Metabolic Response to Klebsiella pneumoniae Infection in an Experimental Rat Model
Bibliographic record
Abstract
Bacteremia, the presence of viable bacteria in the blood stream, is often associated with several clinical conditions. Bacteremia can lead to multiple organ failure if managed incorrectly, which makes providing suitable nutritional support vital for reducing bacteremia-associated mortality. In order to provide such information, we investigated the metabolic consequences of a Klebsiella pneumoniae (K. pneumoniae) infection in vivo by employing a combination of (1)H nuclear magnetic resonance spectroscopy and multivariate data analysis. K. pneumoniae was intravenously infused in rats; urine and plasma samples were collected at different time intervals. We found that K. pneumoniae-induced bacteremia stimulated glycolysis and the tricarboxylic acid cycle and also promoted oxidation of fatty acids and creatine phosphate to facilitate the energy-demanding host response. In addition, K. pneumoniae bacteremia also induced anti-endotoxin, anti-inflammatory and anti-oxidization responses in the host. Furthermore, bacteremia could cause a disturbance in the gut microbiotal functions as suggested by alterations in a range of amines and bacteria-host co-metabolites. Our results suggest that supplementation with glucose and a high-fat and choline-rich diet could ameliorate the burdens associated with bacteremia. Our research provides underlying pathological processes of bacteremia and a better understanding of the clinical and biochemical manifestations of 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".