Lymphocytic microparticles modulate macrophages function in experimental choroidal neovascularization
Bibliographic record
Abstract
Abstract Purpose Pathological choroidal neovascularization (CNV) is the major cause of severe vision loss in patients with age‐related macular degeneration (AMD). Inflammation is a key component in AMD, and macrophages play an important role in CNV generation. We have demonstrated that human T‐lymphocyte‐derived microparticles (LMPs) significantly inhibit angiogenesis in several models of ocular neovascularization. In this study, we investigated whether LMPs modulate angiogenic microenvironment by altering macrophages activities Methods LMPs were produced from apoptotic human T lymphocytes after treated with actinomycin D. The effects of LMPs on cell viability and cell migration were studied in apoptosis assay and migration assay respectively. Cell growth of human retinal microvascular endothelial cells was assessed after cells were co‐cultured with LMPs pre‐treated macrophages. A laser‐induced CNV model was used to determine labelled choroidal flat‐mounts. Results LMPs dose‐dependently inhibited macrophages cell growth without altering cell death. In addition, LMPs dramatically abrogated VEGF‐induced macrophages migration. LMPs‐pretreated macrophages exhibited strong inhibitory effect on endothelial cell growth and this effect was associated with the increased expression of IL‐12, CD36 and HIF‐1α. In vivo, intravitreal injection of LMPs significantly suppressed laser‐induced CNV and reduced macrophages infiltration at the lesion sites. Conclusion These results suggest that LMPs are potent antiangiogenic therapeutic agent. In addition to the direct effects on endothelial cells, LMPs may interfere the proangiogenic environment through modulation of macrophages function during pathophysiological conditions.
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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 | 0.001 |
| 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".