Bibliographic record
Abstract
With the growing epidemics of childhood and adult obesity and the increasing social and medical burden for our societies, research into the long-term effects of early nutrition has become critical to understanding the underlying mechanisms and to curbing, if at all possible, the progression of the epidemics. Most of the studies in the last decades have focused on models of restricted intrauterine growth and/or maternal diabetes and neonatal overfeeding, as both these extremes are known to lead to the same adult phenotype (metabolic syndrome), which includes insulin and leptin resistance, diabetes, obesity, and hypertension (1–3). To understand the early “programming” effects of caloric intake on adult pathology, many crucial questions need answers. For instance, when is the critical timing for the fetal/neonatal dietary changes to exert enduring effects, what is the duration of the effects, what is the nature of the interactions between predisposing genes and the environmental changes that will lead to adult pathology, and what are the underlying mechanisms? In this issue of Endocrinology, Xiao et al. (4) address some of these questions by demonstrating that overfeeding during the suckling period in rat pups might permanently impair energy expenditure through altering the functional activity of brown adipose tissue (BAT). This particular adipose tissue and its specific expression of uncoupling proteins (mainly UCP1) is recognized to represent an important “gate” against obesity in rodents, although less so in humans (5). Because UCP1 uncouples mitochondrial oxidation from phosphorylation, BAT can oxidize energy substrates, particularly fatty acids, to produce heat instead of ATP.
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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.061 | 0.058 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".