Objective To investigate the research progress of drug-loaded antibacterial coating of orthopedic metal implants in recent years. Methods The recent literature on the drug-loaded antibacterial coating of orthopedic metal implants were reviewed. The research status, classification, and development trend of drug-loaded antibacterial coating were summarized. Results The drug-loaded antibacterial coating of orthopedic metal implants can be divided into passive release type and active release type according to the mode of drug release. Passive drug release coating can release the drug continuously regardless of whether the presence of bacteria around the implants. Active drug release coating do not release the drug unless the presence of bacteria around the implants. Conclusion The sustained and stable release of drugs is a key problem to be solved in various antibacterial coatings research. The intelligent antibacterial coating which release antibiotics only in the presence of bacteria is the future direction of development.
ObjectiveTo discuss the clinical characteristics and the management of major complications after thoracic surgery.MethodsRetrospective research was conducted on 15 213 patients who underwent thoracic surgery from January 2008 to September 2018 in our hospital. Thirty-six (0.24%) patients died of postoperative complications. Based on whether major complications such as severe pulmonary pneumonia and other 13 complications were presented postoperatively, the patients were divided into a complication group (n=389, 294 males and 95 females, aged 61.93±10.23 years) and a non-complication group (n=14 785, 8 636 males and 6 149 females, aged 55.27±13.21 years) after exclusion of unqualified patients. The age, gender distribution, diagnosis, surgical approach, postoperative hospital stay, in-hospital costs and other clinical data were analyzed. And the treatment and outcomes of the complications were summarized.ResultsThe age, proportion of male, malignancy and esophageal diseases, postoperative hospital stay and in-hospital costs in the complication group were significantly more or higher than those in the non-complication group (P<0.05). The top three causes of death among the 36 deaths were pulmonary embolism (PE, 25.00%), severe pulmonary pneumonia (16.67%) and acute respiratory failure (16.67%), respectively. The top five complications among the severe complication group were pulmonary pneumonia (24.73%), pleural space (19.83%), anastomotic leak (17.48%), pulmonary atelectasis (11.51%) and PE (6.18%).ConclusionThoracic surgeons should recognize patients with high risk of severe complications preoperatively based on clinical characteristics and perform multi-disciplinary treatment for severe complications.
Macaque is a common animal model in drug safety assessment. Its behavior reflects its health condition before and after drug administration, which can effectively reveal the side effects of drugs. At present, researchers usually rely on artificial methods to observe the behavior of macaque, which cannot achieve uninterrupted 24-hour monitoring. Therefore, it is urgent to develop a system to realize 24-hour observation and recognition of macaque behavior. In order to solve this problem, this paper constructs a video dataset containing nine kinds of macaque behaviors (MBVD-9), and proposes a network called Transformer-augmented SlowFast for macaque behavior recognition (TAS-MBR) based on this dataset. Specifically, the TAS-MBR network converts the red, green and blue (RGB) color mode frame input by its fast branches into residual frames on the basis of SlowFast network and introduces the Transformer module after the convolution operation to obtain sports information more effectively. The results show that the average classification accuracy of TAS-MBR network for macaque behavior is 94.53%, which is significantly improved compared with the original SlowFast network, proving the effectiveness and superiority of the proposed method in macaque behavior recognition. This work provides a new idea for the continuous observation and recognition of the behavior of macaque, and lays the technical foundation for the calculation of monkey behaviors before and after medication in drug safety evaluation.
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.
ObjectiveTo evaluate the possible role of the expression of insulin-like growth factor-1 receptor (IGF-1R) in determining rectal cancer radiosensitivity. MethodsThe paired preradiation biopsy specimens and postoperative specimens were obtained from 87 patients with rectal cancer in the department of digestive tumor surgery, Jiangsu Province Hospital of Traditional Chinese Medicine, Affiliated Hospital of Nanjing University of Traditional Chinese Medicine from January 2009 to December 2010. The IGF-1R expression was examined by immunohistochemistry (IHC) and reverse transcription-polymerase chain reaction (RT-PCR). The tumor radiosensitivity was defined according to Rectal Cancer Regression Grade, then the relation between the IGF-1R expression and tumor radiosensitivity was evaluated. ResultsCompared with the preradiation biopsy specimens, IGF-1R expression significantly increased in the paired postoperative specimens of the residual cancer cells (Plt;0.001). The IHC result demonstrated IGF-1R overexpression was significantly associated with a poor response to radiotherapy (rs=0.401, Plt;0.001); RT-PCR detection of IGF-1R expression on preradiation biopsy specimens also showed that IGF-1R mRNA negative patients had a higher radiation sensitivity (rs=0.497, Plt;0.001). ConclusionDetection of IGF-1R expression may predict radiosensitivity of preoperative irradiation for rectal cancer.