Application of histochemical and immunohistochemical techniques for detection of lung tissue in cooked sausage.
Bibliographic record
Abstract
Background: Using unauthorized tissues in sausage is a common food adulteration in some parts of the world. Aims: This study was designed to compare the accuracy of histochemical and immunohistochemical techniques for the detection of lung tissue in cooked sausage samples. Methods: Samples with different levels of sheep lung tissues (1, 2.5, and 5%) and a control group were prepared and stained histochemically using H&E, Masson trichrome, and Periodic Acid-Schiff (PAS) stainings, and immunohistochemically using two different commercially-available antibodies of TTF1 Pan-cytokeratin. Results: The highest positive results of lung tissue detection were achieved in sausage samples stained with anti-TTF1 immunohistochemical staining method. Both anti-TTF1 and anti-pan-cytokeratin immunohistochemical techniques detected all contaminated sausage samples treated with 50 g/kg lung tissues. Anti-TTF1 staining method had the highest odds ratio (7.4), followed by anti-pan-cytokeratin method (6.0). Reversely, PAS staining method had the lowest odds ratio (0.21), followed by Masson trichrome method (1.7). Additionally, anti-TTF1 method had the highest (1.8-31.0) confidence intervale (95%), while PAS had the lowest (0.02-2.1). Totally, the odds ratio of lung tissue detected by immunohistochemical methods were higher than those detected by histochemical staining. Conclusion: This is the first report on the comparison of histochemical and immunohistochemical techniques for lung tissue detection in cooked sausage. Anti-TTF1 immunohistochemical staining proved to be the most useful technique for the detection of unauthorized lung tissue in cooked sausages.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".