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Record W1939480328 · doi:10.5430/jst.v5n2p112

HPV prophylactic vaccines: Second-generation or first-generation vaccines

2015· article· en· W1939480328 on OpenAlexvenueno aff
Kimia Kardani, Golnaz Mardani, Azam Bolhassani

Bibliographic record

VenueJournal of Solid Tumors · 2015
Typearticle
Languageen
FieldMedicine
TopicCervical Cancer and HPV Research
Canadian institutionsnot available
FundersIran National Science FoundationNational Science Foundation
KeywordsGardasilHPV vaccinesCapsidMedicineVirologyVirus-like particleGenital wartsVaccinationFirst generationHuman papillomavirusAntibodyCervical cancerImmunologyVirusHPV infectionCancerBiologyRecombinant DNAInternal medicinePopulationGene

Abstract

fetched live from OpenAlex

High-risk genotypes of human papillomavirus (HPV) are associated with genital cancers especially cervical cancer. United State Food and Drug Administration (USFDA) has recently licensed two first-generation prophylactic vaccines ( i.e. , Gardasil and Cervarix), for control of HPV 16 and 18 infections. Both vaccines are able to generate neutralizing antibodies against major capsid protein L1 assembled as virus-like particles (VLPs). To enhance protection against other HPV genotypes, second-generation vaccines are underway. A HPV L1-based nonavalent vaccine showed is potent and safe in prevention of precancerous lesions associated with HPV types 16/18/31/33/45/52/58, as well as anogenital warts associated with HPV types 6/11. This vaccine is in the advanced stage of phase III clinical trials. Other second-generation vaccines were based on L1-pentameric subunits and also the minor capsid protein L2 that have shown to be effective in preclinical studies. The L2 protein co-assembles with the L1 protein for VLP formation increasing virion aggregation. This mini-review describes two vaccination strategies including first-generation and second-generation vaccines against HPV infections.

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)0.000
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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.103
GPT teacher head0.362
Teacher spread0.259 · 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

Citations1
Published2015
Admission routes1
Has abstractyes

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