Cell freeze-drying can be divided into the freezing and drying processes. Mechanical damage caused by ice crystals and damage from solute during freezing shall not be ignored and lyoprotectants are commonly used to reduce those damages on cells. In order to study the mechanism of lyoprotectants to protect cells and determine an optimal lyoprotectant formula, the thermophysical properties and percentage of unfrozen water of different lyoprotectants in freezing were investigated with differential scanning calorimeter (DSC). The survival rate indicated by trypan blue exclusion test and cell-attachment rate after 24 h using different lyoprotectants to freeze hepatoma Hep-G2 cells were measured after cell cryopreservation. The results show that 40% (W/V) PVP + 10% (V/V) glycerol + 15% (V/V) fetal bovine serum + 20% (W/V) trehalose formula of lyoprotectant demonstrate the best effect in protecting cells during freezing, for cell-attachment rate after 24 h is 44.56% ± 2.73%. In conclusion, the formula of lyoprotectant mentioned above can effectively protect cells.
Objective To systematically review the rate of delayed consultation among older pulmonary tuberculosis patients in China. Methods Databases including Web of Science, PubMed, The Cochrane Library, CBM, CNKI, VIP, and WanFang Data were electronically searched to collect cross-sectional studies on the incidence of delayed consultation in older patients with tuberculosis in China from January 2000 to August 2021. Two reviewers independently screened literature, extracted data, and assessed the risk of bias of the included studies. Meta-analysis was then performed by Stata 15.0 software. Results In total, 76 cross-sectional studies with 461 896 cases involving 321 411 elderly delayed consultation tuberculosis patients were included. The results of meta-analysis showed that the rate of delayed consultation was 55.1% (95%CI 52.0% to 58.1%) in older Chinese adults with tuberculosis. The results of the subgroup analysis showed that the delayed consultation rate of male tuberculosis patients was 57.1% and that in female tuberculosis patients was 60.3%. The delayed consultation rates of patients from the eastern, central, western, and northeastern regions were 54.1%, 58.0%, 56.0%, and 53.3%, respectively, and those of patients aged 60 to 69, 70 to 79, and 80 years or older were 73.1%, 76.8%, and 78.1%, respectively. The delayed consultation rates of tuberculosis patients with illiteracy, primary school education, junior high school education, and above were 50.0%, 56.0%, and 53.4%, respectively. The delayed consultation rates of the patients in the papers published between 2000 and 2005, 2006-2010, 2011-2015, and 2016-2021 were 39.3%, 53.3%, 58.3%, and 54.4%, respectively. Among the different detection methods, the delayed consultation rates of tuberculosis patients due to symptoms or recommendations, referrals, follow-ups, and other detection methods were 72.9%, 69.0%, 73.4%, and 57.2%, respectively. Regarding treatment classification, the delayed consultation rates of initial treatment and the retreatment of pulmonary tuberculosis were 72.3% and 75.2%, respectively. The delayed consultation rates of pulmonary tuberculosis patients with negative and positive etiological examinations were 73.9% and 65.2%, respectively. The delayed consultation rates of farmers and non-farmers with pulmonary tuberculosis were 74.3% and 71.8%, respectively. Conclusion The incidence of delayed consultation among older tuberculosis patients in China remains high and shows a fluctuating upwards trend. Additionally, there are substantial differences in the rates of delayed consultation by gender, age, geographical location, educational level, discovery method, occupation, and so on.
Objective Using the evidence-based management to manage the flexible endoscope based on the data collected by information means, to reduce the rate of serious faults and control maintenance costs. Methods From January 2017 to December 2018, we collected and analyzed the flexible endoscope data of the use, leak detection, washing and disinfection, and maintenance between 2015 and 2018 from the Gastroenterology Department of our hospital. Three main causes of flexible endoscope faults were found: delayed leak detection, irregular operation, and physical/chemical wastage. Management schemes (i.e., leak detection supervision, fault tracing, and reliability maintenance) were enacted according to these reasons. These schemes were improved continuously in the implementation. Finally, we calculated the changes of the fault rate of each grade and the maintenance cost. Results By two years management practice, compared with those from 2015 to 2016, the annual rates of grade A and grade C faults of flexible endoscope from 2017 to 2018 decreased by 10.3% and 16.7% respectively, and the annual average maintenance cost fell by 53.2%. Conclusions The maintenance costs of flexible endoscope could be effectively controlled by enacting and implementing a series of targeted management schemes based on the data from the root causes of faults applying the evidence-based management. Evidence-based management based on data has a broad application prospect in the management of medical equipment faults.
Objective To develop a neural network architecture based on deep learning to assist knee CT images automatic segmentation, and validate its accuracy. Methods A knee CT scans database was established, and the bony structure was manually annotated. A deep learning neural network architecture was developed independently, and the labeled database was used to train and test the neural network. Metrics of Dice coefficient, average surface distance (ASD), and Hausdorff distance (HD) were calculated to evaluate the accuracy of the neural network. The time of automatic segmentation and manual segmentation was compared. Five orthopedic experts were invited to score the automatic and manual segmentation results using Likert scale and the scores of the two methods were compared. Results The automatic segmentation achieved a high accuracy. The Dice coefficient, ASD, and HD of the femur were 0.953±0.037, (0.076±0.048) mm, and (3.101±0.726) mm, respectively; and those of the tibia were 0.950±0.092, (0.083±0.101) mm, and (2.984±0.740) mm, respectively. The time of automatic segmentation was significantly shorter than that of manual segmentation [(2.46±0.45) minutes vs. (64.73±17.07) minutes; t=36.474, P<0.001). The clinical scores of the femur were 4.3±0.3 in the automatic segmentation group and 4.4±0.2 in the manual segmentation group, and the scores of the tibia were 4.5±0.2 and 4.5±0.3, respectively. There was no significant difference between the two groups (t=1.753, P=0.085; t=0.318, P=0.752). Conclusion The automatic segmentation of knee CT images based on deep learning has high accuracy and can achieve rapid segmentation and three-dimensional reconstruction. This method will promote the development of new technology-assisted techniques in total knee arthroplasty.