Diabetic Nephropathy, Focal Segmental Glomerulosclerosis (FSGS), IgA Nephropathy (IgAN), Membranous Nephropathy
Conditions
Brief summary
This research plan, from January 2021 to December 2024, aims to collect serum and morning urine from patients diagnosed with IgA nephropathy, idiopathic membranous nephropathy, diabetic nephropathy, and focal segmental glomerulosclerosis the Nephrology Department of Qianfoshan Hospital in Shandong Province, through renal biopsy. These samples will be scanned using a Raman spect to obtain Raman spectral data. The scattering peaks in the Raman spectra will be analyzed using Origin software for Gaussian curve fitting. The position of the peaks will used to query relevant literature to identify the corresponding chemical bonds and confirm the presence of compounds. The intensity and area of the chemical substance peaks in the Raman will be calculated and used to plot calibration curves, thereby establishing a quantitative analysis equation. This equation will be used to accurately calculate the concentration of each analyte in serum and urine samples. Based on the average concentration data for each patient group, multivariate analysis methods, such as principal component analysis (PCA) and Mahalanis distance discriminant model, will be used to classify and predict the disease types. The preliminary data for this study comes from the Nephrology Department ofianfoshan Hospital, where different types of glomerular diseases have been pathologically classified using tools such as light microscopy, electron microscopy, and immunoforescence microscopy. By combining Raman spectroscopy technology and statistical analysis, this study aims to establish a non-invasive and efficient diagnostic tool to assist in the of kidney diseases and predict treatment outcomes.
Interventions
DIAGNOSTIC_TESTImaging Time
Raman spectroscopy images of blood and urine
Sponsors
Zunsong Wang
Study design
Observational model
OTHER
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Healthy volunteers
Yes
Inclusion criteria
1. Age 18 years or older; 2. Patients diagnosed with IgA nephropathy, idiopathic membranous nephrop, diabetic nephropathy, or focal segmental glomerulosclerosis confirmed by renal biopsy; 3. Patients who have not received hormone and/or immunosup therapy before the renal biopsy;
Exclusion criteria
1. Presence of factors causing secondary membranous nephropathy: such as autoimmune diseases (systemic lupus erythematosus),/infections (viral hepatitis), drugs or toxins, etc.; 2. Severe infection: clinical manifestations such as fever, cough and sputum, throat, abdominal pain, diarrhea, boils and other skin and soft tissue infections, with white blood cell count in blood routine exceeding the normal range (10×09/L); 3. Severe cardiovascular disease: including chronic heart failure of grade 3 or above and various arrhythmias; 4. Infect diseases: active phase of various types of hepatitis, AIDS, syphilis, etc.; 5. Evidence of tumor: already diagnosed with a certain tumor or manifestations, tumor markers, etc. indicating the possibility of a tumor; 6. Patients with incomplete data or missed diagnosis.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Raman spectroscopy images of blood and urine | From the time of enrollment to the completion of blood and urine collection within 2 days | The samples were scanned using a Raman spectrometer to obtain Raman spectral data. The scattering peaks in the Raman spectra were analyzed by fitting Gaussian curves using Origin software. The chemical bonds were identified and the presence of compounds was confirmed by referring to the literature based on the peak positions. The peak and area of the chemical substances in the Raman spectra were calculated and used to plot calibration curves, thereby establishing the quantitative analysis equation. This equation was used to calculate the concentrations of each analyte in the serum and urine. The average concentration data for each pathological patient group were used as the basis for multivariate analysis, such as principal component analysis (PCA) and Mahalanobis distance discriminant model, to classify and predict the types of diseases. |
Countries
China
Contacts
Primary ContactZunsong Wang, Dr
Backup ContactMengyu Wu, Dr
Outcome results
None listed