Secreted Frizzled-Related Protein 1 as a Biomarker against Incomplete Age-Related Lobular Involution and Microcalcifications’ Development
Bibliographic record
Abstract
As a downregulator of the Wnt signaling pathway, SFRP1 is involved in several components of the age-related lobular involution process such as inflammation, apoptosis, and adipogenesis. Because microcalcifications are associated with inflammation, we aimed to demystify the cross talk between SFRP1, inflammatory markers, and microcalcifications by assessing SFRP1 expression (immunohistochemistry) in a cohort of 162 women with different degrees of lobular involution. SFRP1 expression was inversely associated with the degree of lobular involution (OR = 0.84; p-value < 0.01). SFRP1 expression, age at mastectomy, and waist circumference taken together predicted the degree of lobular involution (AUC = 78.1). This predictive model was best in patients with microcalcifications (AUC = 81.1) and in parous women (AUC = 87.8). SFRP1 expression was correlated with leptin (rho = 0.32), TNF-α (rho = 0.21), and IL-6 (rho = 0.21) expression by epithelial cells (all p-values <0.001). SFRP1 expression was lower in nulliparous women with involuted breast tissue compared with parous women with involuted breast tissue (Δmean = −2.31; p-value < 0.01) and was higher in nulliparous women with microcalcifications compared with nulliparous women without microcalcifications (Δmean = 2.4; p-value < 0.05). In this study, we highlighted two SFRP1-based predictive models for incomplete lobular involution and the development of microcalcifications and identified two distinct inflammatory profiles associated with age-related lobular involution in parous and nulliparous women.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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".