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C26H26N6O5, monoclinic, P21/c(no. 14), a= 10.9393(1) Å, b= 16.8724(2) Å, c= 13.5836(2) Å, β= 102.843(1)°, V= 2444.43(5) Å3, Z= 4, Rgt(F) = 0.0318, wRref(F2) = 0.0827, T = 173 K.

Objectives: The process of measuring morphological parameters of the cervical spine through computed tomography (CT) is repetitive and time-consuming. Deep learning (DL) improves efficiency and consistency. In this study, we built a series of DL-based segmentation algorithms to automatically measure five key parameters for the evaluation of cervical spondylotic myelopathy. Subsequently, we compared the performance of our algorithm with that of physicians to assess its accuracy and clinical application value. Methods: Cervical spine CT images of 685 patients were divided into a training (n= 548) and a test set (n= 137). The training set was used to develop a VB-Net DL model, including a 3D segmentation model of multiple cervical spine subregions and a key point location model on the midsagittal slice of the cervical spine CT. The parameters measured included sagittal vertebral canal diameter (SCD), sagittal vertebral body diameter (VBD), Pavlov’s ratio, transverse vertebral canal diameter (TCD), and osseous spinal canal area (OSCA). Manual measurements were performed by a radiologist and a spinal surgeon. The model’s performance was evaluated using the Mann–Whitney Utest, Pearson correlation, mean absolute error, and Bland–Altman plots. Results: DL and manual measurements of the Pavlov’s ratio, SCD, VBD, TCD, and OSCA in the test set showed similar accuracy and consistency. The VB-Net model’s Pearson correlation coefficient exceeded 0.8 for most parameters. Conclusions: Our VB-Net-based DL approach can effectively approximate the manual measurements of cervical CT morphological parameters in humans, offing physicians an accurate and efficient auxiliary diagnostic tool.

Triple-negative breast cancer shows worse outcome compared with other subtypes of breast cancer. The discovery of dysregulated microRNAs and their roles in the progression of triple-negative breast cancer provide novel strategies for the treatment of patients with triple-negative breast cancer. In this study, we identified the significant reduction of miR-133 in triple-negative breast cancer tissues and cell lines. Ectopic overexpression of miR-133 suppressed the proliferation, colony formation, and upregulated the apoptosis of triple-negative breast cancer cells. Mechanism study revealed that the YES Proto-Oncogene 1 was a target of miR-133. miR-133 bound the 3′-untranslated region of YES Proto-Oncogene 1 and decreased the level of YES Proto-Oncogene 1 in triple-negative breast cancer cells. Consistent with miR-133 downregulation, YES1 was significantly increased in triple-negative breast cancer, which was inversely correlated with the level of miR-133. Restoration of YES Proto-Oncogene 1 attenuated the inhibitory effects of miR-133 on the proliferation and colony formation of triple-negative breast cancer cells. Consistent with the decreased expression of YES Proto-Oncogene 1, overexpression of miR-133 suppressed the phosphorylation of YAP1 in triple-negative breast cancer cells. Our results provided novel evidence for the role of miR-133/YES1 axis in the development of triple-negative breast cancer, which indicated miR-133 might be a potential therapeutic strategy for triple-negative breast cancer.