Lactation persistency: Insights from mammary cell proliferation studies
Bibliographic record
Abstract
A persistent lactation is dependent on maintaining the number and activity of milk secreting cells with advancing lactation. When dairy cows are milked twice daily, the increase in milk yield from parturition to peak lactation is due to increased secretory activity per cell rather than to accretion of additional epithelial cells. After peak lactation, declining milk yield is due to loss of mammary epithelial cells by apoptosis. During lactation, only 0.3% of mammary cells proliferate in a 24-h period. Yet this proliferative rate is sufficient to replace most mammary epithelial cells by the end of lactation. Management practices can influence lactation persistency. Administration of bovine somatotropin may enhance persistency by increasing cell proliferation and turnover, or by reducing the rate of apoptosis. Increased photoperiod may also increase persistency of lactation by mechanisms that are as yet undefined. Increased milking frequency during the first weeks of lactation increases milk yield, even after return to less frequent milking, with increases of approximately 8% over the entire lactation. A mammary cell proliferation response to frequent milking during early lactation appears to be involved. Conversely, advanced pregnancy, infrequent milking, and mastitis increase death of epithelial cells by apoptosis. Regulation of mammary cell renewal provides a key to increasing persistency. Investigations to characterize epithelial cells that serve as the proliferative population in the bovine mammary gland have been initiated. Epithelial cells that stain lightly in histological sections are evident through all phases of mammary development and secretion and account for nearly all proliferation in the prepubertal gland. Characterization of these cells may provide a means to regulate mammary cell proliferation and thus to enhance persistency, reduce the effects of mastitis, and decrease the necessity for a dry period.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".