Pulmonary Neutrophil Recruitment in Response to Inhaled LPS is Impaired in CD4 Deficient Mice
Bibliographic record
Abstract
Abstract Objectiv e While the role of CD4+ regulatory T cells in the resolution of lung inflammation is well documented, their potential contribution to the onset of the inflammatory response has been largely neglected. Herein, we assessed the impact of germline CD4 deficiency on the alveolar macrophage (AMΦ)-neutrophil (PMN) axis in initiating the LPS-induced lung inflammation. Methods In vivo, The levels of AMΦ and PMN in bronchoalveolar lavage fluid (BALF) and tissue histopathology were compared in wild type (WT) and CD4-deficient (CD4−/−) mice at 4 hours after local LPS challenge (2–3 µg/g: intranasally). CD4 expression (flow cytometry) and representative functions of AMΦ (phagocytosis, cytokine secretion) and PMN (migration) of WT and CD4−/− mice were compared ex vivo. Results LPS increased PMN infiltration of alveoli in WT mice; a response severely blunted in CD4−/− mice. AMΦ isolated from WT mice captured more microbeads, but secreted less tumor necrosis factor-α, than AMΦ from CD4−/− mice. Notably, as compared to PMN of WT mice, PMN of CD4−/− mice had a diminished motility response to a relevant chemotactic stimulus (cell-free BALF). PMN expressed CD4 while AMΦ did not. Conclusions The motility defect of PMN derived from CD4−/− mice is most likely due to an intrinsic loss of CD4, whereas the quantitative and qualitative shift of the AMΦ pool of CD4−/− mice cannot.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".