Author response: Long-term consequences of the absence of leptin signaling in early life
Bibliographic record
Abstract
Leptin is a hormone that keeps us healthy in many ways. It regulates our body weight by reining in our appetite and fine-tuning the energy we burn, and it helps us establish and maintain our fertility. It also participates in brain development. Leptin performs these roles by attaching to specific receptors in nerve cells and relaying relevant information to the brain. Early events can trigger life-long changes in the way our body works, a process called metabolic programming. Leptin is believed to participate in this reprogramming mechanism, but its role remains uncertain. In particular, it is still unclear which leptin-driven changes are permanent, and which ones are reversible. Being able to distinguish between the two types of alterations would help to better grasp the role leptin plays in early development. Here, Ramos-Lobo et al. examined genetically engineered mice born without a working leptin receptor. These animals were impervious to the effects of leptin. Then, once the rodents were adults, they were treated with a drug that restored their leptin receptors, making them sensitive to the hormone again. These experiments revealed that mice without leptin receptors during early life developed obesity, were less able to lose weight and burned less energy. Their reproductive success was also compromised. Finally, the lack of leptin during development caused permanent reduction of the animals’ brains, and changes in the activity of certain genes in the organ. The work by Ramos-Lobo et al. indicates that in mice, lacking leptin sensibility early in life conditions the body to permanently become ‘thrifty’, burning less energy and making it harder to lose weight. It is rare for humans to be born completely without leptin activity. Yet, having too much or too little food as a baby affects the level of the hormone, or our sensitivity to it: this may permanently change the way our bodies manage energy. Ultimately, learning more about these mechanisms could help us ward off or treat obesity.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.165 | 0.057 |
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".