<i>N</i> ‐glycolylneuraminic acid in red meat and processed meat is a health concern: A review on the formation, health risk, and reduction
Bibliographic record
Abstract
One of the most consistent epidemiological associations between diet and human disease risk is the impact of consuming red meat and processed meat products. In recent years, the health concerns surrounding red meat and processed meat have gained worldwide attention. The fact that humans have lost the ability to synthesize N-glycolylneuraminic acid (Neu5Gc) makes red meat and processed meat products the most important source of exogenous Neu5Gc for humans. As our research of Neu5Gc has increased, it has been discovered that Neu5Gc in red meat and processed meat is a key factor in many major diseases. Given the objective evidence of the harmful risk caused by Neu5Gc in red meat and processed meat to human health, there is a need for heightened attention in the field of food. This updated review has several Neu5Gc aspects given including biosynthetic pathway of Neu5Gc and its accumulation in the human body, the distribution of Neu5Gc in food, the methods for detecting Neu5Gc, and most importantly, a systematic review of the existing methods for reducing the content of Neu5Gc in red meat and processed meat. It also provides some insights into the current status and future directions in this area.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".