Biomimetic Nanomaterials for the Immunomodulation of the Cardiosplenic Axis Postmyocardial Infarction
Bibliographic record
Abstract
The spleen is an important mediator of both adaptive and innate immunity. As such, attempts to modulate the immune response provided by the spleen may be conducive to improved outcomes for numerous diseases throughout the body. Here, biomimicry is used to rationally design nanomaterials capable of splenic retention and immunomodulation for the treatment of disease in a distant organ, the postinfarct heart. Engineered senescent erythrocyte-derived nanotheranostic (eSENTs) are generated, demonstrating significant uptake by the immune cells of the spleen including T and B cells, as well as monocytes and macrophages. When loaded with suberoylanilide hydroxamic acid (SAHA), the nanoagents exhibit a potent therapeutic effect, reducing infarct size by 14% at 72 h postmyocardial infarction when given as a single intravenous dose 2 h after injury. These results are supportive of the hypothesis that RBC-derived biomimicry may provide new approaches for the targeted modulation of the pathological processes involved in myocardial infarction, thus further experiments to decisively confirm the mechanisms of action are currently underway. This novel concept may have far-reaching applicability for the treatment of a number of both acute and chronic conditions where the immune responses are either stimulated or suppressed by the splenic (auto)immune milieu.
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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".