Editorial: Interdependencies and interfaces in bone regeneration – the immune status at its core
Bibliographic record
Abstract
Interdependencies and interfaces in bone regeneration -the immune system at its core Incentive Regeneration stands as the optimal outcome following an injury.However, leveraging endogenous regenerative mechanisms for therapeutic purposes, necessitates a profound understanding of the underlying processes.Researchers utilize bone as a model for regeneration, aiming to elucidate the interdependencies and interfaces within the regenerative process.Bone healing is a highly complex process that is tightly orchestrated and the immune reaction evolves as a crucial control system herein (1, 2).Distinct phases, each with unique characteristics, succeed one another, overlapping and dependent on each other, resulting in complete form and functional restoration upon successful accomplishment.However, the complexity of this intricate process exposes it to potential derailments, leading to unsatisfactory outcomes.Bone healing processes can be affected by underlying genetic, metabolic, traumatic and neoplastic conditions, all of which are interdependent with immune cell functions.Recent findings highlight the pivotal role played by the interaction between the inflammatory response and its surrounding mechanical environment (3), metabolism (4), and revascularization (5) in the facilitating successful regenerative processes.This Research Topic provided an opportunity to compile papers exploring the regenerative process, including the healing environment beyond the scope of bone cells.Nine papers were selected from 24 submitted, comprising four original papers, four review articles, and one perspective paper.The contributions of 55 authors from around the world, including Belgium, Chile, China, Germany, New Zealand, Sweden, the United Kingdom, and the United States, are included in this Research Topic.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.016 | 0.021 |
| Insufficient payload (model declined to judge) | 0.013 | 0.010 |
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".