Neonatal low-protein diet reduces the masticatory efficiency in rats
Bibliographic record
Abstract
Little is known about the effects of undernutrition on the specific muscles and neuronal circuits involved in mastication. The aim of this study was to document the effects of neonatal low-protein diet on masticatory efficiency. Newborn rats whose mothers were fed 17% (nourished (N), n 60) or 8% (undernourished (U), n 56) protein were compared. Their weight was monitored and their masticatory jaw movements were video-recorded. Whole-cell patch-clamp recordings were performed in brainstem slice preparations to investigate the intrinsic membrane properties and N-methyl-d-aspartate-induced bursting characteristics of the rhythmogenic neurons (N, n 43; U, n 39) within the trigeminal main sensory nucleus (NVsnpr). Morphometric analysis (N, n 4; U, n 5) were conducted on masseteric muscles serial cross-sections. Our results showed that undernourished animals had lower numbers of masticatory sequences (P=0·049) and cycles (P=0·045) and slower chewing frequencies (P=0·004) (N, n 32; U, n 28). Undernutrition reduced body weight but had little effect on many basic NVsnpr neuronal electrophysiological parameters. It did, however, affect sag potentials (P<0·001) and rebound firing (P=0·005) that influence firing pattern. Undernutrition delayed the appearance of bursting and reduced the propensity to burst (P=0·002), as well as the bursting frequency (P=0·032). Undernourished animals showed increased and reduced proportions of fibre type IIA (P<0·0001) and IIB (P<0·0001), respectively. In addition, their fibre areas (IIA, P<0·001; IIB, P<0·001) and perimeters (IIA, P<0·001; IIB, P<0·001) were smaller. The changes observed at the behavioural, neuronal and muscular levels suggest that undernutrition reduces chewing efficiency by slowing, weakening and delaying maturation of the masticatory muscles and the associated neuronal circuitry.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".