Adenoviral-Mediated Modulation of<i>Sim1</i>Expression in the Paraventricular Nucleus Affects Food Intake
Bibliographic record
Abstract
Haploinsufficency of Sim1, which codes for a basic helix-loop-helix-PAS (PER-ARNT-SIM) transcription factor, causes hyperphagia in mice and humans, without decrease in energy expenditure. Sim1 is expressed in several areas of the brain, including the developing and postnatal paraventricular nucleus (PVN), a region of the hypothalamus that controls food intake. We have previously found that the number of PVN cells is decreased in Sim1+/- mice, suggesting that their hyperphagia is caused by a developmental mechanism. However, the possibility that Sim1 functions in the postnatal PVN to control food intake cannot be ruled out. To explore this hypothesis, we used adenoviral vectors to modulate Sim1 expression in the postnatal PVN of wild-type mice. Unilateral stereotaxic injection into the PVN of an adenoviral vector producing a short hairpin RNA directed against Sim1 resulted in a significant increase in food intake, which peaked to 22% 6 d after the procedure, compared with the injection of a control virus. In contrast, injection of an adenovirus that expresses Sim1 induced a decrease in food intake that was maximal on the seventh day after the procedure, reaching 20%. The impact of bilateral injections of these vectors into the PVN was not greater than that of unilateral injections. Together, these results strongly suggest that Sim1 functions along a physiological pathway to control food intake.
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 | 0.001 |
| 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".