Immune profiling of COVID-19 vaccine responses in people with multiple sclerosis on B cell-depleting therapy (preprint)

Background and ObjectivePeople with multiple sclerosis (pwMS) receiving B cell-depleting therapies have impaired antibody responses to vaccination. In a proportion of individuals, repeat vaccination against COVID-19 leads to seroconversion. We sought to describe the immune phenotype of pwMS on ocrelizumab, and identify clinical and immunological determinants of an effective vaccine response. MethodsThis was a single-centre, prospective cohort study. Peripheral blood samples were collected from pwMS receiving ocrelizumab (n = 38) pre and post administration of a third dose of mRNA COVID-19 vaccine. Immunogenicity was measured by T cell IFN{gamma} ELISpot, antibody titres, and live virus neutralisation. Humoral immunity was benchmarked against pwMS receiving natalizumab (n = 15), and against a correlate of real-world protection (50% reduction in incidence of infection) from SARS-CoV-2 ancestral and omicron BA.5 variants. The peripheral immune phenotype was comprehensively assessed by flow cytometry, and potential clinical and phenotypic determinants of response to vaccination identified. ResultsImmune cell populations relevant to disease and vaccine response were altered in pwMS receiving ocrelizumab versus natalizumab treatment, including depleted CD20-expressing B cell, T cell and NK cell populations, and elevated CD27+CD38+ T cell and NK8 cell frequencies. Following a third vaccine dose, 51% of pwMS on ocrelizumab were seropositive for SARS-CoV-2 receptor-binding-domain IgG, and 25% and 14% met the threshold for effective neutralisation of live SARS-CoV-2 ancestral and omicron BA.5 virus, respectively. B cell frequency at the time of vaccination, but not time since ocrelizumab infusion, was positively correlated with antibody response, while a strong negative correlation was observed between CD56bright NK cell frequency and antibody response in the ocrelizumab group. In this exploratory cohort, CD3-CD20+ B cells (% of lymphocytes; OR=3.92) and CD56bright NK cells (% of NK cells; OR=0.94) were predictive of an effective neutralising antibody response in second dose non-responders (AUC: 0.98). DiscussionOcrelizumab treatment was associated with an altered immune phenotype, including recently described T cell and NK populations with potential roles in disease pathogenesis. However, seroconversion was severely impaired by ocrelizumab, and less than half of those who seroconverted following a third vaccine dose demonstrated effective immunity against SARS-CoV-2 ancestral or omicron BA.5. B cell frequency was associated with an effective antibody response, while immunomodulatory CD56bright NK cells were identified as a potential negative determinant of response in those with inadequate B cell numbers. Immune phenotype rather than time since ocrelizumab infusion may help to stratify individuals for prophylaxis.

Global evidence of the exposure-lag-response associations between temperature anomalies and food markets

Recent years have witnessed a landmark shift in global food prices due to the frequency of extreme weather events caused by temperature anomalies as well as the overlapping risks of COVID-19. Notably, the threat posed by temperature anomalies has spread beyond agricultural production to all aspects across food supply and demand channels, further amplifying volatility in food markets. Exploring trends in global food prices will give nations early warning signs to ensure the stability of food market. Accordingly, we utilize the Distributed Lag Non-Linear Model (DLNM) to simultaneously establish the exposure-lag-response associations between global temperature anomalies and food price returns in two dimensions: "Anomaly Degree" and "Response Time". Meanwhile, we also examine the cumulative lagged effects of temperature anomalies in terms of different quantiles and lag times. Several conclusions have been drawn. First, global food price returns will continue to decrease when the average temperature drops or rises slightly. While it turns up once the average temperature rises more than 1.1 degrees C. Second, major food commodities are more sensitive to temperature changes, and their price returns may also trend in a directional shift at different lags, with the trend in meat price being more particular. Third, food markets are more strongly affected in the case of extreme temperature anomalies. Many uncertainties still exist regarding the impact of climate change on food markets, and our work serves as a valuable reference for international trade regulation as well as the creation of dynamic climate risk hedging strategies.

mTOR inhibition improves the formation of functional T cell memory following COVID-19 vaccination of kidney transplant recipients (preprint)

