Ignite Creation Date:
2024-10-25 @ 7:49 PM
Last Modification Date:
2024-10-26 @ 3:42 PM
Study NCT ID:
NCT06640348
Status:
RECRUITING
Last Update Posted:
None
First Post:
2024-10-11
Brief Title:
Ultrasensitive SERS Platform With Highly Efficient Enrichment of Analyte for Screening and Diagnosis of Epithelial Ovarian Cancer
Sponsor:
None
Organization:
None
Study Overview
Official Title:
Ultrasensitive SERS Platform With Highly Efficient Enrichment of Analyte for Screening and Diagnosis of Epithelial Ovarian Cancer
Status:
RECRUITING
Status Verified Date:
2024-10
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
No
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief Summary:
This project is an open single-center prospective study aimed at developing high-sensitivity high-specificity enrichment SERS chips using femtosecond laser processing technology It involves extracting information from blood samples of ovarian cancer patients and normal controls specifically identifying cancer and non-cancer signals The study will construct a statistical algorithm model for the early diagnosis of ovarian cancer enabling early identification and intervention for ovarian cancer patients
Detailed Description:
Epithelial Ovarian Cancer EOC poses a significant challenge in the field of gynecological oncology regarding precise early screening In response to this critical scientific issue the research team has designed and developed a high-sensitivity high-specificity enrichment SERS chip exploring its applications in the screening and diagnosis of ovarian cancer The development of the SERS chip and its functional implementation has been doneClinical research trials are conducted for ovarian cancer screening and diagnosis analyzing the physicochemical properties of key biomolecules in the blood of ovarian cancer patients The study reveals the interaction patterns between SERS active particles and biomolecules establishing a competitive adsorption model between multiple biomolecules and active particles Raman spectra of individual components are collected to create a characteristic Raman information database for key biomolecules
The analysis of Raman spectra from ovarian cancer patients and healthy individuals delves into the characteristic signals constructing a statistical classification model for patient and normal Raman signals Different tissue types and grades of ovarian cancer patients Raman spectra signals are analyzed establishing high-throughput classification methods for various ovarian cancers By combining clinical gold-standard detection techniques the sources of characteristic signals are determined providing a theoretical foundation and technical support for conducting ovarian cancer research and establishing treatment plans in clinical settings
The analysis of Raman spectra from ovarian cancer patients and healthy individuals delves into the characteristic signals constructing a statistical classification model for patient and normal Raman signals Different tissue types and grades of ovarian cancer patients Raman spectra signals are analyzed establishing high-throughput classification methods for various ovarian cancers By combining clinical gold-standard detection techniques the sources of characteristic signals are determined providing a theoretical foundation and technical support for conducting ovarian cancer research and establishing treatment plans in clinical settings
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
None
Is a FDA Regulated Device?:
None
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None