Ameliorating effects of <i>Sporidiobolus pararoseus</i> extract on dyslipidemia in mice with high fat diet induced obesity
Bibliographic record
Abstract
The study investigated how an extract of Sporidiobolus pararoseus (S.p.) affects lipid metabolism in Kunming mice that were obese as a result of being fed a high-fat diet; the control group were administered Max EPA fish oil. Ten mice were randomly selected from a pool of 60 mice for the control group and the remaining 50 mice were fed with a high-fat diet to establish a dyslipidemia model. After 4 weeks, these 50 mice were randomly distributed among 5 groups: high-fat model group; Max EPA group; and 3 groups of mice fed different doses of S.p. extract (low dose, medium dose, and high dose). After 8 weeks, the mice were sacrificed and the relevant parameters were measured. Compared with the high-fat model group, the group administered the high dose of S.p. extract showed significantly decreased body mass and serum levels of total cholesterol, triglycerides, and low-density lipoprotein cholesterol, and increased levels of high-density lipoprotein cholesterol. The results from RT-PCR showed that the mRNA expression of sterol regulatory element-binding protein 1c, fatty acid synthesis enzyme, and acetyl-CoA carboxylase was lower in the groups supplemented with S.p. extract than in the high-fat model group, whereas the expression of carnitine palmitoyltransferase 1 was higher in the group supplemented with S.p. extract than in the high-fat model group. Our results suggest that taking S.p. extract could benefit patients with dyslipidemia. Therefore, S.p. extract should be developed as a dietary supplement to improve lipid metabolism in obese people.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".