Primary human monocytes differentiate into M2 macrophages and involve Notch-1 pathway
Bibliographic record
Abstract
The current study investigates whether inhibiting the Notch-1 signaling pathway in primary human monocytes enhances M2 macrophage differentiation. We generated a primary human monocyte cell culture model to understand the effect of the Notch-1 signaling pathway. Monocytes were treated with Notch-1 inhibitors DAPT or siRNA. Our data show that there was a significant increase in the M1 macrophage population demonstrated by iNOS marker in the primary human monocytes treated with apoptotic-conditioned medium (ACM). Next, the levels of pro-inflammatory cytokines IL-6 and MCP-1, as well as TNF-α, increased in ACM media (p < 0.05). Furthermore, M1 macrophages and pro-inflammatory cytokines were reduced following DAPT or siRNA treatment. Comparatively, there was a significant increase in M2 macrophages, as demonstrated by an increase in CD206 and arginase-1 positive cells treated with DAPT or siRNA (p < 0.05). Furthermore, a significant increase in the associated anti-inflammatory cytokines IL-10 and IL-1RA was also observed with respect to control groups (p < 0.05). We conclude that blocking the Notch-1 pathway with DAPT or siRNA attenuates pro-inflammatory cytokines, enhances M2 macrophage differentiation, and increases anti-inflammatory cytokines in primary human monocytes. As a result, Notch-1 pathway inhibition has potential therapeutic applications of inflammatory disease.
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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 | 0.001 |
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".