Investigating the potential role of swertiamarin on insulin resistant and non-insulin resistant granulosa cells of poly cystic ovarian syndrome patients
Bibliographic record
Abstract
BACKGROUND AND AIM: Conventional drugs have limitations due to prevalence of contraindications in PCOS patients. To explore the potential effects of swertiamarin, on abrupted insulin and steroidogenic signaling in human luteinized granulosa cells from PCOS patients with or without insulin resistance. EXPERIMENTAL PROCEDURE: hLGCs from 8 controls and 16 PCOS patients were classified for insulin resistance based on down regulation of protein expression of insulin receptor-β (INSR- β) as shown in our previous paper. Cells were grouped as control, PCOS-IR and PCOS-NIR, treated with swertiamarin (66 µM) and metformin (1 mM). Expression of key molecules involved in insulin signaling, fat metabolism, IGF system and steroidogenesis were compared between groups. RESULTS: Swertiamarin significantly (P < 0.05) reversed the expression of INSR-β, PI(3)K, p-Akt, PKC-ζ, PPARγ, (P < 0.01) IRS (Ser 307) and IGF system in PCOS-IR group and was equally potent to metformin. In the same group, candidate genes viz SREBP1c, FAS, ACC-1 and CPT-1 were down regulated by swertiamarin (P < 0.001) and metformin (P < 0.001). Significant upregulation was demonstrated in expression of StAR, CYP19A1, 17β-HSD and 3β-HSD when treated with swertiamarin (P < 0.01) and metformin (P < 0.01) in PCOS-IR followed by increase in 17β-HSD and 3β-HSD enzyme activity along with estradiol and progesterone secretions. However, swertiamarin did not reveal any effect on PCOS-NIR group as compared to metformin that significantly (P < 0.01) reversed all the parameters related to steroidogenesis and down regulated basal expression of insulin signaling genes. CONCLUSION: Swertiamarin, presents itself as a potential fertility drug in hLGCs from PCOS-IR 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) | 0.000 | 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 | 0.000 |
| 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".