Prediction efficacy of feature classification of solitary pulmonary nodules based on CT radiomics.

OBJECTIVE: To investigate the relationship between CT radiomic features, pathological classification of pulmonary nodules, and evaluate the prediction effect of different stratified progressive radiomic models on the pathological classification of pulmonary nodules. METHODS: Altogether, 189 patients pathologically confirmed with pulmonary nodules from July 2017 to August 2019 who had complete data were enrolled, including 71 patients with benign nodules, 51 with malignant non-invasive nodules, and 67 with invasive nodules. Three CT radiomic models were established respectively. Model 1 classified benign and malignant nodules (including malignant non-invasive and invasive nodules). Model 2 classified malignant non-invasive and invasive nodules. Model 3 classified benign, malignant non-invasive, and invasive nodules. High-throughput feature collection was carried out for all delineated regions of interest (ROIs), and the best models were established by screening features and classifiers using intelligent methods. ROC curves and areas under the curve (AUCs) were used to evaluate the prediction efficacy of the models by calculating the sensitivity, specificity, accuracies, positive predictive values, and negative predictive values. RESULTS: Through Models 1, 2, and 3, we screened out 20, 2, and 20 radiomic features, respectively, and plotted the ROC curves. In the test group, the AUC values were 0.85, 0.89, and 0.84, respectively; the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were 79.66 %, 70.42 %, 84.59 %, and 81.74 % and 67.57％ for Model 1, 88.06 %, 74.51 %, 82.2 %, 81.94 %, and 82.61 % for Model 2, and 71.34 %, 85.05 %, 70.37 %, 83.2 %, and 76.3 % for Model 3. CONCLUSION: The radiomic feature models based on CT images could well reflect the differences between benign nodules, malignant non-invasive nodules, and invasive nodules, and assist in their classification.

Bufalin inhibits PDGF-BB-induced mesangial cell proliferation via mediating gap junctional intercellular communication / 中国当代儿科杂志

<p><b>OBJECTIVE</b>To study the role and possible mechanisms of gap junctional intercellular communication (GJIC) involved in mesangial cell (MC) proliferation which could be inhibited by bufalin.</p><p><b>METHODS</b>Rat mesangial cells were cultured in vitro. The effect of bufalin on platelet-derived growth factor-BB (PDGF-BB)-induced MC proliferation was evaluated by MTT assay. The function of GJIC was detected by Lucifer Yellow scrape loading and dye transfer (SLDT). mRNA levels of Cx43, Cx45 and Cx40 were measured by RT-PCR. Intracellular calcium concentrations ([Ca(2+)]i) were examined in laser scanning confocal microscopy after loading by Fura-3/AM.</p><p><b>RESULTS</b>MTT indicated that bufalin could inhibited PDGF-BB-induced MC proliferation (P<0.01). Compared with the hormal control group, PDGF-BB inhibited GJIC function, increased the expression of Cx45 and Cx40 (P<0.01) without altering the Cx43 (P>0.05) in gene level and also increased [Ca(2+)]i. However, bufalin treatment enhanced GJIC function, decreased Cx45 mRNA and Cx40 mRNA expression (P<0.01), and reduced [Ca(2+)]i (P<0.01).</p><p><b>CONCLUSIONS</b>Bufalin inhibits PDGF-BB-induced MC proliferation, and its possible mechanisms may be related to regulation of Cx45 and Cx40 expression in the gene level, reduction of [Ca(2+)]i and enhancement of GJIC function.</p>