North American ginseng influences adipocyte–macrophage crosstalk regulation of inflammatory gene expression
Bibliographic record
Abstract
BACKGROUND: Adipocyte-macrophage communication plays a critical role regulating white adipose tissue (WAT) inflammatory gene expression. Because WAT inflammation contributes to the development of metabolic diseases, there is significant interest in understanding how exogenous compounds regulate the adipocyte-macrophage crosstalk. An aqueous (AQ) extract of North American (NA) ginseng (Panax quinquefolius) was previously shown to have strong inflammo-regulatory properties in adipocytes. This study examined whether different ginseng extracts influence adipocyte-macrophage crosstalk, as well as WAT inflammatory gene expression. METHODS: The effects of AQ and ethanol (EtOH) ginseng extracts (5 μg/mL) on adipocyte and macrophage inflammatory gene expression were studied in 3T3-L1 and RAW264.7 cells, respectively, using real-time reverse transcription polymerase chain reaction. Adipose tissue organ culture was also used to examine the effects of ginseng extracts on epididymal WAT (EWAT) and inguinal subcutaneous WAT (SWAT) inflammatory gene expression. RESULTS: The AQ extract caused significant increases in the expression of common inflammatory genes (e.g., Mcp1, Ccl5, Tnf-α, Nos2) in both cell types. Culturing adipocytes in media from macrophages treated with the AQ extract, and vice versa, also induced inflammatory gene expression. Adipocyte Ppar-γ expression was reduced with the AQ extract. The AQ extract strongly induced inflammatory gene expression in EWAT, but not in SWAT. The EtOH extract had no effect on inflammatory gene expression in either both cell types or WAT. CONCLUSION: These findings provide important new insights into the inflammo-regulatory role of NA ginseng in WAT.
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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) | 0.001 | 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".