Objective To investigate the effects and mechanism of Yuyin Qianyang Tongluo Formula on cognitive function in Alzheimer's disease mice.Methods Transgenic negative mice were designated as the normal group,while APP/PS1 double transgenic mice were randomly divided into six groups(ni=6 per group):model group,low-dose Yuyin Qianyang Tongluo Formula group,medium-dose group,high-dose group,and positive drug group.The Yuyin Qianyang Tongluo Formula low-,medium-,high-dose groups correspondingly received intragastric administration of 0.75,1.5,and 3 g·kg-1·d-1,respectively.The positive drug group received 2 mg·kg-1·d-1 donepezil hydrochloride tablets,while the model and normal groups received equal volumes of saline.All groups were treated once daily for 8 weeks.Behavioral tests were conducted using the Morris water maze and Y-maze.Hippocampal β-amyloid(Aβ)1-42 level was measured by enzyme-linked immunosorbent assay(ELISA).Hippocampal CA1 and CA3 neurons were observed hematoxylin-eosin(HE)staining,and Nissl bodies were examined Nissl staining.Protein expression levels of Aβ transporter low-density lipoprotein receptor-related protein 1(LRP-1)and endoplasmic reticulum stress marker glucose-regulated protein 78(GRP78)were detected by Western Blot analysis.Results(1)Compared with the normal group,the model group showed significantly prolonged escape latency(P＜0.000 1),reduced platform crossings,decreased movement distance and time around the platform of Morris water maze test(P＜0.05 or P＜0.01),significantly lower spontaneous alternation percentage in Y-maze test(P＜0.05),and significantly elevated hippocampal Aβ1-42 level(P＜0.000 1).Compared with the model group,all Yuyin Qianyang Tongluo Formula dose groups and the positive drug group exhibited significantly shortened escape latency of Morris water maze test(P＜0.000 1).The high-dose Yuyin Qianyang Tongluo Formula group and positive drug group showed significantly increased platform crossings,movement distance and time around the platform of Morris water maze test(P＜0.05),significantly higher spontaneous alternation percentage in Y-maze test(P＜0.05).All Yuyin Qianyang Tongluo Formula dose groups and the positive drug group showed significantly reduced hippocampal Aβ1-42 level(P＜0.000 1).(2)Compared with the normal group,the model group exhibited disordered and deformed hippocampal neurons and Nissl bodies.Compared with the model group,all Yuyin Qianyang Tongluo Formula groups and positive drug group showed improved neuronal and Nissl body organization,with the improvement degree positively correlated with dosage.(3)Compared with the normal group,the model group showed decreased LRP1 level and increased GRP78 level,but there were no statistically significant differences(P＞0.05).Compared with the model group,the high-dose Yuyin Qianyang Tongluo Formula group and positive drug group showed increased LRP1 level,while all Yuyin Qianyang Tongluo Formula dose groups and positive drug group exhibited decreased GRP78 level,but there were no statistically significant differences(P＞0.05).Conclusion Yuyin Qianyang Tongluo Formula significantly improves cognitive and memory function in model mice in a dose-dependent manner,potentially through reducing Aβ aggregation in the brain via endoplasmic reticulum stress pathway.

OBJECTIVE: To establish a deep-learning architecture based on faster region-based convolutional neural networks (Faster R-CNN) algorithm for detection and sorting of red ginseng (Ginseng Radix et Rhizoma Rubra) with internal defects automatically on an online X-ray machine vision system. METHODS: A Faster R-CNN based classifier was trained with around 20 000 samples with mean average precision value (mAP) of 0.95. A traditional image processing method based on feedforward neural network (FNN) obtained a bad performance with the accuracy, recall and specificity of 69.0%, 68.0%, and 70.0%, respectively. Therefore, the Faster R-CNN model was saved to evaluate the model performance on the defective red ginseng online sorting system. RESULTS: An independent set of 2 000 red ginsengs were used to validate the performance of the Faster R-CNN based online sorting system in three parallel tests, achieving accuracy of 95.8%, 95.2% and 96.2%, respectively. CONCLUSION The overall results indicated that the proposed Faster R-CNN based classification model has great potential for non-destructive detection of red ginseng with internal defects.