Objective To study the differential diagnostic value of interferon-γ inducible protein 10 (IP-10),macrophage inflammatory protein-1 α (MIP-1 α) and monocyte chemoattractant-1 (MCP-1) level in the tuberculous,malignant pleural effusion.Methods Enzyme-linked immunosorbent assay was used to detect the level of IP-10,MIP-1 α and MCP-1 in tuberculous pleural fluid (tuberculous pleural fluid group,43 cases) and malignant pleural fluid (malignant pleural fluid group,45 cases).The level of IP-10,MIP-1 α and MCP-1 and the significance were analyzed by ROC curve.Results The level of IP-10,MIP-1 α and MCP-1 were significantly higher in tuberculous pleural fluid group than those in malignant pleural fluid group,and there were significant differences(t =4.931,3.106,2.385 ; P =0.000,0.004,0.041).ROC curve analysis showed that the critical value of IP-10,MIP-1 α and MCP-1 in diagnosis of pleural effusion was respectively 1 589.73,213.50,1 452.63 ng/L.The sensitivity and specificity of IP-10,MIP-1 α and MCP-1 in pleural fluid were 68.8％,81.3％,87.5％ and 87.5％,68.8％,56.3％,respectively.Conclusion The level of IP-10,MIP-1 α and MCP-1 in tuberculous and malignant pleural fluid are significant for the early diagnosis and differential diagnosis.

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.

17q12 deletion syndrome is a rare autosomal dominant disorder affecting multiple organ systems caused by the deletion of DNA fragments approximately 1.4～1.8 Mb in band 2 of region 1,the long arm of chromosome 17,including hepatocyte nuclear factor 1B.The clinical manifestation of the disease ismaturity-onset diabetes of youth type 5,abnormalities in renalstructure or function,as well as in neurodevelopment or psychiatric systems.

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

Total mesorectal excision (TME) has become the basic principle of surgical treat-ment for middle and low rectal cancer. Some of patients with ultra-low rectal cancer require under-going intersphincteric resection (ISR). Due to the limitation of the narrow pelvis, TME and ISR put forward higher requirements for the precise separation of the anatomical level and the protection of neurological function during the operation. At present, evaluation of the difficulty of surgery for middle and low rectal cancer is mainly based on the subjective judgment of chief surgeon, and there is no unified and objective scoring system or prediction model that can classify the difficulty of surgery for middle and low rectal cancer before surgery. The authors review relevant literatures and summarize the existing studies related to pelvic measurement for predicting the difficulty of surgery for middle and low rectal cancer, in order to provide significant guidance for the selection of surgical approach for patients with middle and low rectal cancer.

Intersphincteric resection (ISR) is an advanced sphincter-preserving surgery for low rectal cancer. Accumulating evidences from clinical studies indicate that ISR can spare some pati-ents with low rectal cancer from the distress of anal amputation while ensuring oncological efficacy. However, due to the necessity of removing part or all of the internal sphincter during rectal resection and the extremely low anastomosis level, a subset of patients may experience low anterior resection syndrome (LARS) after surgery. LARS is characterized by symptoms such as anal incontinence, increased bowel frequency, urgency, incomplete evacuation, and obstructed defecation. Based on relevant literature and team practice, the authors provide an overview of the diagnosis and treat-ment progress of LARS following ISR.