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Record W4245781185 · doi:10.1371/journal.pone.0004505

International Network for Comparison of HIV Neutralization Assays: The NeutNet Report

2009· article· en· W4245781185 on OpenAlexfundno aff
Éva Mária Fenyõ, Alan Heath, Stefania Dispinseri, Harvey Holmes, Paolo Lusso, Susan Zolla‐Pazner, Helen Donners, Leo Heyndríckx, José Alcamı́, Vera Bongertz, Christian Jassoy, Mauro Malnati, David C. Montefiori, Christiane Moog, Lynn Morris, Saladin Osmanov, Victoria R. Polonis, Quentin J. Sattentau, Hanneke Schuitemaker, Ruengpung Sutthent, Terri Wrin, Gabriella Scarlatti

Bibliographic record

VenuePLoS ONE · 2009
Typearticle
Languageen
FieldImmunology and Microbiology
TopicHIV Research and Treatment
Canadian institutionsnot available
FundersNational Institute of Allergy and Infectious DiseasesNational Institutes of HealthFundación Para La Innovación Y La Prospectiva En Salud En EspañaInstituto de Salud Carlos IIIBill and Melinda Gates FoundationLunds UniversitetFonds Wetenschappelijk OnderzoekVlaamse regeringDepartment of Science and Technology, Ministry of Science and Technology, IndiaCenter for AIDS Research, University of WashingtonUniversity of OxfordYork UniversityStyrelsen för Internationellt UtvecklingssamarbeteVetenskapsrådetInternational AIDS Vaccine Initiative
KeywordsVirologyInfectivityVirusNeutralizationPeripheral blood mononuclear cellBiologyIn vitro toxicologyAntibodyMolecular biologyIn vitroImmunologyGenetics

Abstract

fetched live from OpenAlex

BACKGROUND: Neutralizing antibody assessments play a central role in human immunodeficiency virus type-1 (HIV-1) vaccine development but it is unclear which assay, or combination of assays, will provide reliable measures of correlates of protection. To address this, an international collaboration (NeutNet) involving 18 independent participants was organized to compare different assays. METHODS: Each laboratory evaluated four neutralizing reagents (TriMab, 447-52D, 4E10, sCD4) at a given range of concentrations against a panel of 11 viruses representing a wide range of genetic subtypes and phenotypes. A total of 16 different assays were compared. The assays utilized either uncloned virus produced in peripheral blood mononuclear cells (PBMCs) (virus infectivity assays, VI assays), or their Env-pseudotyped (gp160) derivatives produced in 293T cells (PSV assays) from molecular clones or uncloned virus. Target cells included PBMC and genetically-engineered cell lines in either a single- or multiple-cycle infection format. Infection was quantified by using a range of assay read-outs that included extracellular or intracellular p24 antigen detection, RNA quantification and luciferase and beta-galactosidase reporter gene expression. FINDINGS: PSV assays were generally more sensitive than VI assays, but there were important differences according to the virus and inhibitor used. For example, for TriMab, the mean IC50 was always lower in PSV than in VI assays. However, with 4E10 or sCD4 some viruses were neutralized with a lower IC50 in VI assays than in the PSV assays. Inter-laboratory concordance was slightly better for PSV than for VI assays with some viruses, but for other viruses agreement between laboratories was limited and depended on both the virus and the neutralizing reagent. CONCLUSIONS: The NeutNet project demonstrated clear differences in assay sensitivity that were dependent on both the neutralizing reagent and the virus. No single assay was capable of detecting the entire spectrum of neutralizing activities. Since it is not known which in vitro assay correlates with in vivo protection, a range of neutralization assays is recommended for vaccine evaluation.

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)0.080
metaresearch head score (Gemma)0.062
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.080
Threshold uncertainty score0.421

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0800.062
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0130.015
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0040.007
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0290.013

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 head0.068
GPT teacher head0.311
Teacher spread0.243 · 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".

Quick stats

Citations119
Published2009
Admission routes1
Has abstractyes

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