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Record W2057904814 · doi:10.3791/751

Titration of Human Coronaviruses Using an Immunoperoxidase Assay

2008· article· en· W2057904814 on OpenAlexaff
F. Lambert, Hélène Jacomy, Gabriel Marceau, Pierre J. Talbot

Bibliographic record

VenueJournal of Visualized Experiments · 2008
Typearticle
Languageen
FieldNursing
TopicVitamin C and Antioxidants Research
Canadian institutionsArmand Frappier MuseumInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsVirologyImmunoperoxidaseCoronavirusTitrationCoronavirus disease 2019 (COVID-19)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)BiologyMedicineAntibodyPathologyChemistryImmunologyMonoclonal antibodyInfectious disease (medical specialty)

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 are liable method for the titration of HCoV-229E and HCoV-OC43 in biological samples such as cells, tissues and fluids [corrected].

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.003
Threshold uncertainty score0.476

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.151
GPT teacher head0.508
Teacher spread0.358 · 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 teacher head, 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

Citations14
Published2008
Admission routes1
Has abstractyes

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