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17,833 results on '"Chen, Pei"'

209. Neutralizing antibody activity against SARS-CoV-2 variants in gestational age-matched mother-infant dyads after infection or vaccination

Author

Matsui, Yusuke, Li, Lin, Prahl, Mary, Cassidy, Arianna G, Ozarslan, Nida, Golan, Yarden, Gonzalez, Veronica J, Lin, Christine Y, Jigmeddagva, Unurzul, Chidboy, Megan A, Montano, Mauricio, Taha, Taha Y, Khalid, Mir M, Sreekumar, Bharath, Hayashi, Jennifer M, Chen, Pei-Yi, Kumar, G Renuka, Warrier, Lakshmi, Wu, Alan HB, Song, Dongli, Jegatheesan, Priya, Rai, Daljeet S, Govindaswami, Balaji, Needens, Jordan M, Rincon, Monica, Myatt, Leslie, Asiodu, Ifeyinwa V, Flaherman, Valerie J, Afshar, Yalda, Jacoby, Vanessa L, Murtha, Amy P, Robinson, Joshua F, Ott, Melanie, Greene, Warner C, and Gaw, Stephanie L

Subjects
Vaccine Related, Prevention, Immunization, Biodefense, Biotechnology, Infectious Diseases, Emerging Infectious Diseases, Infection, Reproductive health and childbirth, Good Health and Well Being, Antibodies, Neutralizing, Antibodies, Viral, COVID-19, COVID-19 Vaccines, Female, Gestational Age, Humans, Mothers, Neutralization Tests, SARS-CoV-2, Vaccination, Adaptive immunity, Immunoglobulins, Infectious disease
Abstract

Pregnancy confers unique immune responses to infection and vaccination across gestation. To date, there are limited data comparing vaccine- and infection-induced neutralizing Abs (nAbs) against COVID-19 variants in mothers during pregnancy. We analyzed paired maternal and cord plasma samples from 60 pregnant individuals. Thirty women vaccinated with mRNA vaccines (from December 2020 through August 2021) were matched with 30 naturally infected women (from March 2020 through January 2021) by gestational age of exposure. Neutralization activity against the 5 SARS-CoV-2 spike sequences was measured by a SARS-CoV-2-pseudotyped spike virion assay. Effective nAbs against SARS-CoV-2 were present in maternal and cord plasma after both infection and vaccination. Compared with WT spike protein, these nAbs were less effective against the Delta and Mu spike variants. Vaccination during the third trimester induced higher cord-nAb levels at delivery than did infection during the third trimester. In contrast, vaccine-induced nAb levels were lower at the time of delivery compared with infection during the first trimester. The transfer ratio (cord nAb level divided by maternal nAb level) was greatest in mothers vaccinated in the second trimester. SARS-CoV-2 vaccination or infection in pregnancy elicits effective nAbs with differing neutralization kinetics that are influenced by gestational time of exposure.

Published
2022

217. Neutralizing immunity in vaccine breakthrough infections from the SARS-CoV-2 Omicron and Delta variants

Author

Servellita, Venice, Syed, Abdullah M, Morris, Mary Kate, Brazer, Noah, Saldhi, Prachi, Garcia-Knight, Miguel, Sreekumar, Bharath, Khalid, Mir M, Ciling, Alison, Chen, Pei-Yi, Kumar, G Renuka, Gliwa, Amelia S, Nguyen, Jenny, Sotomayor-Gonzalez, Alicia, Zhang, Yueyuan, Frias, Edwin, Prostko, John, Hackett, John, Andino, Raul, Wadford, Debra A, Hanson, Carl, Doudna, Jennifer, Ott, Melanie, and Chiu, Charles Y

Subjects
Medical Microbiology, Biomedical and Clinical Sciences, Prevention, Immunization, Biodefense, Emerging Infectious Diseases, Infectious Diseases, Vaccine Related, Infection, Good Health and Well Being, Antibodies, Neutralizing, Antibodies, Viral, BNT162 Vaccine, COVID-19, COVID-19 Vaccines, Humans, SARS-CoV-2, B.1.1.529, B.1.617.2, Delta variant, Omicron variant, VLP, antibody neutralization, boosted breakthrough infection, breakthrough infection, humoral immunity, pseudovirus infectivity studies, quantitative antibody assay, variant of concern, variant severity, virus-like particle, Biological Sciences, Medical and Health Sciences, Developmental Biology, Biological sciences, Biomedical and clinical sciences
Abstract

