Swim training induces distinct osseous gene expression patterns in anosteocytic and osteocytic teleost fish
Bibliographic record
Abstract
The traditional understanding of bone mechanosensation implicates osteocytes, canaliculi, and the lacunocanalicular network in biomechanical adaptation. However, recent findings challenge this notion, as shown in advanced teleost fish where anosteocytic bone lacking osteocytes are nevertheless responsive to mechanical load. To investigate specific molecular mechanisms involved in bone mechanoadaptation in osteocytic and anosteocytic fish bone, we conducted a 5-min single swim-training experiment with zebrafish and ricefish, respectively. Through RNASeq analysis of fish spines, analyzed at various time points following swim training, we uncovered distinct gene expression patterns in osteocytic and anosteocytic fish bones. Notably, osteocytic fish bone exhibited an early response to mechanical load, contrasting to a delayed response observed in anosteocytic fish bones, both within 8 h following stimulation. We identified an increase in osteoblast differentiation in anosteocytic bone following training, while chordoblast activity was delayed. This temporal response suggests a time-dependent adaptation in anosteocytic bone, indicating the presence of intricate feedback networks within bone that lacks osteocytes.
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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 | 0.000 |
| 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".