ABSTRACT
Objective: Contrast-enhanced CT is an important method of preoperative diagnosis and evaluation for the malignant potential of gastric submucosal tumor (SMT). It has a high diagnostic accuracy rate in differentiating gastric gastrointestinal stromal tumor (GIST) with a diameter greater than 5 cm from gastric benign SMT. This study aimed to use deep learning algorithms to establish a diagnosis model (GISTNet) based on contrast-enhanced CT and evaluate its diagnostic value in distinguishing gastric GIST with a diameter ≤ 5 cm and other gastric SMT before surgery. Methods: A diagnostic test study was carried out. Clinicopathological data of 181 patients undergoing resection with postoperative pathological diagnosis of gastric SMT with a diameter ≤ 5 cm at Department of Gastrointestinal Surgery of Renji Hospital from September 2016 to April 2021 were retrospectively collected. After excluding 13 patients without preoperative CT or with poor CT imaging quality, a total of 168 patients were enrolled in this study, of whom, 107 were GIST while 61 were benign SMT (non-GIST), including 27 leiomyomas, 24 schwannomas, 6 heterotopic pancreas and 4 lipomas. Inclusion criteria were as follows: (1) gastric SMT was diagnosed by contrast-enhanced CT before surgery; (2) preoperative gastroscopic examination and biopsy showed no abnormal cells; (3) complete clinical and pathological data. Exclusion criteria were as follows: (1) patients received anti-tumor therapy before surgery; (2) without preoperative CT or with poor CT imaging quality due to any reason; (3) except GIST, other gastric malignant tumors were pathologically diagnosed after surgery. Based on the hold-out method, 148 patients were randomly selected as the training set and 20 patients as the test set of the GISTNet diagnosis model. After the GISTNet model was established, 5 indicators were used for evaluation in the test set, including sensitivity, specificity, positive predictive value, negative predictive value and the area under the receiver operating curve (AUC). Then GISTNet diagnosis model was compared with the GIST-risk scoring model based on traditional CT features. Besides, in order to compare the accuracy of the GISTNet diagnosis model and the imaging doctors in the diagnosis of gastric SMT imaging, 3 radiologists with 3, 9 and 19 years of work experience, respectively, blinded to clinical and pathological information, tested and judged the samples. The accuracy rate between the three doctors and the GISTNet model was compared. Results: The GISTNet model yielded an AUC of 0.900 (95% CI: 0.827-0.973) in the test set. When the threshold value was 0.345, the sensitivity specificity, positive and negative predictive values of the GISTNet diagnosis model was 100%, 67%, 75% and 100%, respectively. The accuracy rate of the GISTNet diagnosis model was better than that of the GIST-risk model and the manual readings from two radiologists with 3 years and 9 years of work experience (83% vs. 75%, 60%, 65%), and was close to the manual reading of the radiologist with 19 years of work experience (83% vs. 80%). Conclusion: The deep learning algorithm based on contrast-enhanced CT has favorable and reliable diagnostic accuracy in distinguishing gastric GIST with a diameter ≤ 5 cm and other gastric SMT before operation.
