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Record W2809218127 · doi:10.1155/2018/5809028

Development of Novel Single-Chain Antibodies against the Hydrophobic HPV-16 E5 Protein

2018· article· en· W2809218127 on OpenAlexaff
César U. Monjarás-Ávila, Sofía Bernal‐Silva, Horacio Bach

Bibliographic record

VenueBioMed Research International · 2018
Typearticle
Languageen
FieldMedicine
TopicCervical Cancer and HPV Research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAntibodyBiologyL1VirusCarcinogenesisMalignant transformationGroup-specific antigenColocalizationVirologyCellImmunologyMolecular biologyCancerCancer researchGeneBiochemistry

Abstract

fetched live from OpenAlex

The human papilloma virus type 16 infects genital mucosa with high prevalence in the oncogenesis of cervical and oropharyngeal cancers. The E5 protein of this virus is a small hydrophobic protein, whose expression generally decreases as the infection progresses to malignancy. These characteristics point to a role of E5 in the establishment of HPV infection and the initiation into cell transformation. The study of the HPV-16 E5 functions has been hindered because of the lack of antibodies. Detection is very difficult because of its hydrophobic nature, membrane location, and very low levels of expression. Thus, the objective of this study was to select single-chain antibodies against the full size E5 protein, which was coexpressed with maltose-binding protein. We report that the E5 protein was recognized by the antibody and was validated in W12 cells by fluorescent microscopy, including a colocalization with one of its host substrates. The use of this antibody could increase our knowledge about the functions of the oncogenic HPV-16 E5 protein during the earliest stages of keratinocyte infection in human.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.181
GPT teacher head0.429
Teacher spread0.248 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations2
Published2018
Admission routes1
Has abstractyes

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