Chitosan Interferon-γ Nanogene Therapy for Lung Disease: Modulation of T-Cell and Dendritic Cell Immune Responses
Bibliographic record
Abstract
: The use of chitosan nanoparticles as carriers for expression plasmids represents a major improvement in gene expression technology. We demonstrated previously that treatment with chitosan interferon-gamma (IFN-gamma) plasmid deoxyribonucleic acid (DNA) nanoparticles (chitosan interferon-gamma nanogene [CIN]) led to in situ production of IFN-gamma and a reduction in inflammation and airway reactivity in mice, but the mechanism underlying the immunomodulatory effects of CIN remains unclear. In this report, the effect of CIN treatment on the immune responses of CD8+ T cells and dendritic cells was examined in a BALB/c mouse model of ovalbumin (OVA)-induced allergic asthma. OT1 mice (OVA-T cell receptor [TCR] transgenic) were also used to test the effects of CIN on OVA-specific CD8+ T cells. CIN treatment caused a reduction in IFN-gamma production in a subpopulation of OVA-specific CD8+ T cells cultured in vitro in the presence of OVA. CIN also reduced apoptosis of the CD8+ T cells. Examination of dendritic cells from lung and lymph nodes indicated that CIN treatment decreased their antigen-presenting activity, as evident from the reduction in CD80 and CD86 expression. Furthermore, CIN treatment significantly decreased the number of CD11c+b+ dendritic cells in lymph nodes, suggesting that endogenous IFN-gamma expression may immunomodulate dendritic cell migration and activation. CIN therapy results in a reduction in proinflammatory CD8+ T cells and decreases the number and antigen-presenting activity of dendritic cells.
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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".