Preventing protein-energy malnutrition after cortical stroke enhances recovery of symmetry in forelimb use during spontaneous exploration
Bibliographic record
Abstract
Protein-energy malnutrition (PEM) commonly arises after stroke. We investigated the effects of preventing PEM on spontaneous recovery of forelimb use, infarct size, and the acute phase response in the chronic post-stroke period. Male, adult, Sprague–Dawley rats were acclimatized to control diet (12.5% protein), tested for pre-stroke forelimb use symmetry in the cylinder test, and exposed to photothrombotic cortical stroke or sham surgery. Food intake was monitored daily, and body weight weekly. Forelimb use was tested on day 4 after surgery, before assignment to control diet or PEM (0.5% protein), with subsequent testing on days 16 and 29. Blood, brain, and liver were collected on day 30. The low protein diet resulted in PEM, measured by decreased body weight (p < 0.001) and food intake (p = 0.016) and increased liver lipid (p < 0.001). Stroke (p = 0.016) and PEM (p = 0.001) independently elicited increases in serum α-2-macroglobulin concentration, whereas PEM alone decreased albumin (p < 0.001). PEM reduced recovery of forelimb use symmetry during exploration on days 16 (p = 0.024) and 29 (p = 0.013) but did not influence infarct size (p = 0.775). Stroke reduced reliance on the stroke-affected forelimb to initiate exploration up until day 29 (p < 0.001); PEM had no influence (p ≥ 0.463). Preventing post-stroke PEM appears to yield direct benefits for certain types of motor recovery. Novelty Preventing post-stroke malnutrition benefits certain types of motor recovery. An acute phase response may contribute to the poorer recovery with malnutrition.
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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) | 0.001 | 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".