Reference Interval for Non-HDL-Cholesterol, Remnant Cholesterol and Other Lipid Parameters in the Southern Iranian Population; Findings From Bandare Kong and Fasa Cohort Studies.

BACKGROUND: Growing evidence shows the undisputable role of non-HDL-C and remnant cholesterol (remnant-C) in cardiovascular disease (CVD) risk assessment and treatment. However, the reference interval (RI) for these lipid parameters is not readily available. The aim of the present investigation was to determine the age and sex-specific RIs for non-HDL-C and remnant-C as well as other lipid parameters among a healthy population in southern Iran. We also report the RI of lipid parameters in rural and urban residents, smokers and post-menopausal women. METHODS: Among 14063 participants of Bandare Kong and Fasa cohort studies, 792 healthy subjects (205 men and 578 women) aged 35-70 years were selected. Fasting blood samples were used for determination of total cholesterol (TC), triglycerides (TG) and HDL-C using colorimetric methods. Non-HDL-C and remnant-C were calculated using the valid formula. The 2.5th and 97.5th percentiles were calculated and considered as RI. RESULTS: In the total population (n=792, age 35-70), RIs for non-HDL-C and remnant-C was 74.0-206.8 and 8.0-52.7 mg/dL, respectively. Age (35-44 and≥45 years) and gender-specific RIs for serum non-HDL-C and remnant-C were determined. Remnant-C and non-HDL-C level were different between sex and age categories. The mean value of all lipid parameters except HDL-C was higher in men, urban residents, subject with age≥45 years and smokers. CONCLUSION: This is the first study in which the RIs for non-HDL-C and remnant-C in southern Iran are reported. This may help physicians to conveniently use these lipid parameters for patient care and better cardiovascular risk assessment.

Detection of prostate cancer using temporal sequences of ultrasound data: a large clinical feasibility study.

PURPOSE: This paper presents the results of a large study involving fusion prostate biopsies to demonstrate that temporal ultrasound can be used to accurately classify tissue labels identified in multi-parametric magnetic resonance imaging (mp-MRI) as suspicious for cancer. METHODS: We use deep learning to analyze temporal ultrasound data obtained from 255 cancer foci identified in mp-MRI. Each target is sampled in axial and sagittal planes. A deep belief network is trained to automatically learn the high-level latent features of temporal ultrasound data. A support vector machine classifier is then applied to differentiate cancerous versus benign tissue, verified by histopathology. Data from 32 targets are used for the training, while the remaining 223 targets are used for testing. RESULTS: Our results indicate that the distance between the biopsy target and the prostate boundary, and the agreement between axial and sagittal histopathology of each target impact the classification accuracy. In 84 test cores that are 5 mm or farther to the prostate boundary, and have consistent pathology outcomes in axial and sagittal biopsy planes, we achieve an area under the curve of 0.80. In contrast, all of these targets were labeled as moderately suspicious in mp-MR. CONCLUSION: Using temporal ultrasound data in a fusion prostate biopsy study, we achieved a high classification accuracy specifically for moderately scored mp-MRI targets. These targets are clinically common and contribute to the high false-positive rates associated with mp-MRI for prostate cancer detection. Temporal ultrasound data combined with mp-MRI have the potential to reduce the number of unnecessary biopsies in fusion biopsy settings.