Analysis of Pulmonary Dendritic Cell Maturation and Migration during Allergic Airway Inflammation
Bibliographic record
Abstract
Dendritic cells (DCs) are the key players involved in initiation of adaptive immune response by activating antigen-specific T cells. DCs are present in peripheral tissues in steady state; however in response to antigen stimulation, DCs take up the antigen and rapidly migrate to the draining lymph nodes where they initiate T cell response against the antigen. Additionally, DCs also play a key role in initiating autoimmune as well as allergic immune response. DCs play an essential role in both initiation of immune response and induction of tolerance in the setting of lung environment. Lung environment is largely tolerogenic, owing to the exposure to vast array of environmental antigens. However, in some individuals there is a break in tolerance, which leads to induction of allergy and asthma. In this study, we describe a strategy, which can be used to monitor airway DC maturation and migration in response to the antigen used for sensitization. The measurement of airway DC maturation and migration allows for assessment of the kinetics of immune response during airway allergic inflammation and also assists in understanding the magnitude of the subsequent immune response along with the underlying mechanisms. Our strategy is based on the use of ovalbumin as a sensitizing agent. Ovalbumin-induced allergic asthma is a widely used model to reproduce the airway eosinophilia, pulmonary inflammation and elevated IgE levels found during asthma. After sensitization, mice are challenged by intranasal delivery of FITC labeled ovalbumin, which allows for specific labeling of airway DCs which uptake ovalbumin. Next, using several DC specific markers, we can assess the maturation of these DCs and can also assess their migration to the draining lymph nodes by employing flow cytometry.
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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".