@@

Objective To investigate the effects of alcohol extract of Kunmu decoction on proliferation and apoptosis of rheumatoid arthritis fibroblast-like synoviocytes (RA-FLS). Methods Synovial tissues were obtained from patients with active RA received joint replacement or arthroscopy. The surface antigen and the amount of apoptotic cells were determined by flow cytometry. The inhibitive effect was detected by MTT assay. Results The CD90+surface antigen of synoviocytes was (94.78 ± 0.98)%. The inhibitive effect on the proliferation in all treatment groups were in a time-and dose-dependent manner. The apoptosis rate was increased in a dose-dependent manner among all dosage alcohol extract groups. Conclusion Kunmu decoction might inhibit proliferation and induce apoptosis of RA-FLS.

OBJECTIVE: To study the effects of Tongbi Mixture 2, a compound traditional Chinese herbal medicine for treating rheumatoid arthritis (RA), on immunoregulation of T lymphocytes in rats with collagen-induced arthritis (CIA). METHODS: Forty Wistar rats were randomly divided into normal control group, untreated group, Tongbi Mixture 2-treated group, methotrexate (MTX)-treated group and Tripterygium wilfordii polyglycoside (TWP)-treated group. Except for the rats of the normal control group (injection with normal saline), rats of the other four groups were subcutaneouly multipoint-injected with collagen protein II to induce CIA. The rats were treated with normal saline, Tongbi Mixture 2, MTX tablets and TWP tablets respectively for 36 days. The expressions of CD28 and CD152 were detected by flow cytometry, while the content of tumor necrosis factor-alpha (TNF-alpha) was analyzed by enzyme-linked immunosorbent assay. RESULTS: The expression of CD28 among the three drug treated groups had no statistical difference (P>0.05), while significantly higher than that of the normal control group (P<0.01) and lower than that of the untreated group (P<0.01). The expression of CD152 in the Tongbi Mixture 2 treated-group was lower than those of the MTX- and TWP-treated groups (P<0.05, P<0.01), but had no statistical difference as compared with the normal control group (P>0.05). There were no statistical differences in content of TNF-alpha between the drug treated groups and the normal control group (P>0.05), but the content of TNF-alpha of the drug treated groups was lower than that of the untreated group (P<0.05 or P<0.01). CONCLUSION: Tongbi Mixture 2 can inhibit T lymphocytes through down-regulating the expressions of CD28 and CD152 and the content of TNF-alpha, which may be the major mechanisms in treating RA.

Circadian rhythm is mainly regulated by circadian clock genes, which is the result of biological evolution and plays an important role in maintaining the normal structure and function of organisms. When the circadian rhythm is disturbed or out of balance, it will have adverse health consequences. In addition to studies on diseases of nervous system, endocrine system or cardiovascular system, it has been found that circadian rhythm disorder mediated by circadian clock gene also plays a key regulatory role in the occurrence and development of hepatocellular carcinoma, which is a potential diagnostic marker and therapeutic target. In this paper, the research on the role of circadian clock gene in hepatocellular carcinoma in recent years is reviewed, and the research progresis and existing problems of targeting circadian clock gene in the treatment of hepatocellular carcinoma are discussed.

