Highly purified human peripheral blood monocytes produce IL-6 but not TNFα in response to angiotensin II
Bibliographic record
Abstract
HYPOTHESIS: Monocytes produce pro-inflammatory cytokines in response to Angiotensin II (AngII). INTRODUCTION: AngII has been suggested by many to be pro-inflammatory and likely to contribute to the migration of leukocytes in patients with cardiovascular conditions. MATERIALS AND METHODS: Monocytes were purified from peripheral blood mononuclear cells (PBMCs) by negative selection using antibodies conjugated to magnetic beads. Detection of CD14(+) and AT(1)R expression was achieved by double-labeling flow cytometry. Highly purified monocytes were then stimulated with AngII (6 and 24 h) to assess IL-6 and TNF-α transcript levels by qRT-PCR and protein secretion by ELISA. RESULTS: Monocytes comprised 9.7 ± 2.0% of the PBMCs. Monocyte isolation by negative selection yielded a purity of up to 99.8%. We demonstrated AT(1)R expression on 9.5 ± 0.3% of highly purifed CD14(+)/CD16(-) monocytes. Stimulation of highly purified monocytes with AngII resulted in increased transcript levels of IL-6 at 6 h but not at 24 h, and increased secretion of IL-6 in a dose-dependent manner compared with controls (p <0.01). Conversely, there was no increase in TNF-α mRNA transcripts or protein secretion. CONCLUSIONS: We provide evidence that a CD14(+)/CD16(-) subset of highly purified human monocytes express AT(1)R and respond to AngII exposure in vitro by producing IL-6 but not TNF-α.
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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) | 0.004 | 0.002 |
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".