The Pattern of Expression of Human Placental Lactogen Across Normal, Lactational, and Malignant Mammary Epithelium
Bibliographic record
Abstract
The immunoexpression of human placental lactogen (hPL) in mammary epithelium is not well studied in the literature. Our overall objective was to delineate the distribution pattern of hPL across mammary epithelia of varying levels of differentiation. This is the first research to study the level of expression of hPL in human lactational change epithelium. Immunohistochemistry (IHC) for hPL was performed on archival formalin-fixed paraffin-embedded tissue blocks of 97 cases. These consisted of 53 invasive ductal carcinomas, 21 lactational change cases, and 23 cases of normal mammary tissue. The results of this study show underexpression of hPL in malignant epithelium compared to normal and lactational groups individually and combined as a non-malignant group. However, a higher expression of hPL was noted in mammary carcinoma of axillary lymph node (ALN)-positive patients compared to ALN-negative cases. There was no statistically significant difference between hPL expression and tumor grade, estrogen receptors (ER), progesterone receptors (PR), or human epidermal growth factor receptor 2 (HER2) status. The comparison of the immunoexpression of hPL in malignant epithelium versus lactational change epithelium may provide the basis for future studies on the possible role of hPL in the protective mechanism of lactation tissue from carcinogenesis. Our results could be explained by the proposed mechanism in the literature, which is that breast cancer cells have a potential inhibitory effect on the translation of human chorionic somatotropin hormone (CSH) mRNA into hPL protein. Our results support the literature findings of a poorer prognostic outcome for breast malignancies when hPL is expressed but require further studies using a more comprehensive range of clinical parameters.
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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 | 0.001 |
| 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".