Hyperprolactinemia Impaired the Effects of Lipopolysaccharide on Both Body Temperature and Sickness Behavior in Virgin Female Rats
Bibliographic record
Abstract
OBJECTIVE: Previously we observed an attenuation of body temperature in lactating rats treated with lipopolysaccharide (LPS) compared with virgin saline-treated females. We proposed that high levels of prolactin (PRL) during lactation may induce this attenuation because PRL has a suppressive effect on inflammation. In the present study, we induced hyperprolactinemia in female virgin rats to investigate the effects of PRL on body temperature and sickness behavior induced by LPS. METHODS: To induce hyperprolactinemia, female rats in the estrous phase received domperidone 3 times/day for 5 days and an LPS injection (D + LPS group). Two other groups were treated with saline solution for 5 days, and one of them received a saline injection (S + S group) and the other LPS (S + LPS group). Tympanic temperature was assessed 0, 2, 4, 6, 8, 10, 24, 48, 72, and 96 h after treatment. Body weight gain and food and water consumption were observed 24, 48, 72, and 96 h after treatment. RESULTS: Hyperprolactinemia impaired LPS-induced hypothermia and hyperthermia phases of body temperature. Body weight gains in the S + LPS group and the D + LPS group were similar. A decrease in food consumption was observed in the D + LPS rats at 72 and 96 h compared to the S + LPS group. CONCLUSION: Hyperprolactinemia impaired the body temperature increase induced by LPS and several signs of sickness behavior, suggesting that febrile responses to LPS can be modulated by the physiological state. These phenomena may have adaptive value for reproduction.
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".