Enhanced Infiltration of Central Memory T Cells to the Lung Tissue during Allergic Lung Inflammation
Bibliographic record
Abstract
Memory T cells play a central role in regulating inflammatory responses during asthma. However, tissue distribution of effector memory (TEM) and central memory (TCM) T-cell subtypes, their differentiation, and their contribution to the persistence of lung tissue inflammation during asthma are not well understood. Interestingly, an increase in survival and persistence of memory T cells was reported in asthmatic lungs, which may suggest a shift toward the more persistent TCM phenotype. In this report, we investigated the differential distribution of memory T-cell subtypes during allergic lung inflammation and the mechanism regulating that. Using an OVA-sensitized asthma mouse model, we observed a significant increase in the frequency of TCM cells in inflamed lungs compared to healthy controls. Interestingly, adoptive transfer techniques confirmed substantial infiltration of TCM cells to lung tissues during allergic airway inflammation. Expression levels of TCM homing receptors, CD34 and GlyCAM-1, were also significantly upregulated in the lung tissues of OVA-sensitized mice, which may facilitate the increased TCM infiltration into inflamed lungs. Moreover, a substantial increase in the relative expression of TCM profile-associated genes (EOMES, BCL-6, ID3, TCF-7, BCL-2, BIM, and BMI-1) was noted for TEM cells during lung inflammation, suggesting a shift for TEM into the TCM state. To our knowledge, this is the first study to report an increased infiltration of TCM cells into inflamed lung tissues and to suggest differentiation of TEM to TCM cells in these tissues. Therapeutic interference at TCM infiltration or differentiations could constitute an alternative treatment approach for lung inflammation.
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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".