Role of neuroinflammation in the emotional and cognitive alterations displayed by animal models of obesity
Bibliographic record
Abstract
Obesity is associated with a high prevalence of mood disorders and cognitive dysfunctions in addition to being a significant risk factor for important health complications such as cardiovascular diseases and type 2 diabetes. Identifying the pathophysiological mechanisms underlying these health issues is a major public health challenge. Based on recent findings, from studies conducted on animal models of obesity, it has been proposed that inflammatory processes may participate in both the peripheral and brain disorders associated with the obesity condition including the development of emotional and cognitive alterations. This is supported by the fact that obesity is characterized by peripheral low-grade inflammation, originating from increased adipose tissue mass and/or dysbiosis (changes in gut microbiota environment), both of which contribute to increased susceptibility to immune-mediated diseases. In this review, we provide converging evidence showing that obesity is associated with exacerbated neuroinflammation leading to dysfunction in vulnerable brain regions associated with mood regulation, learning, and memory such as the hippocampus. These findings give new insights to the pathophysiological mechanisms contributing to the development of brain disorders in the context of obesity and provide valuable data for introducing new therapeutic strategies for the treatment of neuropsychiatric complications often reported in obese patients.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".