Abstract:We have previously shown that c-MYC promoter sequences can form stable i-motifs in acidic solution (pH 4.5-5.5). In terms of drug targeting, the question is whether c-MYC promoter sequence i-motifs will exist in the nucleus at neutral pH. In this work, we have investigated the stability of a mutant c-MYC i-motif in solutions containing a molecular crowding agent. The crowded nuclear environment was modeled by the addition of up to 40% w/w polyethylene glycols having molecular weights up to 12,000 g/mol. CD and DSC were used to establish the presence and stability of c-MYC i-motifs in buffer solutions over the pH range 4 to 7. We have shown that the c-MYC i-motif can exist as a stable structure at pH values as high as 6.7 in crowded solutions. Generic dielectric constant effects, e.g., a shift in the pKa of cytosine by more than 2 units (e.g., 4.8 to 7.0), or the formation of non-specific PEG/DNA complexes appear to contribute insignificantly to i-motif stabilization. Molecular crowding, largely an excluded volume effect of added PEG, having a molecular weight in excess of 1,000 g/mol, appears to be responsible for stabilizing the more compact i-motif over the random coil at higher pH values.

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BackgroundTo evaluate the feasibility of using radiomics with precontrast magnetic resonance imaging for classifying hepatocellular carcinoma (HCC) and hepatic haemangioma (HH).MethodsThis study enrolled 369 consecutive patients with 446 lesions (a total of 222 HCCs and 224 HHs). A training set was constituted by randomly selecting 80% of the samples and the remaining samples were used to test. On magnetic resonance (MR) images of HCC and HH obtained with in-phase, out-phase, T2-weighted imaging (T2WI), and diffusion-weighted imaging (DWI) sequences, we outlined the target lesions and extracted 1029 radiomics features, which were classified as first-, second-, higher-order statistics and shape features. Then, the variance threshold, select k best, and least absolute shrinkage and selection operator algorithms were explored for dimensionality reduction of the features. We used four classifiers (decision tree, random forest, K nearest neighbours, and logistic regression) to identify HCC and HH on the basis of radiomics features. Two abdominal radiologists also performed the conventional qualitative analysis for classification of HCC and HH. Diagnostic performances of radiomics and radiologists were evaluated by receiver operating characteristic (ROC) analysis.ResultsValuable radiomics features for building a radiomics signature were extracted from in-phase (n = 22), out-phase (n = 24), T2WI (n = 34) and DWI (n = 24) sequences. In comparison, the logistic regression classifier showed better predictive ability by combining four sequences. In the training set, the area under the ROC curve (AUC) was 0.86 (sensitivity: 0.76; specificity: 0.78), and in the testing set, the AUC was 0.89 (sensitivity: 0.822; specificity: 0.714). The diagnostic performance for the optimal radiomics-based combined model was significantly higher than that for the less experienced radiologist (2-years experience) (AUC = 0.702, p < 0.05), and had no statistic difference with the experienced radiologist (10-years experience) (AUC = 0.908, p>0.05).ConclusionsWe developed and validated a radiomics signature as an adjunct tool to distinguish HCC and HH by combining in-phase, out-phase, T2W, and DW MR images, which outperformed the less experienced radiologist (2-years experience), and was nearly equal to the experienced radiologist (10-years experience).

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