Investigation on the Possible Formation of <i>N</i>-Nitroso-<i>N</i>-methylurea by Nitrosation of Creatinine in Model Systems and in Cured Meats at Gastric pH
Bibliographic record
Abstract
N-Nitroso-N-methylurea (NMU) is a highly potent direct-acting carcinogen that has been shown to induce cancer in a number of animal species. Although previous research has indicated that nitrosation of creatinine (CRN), a common constituent of meats, dried fish, and seafoods, can form traces of NMU, there is uncertainty as to (1) the yield of NMU and (2) whether detectable amounts of NMU can be formed from cured meats following nitrosation under acidic conditions given the low residual levels of nitrite found in cured meats at the present time. Lack of sensitive and specific analytical methods most likely has hindered progress in research in these areas. An HPLC postcolumn denitrosation-thermal energy analyzer technique and a GC-MS confirmation technique were developed for the determination of NMU in cured meats. Both techniques are highly sensitive (0.5 and 0.03 ppb, respectively) and specific. The optimum pH for NMU formation from CRN ranged between pH 1 and pH 3, and the yields of NMU under variable reactant concentrations ranged between 0.00004 and 0.0046%. When 27 samples of various cured meats (10 g aliquots each) were acidified with HCl (final pH values of 0.8-2.5) and incubated at room temperature for 2 h, without any additional nitrite, 24 gave results below detectable levels but 3 formed 2-26 ng of NMU/10 g of meat. Incubation of the negative meats with additional nitrite (50-500 microg/g of meat) formed 0.6-176 ng of NMU/10 g of sample. Although the amounts of NMU formed were extremely small, this seems to be the first reported formation of NMU from cured meats with and without additional nitrite.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".