Association of Lactoferrin and Toll-like Receptor 2 Genotypes with Mastitis and Milk Components in Vietnamese Holstein Cattle
Bibliographic record
Abstract
Mastitis is one of the most widespread diseases in dairy cows and causes huge losses for the dairy industry. Molecular markers can be used for the quick diagnosis of mastitis infection, consequently reducing the loss caused by this disease. Lactoferrin (LTF) and Toll-like receptor 2 (TLR2) have been suggested as candidate genes for mastitis; however, their associations with the mastitis incidence and milk components have not been reported in Vietnamese Holstein cows. This study examined the association of TLR2 and LTF polymorphisms with subclinical mastitis and milk components in the Holstein breed raised in Vietnam. Among 192 samples, we identified 44 mastitis-positive samples (22.92%). The mastitis significantly reduced the fat and lactose components in milk (p < 0.001) but increased the protein concentration in milk. A total of 94 (49%) and 98 (51%) cows had AA and AB genotypes for the LTF gene, respectively. No significant association was found between the LTF genotypes and the milk component traits or mastitis incidence (p > 0.05). The interaction between LTF and mastitis incidence was significantly associated with the protein percentage (p = 0.01). A total of 78, 76, and 38 cows had genotypes GG, GT, and TT for the TLR2 gene, respectively. TLR2 genotypes were not significantly associated with mastitis incidence (p > 0.05) but were significantly associated with pH value (p = 0.03). The interaction between TLR2 and mastitis incidence was significantly associated with the fat (p = 0.02) and protein percentage (p = 0.04). Further studies are required to confirm the roles of LTF and TFL2 in mastitis in the Holstein breed in Vietnam.
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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 | 0.001 | 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".