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Record W4205194146 · doi:10.3791/751-v

Titration of Human Coronaviruses Using an Immunoperoxidase Assay

2008· article· en· W4205194146 on OpenAlexaff
F. Lambert, Hélène Jacomy, Gabriel Marceau, Pierre J. Talbot

Bibliographic record

VenueJournal of Visualized Experiments · 2008
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced Biosensing Techniques and Applications
Canadian institutionsArmand Frappier Museum
Fundersnot available
KeywordsVirologyTiterVirus quantificationVirusSerial dilutionBiologyCytopathic effectMicrobiologyMedicinePathology

Abstract

fetched live from OpenAlex

Determination of infectious viral titers is a basic and essential experimental approach for virologists. Classical plaque assays cannot be used for viruses that do not cause significant cytopathic effects, which is the case for prototype strains 229E and OC43 of human coronavirus (HCoV). Therefore, an alternative indirect immunoperoxidase assay (IPA) was developed for the detection and titration of these viruses and is described herein. Susceptible cells are inoculated with serial logarithmic dilutions of virus-containing samples in a 96-well plate format. After viral growth, viral detection by IPA yields the infectious virus titer, expressed as 'Tissue Culture Infectious Dose 50 percent' (TCID50). This represents the dilution of a virus-containing sample at which half of a series of laboratory wells contain infectious replicating virus. This technique provides a reliable method for the titration of HCoV-229E and HCoV-OC43 in biological samples such as cells, tissues and fluids. This article is based on work first reported in Methods in Molecular Biology (2008) volume 454, pages 93-102.

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)0.001
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: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.083
GPT teacher head0.476
Teacher spread0.393 · 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
GenreMethods

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
Published2008
Admission routes1
Has abstractyes

Explore more

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