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Record W2088490106 · doi:10.1371/journal.pone.0085447

Challenges in the Development of an Immunochromatographic Interferon-Gamma Test for Diagnosis of Pleural Tuberculosis

2013· article· en· W2088490106 on OpenAlexafffund
Claudia M. Denkinger, Yatiraj Kalantri, Samuel G. Schumacher, Joy S. Michael, Deepa Shankar, Arvind Kumar Saxena, Narayanan SRIRAM, Balamugesh Thangakunam, Robert Luo, Nira R. Pollock, Madhukar Pai, Devasahayam Jesudas Christopher

Bibliographic record

VenuePLoS ONE · 2013
Typearticle
Languageen
FieldMedicine
TopicPleural and Pulmonary Diseases
Canadian institutionsMcGill University
FundersFonds de Recherche du Québec - SantéCanadian Institutes of Health ResearchNational Institutes of HealthAmerican Society of Tropical Medicine and HygieneGrand Challenges CanadaEuropean and Developing Countries Clinical Trials PartnershipWellcome TrustMcGill UniversityNational Institute of Allergy and Infectious DiseasesBill and Melinda Gates Foundation
KeywordsTuberculosisMedicineInterferon gammaTuberculosis diagnosisVirologyPathologyMycobacterium tuberculosisInternal medicineCytokine

Abstract

fetched live from OpenAlex

Existing diagnostic tests for pleural tuberculosis (TB) have inadequate accuracy and/or turnaround time. Interferon-gamma (IFNg) has been identified in many studies as a biomarker for pleural TB. Our objective was to develop a lateral flow, immunochromatographic test (ICT) based on this biomarker and to evaluate the test in a clinical cohort. Because IFNg is commonly present in non-TB pleural effusions in low amounts, a diagnostic IFNg-threshold was first defined with an enzyme-linked immunosorbent assay (ELISA) for IFNg in samples from 38 patients with a confirmed clinical diagnosis (cut-off of 300 pg/ml; 94% sensitivity and 93% specificity). The ICT was then designed; however, its achievable limit of detection (5000 pg/ml) was over 10-fold higher than that of the ELISA. After several iterations in development, the prototype ICT assay for IFNg had a sensitivity of 69% (95% confidence interval (CI): 50-83) and a specificity of 94% (95% CI: 81-99%) compared to ELISA on frozen samples. Evaluation of the prototype in a prospective clinical cohort (72 patients) on fresh pleural fluid samples, in comparison to a composite reference standard (including histopathological and microbiologic test results), showed that the prototype had 65% sensitivity (95% CI: 44-83) and 89% specificity (95% CI: 74-97). Discordant results were observed in 15% of samples if testing was repeated after one freezing and thawing step. Inter-rater variability was limited (3%; 1 out of 32). In conclusion, despite an iterative development and optimization process, the performance of the IFNg ICT remained lower than what could be expected from the published literature on IFNg as a biomarker in pleural fluid. Further improvements in the limit of detection of an ICT for IFNg, and possibly combination of IFNg with other biomarkers such as adenosine deaminase, are necessary for such a test to be of value in the evaluation of pleural tuberculosis.

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)0.055
metaresearch head score (Gemma)0.056
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.055
Threshold uncertainty score0.289

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0550.056
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.001
Science and technology studies0.0000.003
Scholarly communication0.0050.002
Open science0.0040.001
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0010.002

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 head0.102
GPT teacher head0.271
Teacher spread0.168 · 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
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

Citations4
Published2013
Admission routes2
Has abstractyes

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Same venuePLoS ONESame topicPleural and Pulmonary DiseasesFrench-language works237,207