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Record W2076205283 · doi:10.1002/bit.20707

Membrane chromatographic immunoassay method for rapid quantitative analysis of specific serum antibodies

2005· article· en· W2076205283 on OpenAlexaff
Raja Ghosh

Bibliographic record

VenueBiotechnology and Bioengineering · 2005
Typearticle
Languageen
FieldMedicine
TopicMonoclonal and Polyclonal Antibodies Research
Canadian institutionsMcMaster University
Fundersnot available
KeywordsChromatographyAntiserumChemistryMembraneImmunoassayAntibodyAntigenHaptenReagentQuantitative analysis (chemistry)BiochemistryBiologyOrganic chemistry

Abstract

fetched live from OpenAlex

This paper discusses a membrane chromatographic immunoassay method for rapid detection and quantitative analysis of specific serum antibodies. A type of polyvinylidine fluoride (PVDF) microfiltration membrane was used in the method for its ability to reversibly and specifically bind IgG antibodies from antiserum samples by hydrophobic interaction. Using this form of selective antibody binding and enrichment an affinity membrane with antigen binding ability was obtained in-situ. This was done by passing a pulse of diluted antiserum sample through a stack of microporous PVDF membranes. The affinity membrane thus formed was challenged with a pulse of antigen solution and the amount of antigen bound was accurately determined using chromatographic methods. The antigen binding correlated well with the antibody loading on the membrane. This method is direct, rapid and accurate, does not involve any chemical reaction, and uses very few reagents. Moreover, the same membrane could be repeatedly used for sequential immunoassays on account of the reversible nature of the antibody binding. Proof of concept of this method is provided using human hemoglobin as model antigen and rabbit antiserum against human hemoglobin as the antibody source.

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)0.000
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 score0.110
Threshold uncertainty score0.543

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.026
GPT teacher head0.322
Teacher spread0.296 · 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

Citations8
Published2005
Admission routes1
Has abstractyes

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