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Record W3086808711 · doi:10.1080/21645515.2020.1812313

Leveraging on the genomics and immunopathology of SARS-CoV-2 for vaccines development: prospects and challenges

2020· review· en· W3086808711 on OpenAlexaff
Idris Nasir Abdullahi, Anthony Uchenna Emeribe, Hafeez Aderinsayo Adekola, Sharafudeen Dahiru Abubakar, Amos Dangana, Halima Ali Shuwa, Sunday T. Nwoba, Jelili Olaide Mustapha, Muyideen Haruna, Kafayat Adepeju Olowookere, Olawale Sunday Animasaun, Charles Egede Ugwu, Solomon Oloche Onoja, Abdullahi Sani Gadama, Musa Mohammed, Isa Muhammad Daneji, Dele Ohinoyi Amadu, Peter Elisha Ghamba, Nkechi Blessing Onukegbe, Muhammad Sagir Shehu, Chiladi Jeff. Isomah, Adamu Babayo, Abdurrahman El-Fulaty Ahmad

Bibliographic record

VenueHuman Vaccines & Immunotherapeutics · 2020
Typereview
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPandemicVaccinationSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)CoronavirusMedicineImmunologyCoronavirus disease 2019 (COVID-19)Public healthDiseaseVirologyIntensive care medicineInfectious disease (medical specialty)Pathology

Abstract

fetched live from OpenAlex

The incidence and case-fatality rates (CFRs) of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection, the etiological agent for Coronavirus Disease 2019 (COVID-19), have been rising unabated. Even though the entire world has been implementing infection prevention and control measures, the pandemic continues to spread. It has been widely accepted that preventive vaccination strategies are the public health measures for countering this pandemic. This study critically reviews the latest scientific advancement in genomics, replication pattern, pathogenesis, and immunopathology of SARS-CoV-2 infection and how these concepts could be used in the development of vaccines. We also offer a detailed discussion on the anticipated potency, efficacy, safety, and pharmaco-economic issues that are and will be associated with candidate COVID-19 vaccines.

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)0.001
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 score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.002

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 head0.216
GPT teacher head0.393
Teacher spread0.178 · 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

Citations4
Published2020
Admission routes1
Has abstractyes

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