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NCT06617988COMPLETEDanonymous

Investigate Differences in SIRT3 Expression in Granulosa Cells and the Levels of Follicular Fluid Metabolites Among Poor Ovarian Responders Subgroups, and Their Association With IVF Outcomes.

Sponsor

Source record

Shandong University of Traditional Chinese Medicine

Phase

Source record

Completed (Observational Study)

Modality

AI-normalized

RNA therapy

Target

AI-normalized

SIRT3 expression levels in granulosa cells and associated follicular fluid metabolites.

Indication / condition

AI-normalized

Poor Ovarian Response

Intervention

Source record

Not applicable- observational study

Source & freshness

Source record

NCT ID

NCT06617988

Original source

ClinicalTrials.gov

Source last updated

Apr 29, 2026

Ingested at

Jun 18, 2026

Internal sync

Jun 18, 2026

Model version

trialsignal-ai-v1

Normalized confidence

96%

Validation status

validated

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NCT ID

NCT06617988

Title

Investigate Differences in SIRT3 Expression in Granulosa Cells and the Levels of Follicular Fluid Metabolites Among Poor Ovarian Responders Subgroups, and Their Association With IVF Outcomes.

Sponsor

Shandong University of Traditional Chinese Medicine

Status

COMPLETED

Phase

Completed (Observational Study)

Condition raw

Poor Ovarian Response

Condition normalized

Poor Ovarian Response

Modality raw

RNA therapy

Modality normalized

RNA therapy

Target raw

SIRT3 expression levels in granulosa cells and associated follicular fluid metabolites.

Target normalized

SIRT3 expression levels in granulosa cells and associated follicular fluid metabolites.

Interventions

Not applicable- observational study

Public preview

Source record

This observational study, sponsored by Shandong University of Traditional Chinese Medicine, aims to elucidate the role of SIRT3 in differentiating subtypes of poor ovarian response (POR) during IVF. The findings could enhance understanding of ovarian response mechanisms and potentially lead to improved IVF outcomes, thereby addressing a significant market need in reproductive health. The study's focus on metabolic profiling may also open avenues for novel therapeutic strategies targeting metabolic pathways in infertility. Given the increasing demand for personalized medicine in reproductive technologies, the insights gained could position the sponsor favorably within the competitive landscape of fertility treatments.

AI-generated analysis supports research triage only. Verify source records, publications, sponsor disclosures and IP databases before making diligence decisions. Model: trialsignal-ai-v1.

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