One-year antibody durability induced by EuCorVac-19, a liposome-displayed COVID-19 receptor binding domain subunit vaccine, in healthy Korean subjects.

OBJECTIVE: EuCorVac-19 (ECV-19), an adjuvanted liposome-displayed receptor binding domain (RBD) COVID-19 vaccine, previously reported interim Phase 2 trial results showing induction of neutralizing antibodies 3 weeks after prime-boost immunization. The objective of this study was to determine the longer-term antibody response of the vaccine. METHODS: To assess immunogenicity 6 and 12 months after vaccination, participants in the Phase 2 trial (NCT04783311) were excluded if they: 1) withdrew, 2) reported COVID-19 infection or additional vaccination, or 3) exhibited increasing Spike (S) antibodies (representing possible non-reported infection). Following exclusions, of the 197 initial subjects, anti-S IgG antibodies and neutralizing antibodies were further assessed in 124 subjects at the 6-month timepoint, and 36 subjects at the 12-month timepoint. RESULTS: Median anti-S antibody half-life was 52 days (interquartile range [IQR]:42-70), in the "early" period from 3 weeks to 6 months, and 130 days (IQR:97-169) in the "late" period from 6 to 12 months. There was a negative correlation between initial antibody titer and half-life. Anti-S and neutralizing antibody responses were correlated. Neutralizing antibody responses showed longer half-lives; the early period had a median half-life of 120 days (IQR:81-207), and the late period had a median half-life of 214 days (IQR:140-550). CONCLUSION: These data establish antibody durability of ECV-19, using a framework to analyze COVID-19 vaccine-induced antibodies during periods of high infection.

End-to-End Model-Based Detection of Infants with Autism Spectrum Disorder Using a Pretrained Model.

In this paper, we propose an end-to-end (E2E) neural network model to detect autism spectrum disorder (ASD) from children's voices without explicitly extracting the deterministic features. In order to obtain the decisions for discriminating between the voices of children with ASD and those with typical development (TD), we combined two different feature-extraction models and a bidirectional long short-term memory (BLSTM)-based classifier to obtain the ASD/TD classification in the form of probability. We realized one of the feature extractors as the bottleneck feature from an autoencoder using the extended version of the Geneva minimalistic acoustic parameter set (eGeMAPS) input. The other feature extractor is the context vector from a pretrained wav2vec2.0-based model directly applied to the waveform input. In addition, we optimized the E2E models in two different ways: (1) fine-tuning and (2) joint optimization. To evaluate the performance of the proposed E2E models, we prepared two datasets from video recordings of ASD diagnoses collected between 2016 and 2018 at Seoul National University Bundang Hospital (SNUBH), and between 2019 and 2021 at a Living Lab. According to the experimental results, the proposed wav2vec2.0-based E2E model with joint optimization achieved significant improvements in the accuracy and unweighted average recall, from 64.74% to 71.66% and from 65.04% to 70.81%, respectively, compared with a conventional model using autoencoder-based BLSTM and the deterministic features of the eGeMAPS.

Deep-Learning-Based Detection of Infants with Autism Spectrum Disorder Using Auto-Encoder Feature Representation.

Autism spectrum disorder (ASD) is a developmental disorder with a life-span disability. While diagnostic instruments have been developed and qualified based on the accuracy of the discrimination of children with ASD from typical development (TD) children, the stability of such procedures can be disrupted by limitations pertaining to time expenses and the subjectivity of clinicians. Consequently, automated diagnostic methods have been developed for acquiring objective measures of autism, and in various fields of research, vocal characteristics have not only been reported as distinctive characteristics by clinicians, but have also shown promising performance in several studies utilizing deep learning models based on the automated discrimination of children with ASD from children with TD. However, difficulties still exist in terms of the characteristics of the data, the complexity of the analysis, and the lack of arranged data caused by the low accessibility for diagnosis and the need to secure anonymity. In order to address these issues, we introduce a pre-trained feature extraction auto-encoder model and a joint optimization scheme, which can achieve robustness for widely distributed and unrefined data using a deep-learning-based method for the detection of autism that utilizes various models. By adopting this auto-encoder-based feature extraction and joint optimization in the extended version of the Geneva minimalistic acoustic parameter set (eGeMAPS) speech feature data set, we acquire improved performance in the detection of ASD in infants compared to the raw data set.

Development of Quenching-qPCR (Q-Q) assay for measuring absolute intracellular cleavage efficiency of ribozyme.

Ribozyme (Rz) is a very attractive RNA molecule in metabolic engineering and synthetic biology fields where RNA processing is required as a control unit or ON/OFF signal for its cleavage reaction. In order to use Rz for such RNA processing, Rz must have highly active and specific catalytic activity. However, current methods for assessing the intracellular activity of Rz have limitations such as difficulty in handling and inaccuracies in the evaluation of correct cleavage activity. In this paper, we proposed a simple method to accurately measure the "intracellular cleavage efficiency" of Rz. This method deactivates unwanted activity of Rz which may consistently occur after cell lysis using DNA quenching method, and calculates the cleavage efficiency by analyzing the cleaved fraction of mRNA by Rz from the total amount of mRNA containing Rz via quantitative real-time PCR (qPCR). The proposed method was applied to measure "intracellular cleavage efficiency" of sTRSV, a representative Rz, and its mutant, and their intracellular cleavage efficiencies were calculated as 89% and 93%, respectively.