The pGenesil-1-Beclin1 eukaryotic expression vectors were constructed to establish an SH-SY5Y cell line stably expressing shRNA-Beclin1. The shRNA was connected to pGenesil-1 to construct the recombinant plasmid pGenesil-1-Beclin1, which was transformed into JM109 E.coli. Positive clones were identified by digestion with restriction endonuclease and DNA sequencing. SH-SY5Y cells were cultured by the conventional method. The pGenesil-1-Beclin1 and pGenesil-1 plasmids were transfected into SH-SY5Ycells, and the cells were screened by G418 until the stable G418-resistant monoclonal cells were acquired. Beclin1 mRNA and Beclin1 protein were detected by RT-PCR and Western blot analysis respectively. The results of restriction endonuclease analysis and DNA sequencing confirmed the correct construction of the eukaryotic expression vector pGenesil-1-Beclin1. Two SH-SY5Y transfected cell lines were successfully selected. Compared with the control group, RT-PCR and Western blot showed that the expression of Beclin1 mRNA and protein were down regulated 71.28%±1.45%(P<0.05)and 75.50%±2.63%(P<0.05), respectively. The results indicated that the eukaryotic expression vector pGenesil-1-Beclin1 was successfully constructed and the SH-SY5Y cell lines with inhibited Beclin1 expression were established. It provides a useful cell model for studying the biological function of Beclin1.
ObjectiveThis study aimed to revise the perioperative recovery scale for integrative medicine (PRSIM) based on item response theory (IRT). MethodsUnder the guidance of IRT, a total of 349 patient data collected during the development of the original version of PRSIM at Guangdong Provincial Hospital of Chinese Medicine were used. Principal component analysis was performed using SPSS 18.0 software to test the unidimensionality. The R language was utilized for parameter estimation, including discrimination coefficient, difficulty parameters and information content, as well as drawing item characteristic curves to assess item quality and estimate item functioning differences. A comprehensive screening process was carried out by combining expert consultations, patient evaluations, and discussions within a core group. ResultsThe degree of discrimination of all items ranged from −0.535 to 2.195. The difficulty coefficient ranged from −10.343 to 5.461, and the average information content of all items ranged from 0.043 to 1.075. Based on the criteria for parameter selection, nine items were retained. The results of expert consultations indicated the removal of 5 items and the modification of 7 items. After discussion within the core group, a final decision was made to remove 5 items. ConclusionBased on a synthesis of IRT and expert consultation feedback, and following discussions within the core group, a revised version comprising 15 items is retained and modified from the original 20 items.
ObjectivesTo explore the effect of the deep learning algorithm convolutional neural network (CNN) in screening of randomized controlled trials (RCTs) in Chinese medical literatures.MethodsLiterature with the topic " oral science” published in 2014 were retrieved from CNKI and exported citations containing title and abstract. RCTs screening was conducted by double independent screening, checking and peer discussion. The final results of the citations were used for CNN algorithm model training. After completing the algorithm model training, a prospective comparative trial was organized by searching all literature with the topic "oral science" published in CNKI from January to March 2018 to compare the sensitivity (SEN) and specificity (SPE) of algorithm with manual screening. The initial results of a single screener represented the performance of manual screening, and the final results after peer discussion were used as the gold standard. The best thresholds of algorithm were determined with the receptor operative characteristic (ROC) curve.ResultsA total of 1 246 RCTs and 4 754 non-RCTs were eventually included for training and testing of CNN algorithm model. 249 RCTs and 949 non-RCTs were included in the prospective trial. The SEN and SPE of manual screening were 98.01% and 98.82%. For the algorithm model, the SEN of RCTs screening decreased with the increase of threshold value while the SPE increased with the increase of threshold value. After 27 changes of threshold value, ROC curve were obtained. The area under the ROC curve was 0.9977, unveiling the optimal accuracy threshold (Threshold=0.4, SEN=98.39%, SPE=98.84%) and high sensitivity threshold (Threshold=0.06, SEN=99.60%, SPE=94.10%).ConclusionsA CNN algorithm model is trained with Chinese RCTs classification database established in this study and shows an excellent classification performance in screening RCTs of Chinese medical literature, which is proved to be comparable to the manual screening performance in the prospective controlled trial.
As the largest ecosystem of human body, intestinal microorganisms participate in the synthesis and metabolism of uric acid. Developing and utilizing intestinal bacteria to degrade uric acid might provide new ideas for the treatment of hyperuricemia. The fecal samples of people with low uric acid were inoculated into uric acid selective medium with the concentration of 1.5 mmol/L for preliminary screening, and the initially screened strains that may have degradation ability were domesticated by concentration gradient method, and the strains with high uric acid degradation rate were identified by 16S rRNA sequencing method. A strain of high-efficiency uric acid degrading bacteria was screened and domesticated from the feces of people with low uric acid. The degradation rate of uric acid could reach 50.2%. It was identified as Escherichia coli. The isolation and domestication of high efficient uric acid degrading strains can not only provide scientific basis for the study of the mechanism of intestinal microbial degradation of uric acid, but also reserve biological strains for the treatment of hyperuricemia and gout in the future.
Place cell with location tuning characteristics play an important role in brain spatial cognition and navigation, but there is relatively little research on place cell screening and its influencing factors. Taking pigeons as model animals, the screening process of pigeon place cell was given by using the spike signal in pigeon hippocampus under free activity. The effects of grid number and filter kernel size on the place field of place cells during the screening process were analyzed. The results from the real and simulation data showed that the proposed place cell screening method presented in this study could effectively screen out place cell, and the research found that the size of place field was basically inversely proportional to the number of grids divided, and was basically proportional to the size of Gaussian filter kernel in the overall trend. This result will not only help to determine the appropriate parameters in the place cell screening process, but also promote the research on the neural mechanism of spatial cognition and navigation of birds such as pigeons.