Virus-like particle (VLP) and live virus assays were used to investigate neutralizing immunity against Delta and Omicron SARS-CoV-2 variants in 259 samples from 128 vaccinated individuals. Following Delta breakthrough infection, titers against WT rose 57-fold and 3.1-fold compared with uninfected boosted and unboosted individuals, respectively, versus only a 5.8-fold increase and 3.1-fold decrease for Omicron breakthrough infection. Among immunocompetent, unboosted patients, Delta breakthrough infections induced 10.8-fold higher titers against WT compared with Omicron (p = 0.037). Decreased antibody responses in Omicron breakthrough infections relative to Delta were potentially related to a higher proportion of asymptomatic or mild breakthrough infections (55.0% versus 28.6%, respectively), which exhibited 12.3-fold lower titers against WT compared with moderate to severe infections (p = 0.020). Following either Delta or Omicron breakthrough infection, limited variant-specific cross-neutralizing immunity was observed. These results suggest that Omicron breakthrough infections are less immunogenic than Delta, thus providing reduced protection against reinfection or infection from future variants.

Published
2022

218. Impacts of an AI-Based 'Cha'bot on College Students' After-Class Review, Academic Performance, Self-Efficacy, Learning Attitude, and Motivation

Author

Lee, Yen-Fen, Hwang, Gwo-Jen, and Chen, Pei-Ying

Abstract

Review strategies after learning new knowledge are essential for students to consolidate the key points, understand the subject content, analyze aspects of the learning topics, and summarize the knowledge content of learning while mastering new knowledge. However, educators have found that students generally have difficulties seeking help when they encounter learning problems. This could significantly affect their after-class review performances. To cope with this problem, an after-class review approach with an AI (Artificial Intelligence)-based chatbot is proposed in this study to provide students with immediate and quality feedback during the learning process. Moreover, a quasi-experiment was conducted to explore students' learning motivation, attitude, and academic performance when using the AI-based chatbot. Participants were two classes of students from a university in Taiwan. One class with 18 students was the experimental group and the other with 20 students was the control group. The experimental group used the AI-based chatbot in the after-class review, while the control group used the conventional after-class review approach. Research results showed that the application of AI-based chatbots in the review process of public health courses could improve students' academic performance, self-efficacy, learning attitude, and motivation. In other words, chatbots could help students become more active in the learning process. It is noted that after students asked questions, providing them with sufficient feedback during the review process could make them feel recognized and help to establish a relaxing and friendly interaction, thereby improving their academic performance.

Published
2022
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220. Spatiotemporal information conversion machine for time-series prediction

Author

Peng, Hao, Chen, Pei, Liu, Rui, and Chen, Luonan

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Mathematics - Dynamical Systems, I.2.1
Abstract

Making predictions in a robust way is a difficult task only based on the observed data of a nonlinear system. In this work, a neural network computing framework, the spatiotemporal information conversion machine (STICM), was developed to efficiently and accurately render a multistep-ahead prediction of a time series by employing a spatial-temporal information (STI) transformation. STICM combines the advantages of both the STI equation and the temporal convolutional network, which maps the high-dimensional/spatial data to the future temporal values of a target variable, thus naturally providing the prediction of the target variable. From the observed variables, the STICM also infers the causal factors of the target variable in the sense of Granger causality, which are in turn selected as effective spatial information to improve the prediction robustness of time-series. The STICM was successfully applied to both benchmark systems and real-world datasets, all of which show superior and robust performance in multistep-ahead prediction, even when the data were perturbed by noise. From both theoretical and computational viewpoints, the STICM has great potential in practical applications in artificial intelligence (AI) or as a model-free method based only on the observed data, and also opens a new way to explore the observed high-dimensional data in a dynamical manner for machine learning., Comment: 28 pages, 6 figures

Published
2021
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