αGalCer/CD1d treatment induces pro-inflammatory iNKT1-associated immune responses without aggravating doxorubicin-induced cardiotoxicity
Bibliographic record
Abstract
Abstract Invariant natural killer T (iNKT) cells are a unique subset of innate immune cells activated by T cell receptor (TCR) signaling through CD1d molecules presenting lipid antigens. By localizing to specific tissues, iNKT cells play critical roles in maintaining homeostasis and regulating pathophysiology, making them attractive therapeutic targets for inflammatory diseases. However, their frequency in the heart and potential roles in mitigating inflammation-induced cardiac damage remain poorly understood. In this study, we examined the distribution of cardiac iNKT subsets and their response to TCR-directed alpha Galactosyl ceramide/cluster of differentiation 1d (αGalCer/CD1d) complexes in a mouse model of Doxorubicin (Dox)-induced cardiotoxicity. In healthy mice, the heart harbored distinct iNKT subsets, dominated by pro-inflammatory iNKT1 cells, followed by iNKT2 subsets. Treatment with αGalCer/CD1d complexes preferentially expanded the iNKT1 subset. In Dox-induced cardiotoxicity, this treatment unexpectedly increased both IFN-γ, a hallmark cytokine of iNKT1 cells, and IL-10, a key anti-inflammatory mediator typically associated with regulatory iNKT subsets. Despite the expansion of iNKT1 cells and elevated IFN-γ levels, αGalCer/CD1d treatment did not exacerbate cardiac damage and showed modest clinical improvements compared to controls. These findings highlight the complexity of iNKT-mediated immune regulation in the heart and emphasize the need for modified TCR-directed immunotherapeutics to skew immune responses toward a regulatory phenotype. Such approaches could hold promise for treating inflammatory cardiac diseases, including Dox-induced myocarditis, myocardial infarction, and autoimmune myocarditis.
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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".