Short salsalate administration affects cell proliferation, metabolism, and inflammation in polycystic kidney disease
Bibliographic record
Abstract
Metabolic reprogramming is a driver of autosomal dominant polycystic kidney disease (ADPKD) progression and a potential therapeutic intervention route. We showed before that the AMP-associated protein kinase (AMPK) activator salsalate attenuates cystic disease progression. Here, we aim to study the early, direct effects of short salsalate treatment in adult-onset conditional Pkd1 deletion mice. Cystic mice were treated with salsalate for two weeks, after which NMR metabolomics and RNA sequencing analyses were performed. Pkd1 deletion resulted in clear metabolomic dysregulation. Short salsalate treatment has small, but significant, effects, reverting acetylcarnitine and phosphocholine concentrations back to wildtype levels, and showing associations with altered purine metabolism. RNA sequencing revealed that short salsalate treatment, next to restoring energy metabolism toward wildtype levels, also affects cell proliferation and inflammation, in PKD. We show that salsalate positively affects major dysregulated processes in ADPKD: energy metabolism, cell proliferation, and inflammation, providing more insights into its working mechanisms.
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) | 0.000 | 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 | 0.001 |
| 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".