Protocol for a scoping review of potential vaccine candidates predicted by VaxiJen for different viral pathogens between 2017-2021.

BACKGROUND: Vaccination is essential for the prevention of infectious diseases and has helped to reduce disease-related mortality, such as pneumonia. However, traditional vaccine development is time-consuming and risky. Reverse vaccinology (RV) is a promising alternative to developing vaccines based on the in silico discovery of antigens, often termed 'potential vaccine candidates' (PVCs), using a pathogen's proteome. RV prediction technologies, such as VaxiJen (founded in 2007), are used to take the first step toward vaccine development. VaxiJen is used by researchers to identify PVCs for various diseases. A 10-year review of these PVCs was published in 2017. There has since been no review of viral PVCs predicted by VaxiJen from 2017 to 2021. The proposed scoping review aims to address this gap. METHODS: This protocol is reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols (PRISMA-P) 2015 checklist. The review will employ Arksey and O'Malley's five-stage methodological framework, which was later enhanced by Levac et al. and the Joanna Briggs Institute (JBI). The PRISMA extension for Scoping Reviews (PRISMA-ScR) reporting guideline will be utilized with this framework. PubMed, Scopus, Web of Science, EBSCOhost, and ProQuest One Academic will be searched using the term 'vaxijen'. The inclusion criteria will be English-only full-text original articles published in peer-reviewed journals and unpublished papers from 2017 to 2021. Rayyan will be used to deduplicate, screen titles and abstracts of articles. The articles' full texts will be examined. The data will be extracted using Microsoft Excel. Using a data charting form, data will be sifted and organized by key categories and themes. DISCUSSION: This protocol was submitted for publication and went through an extensive peer review process. The review has implications for novel vaccine development against various viruses. The key limitation of this study is language bias due to the selection of English-only papers because of limited resources. This study will not require ethical clearance since it will use secondary data and will not include patients. Nevertheless, this research is part of a larger project that was submitted for ethical consideration to the Biomedical Research Ethics Committee of the University of KwaZulu-Natal in South Africa. This study's findings will be published in a peer-reviewed journal and provided to relevant stakeholders. SYSTEMATIC REVIEW REGISTRATION: Open Science Framework (OSF): https://osf.io/ht8wr.

Mapping Potential Vaccine Candidates Predicted by VaxiJen for Different Viral Pathogens between 2017-2021-A Scoping Review.

Reverse vaccinology (RV) is a promising alternative to traditional vaccinology. RV focuses on in silico methods to identify antigens or potential vaccine candidates (PVCs) from a pathogen's proteome. Researchers use VaxiJen, the most well-known RV tool, to predict PVCs for various pathogens. The purpose of this scoping review is to provide an overview of PVCs predicted by VaxiJen for different viruses between 2017 and 2021 using Arksey and O'Malley's framework and the Preferred Reporting Items for Systematic Reviews extension for Scoping Reviews (PRISMA-ScR) guidelines. We used the term 'vaxijen' to search PubMed, Scopus, Web of Science, EBSCOhost, and ProQuest One Academic. The protocol was registered at the Open Science Framework (OSF). We identified articles on this topic, charted them, and discussed the key findings. The database searches yielded 1033 articles, of which 275 were eligible. Most studies focused on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), published between 2020 and 2021. Only a few articles (8/275; 2.9%) conducted experimental validations to confirm the predictions as vaccine candidates, with 2.2% (6/275) articles mentioning recombinant protein expression. Researchers commonly targeted parts of the SARS-CoV-2 spike (S) protein, with the frequently predicted epitopes as PVCs being major histocompatibility complex (MHC) class I T cell epitopes WTAGAAAYY, RQIAPGQTG, IAIVMVTIM, and B cell epitope IAPGQTGKIADY, among others. The findings of this review are promising for the development of novel vaccines. We recommend that vaccinologists use these findings as a guide to performing experimental validation for various viruses, with SARS-CoV-2 as a priority, because better vaccines are needed, especially to stay ahead of the emergence of new variants. If successful, these vaccines could provide broader protection than traditional vaccines.

A five-year (2015 to 2019) analysis of studies focused on breast cancer prediction using machine learning: A systematic review and bibliometric analysis.

The objective 1 of this study was to investigate trends in breast cancer (BC) prediction using machine learning (ML) publications by analysing country, first author, journal, institutional collaborations and co-occurrence of author keywords. The objective 2 was to provide a review of studies on BC prediction using ML and a blood analysis dataset (Breast Cancer Coimbra Dataset [BCCD]), and the objective 3 was to provide a brief review of studies based on BC prediction using ML and patients' fine needle aspirate cytology data (Wisconsin Breast Cancer Dataset [WBCD]). The design of this study was as follows: for objective 1: bibliometric analysis, data source PubMed (2015-2019); for objective 2: systematic review, data source: Google and Google Scholar (2018-2019); for objective 3: systematic review, data source: Google Scholar (2016-2019). The inclusion criteria for objective 1 were all publication results yielded from the searches. All English papers that had a 'PDF' option from the search results were included for objective 2. A sample of the 'PDF' English papers were included for objective 3. All 116 female patients from the BCCD, consisting of 64 positive BC patients and 52 controls were included in the study for objective 2. For the WBCD, all 699 female patients comprising of 458 with a benign BC tumour and 241 with a malignant BC tumour were included for objective 3. All 2928 publications were included for objective 1. The results showed that the United States of America (USA) produced the highest number of publications (n=803). In total, 2419 first authors contributed towards the publications. Breast Cancer Research and Treatment was the highest ranked journal. Institutional collaborations mainly occurred within the USA. The use of ML for BC screening and detection was the most researched topic. A total of 19 distinct papers were included for objectives 2 and 3. The findings from these studies were never presented to clinicians for validations. In conclusion, the use of ML for BC screening and detection is promising.

Comparison of the performance of machine learning algorithms in breast cancer screening and detection: A protocol.

Background: Breast Cancer (BC) is a known global crisis. The World Health Organization reports a global 2.09 million incidences and 627,000 deaths in 2018 relating to BC. The traditional BC screening method in developed countries is mammography, whilst developing countries employ breast self-examination and clinical breast examination. The prominent gold standard for BC detection is triple assessment: i) clinical examination, ii) mammography and/or ultrasonography; and iii) Fine Needle Aspirate Cytology. However, the introduction of cheaper, efficient and noninvasive methods of BC screening and detection would be beneficial. Design and methods: We propose the use of eight machine learning algorithms: i) Logistic Regression; ii) Support Vector Machine; iii) K-Nearest Neighbors; iv) Decision Tree; v) Random Forest; vi) Adaptive Boosting; vii) Gradient Boosting; viii) eXtreme Gradient Boosting, and blood test results using BC Coimbra Dataset (BCCD) from University of California Irvine online database to create models for BC prediction. To ensure the models' robustness, we will employ: i) Stratified k-fold Cross- Validation; ii) Correlation-based Feature Selection (CFS); and iii) parameter tuning. The models will be validated on validation and test sets of BCCD for full features and reduced features. Feature reduction has an impact on algorithm performance. Seven metrics will be used for model evaluation, including accuracy. Expected impact of the study for public health: The CFS together with highest performing model(s) can serve to identify important specific blood tests that point towards BC, which may serve as an important BC biomarker. Highest performing model(s) may eventually be used to create an Artificial Intelligence tool to assist clinicians in BC screening and detection.