[Novel Pulmonary Nodule Position Detection Method Based on Multiscale Convolution].

OBJECTIVE: In order to improve the accuracy of the current pulmonary nodule location detection method based on CT images, reduce the problem of missed detection or false detection, and effectively assist imaging doctors in the diagnosis of pulmonary nodules. METHODS: Propose a novel method for detecting the location of pulmonary nodules based on multiscale convolution. First, image preprocessing methods are used to eliminate the noise and artifacts in lung CT images. Second, multiple adjacent single-frame CT images are selected to be concatenate into multi-frame images, and the feature extraction is carried out through the artificial neural network model U-Net improved by multi-scale convolution to enhanced feature extraction capability for pulmonary nodules of different sizes and shapes, so as to improve the accuracy of feature extraction of pulmonary nodules. Finally, using point detection to improve the loss function of U-Net training process, the accuracy of pulmonary nodule location detection is improved. RESULTS: The accuracy of detecting pulmonary nodules equal or larger than 3 mm and smaller than 3 mm are 98.02% and 96.94% respectively. CONCLUSIONS: This method can effectively improve the detection accuracy of pulmonary nodules on CT image sequence, and can better meet the diagnostic needs of pulmonary nodules.

[Anesthesia Depth Monitoring Based on Anesthesia Monitor with the Help of Artificial Intelligence].

OBJECTIVE: To use the low-cost anesthesia monitor for realizing anesthesia depth monitoring, effectively assist anesthesiologists in diagnosis and reduce the cost of anesthesia operation. METHODS: Propose a monitoring method of anesthesia depth based on artificial intelligence. The monitoring method is designed based on convolutional neural network (CNN) and long and short-term memory (LSTM) network. The input data of the model include electrocardiogram (ECG) and pulse wave photoplethysmography (PPG) recorded in the anesthesia monitor, as well as heart rate variability (HRV) calculated from ECG, The output of the model is in three states of anesthesia induction, anesthesia maintenance and anesthesia awakening. RESULTS: The accuracy of anesthesia depth monitoring model under transfer learning is 94.1%, which is better than all comparison methods. CONCLUSIONS: The accuracy of this study meets the needs of perioperative anesthesia depth monitoring and the study reduces the operation cost.

Relay Catalysis of Rh (II) and Cobaloxime: Stereoselective Synthesis of Spiroindanones from N-Sulfonyl-1,2,3-triazoles.

A novel relay Rh (II)/cobaloxime (III) dual catalysis strategy has been developed for the stereoselective synthesis of indolyl spiroindanones from readily accessible N-sulfonyl-1,2,3-triazoles. This binary-catalyst system enables an aza-vinyl carbene initiated Pictet-Spengler-type cyclization and sequential cobaloxime promoted intramolecular Mannich-type reaction cascade. The easily accessible reagents, high diastereoselectivity, and operational simplicity make this reaction a method of choice for the preparation of functionalized spiroindanones, which are difficult to access by other classic reactions in one step.

Total synthesis of indole alkaloid (±)-subincanadine E.

The first total synthesis of indole alkaloid (±)-subincanadine E has been accomplished. Ni(COD)2-mediated intramolecular Michael addition and zinc-mediated fragmentation reaction served as two key transformations.