Aged ; Barium Sulfate ; adverse effects ; Colorectal Neoplasms ; diagnosis ; physiopathology ; Extravasation of Diagnostic and Therapeutic Materials ; complications ; Female ; Humans ; Male ; Peritonitis ; etiology ; physiopathology ; Retrospective Studies

Objective There is little population-based data on the prevalence and the environmental or genetic determinants of left ventricular hypertrophy (LVH) in China. The purpose of this paper is to study LVH in relation to systolic blood pressure and the angiotensin converting enzyme (ACE) insertion/deletion(I/D) polymorphism in Chinese. Methods We recorded 12-lead ECG (CardioSoft, v4.2) in 1365 residents in the Jingning County, Zhejiang Province, China. LVH was defined according to the gender-specific Sokolow-Lyon and Comell product ECG criteria. Results Regardless of whether the Sokolow-Lyon or Comell product ECG criteria was used, the prevalence of LVH (20.7% and 4.8%, respectively) significantly (P<0.0001) increased with male gender (odds ratio [OR] 2.33 and 7.15) and systolic blood pressure (per 10 mm Hg increase, OR 1.46 and 1.33). If the Sokolow-Lyon criteria was used, the prevalence of LVH was also influenced by alcohol intake (OR 1.44, P=0.03) and body mass index (OR 0.83, P=0.0005). The association between the Sokolow-Lyon voltage amplitude and the ACE I/D polymorphism was dependent on antihypertensive therapy (P=0.01). In 1262 untreated subjects, but not 103 patients on antihypertensive medication, the ACE DD compared with Ⅱ subjects had significantly higher Sokolow-Lyon voltage amplitudes (29.8±0.6 vs. 28.0±20.5 mV, P=0.02) and higher risk of LVH (OR 1.74, 95% CI: 1. 12-2.69, P=0.01). Conclusion LVH is prevalent in Chinese, and is associated with systolic blood pressure and the ACE D allele. The genetic association might be modulated by antihypertensive therapy.

Most of the existing near-infrared noninvasive blood glucose detection models focus on the relationship between near-infrared absorbance and blood glucose concentration, but do not consider the impact of human physiological state on blood glucose concentration. In order to improve the performance of prediction model, particle swarm optimization (PSO) algorithm was used to train the structure paramters of back propagation (BP) neural network. Moreover, systolic blood pressure, pulse rate, body temperature and 1 550 nm absorbance were introduced as input variables of blood glucose concentration prediction model, and BP neural network was used as prediction model. In order to solve the problem that traditional BP neural network is easy to fall into local optimization, a hybrid model based on PSO-BP was introduced in this paper. The results showed that the prediction effect of PSO-BP model was better than that of traditional BP neural network. The prediction root mean square error and correlation coefficient of ten-fold cross-validation were 0.95 mmol/L and 0.74, respectively. The Clarke error grid analysis results showed that the proportion of model prediction results falling into region A was 84.39%, and the proportion falling into region B was 15.61%, which met the clinical requirements. The model can quickly measure the blood glucose concentration of the subject, and has relatively high accuracy.
Algorithms ; Blood Glucose ; Humans ; Neural Networks, Computer

Objective    To systematically evaluate the risk factors for postoperative pulmonary infection in patients with esophageal cancer. Methods    CNKI, Wangfang Data, VIP, CBM, PubMed, EMbase, The Cochrane Library were searched from inception to January 2021 to collect case-control studies, cohort studies and cross-sectional studies about risk factors for postoperative pulmonary infection in patients with esophageal cancer. Two researchers independently conducted literature screening, data extraction and quality assessment. RevMan 5.3 software and Stata 15.0 software were used for meta-analysis. Results    A total of 20 articles were included, covering 5 409 patients of esophageal cancer. The quality score of included studies was 6-8 points. Meta-analysis results showed that age (MD=1.99, 95%CI 0.10 to 3.88, P=0.04), age≥60 years (OR=2.68, 95%CI 1.46 to 4.91, P=0.001), smoking history (OR=2.41, 95%CI 1.77 to 3.28, P<0.001), diabetes (OR=2.30, 95%CI 1.90 to 2.77, P<0.001), chronic obstructive pulmonary disease (OR=3.69, 95%CI 2.09 to 6.52, P<0.001), pulmonary disease (OR=2.22, 95%CI 1.16 to 4.26, P=0.02), thoracotomy (OR=1.77, 95%CI 1.32 to 2.37, P<0.001), operation time (MD=14.08, 95%CI 9.64 to 18.52, P<0.001), operation time>4 h (OR=3.09, 95%CI 1.46 to 6.55, P=0.003), single lung ventilation (OR=3.46, 95%CI 1.61 to 7.44, P=0.001), recurrent laryngeal nerve injury (OR=5.66, 95%CI 1.63 to 19.71, P=0.006), and no use of patient-controlled epidural analgesia (PCEA) (OR=2.81, 95%CI 1.71 to 4.61, P<0.001) were risk factors for postoperative pulmonary infection in patients with esophageal cancer. Conclusion    The existing evidence shows that age, age≥60 years, smoking history, diabetes, chronic obstructive pulmonary disease, pulmonary disease, thoracotomy, operation time, operation time>4 h, single lung ventilation, recurrent laryngeal nerve injury, and no use of PCEA are risk factors for postoperative pulmonary infection in patients with esophageal cancer. Due to the limitation of the quantity and quality of included literature, the conclusion of this study still needs to be confirmed by more high-quality studies.

