Nicotinic α7 receptor inhibits the acylation stimulating protein-induced production of monocyte chemoattractant protein-1 and keratinocyte-derived chemokine in adipocytes by modulating the p38 kinase and nuclear factor-κB signaling pathways
Bibliographic record
Abstract
Obesity is associated with chronic low‑grade inflammation, which is characterized by increased infiltration of macrophages into adipose tissue. Acylation stimulating protein (ASP) is an adipokine derived from the immune complement system, which constitutes a link between adipocytes and macrophages, and is involved in energy homeostasis and inflammation. The purpose of the present study was to preliminarily investigate in vitro, whether functional α7nAChR in adipocytes may suppress ASP‑induced inflammation and determine the possible signaling mechanism. Studies have reported associations between the expression of α7 nicotinic acetylcholine receptor (α7nAChR) and obesity, insulin resistance and diabetes. Additionally, α7nAChRs are important peripheral mediators of chronic inflammation, which is a key contributor to health problems in obesity. The primary aim of the present study was to evaluate the impact of exogenous ASP and α7nAChR on macrophage infiltration in adipose tissue and to examine the potential underlying molecular mechanism. Western blot analysis revealed that recombinant ASP increased the expression levels of monocyte chemoattractant protein‑1 (MCP‑1) and keratinocyte‑derived chemokine (KC) by 3T3‑L1 adipocytes. However, nicotine significantly inhibited the production of ASP‑induced cytokines via the stimulation of α7nAChR. It was also found that α7nAChR inhibited the ASP‑induced activation of p38 kinase and nuclear factor‑κB (NF‑κB), and the production of MCP‑1 and KC. These data indicated that α7nAChR caused the inhibition of ASP‑induced activation of p38 kinase and NF‑κB to inhibit the production of MCP‑1 and KC.
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.
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 | 0.001 |
| 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 | 0.001 |
| 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".