North American ginseng influences adipocyte–macrophage crosstalk regulation of inflammatory gene expression
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it