Potential of artificial intelligence based on chest computed tomography to predict the nature of part-solid nodules.

BACKGROUND: The potential of artificial intelligence (AI) to predict the nature of part-solid nodules based on chest computed tomography (CT) is still under exploration. OBJECTIVE: To determine the potential of AI to predict the nature of part-solid nodules. METHODS: Two hundred twenty-three patients diagnosed with part-solid nodules (241) by chest CT were retrospectively collected that were divided into benign group (104) and malignant group (137). Intraclass correlation coefficient (ICC) was used to assess the agreement in predicting malignancy, and the predictive effectiveness was compared between AI and senior radiologists. The parameters measured by AI and the size of solid components measured by senior radiologists were compared between two groups. Receiver operating characteristic (ROC) curve was chosen for calculating the Youden index of each quantitative parameter, which has statistical significance between two groups. Binary logistic regression performed on the significant indicators to suggest predictors of malignancy. RESULTS: AI was in moderate agreement with senior radiologists (ICC = 0.686). The sensitivity, specificity and accuracy of two groups were close (p > 0.05). The longest diameter, volume and mean CT attenuation value and the largest diameter of solid components between benign and malignant groups were different significantly (p < 0.001). Logistic regression analysis showed that the longest diameter and mean CT attenuation value and the largest diameter of solid components were indicators for malignant part-solid nodules, the threshold of which were 9.45 mm, 425.0 HU and 3.45 mm, respectively. CONCLUSION: Potential of quantitative parameter measured by AI to predict malignant part-solid nodules can provide a certain value for the clinical management.

Breast MRI Segmentation and Ki-67 High- and Low-Expression Prediction Algorithm Based on Deep Learning.

Results: The DSC, PPV, and sensitivity of our combined model are 0.94, 0.93, and 0.94, respectively, with better segmentation performance. And we compare with the segmentation frameworks of other papers and find that our combined model can make accurate segmentation of breast tumors. Conclusion: Our method can adapt to the variability of breast tumors and segment breast tumors accurately and efficiently. In the future, it can be widely used in clinical practice, so as to help the clinic better formulate a reasonable diagnosis and treatment plan for breast cancer patients.

First evidence showing that Pepper vein yellows virus P4 protein is a movement protein.

BACKGROUND: Plant viruses move through plasmodesmata (PD) to infect new cells. To overcome the PD barrier, plant viruses have developed specific protein(s) to guide their genomic RNAs or DNAs to path through the PD. RESULTS: In the present study, we analyzed the function of Pepper vein yellows virus P4 protein. Our bioinformatic analysis using five commonly used algorithms showed that the P4 protein contains an transmembrane domain, encompassing the amino acid residue 117-138. The subcellular localization of P4 protein was found to target PD and form small punctates near walls. The P4 deletion mutant or the substitution mutant constructed by overlap PCR lost their function to produce punctates near the walls inside the fluorescent loci. The P4-YFP fusion was found to move from cell to cell in infiltrated leaves, and P4 could complement Cucumber mosaic virus movement protein deficiency mutant to move between cells. CONCLUSION: Taking together, we consider that the P4 protein is a movement protein of Pepper vein yellows virus.