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Comparison between Phage-ELISA and Phage Dot-Blot Assay Methods for the Detection of Hepatitis B Surface Antigen and its Antibodies in Human Serum

2014· article· en· W2120396598 on OpenAlexvenueno aff
Geok Wee Tan, Wen Siang Tan

Bibliographic record

VenueInternational Journal of Biotechnology for Wellness Industries · 2014
Typearticle
Languageen
FieldMedicine
TopicMonoclonal and Polyclonal Antibodies Research
Canadian institutionsnot available
Fundersnot available
KeywordsDot blotAntibodyAntigenVirologyPhage displayMolecular biologyBiologyImmunology

Abstract

fetched live from OpenAlex

A modified phage-enzyme link immunosorbent assay (phage-ELISA) and a phage dot-blot assay specific for hepatitis B surface antigen (HBsAg) and its antibody were developed by using phage display technology. The phage-ELISA and phage dot-blot assays enabled to detect HBsAg and anti-HBsAg in human sera, and compatible to commercial detection kit. The fusion phages were immobilized onto microtiter plate wells and nitrocellulose membrane sheets, then blocked with 10% milk diluent, and added with human serum at dilution of 1:5000. The absorbance at 405 nm was determined once the colour changes formed. The same human serum also applied on the commercial diagnostic kit for comparison. The statistical analysis was carried out using ANOVA and T Test (LSD) for variable comparison between phage-ELISA and phage dot-blot assays. Based on these studies, the phage-ELISA was found to be more sensitive compared to phage dot-blot assay as the detection of HBsAg in human sera was about 80% as compared to 51.7% by using phage dot-blot assay. Meanwhile, the sensitivity for detection of anti-HBsAg by using phage-ELISA was slightly higher which showed about 83.3%. However, the sensitivity of the assay was dropped almost half when using phage dot-blot assay. Therefore, they are practical to be used as a reliable alternative way for the detection HBsAg and its antibody in human sera.

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)0.002
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.779
Threshold uncertainty score0.426

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.056
GPT teacher head0.420
Teacher spread0.364 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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