Carbohydrates and Epithelial Repair - More Than Just Post-Translational Modification
Bibliographic record
Abstract
Epithelia are the layers of cells that form barriers between external milieu and underlying tissues and thus, are important components of most organs of the body. Epithelial layers of organs, such as the lung, are exposed to various challenges resulting in frequent injury. Epithelial wound healing represents an important process by which repair restores the physical barrier lost as a result of cell damage and apoptosis. The repair of epithelial layers consists of a series of ordered events including epithelial cell spreading, migration proliferation and, differentiation. Carbohydrates attached to cell surface proteins and lipids can modulate the function of structures that they are conjugated to and therefore, can affect cell behavior. Although the basic mechanisms of epithelial repair are not entirely understood, many studies suggest glycoconjugates attached to proteins on the cell surface of epithelial cells play important roles in many of these cellular processes. In the present review, the role of carbohydrates in epithelial repair of different organs, including the sources of epithelial injury and current models of epithelial repair will be discussed with a focus on our understanding of the airway epithelium. With a better understanding of carbohydrates and their role in epithelial repair, new therapeutic targets for diseases involving damage to the epithelium can be identified.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".