Roles of sympathicus, sensory neuropeptides (SNP), metabolites of cyclooxygenase, metabolites of lipoxygenase, endothelium derived relaxing factor (EDRF), reactive oxygen (ROS) and potassium channels (PC) in the hypoxic pulmonary vasoconstriction (HPV) and hypoxic cerebral vasodilation (HCVD) were studied in intact rats, rabbits and dogs. Results were as follows: during hypoxia, the excitation of sympathicus results in a constriction of both pulmonary and cerebral vessels; SNP, EDRF and the opening of 4-AP sensitive PC caused the dilation of both of them; metabolites of lipoxygenase mediated HPV and HCVD, whereas metabolites of cyclooxygenase were their modulators; hypoxia induced blockade of the ATP sensitive PC mediated HPV, but had no effect on HCVD; reduction of O_2~+ in the lung might potentiate HPV, but had no effect on HCVD. It is suggested that the alteration of lipoxygenase metabolites, ROS and ATP sensitive PC are factors accounting for the difference in response of pulmonary and cerebral vassels to hypoxia.

Bowel sounds can reflect the movement and health status of the gastrointestinal tract.However,the traditional manual auscultation method has subjective deviation and is time-consuming and labor-intensive.In order to better assist doctors in diagnosing bowel sounds and improve the reliability and efficiency of bowel sound detection,this study proposed a deep neural network model that combines a residual neural network(ResNet),a bidirectional long short-term memory network(BiLSTM),and an attention mechanism.Firstly,a large number of labeled clinical data was collected using the self-developed multi-channel bowel sound acquisition system,and the multi-scale wavelet decomposition and reconstruction method was used to preprocess the bowel sounds.Then,log Mel spectrogram features were extracted and sent to the network for training.Finally,the performance and effectiveness of the model were evaluated and verified by 10-fold cross-validation and an ablation experiment.The experimental results showed that the precision,recall,and F1 score of the model reached 83%,76%,and 79%,respectively,and it could effectively detect bowel sound segments and locate their start and end times,performing better than previous algorithms.This algorithm can not only provide auxiliary information for doctors in clinical practice but also offer technical support for further analysis and research of bowel sounds.

Bowel sounds is an important indicator to monitor and reflect intestinal motor function, and traditional manual auscultation requires high professional knowledge and rich clinical experience of doctors. In addition, long-time auscultation is time-consuming and laborious， which may lead to misjudgment caused by subjective error. To solve the problem, firstly, the wavelet transform is used to preprocess the bowel sounds signal for noise reduction and enhancement. Secondly, three typical features of intestinal sound were extracted. According to the combination of these features, a three-stage decision was designed to carry out multi-parameter and multi-feature joint threshold detection. This algorithm realized the detection of bowel sound signal and the location of its start and end points, making it possible that the complete bowel sound signal was extracted effectively. In this study, a large number of clinical data and label of bowel sounds were collected, and a new effective evaluation method was proposed to verify the proposed method. The accuracy rate is 83.51%. Results of this study will provide systematic support and theoretical guarantee for the diagnosis of intestinal diseases and the monitoring of postoperative intestinal function recovery of patients.
Algorithms ; Auscultation ; Humans ; Intestines ; Signal Processing, Computer-Assisted ; Wavelet Analysis