Anti-fatigue effects of porcine placenta and its amino acids in a behavioral test on mice
Bibliographic record
Abstract
Placenta extracts are used for their health benefits; however, the anti-fatigue effects of placenta have not been elucidated. Thus, we investigated the anti-fatigue effects of porcine placenta extract (PE) and the amino acids present in the PE (glycine, Gly; proline, Pro; glutamic acid, GA; and arginine, Arg) using a forced swimming test (FST) and a tail-suspension test (TST) on mice. Whole PE or individual amino acids decreased immobility times in the FST. PE, Pro, and Arg all lowered blood levels of lactic acid and alanine aminotransferase (ALT). PE and Gly improved glycogen content and catalase activity. As determined from the serum after the FST: PE regulated the effects of interferon (IFN)-γ and tumor necrosis factor (TNF)-α; GA regulated the effects of IFN-γ; Gly and Arg regulated the effects of interleukin (IL)-6; and all of the amino acids present in PE regulated the effects of TNF-α. As determined from the spleen after the FST: Gly and Arg regulated the effects of IL-1β; Gly, Pro, and Arg regulated the effects of IL-6; PE and all of the amino acids present in PE regulated the effects of TNF-α. After the TST, PE and all of the amino acids present in PE reduced immobility duration as well as levels of aspartate aminotransferase and ALT. As determined from the serum after the TST: PE and Gly regulated the effects of TNF-α; Gly and Arg regulated the effects of IL-1β; Gly, Pro, and Arg regulated the effects of IL-6; PE and all of the amino acids present in PE regulated the effects of TNF-α. These results suggest that PE should be considered a candidate anti-fatigue agent.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".