Lymphocytic Microparticles Modulate Angiogenic Properties of Macrophages in Laser-induced Choroidal Neovascularization
Bibliographic record
Abstract
Pathological choroidal neovascularization (CNV) is the common cause of vision loss in patients with age-related macular degeneration (AMD). Macrophages possess potential angiogenic function in CNV. We have demonstrated that human T lymphocyte-derived microparticles (LMPs) exert a potent antiangiogenic effect in several pathological neovascularization models. In this study, we investigated the alteration of proangiogenic properties of macrophages by LMPs treatment in vitro and in vivo models. LMPs regulated the expression of several angiogenesis-related factors in macrophages and consequently stimulated their antiangiogenic effects evidenced by the suppression of the proliferation of human retinal endothelial cells in co-culture experiments. The involvement of CD36 receptor in LMPs uptake by macrophages was demonstrated by in vitro assays and by immunostaining of choroidal flat mounts. In addition, ex vivo experiments showed that CD36 mediates the antiangiogenic effect of LMPs in murine and human choroidal explants. Furthermore, intravitreal injection of LMPs in the mouse model of laser-induced CNV significantly suppressed CNV in CD36 dependent manner. The results of this study suggested an ability of LMPs to alter the gene expression pattern of angiogenesis-related factors in macrophages, which provide important information for a new therapeutic approach for efficiently interfering with both vascular and extravascular components of CNV.
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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".