Abstract 24: Resolvin D2 Reduces Thrombus Burden and Attenuates Inflammatory Signaling Pathways in a Murine Model of Venous Thrombosis
Bibliographic record
Abstract
Background: Specialized pro-resolving lipid mediators (SPM) derived from polyunsaturated fatty acids, such as Resolvin D2 (RvD2), play a critical role in the resolution of acute inflammation. In recent years, total organic synthesis of SPM have enabled investigation of their bioactivity in-vivo. It is known that the effects of RvD2 include limited neutrophil influx in-vivo, reduced neutrophil transmigration and counter-regulation of cytokines such as tumor necrosis factor-α TNF-α in mice. However, SPMs have not been studied in the context of experimental venous thrombosis (VT). Methods: Mice C57Bl/6, 10 week-old, 25grams. Groups: Control non-VT mice; VT-control-vehicle; VT-RvD2; Flow model: Electrolytic IVC model. Intra-peritoneal injection was used for RvD2 and vehicle administration 4 hours after surgery. Harvest was performed 2 days after surgery, acute VT. Thrombus weight, qRT-PCR for inflammatory cytokines panel and subsets of white blood cell (WBC) counts were performed at the site of thrombosis histologically. Results (Figure 1): We observed that RvD2 significantly reduced thrombus size when compared to vehicle controls (17.9mg±2.1mg vs. 4.5mg±0.8mg). The vein wall cytokine profile demonstrated a significant gene down-regulation of IL-6, CCL2, and TNF-a. In addition, the inflammatory cell counts showed reduction in all white cells subsets. No bleeding complications were observed. Conclusions: For the first time we have demonstrated that RvD2 reduces VT formation. The decreased thrombogenesis was accompanied by a decrease in WBC and proinflammatory cytokines. We demonstrated that RvD2 is a potent anti-inflammatory agent that reduced WBC and attenuated cytokines in the context of experimental VT. Further investigation of RvD2 in VT treatment and prophylaxis appears warranted.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".