Contractile properties and myosin heavy chain composition of rat tongue retrusor musculature show changes in early adulthood after 19 days of artificial rearing
Bibliographic record
Abstract
Previously, we showed that artificial rearing using the "pup in a cup" model results in decreased tongue activity and caused some minor alterations in the tongue retrusor musculature. However, the artificial rearing time frame previously chosen was brief (11 days). The purpose of the present investigation was to extend the artificial rearing period from postnatal days 3 to 21 (P21) to determine whether significant alterations occur as a result of this reduced tongue use. Several changes in contractile properties due to the artificial rearing process were observed, which fully recovered by postnatal days 41 to 42 (P41-2). These changes included a shorter twitch contraction time, shorter twitch half-relaxation time, and decreased fatigue resistance. Styloglossus muscle exhibited more neonatal myosin heavy chain (MHC) isoform at P21 for the artificially reared (AR) group. Changes that were persistent at P41-2 were also observed. Maximum tetanic tension was lower for the AR group at P21 and P41-2 compared with their dam-reared counterparts. Twitch tension was also lower by P41-2 in the AR group. At P41-2, the AR group exhibited an increase in MHC IIa and a decrease in MHC IIb for the styloglossus muscle. In addition, the AR group exhibited a decreased MHC IIb for the long head of the biceps brachii at P41-2. Our results are similar to other models of hindlimb immobilization and suspension. By extending our artificial rearing period, this reduced tongue activity induced acute changes and alterations in the tongue retrusor musculature that persisted into early adulthood.
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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 | 0.000 |
| 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".