Transcriptional Levels of Autophagy and UPR Markers in the Brain and Liver of Pregnant Rats
Bibliographic record
Abstract
Background: Pregnancy is associated with oxidative stress that results in endoplasmic reticulum (ER) stress and unfolded protein response (UPR). Prolonged-unalleviated ER stress causes the activation of the autophagy pathway via UPR. Expression of genes encoding glucose-regulated protein 78 (GRP78) and BECLIN1 are induced in UPR and autophagy. Objectives: We studied the mRNA expression of the aforementioned genes in the liver and brain of Nulligravida versus saline and ethanol-treated pregnant rats. Methods: Control pregnant rats were orally treated with normal saline, and test animals received ethanol 250 mg/kg or resveratrol 120 mg/kg from day 1 to day 21 of gestation. Nulligravida rats treated by saline comprised the non-pregnant control group. On day 21, mRNAs encoding GRP78 and BECLIN1 were extracted from the liver and brain tissues and assessed using real-time PCR. Results: Our results showed that the level of transcripts encoding GRP78 and BECLIN1 was higher in the liver of pregnant rats compared to Nulligravida ones. Further, ethanol decreased the mRNA levels of GRP78 and BECLIN1 in the liver of pregnant rats, an effect that was reversed by resveratrol. Levels of GRP78 transcripts were decreased, and those of BECLIN1 remained unchanged in the brain of ethanol exposed pregnant rats. Conclusions: Levels of mRNAs for GRP78 and BECLIN1 are up-regulated during pregnancy. These levels are reduced in the liver of ethanol-treated rats, and resveratrol compensates these effects.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".