Cargo-less itaconate-based nanoparticles mitigate allergic airway inflammation 3726
Bibliographic record
Abstract
Abstract Description Allergic diseases affect over 50 million annually in the U.S., with standard treatments being non-curative. Allergen-specific immunotherapies represent an antigen (Ag)-specific approach to modulate Th2 dysregulation in allergy, but chronic administration of soluble Ag risks anaphylaxis. Activation of Ag presenting cells by foreign Ags induces metabolic shifts disrupt the tricarboxylic acid (TCA) cycle. Irg1, a gene encoding for cis-aconitate decarboxylase, is upregulated during allergy and produces the immunomodulatory metabolite itaconate. Knockout of Irg1 increases Th2 cytokine production in a house dust mite allergy model, and exogenous delivery of an itaconate derivative can mitigate allergy. Nanoparticles (NPs) can deliver bioactive substances, enabling local retention, and controlled release properties but effective delivery may require higher doses and frequent administration formulation issues. To address this, our lab developed biodegradable, cargo-less itaconate-based NPs for sustained release to target metabolic dysregulation in allergy. Intratracheal delivery abrogated Th2 cytokines and total IgE in a therapeutic OVA-induced allergic airway inflammation model. Furthermore, CD11b+Ly6G+L6Clo cells were significantly increased in the lung after NP treatment, which was not observed for 4-octyl itaconate. These results demonstrate itaconate-based NPs ability to modulate Th2 disease by targeting dysregulated metabolism in allergy. Funding Sources Supported by startup funds provided by the University of Maryland, Baltimore; the National Institute of General Medical Sciences of the National Institutes of Health under award number R35GM142752; Institute for Clinical Translational Sciences (ICTR) and the National Center for Advancing Translational Sciences (NCATS) Clinical Translational Science Award (CTSA) grant number 1UL1TR003098 Topic Categories Vaccines and Immunotherapy (VAC)
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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) | 0.002 | 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".