Filaggrin expression via immunohistochemistry in basal cell carcinoma and squamous cell carcinoma
Bibliographic record
Abstract
BACKGROUND: Filaggrin is a protein integral to the structure and function of the epidermis. Filaggrin (FLG) loss-of-function (LOF) mutations are common and increase the risk of developing atopic dermatitis (AD) and ichthyosis vulgaris (IV). Epidemiologic data suggest a link between skin cancer and AD. We examined if FLG staining pattern can be used to characterize cutaneous squamous cell carcinomas (SCC), basal cell carcinomas (BCC), and reactive squamous epithelium. METHODS: Tissue microarrays (TMAs) were created from 196 cases of formalin-fixed paraffin-embedded (FFPE) SCC and 144 BCC cases. TMAs and sections of reactive squamous epithelium were stained with optimized anti-FLG antibody and evaluated for FLG expression (normal, abnormal, or negative). RESULTS: FLG was absent in poorly differentiated (PD) compared to well-differentiated (WD) SCC (P < .0001) and moderately-differentiated (MD) (P = .0231) SCC, and in MD compared to WD SCC (P = .0099). Abnormal staining was significantly increased in PD compared to WD cases (P = .0039) and in MD compared to WD cases (P = .0006). Most BCC did not exhibit FLG expression (P < .05). Reactive squamous epithelium demonstrated normal, but exaggerated FLG expression. CONCLUSIONS: Our findings demonstrate the differences in FLG expression patterns in types of keratinocyte carcinomas and their mimickers.
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 | 0.001 |
| 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".