Determination of the Presence of Diphtheria Toxin in the Myocardial Tissue of Rabbits and a Female Subject by Using an Immunofluorescent Antibody Method
Bibliographic record
Abstract
BACKGROUND: Clinical diagnosis of diphtheria is often difficult, in particular in countries where the disease is rarely observed, such as Turkey. In 2011, after 12 years of no recorded diphtheria cases in Turkey, a 34-year-old woman was diagnosed with diphtheria; she later died of myocarditis. In this study, we aimed to demonstrate the diagnostic potential of an immunofluorescent antibody method to determine the presence of diphtheria toxin (DT) in the myocardial cells of DT-injected rabbits and the female subject. METHODS: We randomly divided rabbits into two groups: a control group and a DT-injected group. Diphtheria intoxication was simulated in the rabbits by intravenous injection of DT. The myocardium of the rabbits and the female subject were harvested for histopathologic and immunofluorescence examination. A mouse monoclonal anti-DT antibody was used for the immunofluorescent antibody method. RESULTS: The presence of DT in the myocardial cells of both the rabbits and the female subject was visualized using the immunofluorescent method. CONCLUSIONS: Laboratory diagnosis of diphtheria is challenging because of non-toxigenic C. diphtheriae strains and/or the dysfunction of DT. However, visualizing the presence of DT in the myocardial tissue may act as an indicator of biologically active DT. We validated that an immunofluorescent method, which utilizes a monoclonal anti-DT (A-subunit specific) antibody, is a useful diagnostic tool to determine the presence of DT in the myocardium of rabbits and human.
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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 | 0.001 | 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".