Use of CAT scan to determine mammary gland development of sows injected with growth hormone releasing factor during gestation and (or) lactation
Bibliographic record
Abstract
Fifty-two Yorkshire × Landrace gilts were allotted to: (1) saline injections (CTL; n = 17) thrice daily during gestation (day 100 to parturition) and lactation (days 3 to 29); (2) s.c. injections of 12 mg of GRF (1-29)NH2 thrice daily from day 100 of gestation to parturition (GEST; n = 12); (3) s.c. injections of 12 mg of GRF thrice daily from days 3 to 29 of lactation (LACT; n = 11); and (4) s.c. injections of 12 mg of GRF thrice daily during gestation (day 100 to parturition) and lactation (days 3 to 29) (GEST-LACT; n = 12). All sows were slaughtered on day 30 of lactation and mammary glands were excised and stored frozen at -20°C. Mammary development was measured on half of the thawed mammary gland with CAT scanning, and standard dissection and previously published biochemical measurements of mammary development were used to assess the contralateral half; correlation analyses were used to compare the two approaches. Volume of parenchymal tissue of GEST-LACT sows was 19% smaller than that of sows subjected to other treatments (GEST × LACT interaction, P = 0.01). Total mammary gland volume (r = 0.68; P = 0.002) and extraparenchymal tissue volume (r = 0.77; P < 0.001) measured by CAT scanning were correlated with extraparenchymal tissue weight obtained by tissue dissection. However, none of the CAT scan measurements were correlated with parenchymal tissue weight (P > 0.1). In conclusion, exogenous GRF given to sows during lactation affected cell volume/differentiation of the mammary gland. For mammary gland assessment in lactating sows, the CAT scan technique accurately measured the weight and volume of extraparenchymal tissue but not parenchymal tissue mass. Key words: Sows, mammary gland, mammary development, CAT scan, growth hormone-releasing factor, lactation
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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) | 0.000 | 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) | 0.001 | 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".