Immunity to bacterial infection (excluding mycobacteria) (PP-060)
Bibliographic record
Abstract
Previous studies suggest that obesity is associated with poor outcome in sepsis, that the endothelium plays a key role in mediating sepsis morbidity and mortality, and that cross-talk between adipoyctes and endothelial cells is an important determinant of health and disease. To study the role of obesity and adipocyte-endothelial cell interactions in the host response to infection, 12 week-old male diet-induced obese (DIO) mice (60% fatty diet) and age-matched lean mice (10% fatty diet) were injected intraperitoneally with E. coli lipopolysaccharide (LPS) (8 mg/kg). Compared with lean mice, LPS administration in DIO mice resulted in significantly higher circulating levels of leptin (1.9-fold, p<0.01), sVEGFR1 (1.5-fold, p<0.05) and IL-6 (3.8-fold, p<0.05) at 24 hrs. Endotoxemia in obese mice was associated with vascular bed-specific accentuation of inflammatory gene expression. For example, P-selectin levels were significantly higher in the liver (1.7-fold), kidney (1.4-fold) and lung (1.2-fold) (p<0.05), but not fat, of endotoxemic DIO vs. lean. Endotoxemia resulted in lower survival of DIO mice (0% vs 62.5%, p<0.02). Under in vitro conditions, incubation of HUVEC with CM from LPS-treated adipocytes demonstrated significant up-regulation of ICAM-1 (5.1-fold), VCAM-1 (6.1-fold), and E-selectin (5.8-fold) (p<0.0001) compared with CM from control adipocytes. Importantly Bay11-7082, an NF-kB inhibitor completely attenuated adipocyte-mediated endothelial activation.
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) | 0.006 | 0.001 |
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".