Objective To investigate the value of automatic segmentation of carotid vessel wall in multicontrast MR images using U?Net neural network. Methods Patients were retrospectively collected from 2012 to 2015 in Carotid Atherosclerosis Risk Assessment (CARE II) study. All patients who recently suffered ischemic stroke and/or transient ischemic attack underwent identical, state?of?the?art multicontrast MRI technique. A total of 17 568 carotid vessel wall MR images from 658 subjects were included in this study after inclusion criteria and exclusion criteria. All MR images were analyzed using customized analysis platform (CASCADE). Randomly, 10 592 images were assigned into training dataset, 3 488 images were assigned into validating dataset and 3 488 images were assigned into test dataset according to a ratio of 6∶2∶2. Data augmentation was performed to avoid over fitting and improve the ability of model generalization. The fine?tuned U?Net model was utilized in the segmentation of carotid vessel wall in multicontrast MR images. The U?Net model was trained in the training dataset and validated in the validating dataset. To evaluate the accuracy of carotid vessel wall segmentation, the sensitivity, specificity and Dice coefficient were used in the testing dataset. In addition, the interclass correlation and the Bland?Altman analysis of max wall thickness and wall area were obtained to demonstrate the agreement of the U?Net segmentation and the manual segmentation. Results The sensitivity, specificity and Dice coefficient of the fine?tuned U?Net model achieved 0.878,0.986 and 0.858 in the test dataset, respectively. The interclass correlation (95% confidence interval) was 0.921 (0.915-0.925) for max wall thickness and 0.929 (0.924-0.933) for wall area. In the Bland?Altman analysis, the difference of max wall thickness was (0.037±0.316) mm and the difference of wall area was (1.182±4.953) mm2. The substantial agreement was observed between U?Net segmentation method and manual segmentation method. Conclusion Automatic segmentation of carotid vessel wall in multicontrast MR images can be achieved using fine?tuned U?Net neural network, which is trained and tested in the large scale dataset labeled by professional radiologists.

Objective: To investigate the value of automatic segmentation of carotid vessel wall in multicontrast MR images using U-Net neural network. Methods: Patients were retrospectively collected from 2012 to 2015 in Carotid Atherosclerosis Risk Assessment (CARE II) study. All patients who recently suffered ischemic stroke and/or transient ischemic attack underwent identical, state-of-the-art multicontrast MRI technique. A total of 17 568 carotid vessel wall MR images from 658 subjects were included in this study after inclusion criteria and exclusion criteria. All MR images were analyzed using customized analysis platform (CASCADE). Randomly, 10 592 images were assigned into training dataset, 3 488 images were assigned into validating dataset and 3 488 images were assigned into test dataset according to a ratio of 6∶2∶2. Data augmentation was performed to avoid over fitting and improve the ability of model generalization. The fine-tuned U-Net model was utilized in the segmentation of carotid vessel wall in multicontrast MR images. The U-Net model was trained in the training dataset and validated in the validating dataset. To evaluate the accuracy of carotid vessel wall segmentation, the sensitivity, specificity and Dice coefficient were used in the testing dataset. In addition, the interclass correlation and the Bland-Altman analysis of max wall thickness and wall area were obtained to demonstrate the agreement of the U-Net segmentation and the manual segmentation. Results: The sensitivity, specificity and Dice coefficient of the fine-tuned U-Net model achieved 0.878,0.986 and 0.858 in the test dataset, respectively. The interclass correlation (95% confidence interval) was 0.921 (0.915-0.925) for max wall thickness and 0.929 (0.924-0.933) for wall area. In the Bland-Altman analysis, the difference of max wall thickness was (0.037±0.316) mm and the difference of wall area was (1.182±4.953) mm². The substantial agreement was observed between U-Net segmentation method and manual segmentation method. Conclusion Automatic segmentation of carotid vessel wall in multicontrast MR images can be achieved using fine-tuned U-Net neural network, which is trained and tested in the large scale dataset labeled by professional radiologists.

Multiple primary lung cancer (MPLC) refers to lung cancer in which two or more primary lesions occurred simultaneously or successively in different parts of the same patient's lungs. The diagnosis interval is 6 months. MPLC is divided into synchronous MPLC (sMPLC) and metachronous MPLC (mMPLC). sMPLC and intrapulmonary metastasis (IM) are different in treatment strategies and prognosis. However, there are many controversies about the distinction between the two in clinical practice. This article summarizes the current main methods of diagnosing MPLC, and focuses on the latest research progress in distinguishing MPLC from IM. It aims to provide a theoretical basis for accurate diagnosis and treatment of patients with multifocal lung cancer.
.