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Record W4385666276 · doi:10.1002/iid3.946

Vaccination is the most effective and best way to avoid the disease of COVID‐19

2023· review· en· W4385666276 on OpenAlexaboutno aff
Hadi Lotfi, Mina Ghorbani Mazar, Negar Mashuri, M.S. Fahim, Nafiseh Shourideh Yazdi

Bibliographic record

VenueImmunity Inflammation and Disease · 2023
Typereview
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsnot available
Fundersnot available
KeywordsVirologyVaccinationDNA vaccinationImmune systemVirusViral vectorVector (molecular biology)BiologyAntigenCoronavirus disease 2019 (COVID-19)MedicineRecombinant DNADiseaseImmunologyImmunizationInfectious disease (medical specialty)GeneGenetics

Abstract

fetched live from OpenAlex

Most of the vaccines that are effective against SARS-CoV-2 have used the following functional strategies: inactivated viruses, live attenuated viruses, viral vector-based vaccines, subunit vaccines, recombinant proteins, and DNA/RNA vaccines. Among the vaccines that stimulate the host's immune system with the help of DNA are: undergoing Phase 2/3 trials including INO-4800 (International Vaccine Institute; Inovio Pharmaceuticals), Symvivo, Canada-COVID19 (AnGes, Inc.); GX-19 (Genexine, Inc.). BNT162b2 and mRNA-1273 vaccines were made by BioNTech/Pfizer/Fosun Pharma group and Moderna/NIAID group, respectively, which are considered as types of RNA vaccines. Vaccines that are based on the viral vector are AstraZeneca, Sputonium, and Johnson-Jensen. Among the inactive viral vaccines, the following can be mentioned: CoronaVac (Sinovac) WIBP vaccine (Wuhan Institute of Biological Products, Sinopharm), BBIBPCorV (Beijing Institute of Biological Products, Sinopharm), BBV152/Covaxin (Bharat Biotech, ICMR, National Institute of Virology) And among the protein-based/subunit vaccines, the following can be counted: NVX-CoV2373: (Novavax); SCB-2019 vaccine (Clover Biopharmaceuticals AUS Pty Ltd.); Covax-19 (GeneCure Biotechnologies; Vaxine Pty Ltd.) mRNA vaccines, viral vector vaccines, and protein subunit vaccines cannot cause disease because these vaccines stimulate the immune system to produce antibodies against virus proteins instead of the virus itself (or its antigen). MRNA vaccines increase SARS-CoV-2 proteins and ultimately stimulate the production of T and B lymphocytes. The epidemic of HCoVs and their destructive and harmful effects on life has caused the scientific community to seek the production of an effective and efficient vaccine before its catastrophic release. We all need to know that none of us will be healed until the other is healed. The purpose of this review article is to present a selection of existing knowledge in the field of fighting and preventing the coronavirus.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.013
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0130.006

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.064
GPT teacher head0.406
Teacher spread0.342 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations27
Published2023
Admission routes1
Has abstractyes

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