THE COMPLEX INTERACTION BETWEEN SEPSIS, IMMUNITY, AND THE MICROBIOME IN A MURINE MODEL OF OBESITY
Bibliographic record
Abstract
Introduction: Sepsis, a severe and often fatal condition, is influenced by obesity, with some studies suggesting an "obesity paradox" where obesity enhances survival. However, outcomes in murine models of sepsis and obesity show variability. This PhD thesis investigates the role of obesity in sepsis using a murine model of diet-induced obesity (DIO). Additionally, it explores how the gut-lung axis and short-chain fatty acids (SCFAs) influence macrophage function in the context of sepsis and obesity. Aims: This thesis aims to (1) systematically review the literature DIO and sepsis in murine models, (2) describe the impact of sepsis and obesity using a DIO fecal-induced peritonitis (FIP) model, and (3) provide a mechanistic understanding of how sepsis, obesity, and season affect organ function, focusing on circadian clock genes and subsequent dysbiosis impacting macrophage function. Results: The scoping review identified a lack of standardization and high variability in outcomes and methodologies in murine models of obesity and sepsis, complicating translational relevance. In our DIO FIP model, findings revealed no "obesity paradox," with obese mice showing decreased survival. Using a single predefined dose in DIO FIP models is optimal for observing the impact of obesity on sepsis outcomes, minimizing confounding effects. Increased mortality in septic mice during fall was influenced by infradian cycles, with BMAL1 downregulation in fall and obese mice suggesting a link between circadian rhythms and sepsis outcomes. Reduced SCFA levels in fall and high-fat diet mice impaired macrophage function, affecting inflammation resolution. SCFAs play a crucial role in modulating macrophage polarization and inflammation resolution in sepsis and obesity. Conclusion: This study highlights the importance of seasonality, metabolic alterations from dysbiosis, and their collective impact on immune responses in sepsis. Addressing these factors can improve the translational relevance of obesity and sepsis research, enhancing the utility of animal models in clinical applications.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".