Enhancing TB diagnosis: Improving specificity with scFv antibodies targeting the PPE17 epitope
Bibliographic record
Abstract
BACKGROUND: Serological assays have demonstrated enhanced simplicity, accuracy, and effectiveness in detecting Mycobacterium Tuberculosis (Mtb) antigens. The proline-proline-glutamic acid 17 (PPE17) antigen, specifically localized on the surface of Mtb, has been identified as unique to the Mtb species. The unique properties of single-chain antibodies make them well-suited for accurate diagnostic applications. In this study, specialized single-chain antibodies (scFvs) targeting PPE17 were employed to create a precise indirect immunofluorescent assay for diagnosing pulmonary tuberculosis (TB). METHODS: To select an immunodominant epitope of PPE17 in silico analysis was applied. The sequence was evaluated using the BLAST algorithm. A phage antibody display library of scFv was applied and two scFvs were isolated against the epitope by panning process. Specific clones were distinguished through PCR and DNA fingerprinting techniques. The reactivity of the chosen scFvs towards the selected epitope was assessed by ELISA. An Indirect Immunofluorescence Assay (IFA) was performed on 50 positive and 50 negative TB sputum smears, which were confirmed through both culture and genotype methods, to evaluate the performance of anti-PPE17 scFvs in accurately and rapidly detecting TB-positive smears, and TB-negative and Nocardia smears serving as negative controls for comparison. RESULTS: An immunodominant epitope of the PPE17 antigen consisting of amino acids 27-39, was identified. Two specific anti-PPE17-scFvs with frequencies of 25 % and 20 % were selected. ELISA results confirmed the reactivity of the scFvs against the epitope. Immunofluorescence assays demonstrated positive results for both antibodies when tested against positive TB sputum smears, whereas no positive results were obtained in tests against TB-negative and Nocardia smears. CONCLUSION: A fast and accurate indirect immunofluorescence assay was developed to identify Mtb bacteria in TB sputum smears using specific anti-PPE17 scFvs. The results illustrated the capability of both scFvs in detecting Mtb in TB samples and differentiating Mtb from Nocardia smears. This suggests the potential for a novel diagnostic test that ensures precise TB detection in sputum samples, thereby preventing any potential misdiagnosis of tuberculosis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".