Bibliographic record
Abstract
ABSTRACT Background The regrowth of amputated appendage extremities and the distal tips of digits represent models of tissue regeneration in multiple vertebrate taxa. In humans, digit tip injuries, including traumatic amputation and crush injuries, are among the most common type of injury to the human hand. Despite clinical reports demonstrating natural regeneration of appendages in lower vertebrates and human digits, current treatment options are suboptimal, and are complicated by the anatomical complexities and functions of the different tissues within the digits. Results: In light of these challenges, we focus on recent advancements in understanding appendage regeneration from model organisms. We pay special attention to the cellular programs underlying appendage regeneration, where cumulative data from salamanders, fish, frogs, and mice indicate that regeneration occurs by the actions of lineage‐restricted precursors. We focus on pathologic states and the interdependency that exists, in both humans and animal models, between the nail organ and the peripheral nerves for successful regeneration. Conclusions: The increased understanding of regeneration in animal models may open new opportunities for basic and translational research aimed at understanding the mechanisms that support limb regeneration, as well as amelioration of limb abnormalities and pathologies. Developmental Dynamics 244:641–650, 2015. © 2015 Wiley Periodicals, Inc.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.886 | 0.723 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".