Molecular Imprints of Clinical Comorbidities in Hypothalamic Extracellular Vesicles at the Onset of Obesity
Bibliographic record
Abstract
The onset of obesity is characterized by early physiological and molecular changes, including leptin resistance and hypothalamic dysfunction, preceding significant weight gain and metabolic complications. Extracellular vesicles (EVs) are key mediators of intercellular communication, reflecting early pathological shifts in metabolic disorders. This study investigates the role of hypothalamic EVs (hEVs) in early obesogenic insult and their potential implications for obesity-related comorbidities. Using a hamster model fed a high fat diet for 30 days, next-generation proteomics revealed altered hEV protein compositions linked to cellular metabolism, neuroinflammation, and metabolic dysfunction, mirroring early obesity-related dysfunction. These findings highlight the adaptive molecular profiles of hEVs during early obesogenic insult and their potential as biomarkers and molecular mediators in obesity progression and its comorbidities. In conclusion, this study provides new insights into the molecular mechanisms underlying the onset of obesity and highlights hEVs as promising targets for early detection and intervention.
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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".