Severe systemic inflammation promotes persistent brain metabolic abnormalities
Bibliographic record
Abstract
Abstract Background Severe systemic inflammation can spread to the central nervous system, promoting neuronal dysfunction. Individuals who survive to a severe inflammatory episode, have greater chances to develop cognitive impairment. In this context, brain inflammatory changes have been considered a risk factor for Alzheimer’s disease (AD). We previously demonstrated, a significant [18F]FDG hypometabolism 24 h after the induction of severe systemic inflammation by cecal ligation and perforation (CLP), a sepsis model. However, whether severe systemic inflammation causes long‐term effects on brain energy metabolism remains unclear. Here, we aim at Investigating persistent consequences of a severe systemic inflammatory episode on brain energy metabolism. We hypothesized that brain energetic metabolism does not completely recover from a systemic inflammatory episode, which could be a trigger for neurodegeneration. Method Wistar rats (90 days old) were submitted to CLP and, examined in vivo via Micro‐PET [18F]FDG imaging 30 days later. Albumin and cellularity in the cerebrospinal fluid (CSF) were assessed by HPLC and flow cytometry, respectively, as an index of blood brain barrier (BBB) integrity. HPLC was also used for evaluating glutamate levels in CSF and glutamate uptake was evaluated in hippocampal and cortical slices using a radioactive assay. Result We found a persistent brain [18F]FDG hypometabolism in the CLP group. A 4‐fold increase in albumin levels was observed, along with a prominent increase in cellularity in the CSF of CLP animals. Additionally, decreased glutamate uptake in cortex (32%) and hippocampus (16%) along with increased levels of glutamate in CSF was found in the CLP group (Figure 1). Conclusion These results that an episode of severe systemic inflammatory causes persistent brain metabolic disturbances and BBB damage. It is very likely that the long period of BBB disruption, allowing for communication between peripheral mediators/cells and the brain, compromises brain energy metabolism. Together, these findings suggest a link between peripheral inflammation and neurodegeneration.
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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) | 0.000 | 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".