ObjectiveTo summarize the research progress of patient-derived organoid (PDO) and patient-derived xenograft (PDX) models in preclinical drug screening for gastric cancer, aiming to provide a new perspective for precise drug screening and promote the application of personalized medicine and precision medicine for gastric cancer. MethodA literature review was conducted on the use of PDO and PDX models in the basic research and preclinical drug screening for gastric cancer. ResultsThe PDO and PDX models of gastric cancer exhibited a higher tumor biological simulation capability and a relatively accurate preclinical drug response prediction. However, they each have some certain limitations. The advent of organoid models based on xenografting, which combines the advantages of both, is expected to compensate for their respective shortcomings. These models can better reflect the heterogeneity of patients’ tumors and have unique advantages in the evaluation of new targeted drugs for specific molecular targets in gastric cancer, such as epidermal growth factor receptor. They show a certain correlation with the actual clinical response of patients, paving a new way for the development of new drugs, the study of drug action and resistance mechanisms, and personalized therapy. ConclusionPDO and PDX models, as a highly promising research platform, show a great potential in the screening of anti-tumor drugs and the development of personalized medical strategies.
ObjectiveTo explore the selection problem of independent variables and stepwise regression method for multiple logistic regression analysis. MethodsAccording to the data of the case-control investigation for coronary heart disease, age (X1), hypertension history (X2), hypertension family history (X3), smoking (X4), hyperlipidemia history (X5), animal fat intake (X6), weight index (X7), type A personality (X8), and coronary heart disease (CHD, Y) were analyzed by SPSS 18.0 software. The multiple logistic regression analysis was done and the differences of risk factors were compared among 6 kinds stepwise regression variable selection method. ResultsThe univariate analysis showed that no difference was found between CHD group and non-CHD group in age distribution (P=0.116). But the multivariate logistic regression analysis showed that, comparing to population over 65 years old, age was a protective factor on the low age groups (OR< 45=0.100, 0.000 to 0.484, P=0.020; OR45-54=0.051, 0.003 to 0.975, P=0.048). If the age was defined as categorical variable, the risk factors for coronary heart disease were animal fat intake (X6), type A personality (X8), hypertension history (X5) and age (X1), respectively (P < 0.05). If the age was defined as a continuous variable, the effect of age (X1) was not statistically significant (P=0.053). The common risk factors were intake of animal fat (X6) and type a personality (X8) by six kinds method of stepwise variable selection. In addition, the risk factor also included hyperlipidemia history (X5) (forward-condition, forward-LR, forward-wald), hypertension family history (X3), age (X1) (backward-condition, backward-LR) and hypertension history (X2) (backward-wald). ConclusionStepwise regression method should be used to analyze all the variables, including no statistically significant independent variables in univariate analysis. If the categorical variable is regarded as continuous variables, some information may be lost, and even the risk factors may be missed. When the risk factors are not the same by several stepwise regression variable selection method, it should be combined with clinical and epidemiological significance, as well as biological mechanisms and other professional knowledge.
随着人口的老龄化，越来越多的有症状或无症状的冠心病患者需接受非心脏外科手术。接受非心脏外科手术而死亡的患者大约有50%是由于心脏并发症所致［1］。围手术期发生的心脏并发症大约5%～10%为心肌梗死，主要发生于术后头3天，其病死率很高，可达32%～69%［2，3］。术后发生心肌梗死或不稳定型心绞痛的患者发生心血管问题的几率增加20倍［4］。因此，如何评估非心脏外科手术患者的心脏危险性，如何预防围手术期心脏并发症的发生，已成为外科医生十分关注的一个问题。
OBJECTIVE: To investigate the selection and identification of human keratinocyte stem cells(KSC) in vitro. METHODS: According to the characteristics of KSC which can adhere to extracellular matrix very fast, we selected 3 groups of different time(5 minutes, 20 minutes and 60 minutes) and unselected as control group. And the cells were identified by monoclone antibody of beta 1-integrin and cytokeratin 19 (Ck19), then the image analysis was done. Furthermore we analyzed the cultured cells with flow cytometer(FCM) and observed the ultrastructure of the cell by transmission electron microscope(TEM). RESULTS: The cell clones formed in all groups after 10 to 14 days, while the cells of 5 minute group grew more slowly than those of the other groups, however, the clones of this group were bigger. The expression of beta 1-integrin and Ck19 were found in all groups. The positive rate of beta 1-integrin was significant difference between 5 minute group and the other groups (P lt; 0.05). And the expression of Ck19 was no significant difference between 5 minute group and 20 minute group(P gt; 0.05), and between 60 minute group and control group. But significant difference was observed between the former and the later groups(P lt; 0.05). The result of FCM showed that most cells of the 5 minute group lied in G1 period of cell cycle, which was different from those of the other groups. At the same time, the cells of 5 minute group were smaller and contained fewer organelles than those of the other groups. CONCLUSION: The above results demonstrate that the cells of 5 minute group have a slow cell cycle, characteristics of immaturity, and behaving like clonogenic cells in vitro. The cells have the general anticipated properties for KSC. So the KSC can be selected by rapid attachment to extracellular matrix and identified by monoclone antibody of beta 1-integrin and Ck19.