Impact of Female Sex Hormones on Liver Tissue Lactic Acidosis During Ischemia
Bibliographic record
Abstract
BACKGROUND: Lower liver transplant success is observed when the donor is female. Intracellular acidosis during ischemia is proposed to contribute to the injury sustained by the transplanted organ and its role in livers obtained from nonheartbeating donors is unclear. Research has shown that livers of female rats develop a greater degree of intracellular acidosis during ischemia than males. This work explores the role of sex hormones in mediating this sex difference. METHODS: Subgroups of neutered female rats were given 17 beta-estradiol (E), progesterone (P), or combination (E+P). To compare the effects of female sex hormones in males, subgroups of intact and castrated males received 17 beta-estradiol. In vivo and ischemic liver biopsies were taken and analyzed for lactate and H. RESULTS: Although there was no effect of hormone therapy on baseline metabolic parameters, during ischemia compared to neutered females, livers from E females significantly (P<0.01) increased lactate by 56% and H+ by 71%, while E+P significantly increased only lactate (39%; P<0.05). Livers from neutered males given 17 beta-estradiol showed significantly greater (P<0.001) accumulation of lactate (80%) and H+ (79%). This was even shown in intact males, where despite a blunted response, 17 beta-estradiol, significantly (P<0.05) increased lactate by 39% and H+ by 25%. CONCLUSION: This study illustrates the mechanisms for the sex difference in the liver's metabolic response to ischemia are estrogen mediated, which is seen even in the presence of male hormones, thus offering one explanation for the lower liver transplant success when the donor is female.
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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) | 0.003 | 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".