Subject(s)
Humans , Deep Learning , Diagnosis, Differential , Gastrointestinal Stromal Tumors/diagnostic imaging , Retrospective Studies , Stomach Neoplasms/diagnostic imaging , Tomography, X-Ray ComputedABSTRACT
OBJECTIVE@#To explore the effect of microRNA-101 on apoptosis of condylar cartilage cells and the specific mechanism of molecular biology.@*METHODS@#IL-1 was used to stimulate and establish the model of apoptosis of condylar cartilage cells. The expression change of miR-101 in control group was compared with that in IL-1 stimulation group by qRT-PCR. Overexpression and down-regulation models of miR-101 were established by transfecting Mimics and Inhibitor and verified by qRT-PCR. Flow cytometry was used to detect the effect of miR-101 overexpression and down-regulation on apoptosis. Target gene of miR-101 was analyzed and calculated through bioinformatics. Western blot and Luciferase report assay were used to detect whether SOX9 could become the target gene of miR-101.@*RESULTS@#qRT-PCR results showed that IL-1 stimulation could cause the increase of miR-101 expression. After the transfection of rabbit condylar cartilage cells by Mimics and Inhibitor, qRT-PCR results confirmed the significant effect of miR-101 overexpression and down-regulation. It was confirmed by flow cytometry that overexpression of miR-101 could promote the apoptosis of condylar cartilage cells, and down-regulation of miR-101 could reduce the apoptosis. It was confirmed by Western blot and Luciferase report assay that SOX9 was the target gene of miR-101, and miR-101 inhibited SOX9 expression through complementary pairing with 3'UTR of SOX9 mRNA.@*CONCLUSIONS@#miR-101 can promote the apoptosis of condylar cartilage cells through inhibiting the protein level of target gene SOX9.
ABSTRACT
Objective: To explore the effect of microRNA-101 on apoptosis of condylar cartilage cells and the specific mechanism of molecular biology. Methods: IL-1 was used to stimulate and establish the model of apoptosis of condylar cartilage cells. The expression change of miR-101 in control group was compared with that in IL-1 stimulation group by qRT-PCR. Overexpression and down-regulation models of miR-101 were established by transfecting Mimics and Inhibitor and verified by qRT-PCR. Flow cytometry was used to detect the effect of miR-101 overexpression and down-regulation on apoptosis. Target gene of miR-101 was analyzed and calculated through bioinformatics. Western blot and Luciferase report assay were used to detect whether SOX9 could become the target gene of miR-101. Results: qRT-PCR results showed that IL-1 stimulation could cause the increase of miR-101 expression. After the transfection of rabbit condylar cartilage cells by Mimics and Inhibitor, qRT-PCR results confirmed the significant effect of miR-101 overexpression and down-regulation. It was confirmed by flow cytometry that overexpression of miR-101 could promote the apoptosis of condylar cartilage cells, and down-regulation of miR-101 could reduce the apoptosis. It was confirmed by Western blot and Luciferase report assay that SOX9 was the target gene of miR-101, and miR-101 inhibited SOX9 expression through complementary pairing with 3'UTR of SOX9 mRNA. Conclusions: miR-101 can promote the apoptosis of condylar cartilage cells through inhibiting the protein level of target gene SOX9.
ABSTRACT
<p><b>OBJECTIVE</b>To measure lutein, zeaxanthin and β-carotene level in foods commonly consumed in Beijing, and compare the content difference between raw and cooked food.</p><p><b>METHODS</b>Forty-six commonly consumed foods of 8 classes were collected in Haidian district of Beijing from September to October in 2009. A high performance liquid chromatography method was used to determine the content of lutein, zeaxanthin and β-carotene in both raw and cooked samples.</p><p><b>RESULTS</b>Lutein was abundant in cucurbitaceous and solanaceous, allium and nuts, especially in Chinese chive (18 226.9 µg/100 g) and pumpkin (13 265.2 µg/100 g). Major sources of zeaxanthin included round pumpkin, green garlic shoot, corn and eggs, whose level of zeaxanthin were 444.6, 283.5, 279.7, 118.6 - 377.9 µg/100 g, respectively. Zeaxanthin level of those cooked foods changed to 483.9, 239.3, 279.1, 149.5 - 594.7 µg/100 g, respectively. The zeaxanthin level of cooked Chinese chive reached 1081.2 µg/100 g, while we did not detect any zeaxanthin in raw Chinese chive. β-carotene was present in a wide variety of vegetables and fruits. Carrot (17 234.3 µg/100 g) was a good source of β-carotene, while its level in cooked carrot was 17 013.5 µg/100 g.</p><p><b>CONCLUSION</b>Consuming the proper kinds of foods and changing the method of food processing were beneficial to increase the intake of lutein, zeaxanthin and β-carotene.</p>