactivity to natural exposure and inactivated vaccination with peptide microarrays.

Objective: Vaccination is effective tool for preventing and controlling SARS-CoV-2 infections, and inactivated vaccines are the most widely used type of vaccine. In order to identify antibody-binding peptide epitopes that can distinguish between individuals who have been vaccinated and those who have been infected, this study aimed to compare the immune responses of vaccinated and infected individuals. Methods: SARS-CoV-2 peptide microarrays were used to assess the differences between 44 volunteers inoculated with the inactivated virus vaccine BBIBP-CorV and 61 patients who were infected with SARS-CoV-2. Clustered heatmaps were used to identify differences between the two groups in antibody responses to peptides such as M1, N24, S15, S64, S82, S104, and S115. Receiver operating characteristic curve analysis was used to determine whether a combined diagnosis with S15, S64, and S104 could effectively distinguish infected patients from vaccinated individuals. Results: Our findings showed that the specific antibody responses against S15, S64, and S104 peptides were stronger in vaccinators than in infected persons, while responses to M1, N24, S82, and S115 were weaker in asymptomatic patients than in symptomatic patients. Additionally, two peptides (N24 and S115) were found to correlate with the levels of neutralizing antibodies. Conclusion: Our results suggest that antibody profiles specific to SARS-CoV-2 can be used to distinguish between vaccinated individuals and those who are infected. The combined diagnosis with S15, S64, and S104 was found to be more effective in distinguishing infected patients from those who have been vaccinated than the diagnosis using individual peptides. Moreover, the specific antibody responses against the N24 and S115 peptides were found to be consistent with the changing trend of neutralizing antibodies.

Assessing the impact of COVID-19 interventions on influenza-like illness in Beijing and Hong Kong: an observational and modeling study.

BACKGROUND: The impact of coronavirus diseases 2019 (COVID-19) related non-pharmaceutical interventions (NPIs) on influenza activity in the presence of other known seasonal driving factors is unclear, especially at the municipal scale. This study aimed to assess the impact of NPIs on outpatient influenza-like illness (ILI) consultations in Beijing and the Hong Kong Special Administrative Region (SAR) of China. METHODS: We descriptively analyzed the temporal characteristics of the weekly ILI counts, nine NPI indicators, mean temperature, relative humidity, and absolute humidity from 2011 to 2021. Generalized additive models (GAM) using data in 2011-2019 were established to predict the weekly ILI counts under a counterfactual scenario of no COVID-19 interventions in Beijing and the Hong Kong SAR in 2020-2021, respectively. GAM models were further built to evaluate the potential impact of each individual or combined NPIs on weekly ILI counts in the presence of other seasonal driving factors in the above settings in 2020-2021. RESULTS: The weekly ILI counts in Beijing and the Hong Kong SAR fluctuated across years and months in 2011-2019, with an obvious winter-spring seasonality in Beijing. During the 2020-2021 season, the observed weekly ILI counts in both Beijing and the Hong Kong SAR were much lower than those of the past 9 flu seasons, with a 47.5% [95% confidence interval (CI): 42.3%, 52.2%) and 60.0% (95% CI: 58.6%, 61.1%) reduction, respectively. The observed numbers for these two cities also accounted for only 40.2% (95% CI: 35.4%, 45.3%) and 58.0% (95% CI: 54.1%, 61.5%) of the GAM model estimates in the absence of COVID-19 NPIs, respectively. Our study revealed that, "Cancelling public events" and "Restrictions on internal travel" measures played an important role in the reduction of ILI in Beijing, while the "restrictions on international travel" was statistically most associated with ILI reductions in the Hong Kong SAR. CONCLUSIONS: Our study suggests that COVID-19 NPIs had been reducing outpatient ILI consultations in the presence of other seasonal driving factors in Beijing and the Hong Kong SAR from 2020 to 2021. In cities with varying local circumstances, some NPIs with appropriate stringency may be tailored to reduce the burden of ILI caused by severe influenza strains or other respiratory infections in future.

AI-Designed, Mutation-Resistant Broad Neutralizing Antibodies Against Multiple SARS-CoV-2 Strains (preprint)

In this study, we generated a Digital Twin for SARS-CoV-2 by integrating data and meta-data with multiple data types and processing strategies, including machine learning, natural language processing, protein structural modeling, and protein sequence language modeling. This approach enabled the computational design of broadly neutralizing antibodies against over 1300 different historical strains of SARS-COV-2 containing 64 mutations in the receptor binding domain (RBD) region. The AI-designed antibodies were experimentally validated in real-virus neutralization assays against multiple strains including the newer Omicron strains that were not included in the initial design base. Many of these antibodies demonstrate strong binding capability in ELISA assays against the RBD of multiple strains. These results could help shape future therapeutic design for existing strains, as well as predicting hidden patterns in viral evolution that can be learned by AI for developing future antiviral treatments.