At the 4th Guangzhou International Symposium on Oncology, Rui-Hua Xu, Chao-Nan Qian, and Wei Zhang--the chairmen and editors of the Chinese Journal of Cancer--announced and presented awards to 14 authors in recognition of their outstanding contributions to the journal.
Awards and Prizes ; Biomedical Research ; standards ; Congresses as Topic ; Humans ; Medical Oncology ; standards ; Periodicals as Topic

As an important basis for lesion determination and diagnosis, medical image segmentation has become one of the most important and hot research fields in the biomedical field, among which medical image segmentation algorithms based on full convolutional neural network and U-Net neural network have attracted more and more attention by researchers. At present, there are few reports on the application of medical image segmentation algorithms in the diagnosis of rectal cancer, and the accuracy of the segmentation results of rectal cancer is not high. In this paper, a convolutional network model of encoding and decoding combined with image clipping and pre-processing is proposed. On the basis of U-Net, this model replaced the traditional convolution block with the residual block, which effectively avoided the problem of gradient disappearance. In addition, the image enlargement method is also used to improve the generalization ability of the model. The test results on the data set provided by the "Teddy Cup" Data Mining Challenge showed that the residual block-based improved U-Net model proposed in this paper, combined with image clipping and preprocessing, could greatly improve the segmentation accuracy of rectal cancer, and the Dice coefficient obtained reached 0.97 on the verification set.
Algorithms ; Delayed Emergence from Anesthesia ; Humans ; Image Processing, Computer-Assisted ; Rectal Neoplasms/diagnostic imaging* ; Tomography, X-Ray Computed

Sleep staging is the basis for solving sleep problems. There's an upper limit for the classification accuracy of sleep staging models based on single-channel electroencephalogram (EEG) data and features. To address this problem, this paper proposed an automatic sleep staging model that mixes deep convolutional neural network (DCNN) and bi-directional long short-term memory network (BiLSTM). The model used DCNN to automatically learn the time-frequency domain features of EEG signals, and used BiLSTM to extract the temporal features between the data, fully exploiting the feature information contained in the data to improve the accuracy of automatic sleep staging. At the same time, noise reduction techniques and adaptive synthetic sampling were used to reduce the impact of signal noise and unbalanced data sets on model performance. In this paper, experiments were conducted using the Sleep-European Data Format Database Expanded and the Shanghai Mental Health Center Sleep Database, and achieved an overall accuracy rate of 86.9% and 88.9% respectively. When compared with the basic network model, all the experimental results outperformed the basic network, further demonstrating the validity of this paper's model, which can provide a reference for the construction of a home sleep monitoring system based on single-channel EEG signals.
China ; Sleep Stages ; Sleep ; Electroencephalography ; Databases, Factual

