Cell-in-cell-mediated intercellular communication exacerbates the pro-inflammatory progression in asthma
Bibliographic record
Abstract
Cell-in-cell (CIC) structures have been suggested to mediate intracellular substance transport between cells and have been found widely in inflammatory lung tissue of asthma. The aim of this study was to investigate the significance of CIC structures in inflammatory progress of asthma. CIC structures and related inflammatory pathways were analyzed in asthmatic lung tissue and normal lung tissue of mouse model. In vitro, the activation of inflammatory pathways by CIC-mediated intercellular communication was analyzed by RNA-Seq and verified by Western blotting and immunofluorescence. Results showed that CIC structures of lymphocytes and alveolar epithelial cells in asthmatic lung tissue mediated intercellular substance (such as mitochondria) transfer and promoted pro-inflammation in two phases. At early phase, internal lymphocytes triggered inflammasome-dependent pro-inflammation and cell death of itself. Then, degraded lymphocytes released cellular contents such as mitochondria inside alveolar epithelial cells, further activated multi-pattern-recognition receptors and NF-kappa B signaling pathways of alveolar epithelial cells, and thereby amplified pro-inflammatory response in asthma. Our work supplements the mechanism of asthma pro-inflammation progression from the perspective of CIC structure of lymphocytes and alveolar epithelial cells, and provides a new idea for anti-inflammatory therapy of asthma.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".