Deep-learning based bioactive therapeutic peptides generation and screening (preprint)

Many bioactive peptides demonstrated therapeutic effects over-complicated diseases, such as antiviral, antibacterial, anticancer, etc. Similar to the generating de novo chemical compounds, with the accumulated bioactive peptides as a training set, it is possible to generate abundant potential bioactive peptides with deep learning. Such techniques would be significant for drug development since peptides are much easier and cheaper to synthesize than compounds. However, there are very few deep learning-based peptide generating models. Here, we have created an LSTM model (named LSTM_Pep) to generate de novo peptides and finetune learning to generate de novo peptides with certain potential therapeutic effects. Remarkably, the Antimicrobial Peptide Database has fully utilized in this work to generate various kinds of potential active de novo peptide. We proposed a pipeline for screening those generated peptides for a given target, and use Main protease of SARS-COV-2 as concept-of-proof example. Moreover, we have developed a deep learning-based protein-peptide prediction model (named DeepPep) for fast screening the generated peptides for the given targets. Together with the generating model, we have demonstrated iteratively finetune training, generating and screening peptides for higher predicted binding affinity peptides can be achieved. Our work sheds light on to the development of deep learning-based methods and pipelines to effectively generating and getting bioactive peptides with a specific therapeutic effect, and showcases how artificial intelligence can help discover de novo bioactive peptides that can bind to a particular target.

Association of depression symptoms and sleep quality with state-trait anxiety in medical university students in Anhui Province, China: a mediation analysis.

BACKGROUND: The prevalence of depression symptoms among medical students is particularly high, and it has increased during the COVID-19 epidemic. Sleep quality and state-trait anxiety are risk factors for depression, but no study has yet investigated the mediating role of state-trait anxiety in the relationship between poor sleep quality and depression symptoms in medical students. This study aims to investigate the relationship among depression symptoms, sleep quality and state-trait anxiety in medical university students in Anhui Province. METHODS: This was a cross-sectional survey of 1227 students' online questionnaires collected from four medical universities in Anhui Province using a convenience sampling method. We measured respondents' sleep quality, state-trait anxiety, and depression symptoms using three scales: the Pittsburgh Sleep Quality Index (PSQI), the State-Trait Anxiety Inventory (STAI) and the Self-rating Depression Scale (SDS). We analysed the mediating role of STAI scores on the association between PSQI scores and SDS scores through the Sobel-Goodman Mediation Test while controlling for covariates. P < 0.05 was considered statistically significant. RESULTS: A total of 74.33% (912) and 41.40% (518) of the respondents reported suffering from poor sleep quality and depression symptoms. Sleep quality, state-trait anxiety, and depression symptoms were positively associated with each other (ß = 0.381 ~ 0.775, P < 0.001). State-trait anxiety partially mediated the association between sleep quality and depression symptoms (Sobel test Z = 15.090, P < 0.001), and this mediating variable accounted for 83.79% of the association when adjusting for potential confounders. Subgroup analysis further revealed that STAI scores partially mediated the association between PSQI scores and SDS scores in females and rural students and fully mediated the association between PSQI scores and SDS scores in males and urban students. CONCLUSIONS: This study found that sleep quality and state-trait anxiety have a significant predictive effect on depression symptoms. State-trait anxiety mediated the relationship between sleep quality and depression symptoms, with a more complex mechanism observed among rural and female medical students. Multiple pathways of intervention should be adopted, such as encouraging students to self-adjust, providing professional psychological intervention and timely monitoring, enriching extracurricular activities, and making changes in policies regarding long shifts and working hours.

How Healthy Aging and Contact With Children Are Associated With Satisfaction in Middle-Aged and Older Parents in China: A Mediation Analysis.

Objectives: This study aims to examine the mediation role of satisfaction with children on the association between contact with children (CCT) and healthy aging among middle-aged and older parents in China. Methods: Data from 9,575 parents over 45 years old were obtained from the 2018 China Health and Retirement Longitudinal Survey. A multinomial logistic regression model was applied to measure the association between contact, satisfaction, and healthy aging with potential confounders controlled. We used the Sobel-Goodman Mediation test to analyze the mediation role of satisfaction on the association between types of CCT and healthy aging. Results: Parents with contact with adult children had higher satisfaction with children [for contact weekly (satisfied/unsatisfied): relative risk ratio (RRR) = 2.44, CI = 1.92-3.10] and higher healthy aging [for contact weekly (Q5/Q1): RRR = 1.41, CI = 1.13-1.77]. Satisfaction was strongly related to healthy aging [for satisfied (Q5/Q1): RRR = 3.44, CI = 2.14-5.51], and mediated 19.05% of healthy aging for weekly contact (Sobel test z = 4.338; indirect role = 0.014, CI = 0.011-0.018; direct role = 0.061, CI = 0.029-0.094). Subgroup analysis further revealed that satisfaction with contact played a partial mediating role between monthly contact and healthy aging in female and rural groups. Conclusions: Monthly CCT is more appropriate for older parents. Satisfaction with children in older parents seems to act as a significant and partial mediator of the relationship between contact and healthy aging. The contribution of satisfaction to healthy aging could be important to be considered and promoted in women and rural older parents, independent of CCT.

