Canine oral mucosa evaluation as a potential autograft tissue for the treatment of unresponsive keratoconjunctivitis sicca
Bibliographic record
Abstract
PURPOSE: Labial mucosa transplantation for the treatment of canine keratoconjunctivitis sicca (KCS) has been reported recently. Postoperative alleviation of clinical signs was noted and assumed to be the result of labial salivary glands providing lubrication to the ocular tissue. The aim of this study was to evaluate the presence of minor salivary glands (MSG) in the canine oral mucosa. METHODS: Oral mucosal biopsies were collected from six dogs that died (n = 1) or were euthanized (n = 5) for reasons unrelated to this study. The breeds included were two Doberman Pinschers, one Labrador Retriever, one Portuguese Water Dog, one German Shepherd Dog, and one mixed canine. Three were spayed females, and three were castrated males with the median age of 9 years (range, 6-13 years). Samples were obtained by an 8-mm punch biopsy at the following locations of the canine oral cavity: upper rostral labial mucosa at midline, lower rostral labial mucosa at midline, upper labial mucosa near the commissure, lower labial mucosa near the commissure, and buccal mucosa approximately 1 cm caudal to the commissure. Samples were routinely processed with hematoxylin and eosin, and periodic acid-Schiff stains. Samples were evaluated by light microscopy. RESULTS: At the selected locations, no MSG or other secreting cells were detected. CONCLUSIONS: Minor salivary glands are not associated with alleviation of canine KCS symptoms following labial mucosa transplantation. Further studies are needed to determine the mechanism leading to the transient improvement of KCS symptoms in canine patients following labial mucosa transplantation.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".