Scientific Advances of Milk Enrichment with Conjugated Linoleic Acid to Produce Anti-Cancer Milk
Bibliographic record
Abstract
Background: Conjugated linoleic acid (CLA) is an unsaturated fatty acid that after the discovery of its anti-cancerous properties numerous researches conducted to increase its concentration in the food products. The current study aimed to review researches in the field of milk enrichment with CLA to identify the main actors and highlight the achievements in the field; therefore, the effectiveness of these researches for producing anti-cancer milk was surveyed. Methods: The journal of dairy science was selected for data extraction and related papers were identified via review of all published papers during years 2003 - 2013 and then were subjected into content analysis. Statistical analysis was done with the MEANS procedure of SAS software (Version 9.1). Results: Among all collected papers, only 72 were eligible. Data analysis showed that the USA, Canada and France were the most productive countries in the field of milk enrichment with CLA. Surprisingly, Iran did not have any paper in this field in the journal of dairy science. The supplements that were added to the animal diets were enriched by linoleic or linolenic acids. Enhancing the milk cis-9, and trans-11 CLA content were the main objectives of researches. More than 70% of treatments led to the supply of the minimum amount of CLA to prevent cancer with serving milk twice a day. Conclusions: Considerably, milk enrichment with CLA mostly has being attracted by developed countries. Enriching milk with CLA is a suitable scientific strategy to fight against cancer. It is important to increase researches in increasing trans- 10, and cis- 12 CLA.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".