Recent advances and challenges of biosensing in point-of-care molecular diagnosis.

Molecular diagnosis, which plays a major role in infectious disease screening with successful understanding of the human genome, has attracted more attention because of the outbreak of COVID-19 recently. Since point-of-care testing (POCT) can expand the application of molecular diagnosis with the benefit of rapid reply, low cost, and working in decentralized environments, many researchers and commercial institutions have dedicated tremendous effort and enthusiasm to POCT-based biosensing for molecular diagnosis. In this review, we firstly summarize the state-of-the-art techniques and the construction of biosensing systems for POC molecular diagnosis. Then, the application scenarios of POCT-based biosensing for molecular diagnosis were also reviewed. Finally, several challenges and perspectives of POC biosensing for molecular diagnosis are discussed. This review is expected to help researchers deepen comprehension and make progresses in POCT-based biosensing field for molecular diagnosis applications.

A meta-analysis of the association between obesity and COVID-19.

Owing to limited data, we conducted a meta-analysis to re-evaluate the relationship between obesity and coronavirus-2019 (COVID-19). Literature published between 1 January 2020 and 22 August 2020 was comprehensively analysed, and RevMan3.5 was used for data analysis. A total of 50 studies, including data on 18 260 378 patients, were available. Obesity was associated with a higher risk of severe acute respiratory syndrome-coronavirus 2 (SARS-CoV2) infection (odds ratio (OR): 1.39, 95% confidence interval (CI) 1.25-1.54; P < 0.00001) and increased severity of COVID-19 (hospitalisation rate: OR: 2.45, 95% CI 1.78-3.39; P < 0.00001; severe cases: OR: 3.74, 95% CI 1.18-11.87; P: 0.02; need for intensive care unit admission: OR: 1.30, 95% CI 1.21-1.40; P < 0.00001; need for invasive mechanical ventilation: OR: 1.59, 95% CI 1.35-1.88; P < 0.00001 and mortality: OR: 1.65, 95% CI 1.21-2.25; P: 0.001). However, we found a non-linear association between BMI and the severity of COVID-19. In conclusion, we found that obesity could increase the risk of SARS-CoV2 infection and aggregate the severity of COVID-19. Further studies are needed to explore the possible mechanisms behind this association.

Clinical characteristics of 2019 novel coronavirus pneumonia in Zhejiang province, China.

Since December 2019, an increasing number of cases associated with the 2019 novel coronavirus (2019­nCoV) have emerged in Wuhan, China, which has resulted in a rapid outbreak in China and worldwide. The present study aimed to describe the clinical, laboratory and radiological characteristics of 2019­nCoV pneumonia (NCP) in Zhejiang province, outside of Wuhan. A total of 74 patients with 2019­nCoV were continuously enrolled between January 22 and March 2, 2020 at Zhejiang Hospital. Diagnosis was confirmed at Zhejiang Hospital by reverse transcription­PCR (RT­PCR), which was approved by the Chinese government. Subsequently, the clinical features between positive­ and negative­NCP patients in Zhejiang were compared. Among the 74 hospitalized patients with suspected 2019­NCP, six patients (one male and five female patients) were confirmed to be infected with 2019­nCoV by RT­PCR. The average age of the confirmed patients was 40±13 years. There were three family clusters among the confirmed cases, one patient from each of these families had travel history or contact with patients from Wuhan within 2 weeks. Compared with non­NCP patients, the most common symptoms at onset for patients with NCP were fever (5/6; 83.3%) and cough (5/6; 83.3%), followed by dyspnea/pharyngalgia (2/6; 33.3%), whereas myalgia (1/6; 16.7%) and fatigue (1/6; 16.7%) were less common. All 74 patients with suspected NCP exhibited abnormal computerized tomography (CT) images. In total, 2/6 (33.3%) patients with confirmed NCP presented with bilateral pneumonia, and 21/68 (30.9%) non­NCP patients exhibited bilateral pneumonia, with bilateral distribution of patchy shadows or ground glass opacity. The present study revealed that epidemiological history was critical to the diagnosis of 2019­nCoV in low epidemic regions outside Hubei province. It was also identified that chest CT could not replace nucleic acid testing due to similar radiological manifestations.