Induction of endoplasmic reticulum stress markers in an acromegaly model
Bibliographic record
Abstract
Acromegaly is a growth hormone (GH) excess pathological condition in humans. Acromegaly is associated with somatic disfigurement and a wide range of systemic manifestations such as arthritis, neuropathy, carpal tunnel syndrome, reproductive disorders, metabolic disorders, and gastrointestinal complications. The influence of excess GH on the cellular level could aid in understanding the root causes of acromegaly-related health complications. Previously, we found that GH excess induces DNA damage to somatic cells and reduces the stem cells number and causes premature aging. In this study, an in-depth analysis of the acromegaly RNAseq data revealed the disruption of important biological cellular processes. Gene set enrichment analysis, heatmap, and enrichment analysis of acromegaly RNAseq data revealed induction of endoplasmic reticulum (ER) stress markers in various organs. Interestingly, the induction of ER stress was even more apparent than in aged zebrafish. Splicing of box-binding protein-1 (XBP1) mRNA is a hallmark of ER stress. Therefore, we quantified spliced XBP1 mRNA in different organs of our acromegaly model. Thus, our study emphasizes the importance of ER stress in GH oversecretion, which is important for understanding the health complications of acromegaly.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".