Polysomnography (PSG) monitoring is an important method for clinical diagnosis of diseases such as insomnia, apnea and so on. In order to solve the problem of time-consuming and energy-consuming sleep stage staging of sleep disorder patients using manual frame-by-frame visual judgment PSG, this study proposed a deep learning algorithm model combining convolutional neural networks (CNN) and bidirectional gate recurrent neural networks (Bi GRU). A dynamic sparse self-attention mechanism was designed to solve the problem that gated recurrent neural networks (GRU) is difficult to obtain accurate vector representation of long-distance information. This study collected 143 overnight PSG data of patients from Shanghai Mental Health Center with sleep disorders, which were combined with 153 overnight PSG data of patients from the open-source dataset, and selected 9 electrophysiological channel signals including 6 electroencephalogram (EEG) signal channels, 2 electrooculogram (EOG) signal channels and a single mandibular electromyogram (EMG) signal channel. These data were used for model training, testing and evaluation. After cross validation, the accuracy was (84.0±2.0)%, and Cohen's kappa value was 0.77±0.50. It showed better performance than the Cohen's kappa value of physician score of 0.75±0.11. The experimental results show that the algorithm model in this paper has a high staging effect in different populations and is widely applicable. It is of great significance to assist clinicians in rapid and large-scale PSG sleep automatic staging.
Humans ; Polysomnography ; China ; Sleep Stages ; Sleep ; Algorithms

As the most efficient perception system in nature, the perception mechanism of the insect (such as honeybee) antennae is the key to imitating the high-performance sensor technology. An automated experimental device suitable for collecting electrical signals (including antenna reaction time information) of antennae was developed, in response to the problems of the non-standardized experimental process, interference of manual operation, and low efficiency in the study of antenna perception mechanism. Firstly, aiming at the automatic identification and location of insect heads in experiments, the image templates of insect head contour features were established. Insect heads were template-matched based on the Hausdorff method. Then, for the angle deviation of the insect heads relative to the standard detection position, a method that calculates the angle of the insect head mid-axis based on the minimum external rectangle of the long axis was proposed. Eventually, the electrical signals generated by the antennae in contact with the reagents were collected by the electrical signal acquisition device. Honeybees were used as the research object in this study. The experimental results showed that the accuracy of template matching could reach 95.3% to locate the bee head quickly, and the deviation angle of the bee head was less than 1°. The distance between antennae and experimental reagents could meet the requirements of antennae perception experiments. The parameters, such as the contact reaction time of honeybee antennae to sucrose solution, were consistent with the results of the manual experiment. The system collects effectively antenna contact signals in an undisturbed state and realizes the standardization of experiments on antenna perception mechanisms, which provides an experimental method and device for studying and analyzing the reaction time of the antenna involved in biological antenna perception mechanisms.
Animals ; Arthropod Antennae ; Bees

Objective    To investigate the clinical efficacy of minimally invasive Ivor-Lewis esophagectomy (MIILE) with reverse-puncture anastomosis. Methods    Clinical data of the patients with lower esophageal carcinoma who underwent MIILE with reverse-puncture anastomosis in our department from May 2015 to December 2020 were collected. Modified MIILE consisted of several key steps: (1) pylorus fully dissociated; (2) making gastric tube under laparoscope; (3) dissection of esophagus and thoracic lymph nodes under artificial pneumothorax with single-lumen endotracheal tube intubation in semi-prone position; (4) left lung ventilation with bronchial blocker; (5) intrathoracic anastomosis with reverse-puncture anastomosis technique. Results     Finally 248 patients were collected, including 206 males and 42 females, with a mean age of 63.3±7.4 years. All 248 patients underwent MIILE with reverse-puncture anastomosis successfully. The mean operation time was 176±35 min and estimated blood loss was 110±70 mL. The mean number of lymph nodes harvested from each patient was 24±8. The rate of lymph node metastasis was 43.1% (107/248). The pulmonary complication rate was 13.7% (34/248), including 6 patients of acute respiratory distress syndrome. Among the 6 patients, 2 patients needed endotracheal intubation-assisted respiration. Postoperative hemorrhage was observed in 5 patients and 2 of them needed hemostasis under thoracoscopy. Thoracoscopic thoracic duct ligation was performed in 1 patient due to the type Ⅲ chylothorax. TypeⅡ anastomotic leakage was found in 3 patients and 1 of them died of acute respiratory distress syndrome. One patient of delayed broncho-gastric fistula was cured after secondary operation. Ten patients with type Ⅰ recurrent laryngeal nerve injury were cured after conservative treatment. All patients were followe up for at least 16 months. The median follow-up time was 44 months. The 3-year survival rate was 71.8%, and the 5-year survival rate was 57.8%. Conclusion    The optimized MIILE with reverse-puncture anastomosis for the treatment of lower esophageal cancer is safe and feasible, and the long-term survival is satisfactory.