Cell surface markers CD44 and CD166 localized specific populations of salivary acinar cells
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: Experimental approaches tested to date for functional restoration of salivary glands (SGs) are tissue engineering, gene transfer, and cell therapy. To further develop these therapies, identifying specific cell surface markers for the isolation of salivary acinar cells is needed. To test a panel of cell surface markers [used in the isolation of mesenchymal stem cells, (MSCs)] for the localization of salivary acinar cells. MATERIALS: Human submandibular and parotid glands were immunostained with a panel of MSC markers and co-localized with salivary acinar cell differentiation markers [α-amylase, Na-K-2Cl cotransporter-1, aquaporin-5 (AQP5)]. Additional cell markers were also used, such as α-smooth muscle actin (to identify myoepithelial cells), cytokeratin-5 (basal ductal cells), and c-Kit (progenitor cells). RESULTS: CD44 identified serous acini, while CD166 identified mucous acini. Cytokeratin-5 identified basal duct cells and 50% of myoepithelial cells. None of the remaining cell surface markers (Stro-1, CD90, CD106, CD105, CD146, CD19, CD45, and c-Kit) were expressed in any human salivary cell. CONCLUSIONS: CD44 and CD166 localized human salivary serous and mucous acinar cells, respectively. These two cell surface markers will be useful in the isolation of specific populations of salivary acinar cells.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 it