Albuminuria and neck circumference are determinate factors of successful accurate estimation of glomerular filtration rate in high cardiovascular risk patients.

Background: Estimated glomerular filtration rate (eGFR) is used for diagnosis of chronic kidney disease (CKD). The eGFR models based on serum creatinine or cystatin C are used more in clinical practice. Albuminuria and neck circumference are associated with CKD and may have correlations with eGFR.

Neck Circumference as a Predictive Indicator of CKD for High Cardiovascular Risk Patients.

Background: Neck circumference (NC) is an anthropometric measure of obesity for upper subcutaneous adipose tissue distribution which is associated with cardiometabolic risk. This study investigated whether NC is associated with indicators of chronic kidney disease (CKD) for high cardiometabolic risk patients.

Methods: A total of 177 consecutive patients who underwent the outpatient departments of cardiology were prospectively enrolled in the study.

Verification of gene expression profiles for colorectal cancer using 12 internet public microarray datasets.

Aim: To verify gene expression profiles for colorectal cancer using 12 internet public microarray datasets.

Methods: Logistic regression analysis was performed, and odds ratios for each gene were determined between colorectal cancer (CRC) and controls. Twelve public microarray datasets of GSE 4107, 4183, 8671, 9348, 10961, 13067, 13294, 13471, 14333, 15960, 17538, and 18105, which included 519 cases of adenocarcinoma and 88 normal mucosa controls, were pooled and used to verify 17 selective genes from 3 published studies and estimate the external generality.

Gene expression profile of peripheral blood in colorectal cancer.

Aim: Optimal molecular markers for detecting colorectal cancer (CRC) in a blood-based assay were evaluated.

Methods: A matched (by variables of age and sex) case-control design (111 CRC and 227 non-cancer samples) was applied. Total RNAs isolated from the 338 blood samples were reverse-transcribed, and the relative transcript levels of candidate genes were analyzed.

Gene expression profiling of breast cancer survivability by pooled cDNA microarray analysis using logistic regression, artificial neural networks and decision trees.

Background: Microarray technology can acquire information about thousands of genes simultaneously. We analyzed published breast cancer microarray databases to predict five-year recurrence and compared the performance of three data mining algorithms of artificial neural networks (ANN), decision trees (DT) and logistic regression (LR) and two composite models of DT-ANN and DT-LR. The collection of microarray datasets from the Gene Expression Omnibus, four breast cancer datasets were pooled for predicting five-year breast cancer relapse.