Basophils are recruited to inflamed lungs and exacerbate memory <scp>T</scp>h2 responses in mice and humans
Bibliographic record
Abstract
BACKGROUND: Although the contribution of basophils as inducers or amplifiers of Th2 responses is still debated, prolonged basophil/CD4 T cell interactions were observed in lungs but not lymph nodes (LNs) of parasite-infected mice. However, the impact of basophils on the function of tissue CD4 effector T cells remains unknown. METHODS: Basophils were purified from the lungs of ovalbumin (OVA)-sensitized and OVA-challenged (OVA-immunized) mice or human peripheral blood for in vivo and in vitro functional studies. Pulmonary basophils were adoptively transferred to OVA-sensitized hosts to assess airway inflammation in bronchoalveolar lavage fluid (BALF) and Th2 responses in lung explants and draining LNs. Basophils were co-cultured with effector T cells or Ag-specific naïve T cells alone or in combination with dendritic cells (DCs); IL-4 production was determined by flow cytometry and ELISA. RESULTS: Basophils accumulated in lungs of OVA-immunized mice. Adoptive transfer of basophils to OVA-sensitized hosts enhanced lung IL-4 and IL-13 release while co-administration of OVA further aggravated airway inflammation and Th2 responses in LNs. Mechanistic in vitro studies revealed that pulmonary basophils interacted with lung CD4 effectors, in the absence of DCs, to increase T cell survival and Th2 cytokine expression at the single cell level but amplified OVA-loaded DC-driven Th2 differentiation. Finally, human basophils augmented in vitro IL-4 expression in effector memory CD4 T cells that include CRTH2(+) cells through IL-4 and TCR-independent pathways. CONCLUSIONS: Basophils may worsen Th2 inflammatory disorders through direct interactions with pathogenic CD4 T cells as well as by enhancing DC-induced Th2 cell development.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".