Effects of a (pro)renin receptor blocker on weight gain in obese mice
Bibliographic record
Abstract
Objective Elucidate the role of the (pro)renin receptor [(P)RR] in obesity using a mouse model. Methods Mice were fed with a normal (N) or high‐fat/high carbohydrate (HF/HC) diet and treated with saline or a (P)RR blocker [(P)RRB] for 10 weeks. At sacrifice, white adipose tissue (WAT) [subcutaneous fat (SCF), perirenal fat (PRF), perigonadal fat (PGF)] were collected. mRNA was assessed by real‐time PCR, proteins by Western Blot and circulating proteins by array. Results (P)RRB decreased weight gain only in mice receiving a HF/HC diet compared to the saline group. (P)RR and renin mRNA in all WAT were increased with obesity compared to N diet without any effect of the (P)RRB with the exception of the SCF where (P)RR was decreased. (P)RR protein increased similarly to mRNA expression with obesity in PRF and SCF but was unaffected by the (P)RRB. Leptin and mesoderm‐specific transcript/paternally expressed gene 1 (MEST/PEG1), a marker of adipocyte cell size, mRNA increased with obesity in all WAT and decreased with (P)RRB on both diets. A similar effect was observed for resistin mRNA in SCF. Circulating leptin was modified similarly as its mRNA while circulating resistin was increased with obesity with no effect of the (P)RRB. Conclusion Obesity modulates (P)RR in WAT. (P)RRB seems to decrease weight gain, leptin mRNA and circulating level as well as adipocyte size.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".