Improving hepatic mitochondrial biogenesis as a postulated mechanism for the antidiabetic effect of <i>Spirulina platensis</i> in comparison with metformin
Bibliographic record
Abstract
Various nutritional and medicinal potencies have been accredited to metabolites from the cyanobacteria, Spirulina platensis (Arthrospira platensis) sp. Hence, our study was designed to examine whether the Spirulina supplementation would possess beneficial effects in type 2 diabetes mellitus (T2DM) in comparison with metformin. High-fat diet/low-dose streptozotocin (HFD/STZ) model was adopted and the diabetic rats were orally treated with metformin (200 mg/kg) or Spirulina (250 or 500 or 750 mg/kg) for 30 days. Spirulina ameliorated the HFD/STZ-induced elevation of fasting blood glucose, insulin, and hepatic enzymes. Moreover, Spirulina successfully rectified disrupted serum lipid profile and exhibited an anti-inflammatory effect via tumor necrosis factor-α and adiponectin modulation. On the molecular level, Spirulina reduced the expression of hepatic sterol regulatory element binding protein-1c (SREBP-1c), confirming its lipotropic effect. Furthermore, Spirulina amended compromised hepatic mitochondrial biogenesis signaling by significantly increasing peroxisome proliferator-activated receptor-gamma coactivator-1α (PGC-1α), mitochondrial transcription factor A (Tfam), and mitochondrial DNA (mtDNA) copy number. On almost all parameters, the highest dose of Spirulina showed the best effects, which were comparable to that of metformin. To our knowledge, our study is the first to attribute the various aspects of the effect of Spirulina to the SREBP-1c and PGC-1α/Tfam/mtDNA pathways in liver. The present results clearly proved that Spirulina modulated glucose/lipid profile and exhibited prominent anti-inflammatory properties through SREBP-1c inhibition and hepatic mitochondrial biogenesis enhancement. Thus, Spirulina can be considered as an add-on to conventional antidiabetic agents and might influence the whole dynamics of the therapeutic approaches in T2DM.
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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".