Inadequate immune response to vaccination is a long-standing problem faced by immunosuppressed kidney transplant recipients (KTRs), requiring novel strategies to improve vaccine efficacy. In this study, the potential of mechanistic target of rapamycin inhibitors (mTORi) to improve T cell responses to COVID-19 vaccination was investigated. Following primary vaccination with adenoviral (ChAdOx1) or mRNA (BNT162b2) COVID-19 vaccines, KTRs receiving rapamycin demonstrated T cell responses greater than those of healthy individuals, characterized by increased frequencies of vaccine-specific central memory, effector memory and TEMRA T cells, in both the CD4+ and CD8+ compartments. Relative to standard-of-care triple therapy, mTORi-based therapy was associated with a 12-fold greater functional T cell response to primary vaccination of KTRs. The use of rapamycin to augment T cell responses to COVID-19 booster (third dose) vaccination was next investigated in a randomized, controlled trial. Immunosuppression modification with rapamycin was feasible and well-tolerated, but did not improve vaccine-specific T cell responses in this cohort. To understand the parameters for effective use of rapamycin as a vaccine adjuvant, mice were treated with rapamycin before primary or booster vaccination with ancestral and/or Omicron COVID-19 vaccines. Supporting the findings from KTRs, significant enhancement of functional and stem-like memory T cell responses was observed when rapamycin was administered from the time of primary, rather than booster, vaccination. Collectively, a positive effect of mTOR inhibitors on vaccine-induced T cell immunity against COVID-19 in humans was demonstrated.

A Careless Whisper Between Genius Hour and Schooling Under/After the COVID-19 Pandemic

One of the most popular educational trends in 2021, Genius Hour, is an innovative educational technology that allows students to spend an hour a day independently completing self-paced and optional tasks, originated from the policy of Google Inc. using 20% of working hours on "the tasks that are not related to work, but are of interest to oneself". In 2018, the Ministry of Education promulgated the 12-year National Basic Education Curriculum Guidelines of Integrated Activity, in which the learning performance of the self-directed learning and management projects in the second and third learning stages of the Integrated Activity field coincides with the connotation of the so-called "Genius Hour". However, according to the current number of learning periods in the primary and secondary school curriculum syllabus, there will be limitations for the implementation of "genius time" in public primary and secondary schools. This article is based on the examples of "Genius Time" in practical teaching, the expectations for self-directed learning in the field of integrated activities in the curriculum, and the dialog records from the informal online interviews with 12 primary and secondary school principals, directors and teachers on "Genius Hour" to provide a possible imagination for the implementation of "Genius Hour" in the primary and secondary schools in countries where the COVID-19 pandemic still prevail.

Global evidence of the exposure-lag-response associations between temperature anomalies and food markets

Recent years have witnessed a landmark shift in global food prices due to the frequency of extreme weather events caused by temperature anomalies as well as the overlapping risks of COVID-19. Notably, the threat posed by temperature anomalies has spread beyond agricultural production to all aspects across food supply and demand channels, further amplifying volatility in food markets. Exploring trends in global food prices will give nations early warning signs to ensure the stability of food market. Accordingly, we utilize the Distributed Lag Non-Linear Model (DLNM) to simultaneously establish the exposure-lag-response associations between global temperature anomalies and food price returns in two dimensions: “Anomaly Degree” and “Response Time”. Meanwhile, we also examine the cumulative lagged effects of temperature anomalies in terms of different quantiles and lag times. Several conclusions have been drawn. First, global food price returns will continue to decrease when the average temperature drops or rises slightly. While it turns up once the average temperature rises more than 1.1 °C. Second, major food commodities are more sensitive to temperature changes, and their price returns may also trend in a directional shift at different lags, with the trend in meat price being more particular. Third, food markets are more strongly affected in the case of extreme temperature anomalies. Many uncertainties still exist regarding the impact of climate change on food markets, and our work serves as a valuable reference for international trade regulation as well as the creation of dynamic climate risk hedging strategies.

A Multi-Center Study for the Development of the Taiwan Cognition Questionnaire (TCQ) in Major Depressive Disorder.

Cognitive dysfunction is associated with functional impairment of patients with Major Depressive Disorder (MDD). The goals were to explore the associated factors of cognitive impairment in MDD and to develop and validate a brief and culture-relevant questionnaire, the Taiwan Cognition Questionnaire (TCQ), among patients with MDD. This was a cross-sectional, multi-center observational study of MDD patients in Taiwan. Participants of Group 1 from 10 centers contributed to the validation of the TCQ by their response and sociodemographics. The participants of Group 2 from one center received an objective cognitive assessment for clarification of the relationship between the TCQ score and its associated factors. In Group 1, 493 participants were recruited. As for Group 2, an extra 100 participants were recruited. The global Cronbach's alpha for the TCQ was 0.908. According to the coordinates of the ROC curve, 9/10 was the ideal cut-off point. With the criteria, the sensitivity/specificity of the TCQ was 0.610/0.689. The TCQ score was positively associated with a history of being admitted to acute psychiatric care and the severity of depression and negatively associated with objective cognitive measures. The TCQ provides a reliable, valid, and convenient measure of subjective cognitive dysfunction in patients with MDD.

