Application of artificial intelligence-assisted image diagnosis software based on volume data reconstruction technique in medical imaging practice teaching.

BACKGROUND: In medical imaging courses, due to the complexity of anatomical relationships, limited number of practical course hours and instructors, how to improve the teaching quality of practical skills and self-directed learning ability has always been a challenge for higher medical education. Artificial intelligence-assisted diagnostic (AISD) software based on volume data reconstruction (VDR) technique is gradually entering radiology. It converts two-dimensional images into three-dimensional images, and AI can assist in image diagnosis. However, the application of artificial intelligence in medical education is still in its early stages. The purpose of this study is to explore the application value of AISD software based on VDR technique in medical imaging practical teaching, and to provide a basis for improving medical imaging practical teaching. METHODS: Totally 41 students majoring in clinical medicine in 2017 were enrolled as the experiment group. AISD software based on VDR was used in practical teaching of medical imaging to display 3D images and mark lesions with AISD. Then annotations were provided and diagnostic suggestions were given. Also 43 students majoring in clinical medicine from 2016 were chosen as the control group, who were taught with the conventional film and multimedia teaching methods. The exam results and evaluation scales were compared statistically between groups. RESULTS: The total skill scores of the test group were significantly higher compared with the control group (84.51 ± 3.81 vs. 80.67 ± 5.43). The scores of computed tomography (CT) diagnosis (49.93 ± 3.59 vs. 46.60 ± 4.89) and magnetic resonance (MR) diagnosis (17.41 ± 1.00 vs. 16.93 ± 1.14) of the experiment group were both significantly higher. The scores of academic self-efficacy (82.17 ± 4.67) and self-directed learning ability (235.56 ± 13.50) of the group were significantly higher compared with the control group (78.93 ± 6.29, 226.35 ± 13.90). CONCLUSIONS: Applying AISD software based on VDR to medical imaging practice teaching can enable students to timely obtain AI annotated lesion information and 3D images, which may help improve their image reading skills and enhance their academic self-efficacy and self-directed learning abilities.

CircABCB10 Promotes the Apoptosis and Inflammatory Response of 16HBE Cells by Cigarette Smoke Extract by Targeting miR-130a/PTEN Axis.

Background: Chronic obstructive pulmonary disease (COPD) has become a global public health problem due to its high mortality. So there is an urgent need to find an effective treatment. Methods: The targeting relationship among circABCB10, miR-130a and PTEN was predicted by the targetscan database (TargetScanHuman 8.0, https://www.targetscan.org/vert_80/). A total of 60 patients which were from the second affiliated hospital of Qiqihar Medical University from 2022 to 2023 were enrolled. The lung condition was detected by CT (Computed Tomography). The expression levels of circABCB10, miR-130a and PTEN in lung tissues were determined by qRT-PCR. The COPD model was established by stimulating normal and silenced 16HBE cells in circABCB10 genes with cigarette smoke extract (CSE) at different concentrations. qRT-PCR was conducted for the expression levels of circABCB10, miR-130a and PTEN, WB for the expression levels of apoptotic proteins, ELISA for the content of inflammatory factors, and CCK8 for the effect of CSE on the proliferation of cells. Results: CircABCB10 expression increased in lung tissues from patients with COPD and in 16HBE cells treated with CSE. The stimulation on cells with CSE increased the expression of inflammatory factors, while knocking down circABCB10 could reverse this response. The inflammatory response to the knockdown of circABCB10 was reversed by miR-130a inhibitor, which increased the expression of c-caspase 3. The targetscan database predicted the target factor downstream miR-130a was PTEN. Transfecting OE-PTEN reversed the inflammation of knocking down circABCB10, and increased the apoptosis and inflammation. Conclusion: CircABCB10 can cause the inflammatory response by targeting miR-130a/PTEN axis, which is a mechanism that may lead to the occurrence and development of COPD.