Response of the Murine Urinary Bladder Microcirculation to Lipopolysaccharide from <i>Escherichia coli</i> and <i>Pseudomonas aeruginosa</i>
Bibliographic record
Abstract
There is in vitro evidence demonstrating the inflammatory response to lipopolysaccharide (LPS) differs depending on the bacterial source. The objective of our study was to examine the in vivo inflammatory response to LPS from two common uropathogens, Escherichia coli ( Ec ) and Pseudomonas aeruginosa ( Pa ). LPS or saline was injected transurethrally into bladders of female C57Bl/6 mice at varying doses. The bladder was exteriorized, and the bladder microcirculation examined by intravital microscopy. Leukocyte adhesion at 4 hrs post challenge with 5 mg/kg Pa LPS was 2.9±0.6 cells/field of view and at 7 mg/kg it was 3.9±0.8 cells/field of view, a significant increase from saline control. In contrast, leukocyte adhesion 4 hours post 5 mg/kg Ec LPS challenge was 17.4±3.9 and with 7 mg/kg 19.7±2.9 cells/field of view. However, at 4.5 hours adhesion was similar with either LPS (5 mg/kg) with 18.4±3.0 and 21.2±4.0 cells/field of view, respectively. The flux of rolling leukocytes was significantly increased above baseline levels only after Ec LPS challenge. There was also a significant increase in the number of neutrophils with Ec LPS stimulation. In support of the in vitro observations, these in vivo results demonstrate that Ec LPS is more potent and induces a more rapid inflammatory response than Pa LPS. Supported by a Collaborative Health Research Projects grant from CIHR and NSERC.
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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) | 0.001 | 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".