Passivating the Background of Living Microbes with a Zwitterionic Peptide for Therapies.

Living microbial therapies have been proposed as a course of action for a variety of diseases. However, problematic interactions between the host immune system and the microbial organism present significant clinical concerns. Previously, we developed a genetically encoded superhydrophilic zwitterionic peptide, termed EKP, to mimic low-immunogenic zwitterionic materials, which have been used for the chemical modification of biologics such as protein and nucleic acid drugs to increase their in vivo circulation time and reduce their immunogenicity. Herein, we demonstrate the protective effects of the EKP polypeptide genetically cloaking the surface of Saccharomyces cerevisiae as a model microbe in both in vitro and in vivo systems. First, we show that EKP peptide cloaking suppresses the interactions between yeast cells and their specific antibodies, thereby illustrating its cloaking behavior. Then, we examine the in vitro interactions between EKP peptide surface cloaked yeast cells and murine macrophage cells, which exhibit phagocytotic behavior in the presence of foreign microbes. Our results indicate that EKP cloaking suppresses macrophage interactions and thus reduces phagocytosis. Furthermore, EKP cloaked yeast cells demonstrate a prolonged circulation time in mice in vivo.

Effectiveness of mobile augmented reality-integrated oral health education for community-dwelling older adults: A randomized controlled trial.

BACKGROUND & PURPOSE: Oral health education helps older adults optimize their oral health. However, traditional lecture-based oral health education has limitations, exacerbated by the COVID-19 pandemic. Mobile augmented reality (MAR) has emerged as an alternative educational method. This study compared the effectiveness of MAR-integrated oral health education with that of lecture-based education and no education. METHODS: This parallel, randomized controlled, open-label trial enrolled 75 older adults from six activity centers. The participants were randomly assigned, by a random number table technique, to the lecture-based, MAR, or control group. Data on oral healthcare-related knowledge, self-efficacy, and oral health status were collected through questionnaires and oral examinations at baseline, immediately after the intervention, and at a 2-week follow-up. The MAR system's usability was assessed. Statistical analyses, comprising descriptive statistics and inferential tests, were performed. RESULTS: Data from 61 participants were analyzed, 22 in the lecture-based group, 20 in the MAR group, and 19 in the control group. Both lectures and MAR education significantly improved oral health status. However, changes in knowledge and self-efficacy scores were significantly different only for the MAR versus control group (p = .002 and .001, respectively). The MAR group demonstrated better knowledge and self-efficacy retention than did the lecture-based group, without significant difference. Usability assessment revealed potential for improvement in the MAR system. CONCLUSIONS: MAR-integrated oral health education enhanced the knowledge, self-efficacy, and oral health status of community-dwelling older adults. However, addressing technology adoption and usability challenges is vital. Longer-term evaluations and broader geographical studies are recommended.

A mobile augmented reality-integrated oral health education for community dwelling older adults: A pilot study.

Background/purpose: Providing oral healthcare education can be an optimal strategy for imparting knowledge and developing self-care skills for older adults to enhance their oral health conditions. However, traditional passive lecture-based education programs have several limitations. Integrating mobile augmented reality (MAR) into oral health education can potentially enhance the effectiveness of the education by mitigating those limitations. The objectives of this study are to develop and evaluate the effectiveness of MAR-integrated oral health education. The objectives of this study were to develop and evaluate the effectiveness of MAR-integrated oral health education. Materials and methods: Twenty-four older adults from community dwellings participated in this study. The participants received MAR-integrated oral health education once at activity centers. Self-report questionnaires were utilized to assess oral healthcare-related knowledge and self-efficacy, and the usability score of the MAR system. The assessment was conducted before and after the educational program. Data were statistically analyzed using descriptive statistics and paired-t test. Results: The mean scores of oral healthcare-related knowledge and self-efficacy showed a significant increase after the educational program, with P-values of <0.001 and 0.002, respectively. A majority of the participants exhibited a lack of knowledge and self-efficacy regarding the proper selection of toothpaste and interdental brushes. However, there was an improvement after the intervention. The usability score of the system was lower than the average level. Conclusion: MAR-integrated oral health education effectively increases oral health-related knowledge and self-efficacy among community dwelling older adults. Still, the usability of the MAR system needs to be improved. Further investigation of long-term effects and clinical outcomes, and the inclusion of comparative groups are recommended for the future study.

DeepFlu: a deep learning approach for forecasting symptomatic influenza A infection based on pre-exposure gene expression.

BACKGROUND AND OBJECTIVE: Not everyone gets sick after an exposure to influenza A viruses (IAV). Although KLRD1 has been identified as a potential biomarker for influenza susceptibility, it remains unclear whether forecasting symptomatic flu infection based on pre-exposure host gene expression might be possible. METHOD: To examine this hypothesis, we developed DeepFlu using the state-of-the-art deep learning approach on the human gene expression data infected with IAV subtype H1N1 or H3N2 viruses to forecast who would catch the flu prior to an exposure to IAV. RESULTS: The results indicated that such forecast is possible and, in other words, gene expression could reflect the strength of host immunity. In the leave-one-person-out cross-validation, DeepFlu based on deep neural network outperformed the models using convolutional neural network, random forest, or support vector machine, achieving 70.0% accuracy, 0.787 AUROC, and 0.758 AUPR for H1N1 and 73.8% accuracy, 0.847 AUROC, and 0.901 AUPR for H3N2. In the external validation, DeepFlu also reached 71.4% accuracy, 0.700 AUROC, and 0.723 AUPR for H1N1 and 73.5% accuracy, 0.732 AUROC, and 0.749 AUPR for H3N2, surpassing the KLRD1 biomarker. In addition, DeepFlu which was trained only by pre-exposure data worked the best than by other time spans and mixed training data of H1N1 and H3N2 did not necessarily enhance prediction. DeepFlu is available at https://github.com/ntou-compbio/DeepFlu. CONCLUSIONS: DeepFlu is a prognostic tool that can moderately recognize individuals susceptible to the flu and may help prevent the spread of IAV.