Lipocalin 2 Is Required for Pulmonary Host Defense against <i>Klebsiella</i> Infection
Bibliographic record
Abstract
Antimicrobial proteins comprise a significant component of the acute innate immune response to infection. They are induced by pattern recognition receptors as well as by cytokines of the innate and adaptive immune pathways and play important roles in infection control and immunomodulatory homeostasis. Lipocalin 2 (siderocalin, NGAL, 24p3), a siderophore-binding antimicrobial protein, is critical for control of systemic infection with Escherichia coli; however, its role in mucosal immunity in the respiratory tract is unknown. In this study, we found that lipocalin 2 is rapidly and robustly induced by Klebsiella pneumoniae infection and is TLR4 dependent. IL-1beta and IL-17 also individually induce lipocalin 2. Mucosal administration of IL-1beta alone could reconstitute the lipocalin 2 deficiency in TLR4 knockout animals and rescue them from infection. Lipocalin 2-deficient animals have impaired lung bacterial clearance in this model and mucosal reconstitution of lipocalin 2 protein in these animals resulted in rescue of this phenotype. We conclude that lipocalin 2 is a crucial component of mucosal immune defense against pulmonary infection with K. pneumoniae.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".