Bibliographic record
Abstract
Protection of mucosal tissues of the oral cavity, intestines, respiratory tract, and urogenital tract from the constant challenge of pathogens is achieved by the combined barrier function of the lining epithelia and specialized immune cells. Recent studies have indicated that osteopontin (OPN) has a pivotal role in the development of immune responses and in the tissue destruction and the subsequent repair processes associated with inflammatory diseases. While expression of OPN is increased in immune cells--including neutrophils, macrophages, T- and B-lymphocytes--and in epithelial, endothelial, and fibroblastic cells of inflamed tissues, deciphering the specific functions of OPN has been difficult. In part, this is due to the broad range of biological activities of OPN that are mediated by multiple receptors which recognize several signaling motifs whose activities are influenced by post-translational modifications and proteolytic processing of OPN. Understanding the role of OPN in mucosal inflammation is further complicated by its contributions to the barrier function of the lining epithelia and the complexity of the specialized mucosal immune system. In an attempt to provide some insights into the involvement of OPN in mucosal diseases, this review summarizes current knowledge of the biological activities of OPN involved in the development of inflammatory responses and in wound healing, and indicates how these activities may affect the protection of mucosal tissues.
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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".