Nucleic acid sensing Toll-like receptors 3 and 9 play complementary roles in the development of bacteremia after nasal colonization associated with influenza co-infection
Bibliographic record
Abstract
Streptococcus pneumoniae can cause mortality in infant, elderly, and immunocompromised individuals owing to invasion of bacteria to the lungs, the brain, and the blood. In building strategies against invasive infections, it is important to achieve greater understanding of how the pneumococci are able to survive in the host. Toll-like receptors (TLRs), critically important components in the innate immune system, have roles in various stages of the development of infectious diseases. Endosomal TLRs recognize nucleic acids of the pathogen, but the impact on the pneumococcal diseases of immune responses from signaling them remains unclear. To investigate their role in nasal colonization and invasive disease with/without influenza co-infection, we established a mouse model of invasive pneumococcal diseases directly developing from nasal colonization. TLR9 KO mice had bacteremia more frequently than wildtype in the pneumococcal mono-infection model, while the occurrence of bacteremia was higher among TLR3 KO mice after infection with influenza in advance of pneumococcal inoculation. All TLR KO strains showed poorer survival than wildtype after the mice had bacteremia. The specific and protective role of TLR3 and TLR9 was shown in developing bacteremia with/without influenza co-infection respectively, and all nucleic sensing TLRs would contribute equally to protecting sepsis after bacteremia.
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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".