Time-based gene expression programme following diaphragm injury in a rat model
Bibliographic record
Abstract
It was hypothesised that diaphragm injury activates a time-based programme of gene expression in muscle repair. Gene expression of different substances, such as proteases (calpain 94 (p94)), transcription factors (myogenin and cFos), growth factors (both basic fibroblast growth factor (bFGF) and insulin-like growth factor (IGF)-II), and structural proteins (myosin heavy chain (MHC) and titin), was quantified by RT-PCR in rat diaphragms exposed to caffeine-induced injury. Injured and noninjured (control) rat hemidiaphragms were excised at different time points (1-240 h). In injured hemidiaphragms, in comparison with control muscles, p94 expression levels peaked at 1 h post-injury (PI), cFos mRNA levels began to rise, after an initial dip, and peaked at 96 h PI, while myogenin mRNA levels started to increase as early as 12 h PI, IGF-II mRNA levels initially decreased until 48 h PI and increased thereafter, peaking at 72 h PI, bFGF mRNA levels rose to a maximum at 96 h PI, and MHC and titin mRNA levels were significantly elevated at 72 h PI. Caffeine-induced diaphragm injury is followed by a time-based expression programme of different genes tailored to meet muscle repair needs.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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".