155 Effects of sustained hyperprolactinemia in late gestation on the mammary parenchymal transcriptome of gilts
Bibliographic record
Abstract
Abstract The study objective was to determine the effects of hyperprolactinemia on the mammary parenchymal transcriptome in late-pregnant gilts. Gilts were divided into 3 groups on day 90 of gestation to receive IM injections of 1) canola oil (CTL, n = 18) until day 109 of gestation, 2) domperidone (dopamine antagonist) until day 96 (T7, n = 17) or, 3) domperidone until day 109 (T20, n = 17). Mammary glands were collected on day 110 and parenchymal tissue was sampled for transcriptomic analyses. Total RNA was isolated from 6 CTL and 6 T20 gilts for microarray analysis. The GeneChip® Porcine Gene 1.0 ST Array (19,202 genes) was used for hybridization. Array quality control, data normalization and expression level analyses were performed with the Affymetrix Expression Console and Transcriptome Analysis Console (TAC) software. Using a threshold cut-off of 1.5 fold (P < 0.05), a total of 313 upregulated and 480 downregulated gene transcripts were identified in T20 vs CTL gilts. A qPCR validation analysis of selected upregulated (n = 13) and downregulated (n = 13) genes was conducted on all animals (CTL, T7, T20). The MIXED procedure of SAS was used for statistical analyses. All selected genes were validated for the CTL vs T20 comparison (P < 0.01). Only 4 selected genes (CAMK1G, COL9A1, P2RX7, TDRD1) were downregulated in the T7 treatment (vs CTL, P < 0.05). Functional analyses of differentially expressed genes were performed using the PANTHER classification system. The top upregulated Biological Process enriched GO terms were Inflammatory Response (GO:0006954) and Response to Lipid (GO:0033993). The Positive Regulation of Cell Population Proliferation (GO:0008284) and Regulation of Cellular Catabolic Process (GO:0031329) GO terms were identified for downregulated genes. Results suggest that a sustained hyperprolactinemia during late-pregnancy (T20 treatment) may increase mammary inflammatory response and reduce cell proliferation.
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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".