Neural plasticity and the regulation of energy balance
Bibliographic record
Abstract
Energy homeostasis crucially depends on specialized brain circuits that process signals from peripheral organs and the environment to control food intake and energy expenditure. While much detail of this regulatory network has been elucidated in recent years, it is still poorly understood why some humans can maintain their body weight constant over most of their adult life, while others experience substantial gains in weight. These changes in body weight often appear difficult to reverse, both in humans and rodents, indicating that the underlying regulatory circuits may be altered in a very stable way. Such a “memory formation” of a new body weight set point may rely on plastic changes occurring in the brain circuits controlling energy balance. These plastic changes may be brought about by altering the connectome of these circuits, i.e., changing their wiring diagram or by altered neural genesis in the hypothalamus, a brain region key to body weight control. We now know that cell proliferation in the hypothalamus continues into adulthood and that it is responsive to growth factors. There is furthermore evidence that alterations in hypothalamic neural genesis can affect bodyweight. However, we still know only very little about the identity, fate and origin of hypothalamic cells that are either exogenously induced or constitutively born during adulthood. Progress in these areas will be presented.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".