Mammary tissue damage during bovine mastitis: Causes and control1
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Mastitis, an inflammatory reaction of the mammary gland that is usually caused by a microbial infection, is recognized as the most costly disease in dairy cattle. Decreased milk production accounts for approximately 70% of the total cost of mastitis. Mammary tissue damage reduces the number and activity of epithelial cells and consequently contributes to decreased milk production. Mammary tissue damage has been shown to be induced by either apoptosis or necrosis. These 2 distinct types of cell death can be distinguished by morphological, biochemical, and molecular changes in dying cells. Both bacterial factors and host immune reactions contribute to epithelial tissue damage. During infection of the mammary glands, the tissue damage can initially be caused by bacteria and their products. Certain bacteria produce toxins that destroy cell membranes and damage milk-producing tissue, whereas other bacteria are able to invade and multiply within the bovine mammary epithelial cells before causing cell death. In addition, mastitis is characterized by an influx of somatic cells, primarily polymorphonuclear neutrophils, into the mammary gland. With more immune cells migrating into the mammary gland and the breakdown of the blood-milk barrier, damage to the mammary epithelium worsens. It is well known that breakdown of the extracellular matrix can lead to death of the epithelial cells. Meanwhile, polymorphonuclear neutrophils can harm the mammary tissue by releasing reactive oxygen intermediates and proteolytic enzymes. In vitro and in vivo studies suggest that the use of antioxidants and other protective compounds in mastitis control programs is worth investigating, because they may aid in alleviating damage to secretory cells and thus reduce subsequent milk loss.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it