CHARACTERIZATION OF S. PNEUMONIAE PNEUMONIA-INDUCED MULTIPLE ORGAN DYSFUNCTION SYNDROME
Bibliographic record
Abstract
Streptococcus pneumoniae, a gram-positive bacteria, is the most common cause of community-acquired pneumonia. It is a common cause of septic shock with multiple organ dysfunction syndrome (MODS) resulting in significant mortality. Gram-positive mouse models of sepsis with MODS are required to examine mechanisms of immune responses in severe sepsis. To assess whether lung infection due to S. pneumoniae in a nonventilated mouse model can induce multiple organ dysfunction. S. pneumoniae, SPN 15814 strain, harvested at log phase, was injected intratracheally in C57BL/6 mice at OD 600 between 0.35 and 0.63. A dose of bacteria at OD 600 = 0.63 conferred approximately 30% mortality in 36 h. Lung pneumonia was assessed by histology, lung myeloperoxidase activity, and lung bacterial load; intestinal epithelial barrier integrity was assessed by measuring blood-to-lumen clearance of Cr-EDTA; renal function was assessed by measuring plasma creatinine and urea; and myocardiac function was assessed using an isolated perfused mouse heart model. S. pneumoniae-induced pneumonia resulted in neutrophil infiltration into the lungs and increased lung bacterial load. Although relatively few bacteria gained access to the blood stream, the pneumonia was accompanied by increased intestinal epithelial barrier permeability, increased plasma creatinine, and decreased cardiac output and stroke volume. These data clearly show that intratracheal S. pneumoniae induced not only pneumonia but also MODS, despite the fact that few organisms gain access to the blood stream. This model can be used as a good gram-positive model of sepsis and MODS for further studies.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".