Host defence during<i>Klebsiella</i>pneumonia relies on haematopoietic-expressed Toll-like receptors 4 and 2
Bibliographic record
Abstract
In this study, the relative roles of Toll-like receptor (TLR)2 and TLR4 were investigated independently and together. Moreover, we studied the role of haematopoietic compartment in anti-Klebsiella host defence. We infected TLR2 and TLR4 single-, and TLR2×4 double knockout (KO) animals with different doses of Klebsiella pneumoniae. In addition, bone marrow chimeric mice were created and infected. TLR4 played a more prominent role in antibacterial defence than TLR2, considering that only TLR4 KO mice demonstrated enhanced bacterial growth in lungs and spleen 24 h after infection with 3×10³ colony-forming units of Klebsiella compared with wild-type (WT) mice. In late-stage infection or after exposure to a higher infectious dose, bacterial counts in lungs of TLR2 KO animals were elevated compared with WT mice and TLR2×4 KO animals were more susceptible to infection than TLR4 KO mice. TLR signalling in cells of haematopoietic origin is of primary importance in host defence against K. pneumoniae. These data suggest that: 1) TLR4 drives the antibacterial host response after induction of pneumonia with relatively low Klebsiella doses; 2) TLR2 becomes involved at a later phase of the infection and/or upon exposure to higher bacterial burdens; and 3) haematopoietic TLR2 and TLR4 are important for an adequate host response during Klebsiella pneumonia.
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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) | 0.002 | 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".