Passive immunization of lactating mice with stanniocalcin-1 antiserum reduces mammary gland development, milk fat content, and postnatal pup growth
Bibliographic record
Abstract
During pregnancy and lactation in rodents, stanniocalcin-1 (STC-1) production by the ovaries is upregulated markedly and released into the circulation. The mammary glands are one target of this systemically delivered hormone. The purpose of this study was to lower serum levels of STC-1 in lactating mice through passive immunization so as to monitor the effects on mammary gland function and postnatal pup growth. Passive immunization significantly reduced circulating hormone levels, and pup growth was significantly compromised (30%), even though control and experimental litters had ingested equal amounts of milk. When mammary glands were analyzed, the alveolar area was significantly reduced in antibody-treated mothers. An analysis of milk composition revealed no changes in lactose, protein, or electrolyte levels but an approximately 40% reduction in triglyceride levels. The latter was due to a significant reduction in mammary gland lipoprotein lipase activity and led to a significant buildup of triglycerides in the serum. Body fat content was also significantly reduced in pups from antibody-treated mothers, whereas pup fecal fat content was increased. In mothers, passive immunization also caused significant behavioral effects, in particular, increased locomotor and hindleg rearing activities. Collectively, the results suggest that systemically derived STC-1 has important effects on mammary gland development and the transfer of serum-based triglycerides into milk. Locomotor effects suggest that STC-1 also has a role in maternal behavior.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".