This study provides tools and outcome metrics that will aid researchers and clinicians in choosing an appropriate quantification method, processing large amounts of data efficiently, and overall generating valuable information that can contribute to our understanding of small non-coding RNAs as potential biomarkers and mediators of biological functions and disease.
Fast and robust detection, segmentation and tracking of anatomical structures or pathologies support the entire clinical workflow enabling real-time guidance, quantification, and processing in the operating room.
Among these, methodological aspects related to the preparation of the patient and his/her own physiology, and also those related to the quantification and processing of the PET study which can account for up to 20% of the variation obtained when acquiring the SUV value [1].
16-bit grayscale tiffs were subsequently exported to ImageJ for quantification and processing into rendered colored images.
The objective of this study is to provide researchers with general guidelines for quantification, data processing and analysis of miRNA, and other small non-coding RNAs (sncRNAs), from human clinical samples using NGS.
AKC performed RNA and protein isolation and quantification, RNA processing and microarray scanning and data mining, realtime quantitative RT-PCR and Western blot validation experiments, confocal immunofluorescence and histological analysis of suture tissues, and drafted the paper.
The goal of this study was to highlight some fundamental details of small ncRNA profiling, and provide the reader with general guidelines for quantification, data processing and analysis of sncRNAs from clinical samples using NGS.
NP quantification was achieved by processing 3D microscopy image stacks.
Advances in MRI acquisitions including diffusion-weighted imaging, post-acquisition image processing techniques, and quantification of imaging data are increasing the accuracy of lesion detection.
After processing the data from quantification of endothelial area of interest, we noticed a significant difference of CD34 in tumour (T-REA = 1.95%) versus stroma (S-REA = 0.91%).
