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Record W3034138174 · doi:10.1099/jmm.0.001213

Correlation between cervical HPV DNA detection and HPV16 seroreactivity measured with L1-only and L1+L2 viral capsid antigens

2020· article· en· W3034138174 on OpenAlexaffabout
Andrea Trevisan, João Manuel Grisi Candeias, Patrícia Thomann, Luisa L. Villa, Eduardo L. Franco, Helen Trottier

Bibliographic record

VenueJournal of Medical Microbiology · 2020
Typearticle
Languageen
FieldMedicine
TopicCervical Cancer and HPV Research
Canadian institutionsMcGill UniversityUniversité de MontréalCentre Hospitalier Universitaire Sainte-Justine
FundersNational Cancer Institute
KeywordsSerologyVirologyImmunologyGenotypingMedicineViral loadAntigenAntibodyCervical cancerHPV infectionBiologyGenotypeCancerVirusInternal medicineGeneGenetics

Abstract

fetched live from OpenAlex

Introduction.Persistent human papillomavirus (HPV) type 16 infection is the main causal agent of cervical cancer. Most HPV infections clear spontaneously within 1–2 years. Although not all infected women develop detectable HPV antibodies, about 60–70 % seroconvert and retain their antibodies at low levels. Aim.We investigated if cervical HPV16 DNA positivity was associated with HPV16 seroreactivity measured with two different antigen formulations. We assessed if associations were influenced by co-infection with other HPV types and HPV16 viral load. Methodology.We used baseline data for women participating in the Ludwig–McGill cohort, a longitudinal investigation of the natural history of HPV infection and cervical neoplasia. The study enrolled 2462 Brazilian women from 1993 to 1997 (pre-vaccination). ELISA assays were based on L1-only or L1+L2 virus-like particles (VLPs). Seroreactivity was expressed as normalized absorbance ratios. HPV genotyping and viral load were evaluated by PCR protocols. Pearson’srwas used to measure correlations between interval-scaled variables. Serological accuracy in HPV16 DNA detection was assessed using receiver operating characteristic (ROC) curves. We analysed the association between HPV DNA positivity and HPV16 seroreactivity by linear regression. Results.Correlations between L1+L2 and L1-only VLPs for detection of HPV16 were poor (r=0.43 and 0.44 for dilutions 1 : 10 and 1 : 50, respectively). The protocol with the best accuracy was L1+L2 VLPs at serum dilution 1 : 10 (ROC area=0.73, 95 % CI: 0.65–0.85). HPV16 DNA positivity was correlated with HPV16 seroreactivity and was not influenced by co-infection or viral load. To a lesser degree, HPV16 seroreactivity was correlated with infection by other Alpha-9 papillomavirus species. Conclusion.HPV16 DNA positivity and HPV16 seroreactivity are strongly correlated. L1+L2 VLPs perform better than L1-only VLPs for detecting IgG antibodies to HPV16 in women infected with HPV16 or other Alpha-9 HPV species. This study advances our understanding of humoral immune responses against HPV16 by providing insights about the influence of VLP antigen composition to measure humoral immune response against naturally acquired HPV infection.

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)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.029
GPT teacher head0.297
Teacher spread0.268 · 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 designObservational
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".

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Citations2
Published2020
Admission routes2
Has abstractyes

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