Nesfatin-1-like peptide is a novel metabolic factor that suppresses feeding, and regulates whole-body energy homeostasis in male Wistar rats
Bibliographic record
Abstract
Nucleobindin-1 has high sequence similarity to nucleobindin-2, which encodes the anorectic and metabolic peptide, nesfatin-1. We previously reported a nesfatin-1-like peptide (NLP), anorectic in fish and insulinotropic in mice islet beta-like cells. The main objective of this research was to determine whether NLP is a metabolic regulator in male Wistar rats. A single intraperitoneal (IP) injection of NLP (100 μg/kg BW) decreased food intake and increased ambulatory movement, without causing any change in total activity or energy expenditure when compared to saline-treated rats. Continuous subcutaneous infusion of NLP (100 μg/kg BW) using osmotic mini-pumps for 7 days caused a reduction in food intake on days 3 and 4. Similarly, water intake was also reduced for two days (days 3 and 4) with the effect being observed during the dark phase. This was accompanied by an increased RER and energy expenditure. However, decreased whole-body fat oxidation, and total activity were observed during the long-term treatment (7 days). Body weight gain was not significantly different between control and NLP infused rats. The expression of mRNAs encoding adiponectin, resistin, ghrelin, cholecystokinin and uncoupling protein 1 (UCP1) were significantly upregulated, while leptin and peptide YY mRNA expression was downregulated in NLP-treated rats. These findings indicate that administration of NLP at 100 μg/kg BW reduces food intake and modulates whole body energy balance. In summary, NLP is a novel metabolic peptide in rats.
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 | 0.000 |
| 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) | 0.002 | 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".