Impact of COVID-19 outbreak on multi-scale asymmetric spillovers between food and oil prices.

This paper analyzes the time-frequency spillover effects between food and crude oil markets, two particularly important commodity markets, under the impact of the pandemic. Using the BK frequency domain spillover index and the rolling window method, we explore the spillover effects between the food and crude oil markets under the influence of COVID-19, and compare the changes of spillover effects in each market before and during the pandemic. Based the network connectedness method and the Bayesian structural time series method, we further reveal the changes of the pairwise spillover effects between markets on different time scales. Our study shows that the food-oil market system has the strongest spillover effect in the short term, and the spillovers during the pandemic are significantly weaker than that under the financial crisis. In addition, the pandemic has significantly increased the impact of corn on the crude oil market, but reduced its spillovers on soybeans and rice. Finally, during the COVID-19 period, the wheat market is likely to receive more spillovers from other markets, particularly corn and soybeans. These findings are of great significance for market participants with different horizons to understand the spillover effects of food and oil markets under the impact of the pandemic and to avoid the risk transmission across markets or assets.

Correction: A COVID-19 Pandemic Artificial Intelligence-Based System With Deep Learning Forecasting and Automatic Statistical Data Acquisition: Development and Implementation Study.

[This corrects the article DOI: 10.2196/27806.].

A COVID-19 Pandemic Artificial Intelligence-Based System With Deep Learning Forecasting and Automatic Statistical Data Acquisition: Development and Implementation Study.

BACKGROUND: More than 79.2 million confirmed COVID-19 cases and 1.7 million deaths were caused by SARS-CoV-2; the disease was named COVID-19 by the World Health Organization. Control of the COVID-19 epidemic has become a crucial issue around the globe, but there are limited studies that investigate the global trend of the COVID-19 pandemic together with each country's policy measures. OBJECTIVE: We aimed to develop an online artificial intelligence (AI) system to analyze the dynamic trend of the COVID-19 pandemic, facilitate forecasting and predictive modeling, and produce a heat map visualization of policy measures in 171 countries. METHODS: The COVID-19 Pandemic AI System (CPAIS) integrated two data sets: the data set from the Oxford COVID-19 Government Response Tracker from the Blavatnik School of Government, which is maintained by the University of Oxford, and the data set from the COVID-19 Data Repository, which was established by the Johns Hopkins University Center for Systems Science and Engineering. This study utilized four statistical and deep learning techniques for forecasting: autoregressive integrated moving average (ARIMA), feedforward neural network (FNN), multilayer perceptron (MLP) neural network, and long short-term memory (LSTM). With regard to 1-year records (ie, whole time series data), records from the last 14 days served as the validation set to evaluate the performance of the forecast, whereas earlier records served as the training set. RESULTS: A total of 171 countries that featured in both databases were included in the online system. The CPAIS was developed to explore variations, trends, and forecasts related to the COVID-19 pandemic across several counties. For instance, the number of confirmed monthly cases in the United States reached a local peak in July 2020 and another peak of 6,368,591 in December 2020. A dynamic heat map with policy measures depicts changes in COVID-19 measures for each country. A total of 19 measures were embedded within the three sections presented on the website, and only 4 of the 19 measures were continuous measures related to financial support or investment. Deep learning models were used to enable COVID-19 forecasting; the performances of ARIMA, FNN, and the MLP neural network were not stable because their forecast accuracy was only better than LSTM for a few countries. LSTM demonstrated the best forecast accuracy for Canada, as the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) were 2272.551, 1501.248, and 0.2723075, respectively. ARIMA (RMSE=317.53169; MAPE=0.4641688) and FNN (RMSE=181.29894; MAPE=0.2708482) demonstrated better performance for South Korea. CONCLUSIONS: The CPAIS collects and summarizes information about the COVID-19 pandemic and offers data visualization and deep learning-based prediction. It might be a useful reference for predicting a serious outbreak or epidemic. Moreover, the system undergoes daily updates and includes the latest information on vaccination, which may change the dynamics of the pandemic.

Variation in human mobility and its impact on the risk of future COVID-19 outbreaks in Taiwan.

