Pioglitazone Ameliorates Lipid Metabolic Disorder in KKAy Mice

Pioglitazone (pio) has been used as an effective hypoglycemic drug in medicine, however, the effects and mechanisms of pio on lipid metabolic disorder are still largely unknown. To explore the effects of pio on serum and liver lipid level and antioxidant ability of mice with lipid metabolic disorder, KKAy mice were treated with piofor 12 weeks and their lipid and antioxidant indices were compared to those of KKAy mice without pio treatment. C57BL/6J mice were used as control animals. The results show that pio treatment reduces serum and liver TG, elevates serum HDL-C level, increases serum and liver SOD activity, attenuates serum MDA content, ameliorates liver steatosis, induces liver PPARγexpression and enhances AMPKα phosphorylation level. In conclusion, the results indicate that pio could regulate blood lipid level, reduce liver lipid deposition and enhance antioxidant capacity of mice with lipid metabolic disorder, which is possibly through increasing AMPKα phosphorylation

Graph of survival path computed as demonstration.

The graph was built using ggtree based on tree-structure data.</p

Development and validation of an endoscopic images-based deep learning model for detection with nasopharyngeal malignancies

Abstract Background Due to the occult anatomic location of the nasopharynx and frequent presence of adenoid hyperplasia, the positive rate for malignancy identification during biopsy is low, thus leading to delayed or missed diagnosis for nasopharyngeal malignancies upon initial attempt. Here, we aimed to develop an artificial intelligence tool to detect nasopharyngeal malignancies under endoscopic examination based on deep learning. Methods An endoscopic images-based nasopharyngeal malignancy detection model (eNPM-DM) consisting of a fully convolutional network based on the inception architecture was developed and fine-tuned using separate training and validation sets for both classification and segmentation. Briefly, a total of 28,966 qualified images were collected. Among these images, 27,536 biopsy-proven images from 7951 individuals obtained from January 1st, 2008, to December 31st, 2016, were split into the training, validation and test sets at a ratio of 7:1:2 using simple randomization. Additionally, 1430 images obtained from January 1st, 2017, to March 31st, 2017, were used as a prospective test set to compare the performance of the established model against oncologist evaluation. The dice similarity coefficient (DSC) was used to evaluate the efficiency of eNPM-DM in automatic segmentation of malignant area from the background of nasopharyngeal endoscopic images, by comparing automatic segmentation with manual segmentation performed by the experts. Results All images were histopathologically confirmed, and included 5713 (19.7%) normal control, 19,107 (66.0%) nasopharyngeal carcinoma (NPC), 335 (1.2%) NPC and 3811 (13.2%) benign diseases. The eNPM-DM attained an overall accuracy of 88.7% (95% confidence interval (CI) 87.8%–89.5%) in detecting malignancies in the test set. In the prospective comparison phase, eNPM-DM outperformed the experts: the overall accuracy was 88.0% (95% CI 86.1%–89.6%) vs. 80.5% (95% CI 77.0%–84.0%). The eNPM-DM required less time (40 s vs. 110.0 ± 5.8 min) and exhibited encouraging performance in automatic segmentation of nasopharyngeal malignant area from the background, with an average DSC of 0.78 ± 0.24 and 0.75 ± 0.26 in the test and prospective test sets, respectively. Conclusions The eNPM-DM outperformed oncologist evaluation in diagnostic classification of nasopharyngeal mass into benign versus malignant, and realized automatic segmentation of malignant area from the background of nasopharyngeal endoscopic images