Mechanical Stretch Induces Monocyte Chemoattractant Activity via an NF-κB-Dependent Monocyte Chemoattractant Protein-1-Mediated Pathway in Human Mesangial Cells
Bibliographic record
Abstract
Hemodynamic abnormalities are important in the pathogenesis of the glomerular damage in diabetes. Glomerular macrophage infiltration driven by the chemokine monocyte chemoattractant protein-1 (MCP-1) is an early event in diabetic nephropathy. The thiazolidinedione rosiglitazone ameliorates albumin excretion rate in diabetic patients with microalbuminuria and has anti-inflammatory properties, raising the possibility of a relationship between its renoprotective and anti-inflammatory activity. Investigated was whether mesangial cell stretching, mimicking in vitro glomerular capillary hypertension, enhances MCP-1 expression and monocyte chemoattractant activity. The effect of the combination of stretch with high glucose on MCP-1 production was studied and, finally, the effect of rosiglitazone on these processes was assessed. Stretching of human mesangial cells significantly enhanced their monocyte chemoattractant activity. This effect was mediated by MCP-1 as it was paralleled by a significant rise in both MCP-1 mRNA and protein levels and was completely abolished by MCP-1 blockade. Combined exposure to both stretch and high glucose further increased MCP-1 production. Stretch activated the IkappaB-NF-kappaB pathway, and NF-kappaB inhibition, with the use of the specific inhibitor SN50, completely abolished stretch-induced MCP-1, indicating that stretch-induced MCP-1 was NF-kappaB dependent. The addition of rosiglitazone significantly diminished stretch-induced NF-kappaB activation, MCP-1 production, and monocyte chemotaxis. In conclusion, stretching of mesangial cells stimulates their monocyte chemoattractant activity via an NF-kappaB-mediated, MCP-1-dependent pathway, and this effect is prevented by rosiglitazone.
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 | 0.000 |
| 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".