Differential Expression of Myoepithelial Markers in Salivary, Sweat and Mammary Glands
Bibliographic record
Abstract
Myoepithelial cells (MECs) are contractile elements showing a combined epithelial and smooth muscle phenotype. Among the numerous immunohistochemical markers employed to detect MECs, smooth muscle actin (SMA) is the most widely used. Recently, other markers of smooth muscle differentiation have been demonstrated in MECs, such as calponin, heavy caldesmon (h-caldesmon), and smooth muscle myosin heavy chain (SMM-HC). In the present study normal salivary, mammary, and sweat glands have been studied with four markers of smooth muscle differentiation (SMA, calponin, h-caldesmon, and SMM-HC). The four markers were differentially expressed in the various types of glands. In parotid glands MECs mainly expressed calponin and caldesmon; in submandibular and in cutaneous apocrine and eccrine glands, MECs strongly expressed SMA, calponin, and caldesmon; in minor salivary glands all four markers were equally strongly expressed; and in mammary glands SMA, calponin, and SMM-HC were present both in periductal and periacinar MECs while caldesmon was present in periductal MECs only. In addition to MECs, SMA stained stromal myofibroblasts, sometimes hampering the identification of MECs. Among the other markers, calponin stained only rare stromal myofibroblasts, while caldesmon and SMM-HC were confined to MECs. In conclusion, these latter markers are very useful for identifying MECs. It is suggested that the differential expression of smooth muscle contractile proteins might reflect different functions of MECs in the various sites. Int J Surg Pathol 8(1):29-37, 2000
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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 | 0.000 |
| 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) | 0.001 | 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".