Supplementation of grape seed and skin extract to orlistat therapy prevents high-fat diet-induced murine spleen lipotoxicity
Bibliographic record
Abstract
Spleen is the largest lymphoid organ and obesity is related to an elevated risk of immunity dysfunction. The mechanism whereby fat adversely affects the spleen is poorly understood. This study was designed to assess the effectiveness of grape seed and skin extract (GSSE) and orlistat (Xenical, Xe) on high-fat diet (HFD)-induced spleen lipotoxicity. Obese rats were treated either with GSSE (4 g/kg body weight) or Xe (2 mg/kg body weight) or GSSE+Xe and monitored for weight loss for 3 months. Animals were then sacrificed and their spleen used for the evaluation of lipotoxicity-induced oxidative stress and inflammation as well as the putative protection afforded by GSSE and Xe treatment. HFD induced body weight gain and glycogen accumulation into the spleen; ectopic deposition of cholesterol and triglycerides and an oxidative stress characterized by increased lipoperoxidation and carbonylation; inhibition of antioxidant enzyme activities, such as catalase, glutathione peroxidase, and superoxide dismutase; depletion of zinc and copper; and a concomitant increase in calcium. HFD also increased plasma pro-inflammatory cytokines, such as interleukin (IL)-6, IL-17A, tumour necrosis factor alpha, and C-reactive protein, and decreased plasma IL-10 and adiponectin. Importantly, GSSE counteracted all the deleterious effects of HFD on spleen (i.e., lipotoxicity, oxidative stress, and inflammation) and the best protection was obtained when combining Xe+GSSE. Combining GSSE with Xe prevented against fat-induced spleen lipotoxicity, oxidative stress, and inflammation; this combination may be beneficial in other diseases related to the spleen.
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) | 0.000 | 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".