Protein deficiency, but not zinc deficiency, reduces recovery of type 1 and type 2 muscle fibre diameters in the gastrocnemius muscle of growing rats
Bibliographic record
Abstract
The present study examines the effects of protein- and energy-type malnutrition in combination with Zn deficiency on the growth, serum insulin-like growth factor-1 (IGF-1), gastrocnemius muscle mass and fibre diameter of growing rats during a deficiency phase followed by nutritional rehabilitation. Rats (3-weeks old) were randomly assigned to baseline, or Zn-deficient (Z, < 1 mg Zn/kg), protein-deficient (P, 20 g protein/kg), combined Zn- and protein-deficient (ZP), energy-deficient (E, feed intake pair-fed to Z) or control (C, 30 mg Zn/kg and 170 g protein/kg) groups for a 3-week deficiency phase, followed by a 3-week repletion phase with the control diet. ATPase histochemical staining at pH 9·4 was used to differentiate type 1 and type 2 muscle fibres. After the deficiency phase, the ZP and P groups had lower body weight and smaller gastrocnemius muscle mass than the Z and E groups. Type 1 and 2 muscle fibre diameters (T1- and T2-MFD, respectively) were reduced in the ZP, P and Z groups compared with the E and C groups. Serum Zn was reduced in the ZP, P and Z groups, but serum IGF-1 was lowest in the Z and E groups. After the repletion phase, T1-MFD did not recover in the P and E groups nor T2-MFD in the P group, despite the P and E groups having a better recovery of body weight. In summary, previous protein deficiency, but not Zn deficiency, limited the recovery of both T1- and T2-MFD during nutritional repletion. The quality of skeletal muscle recovery in the malnourished groups was not associated with body weight, muscle mass, serum Zn or IGF-1 concentrations.
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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) | 0.001 | 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 | 0.000 |
| 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".