Human placental extract suppresses mast cell activation and induces mast cell apoptosis
Bibliographic record
Abstract
BACKGROUND: Human placental extract (HPE) has been documented to facilitate the healing of certain disorders including allergy. However, the effects of HPE on the functionality of mast cells, a critical cell type in allergic diseases, have not been reported. METHODS: To investigate the effects of HPE on the regulation of allergy with respect to the biological functions of mast cells, the mast cell line C57 or HMC-1 cells were treated with HPE followed by the assessment of cell proliferation, apoptosis, activation, chemotaxis and phagocytosis. Mouse peritoneal mast cells were also investigated for their responses to induction of apoptosis by HPE in vivo. Furthermore, the effect of HPE on mast cell degranulation was confirmed using the passive cutaneous anaphylaxis (PCA) assay, an acute allergy model. RESULTS: HPE was capable of suppressing mast cell proliferation and inducing mast cell apoptosis. Mast cell degranulation in response to compound 48/80- or anti-DNP IgE and DNP-mediated activation was suppressed. In addition, treatment with HPE compromised the production of cytokines by mast cells and cell chemotaxis. These observations were consistent with the dampened passive cutaneous anaphylaxis (PCA) assay following treatment with HPE. CONCLUSION: This study revealed a suppressive effect of HPE on overall mast cell activities, suggesting a potential regulatory role of HPE on the alleviation of allergic diseases through mast cells.
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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 | 0.000 | 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 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".