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      Challenges and opportunities in current vaccine technology and administration: A comprehensive survey examining oral vaccine potential in the United States

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          ABSTRACT

          This study provides a snapshot of the current vaccine business ecosystem, including practices, challenges, beliefs, and expectations of vaccine providers. Our team focused on providers’ firsthand experience with administering vaccines to determine if an oral vaccine (e.g. pill or oral-drop) would be well-received. We interviewed 135 healthcare providers and vaccine specialists across the US, focusing questions on routine vaccinations, not COVID-19 vaccines. Improving workflow efficiency is a top concern among vaccine providers due to shrinking reimbursement rates—determined by pharmacy benefit managers (PBMs)—and the time-intensiveness of injectable vaccines. Administering injectable vaccines takes 23 minutes/patient on average, while dispensing pills takes only 5 minutes/patient. An average of 24% of patients express needle-fear, which further lengthens the processing time. Misaligned incentives between providers and PBMs could reduce the quality and availability of vaccine-related care. The unavailability of single-dose orders prevents some rural providers from offering certain vaccines. Most interviewees (74%) believe an oral vaccine would improve patient–provider experience, patient-compliance, and workflow efficiency, while detractors (26%) worry about the taste, vaccine absorption, and efficacy. Additional research could investigate whether currently non-vaccinating pharmacies would be willing to offer oral vaccines, and the impact of oral vaccines on vaccine acceptance.

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          Most cited references42

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          BioBERT: a pre-trained biomedical language representation model for biomedical text mining

          Abstract Motivation Biomedical text mining is becoming increasingly important as the number of biomedical documents rapidly grows. With the progress in natural language processing (NLP), extracting valuable information from biomedical literature has gained popularity among researchers, and deep learning has boosted the development of effective biomedical text mining models. However, directly applying the advancements in NLP to biomedical text mining often yields unsatisfactory results due to a word distribution shift from general domain corpora to biomedical corpora. In this article, we investigate how the recently introduced pre-trained language model BERT can be adapted for biomedical corpora. Results We introduce BioBERT (Bidirectional Encoder Representations from Transformers for Biomedical Text Mining), which is a domain-specific language representation model pre-trained on large-scale biomedical corpora. With almost the same architecture across tasks, BioBERT largely outperforms BERT and previous state-of-the-art models in a variety of biomedical text mining tasks when pre-trained on biomedical corpora. While BERT obtains performance comparable to that of previous state-of-the-art models, BioBERT significantly outperforms them on the following three representative biomedical text mining tasks: biomedical named entity recognition (0.62% F1 score improvement), biomedical relation extraction (2.80% F1 score improvement) and biomedical question answering (12.24% MRR improvement). Our analysis results show that pre-training BERT on biomedical corpora helps it to understand complex biomedical texts. Availability and implementation We make the pre-trained weights of BioBERT freely available at https://github.com/naver/biobert-pretrained, and the source code for fine-tuning BioBERT available at https://github.com/dmis-lab/biobert.
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            The fear of needles: A systematic review and meta-analysis

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              XLNnet: Generalized autoregressive pretraining for language understanding

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                Author and article information

                Journal
                Hum Vaccin Immunother
                Hum Vaccin Immunother
                Human Vaccines & Immunotherapeutics
                Taylor & Francis
                2164-5515
                2164-554X
                9 September 2022
                2022
                9 September 2022
                : 18
                : 6
                : 2114422
                Affiliations
                [a ]Research Division, FruitVaccine, Inc; ., Champaign, IL, USA
                [b ]School of Computing, University of North Florida; , Jacksonville, FL, USA
                Author notes
                CONTACT S. Indu Rupassara indurupassara@ 123456gmail.com FruitVaccine, Inc; ., 60 Hazelwood Drive, Champaign, IL 61820, USA.
                Author information
                https://orcid.org/0000-0002-5090-9664
                https://orcid.org/0000-0003-3610-455X
                https://orcid.org/0000-0002-4536-6917
                Article
                2114422
                10.1080/21645515.2022.2114422
                9746384
                36082816
                34963e44-bb17-4bb1-b33a-55dd895acc76
                © 2022 The Author(s). Published with license by Taylor & Francis Group, LLC.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License ( http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.

                History
                Page count
                Figures: 6, Tables: 1, References: 47, Pages: 10
                Categories
                Brief Report
                Technology – Brief Report

                Molecular medicine
                vaccine delivery methods,vaccine technology,oral vaccines,oral administration,customer discovery survey

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