BACKGROUND: As COVID-19 continues to spread around the world, understanding how patterns of human mobility and connectivity affect outbreak dynamics, especially before outbreaks establish locally, is critical for informing response efforts. In Taiwan, most cases to date were imported or linked to imported cases. METHODS: In collaboration with Facebook Data for Good, we characterized changes in movement patterns in Taiwan since February 2020, and built metapopulation models that incorporate human movement data to identify the high risk areas of disease spread and assess the potential effects of local travel restrictions in Taiwan. RESULTS: We found that mobility changed with the number of local cases in Taiwan in the past few months. For each city, we identified the most highly connected areas that may serve as sources of importation during an outbreak. We showed that the risk of an outbreak in Taiwan is enhanced if initial infections occur around holidays. Intracity travel reductions have a higher impact on the risk of an outbreak than intercity travel reductions, while intercity travel reductions can narrow the scope of the outbreak and help target resources. The timing, duration, and level of travel reduction together determine the impact of travel reductions on the number of infections, and multiple combinations of these can result in similar impact. CONCLUSIONS: To prepare for the potential spread within Taiwan, we utilized Facebook's aggregated and anonymized movement and colocation data to identify cities with higher risk of infection and regional importation. We developed an interactive application that allows users to vary inputs and assumptions and shows the spatial spread of the disease and the impact of intercity and intracity travel reduction under different initial conditions. Our results can be used readily if local transmission occurs in Taiwan after relaxation of border control, providing important insights into future disease surveillance and policies for travel restrictions.

Evaluating the Need for Routine COVID-19 Testing of Emergency Department Staff: Quantitative Analysis.

BACKGROUND: As the number of COVID-19 cases in the US continues to increase and hospitals experience shortage of personal protective equipment (PPE), health care workers have been disproportionately affected. However, since COVID-19 testing is now easily available, there is a need to evaluate whether routine testing should be performed for asymptomatic health care workers. OBJECTIVE: This study aimed to provide a quantitative analysis of the predicted impact that regular testing of health care workers for COVID-19 may have on the prevention of the disease among emergency department patients and staff. METHODS: Using publicly available data on COVID-19 cases and emergency department visits, as well as internal hospital staffing information, we developed a mathematical model to predict the impact of periodic COVID-19 testing of asymptomatic staff members of the emergency department in COVID-19-affected regions. We calculated various transmission constants based on the Diamond Princess cruise ship data, used a logistic model to calculate new infections, and developed a Markov model based on the average incubation period for COVID-19. RESULTS: Our model predicts that after 180 days, with a transmission constant of 1.219e-4 new infections/person2, weekly COVID-19 testing of health care workers would reduce new health care worker and patient infections by approximately 3%-5.9%, and biweekly testing would reduce infections in both by 1%-2.1%. At a transmission constant of 3.660e-4 new infections/person2, weekly testing would reduce infections by 11%-23% and biweekly testing would reduce infections by 5.5%-13%. At a lower transmission constant of 4.067e-5 new infections/person2, weekly and biweekly COVID-19 testing for health care workers would result in an approximately 1% and 0.5%-0.8% reduction in infections, respectively. CONCLUSIONS: Periodic COVID-19 testing for emergency department staff in regions that are heavily affected by COVID-19 or are facing resource constraints may significantly reduce COVID-19 transmission among health care workers and previously uninfected patients.

Modeling the impact of human mobility and travel restrictions on the potential spread of SARS-CoV-2 in Taiwan (preprint)

Background: As COVID-19 continues to spread around the world, understanding how patterns of human mobility and connectivity affect outbreak dynamics, especially before outbreaks establish locally, is critical for informing response efforts. Methods: Here, in collaboration with Facebook Data for Good, we built metapopulation models that incorporate human movement data with the goals of identifying the high risk areas of disease spread and assessing the potential effects of local travel restrictions in Taiwan. We compared the impact of intracity vs. intercity travel restrictions on both the total number of infections and the speed of outbreak spread and developed an interactive application that allows users to vary inputs and assumptions. Findings: We found that intracity travel reductions have a higher impact on overall infection numbers than intercity travel reductions, while intercity travel reductions can narrow the scope of the outbreak and help target resources. We also identified the most highly connected areas that may serve as sources of importation during an outbreak. The timing, duration, and level of travel reduction together determine the impact of travel reductions on the number of infections, and multiple combinations of these can result in similar impact. Interpretation: In Taiwan, most cases to date were imported or linked to imported cases. To prepare for the potential spread within Taiwan, we utilized Facebook's aggregated and anonymized movement and colocation data to identify cities with higher risk of infection and regional importation. Both intracity and intercity movement affect outbreak dynamics, with the former having more of an impact on the total numbers of cases and the latter impacting geographic scope. These findings have important implications for guiding future policies for travel restrictions during outbreaks in Taiwan.