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.
ObjectiveTo summarize the clinical application and future application prospects of organoid model in pancreatic cancer. MethodThe domestic and foreign literature related on the application of organoid model in pancreatic cancer was reviewed. ResultsIn recent years, the organoid model of pancreatic cancer was constructed mainly using patient-derived tissues, fine-needle aspiration samples, and human pluripotent stem cells. The biomarkers of pancreatic cancer were screened according to the histological and structural heterogeneities of the primary tumor retained in organoid model, such as microRNA, glypican-1, annexin A6 and protein biomarkers cytokeratin 7 and 20, cell tumor antigen p53, Claudin-4, carbohydrate antigen 19-9, etc.in the extracellular vesicles. The results of organoid model could maintain the original tumor characteristics and the higher correlation between the organoid model drug sensitivity data and the clinical results of pancreatic cancer patients suggested that, the drug sensitivity data of organoid model could be used to avoid ineffective chemotherapy, so as to improve the treatment response rate and reduce the toxicity of chemical drug treatment, and reasonably select individualized treatment plans for pancreatic cancer patients in future. ConclusionsOrganoid model has many research in screening biomarkers of pancreatic cancer, individualized drug screening, and drug sensitivity test. It can simulate the complex pathophysiological characteristics of pancreatic cancer in vitro, and retain the physiological characteristics and gene phenotype of original tumor cells. It is expected to become a new platform for selecting biomarkers of pancreatic cancer, testing drug sensitivity, and formulating individualized treatment methods for pancreatic cancer, which might further accelerate the research progress of pancreatic cancer.
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.
Subject recruitment is a key component that affects the progress and results of clinical trials, and generally conducted with eligibility criteria (includes inclusion criteria and exclusion criteria). The semantic category analysis of eligibility criteria can help optimizing clinical trials design and building automated patient recruitment system. This study explored the automatic semantic categories classification of Chinese eligibility criteria based on artificial intelligence by academic shared task. We totally collected 38 341 annotated eligibility criteria sentences and predefined 44 semantic categories. A total of 75 teams participated in competition, with 27 teams having submitted system outputs. Based on the results, we found out that most teams adopted mixed models. The mainstream resolution was applying pre-trained language models capable of providing rich semantic representation, which were combined with neural network models and used to fine-tune the models with reference to classifier tasks, and finally improved classification performance could be obtained by ensemble modeling. The best-performing system achieved a macro F1 score of 0.81 by using a pre-trained language model, i.e. bidirectional encoder representations from transformers (BERT) and ensemble modeling. With the error analysis we found out that from the point of data processing steps the data pre-processing and post-processing were very important for classification, while from the point of data volume these categories with less data volume showed lower classification performance. Finally, we hope that this study could provide a valuable dataset and state-of-the-art result for the research of Chinese medical short text classification.
Systematic reviews (SRs) serve as a core methodology in evidence-based medicine (EBM), providing critical evidence for clinical practice and health decision-making. However, the manual screening of titles and abstracts in SRs is labor-intensive and time-consuming, becoming a major bottleneck in research efficiency. Recent advancements in artificial intelligence (AI), particularly large language models (LLMs), have introduced new opportunities and transformations in this field. This article provided an overview of the current status of intelligent screening for titles and abstracts in systematic reviews, with a focus on the application and effectiveness of LLMs. It aims to provide recommendations for users and developers, facilitating the better integration of automation algorithms into the SR process.
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.
This study aimed to comprehensively evaluate the biological activity in different passage populations of mesenchymal stem cells (BMSCs) derived from bone marrow in ovariectomy osteoporotic rats (named OVX-rBMSCs), providing experimental basis for new osteoporotic drug development and research. OVX-rBMSCs were isolated and cultured in vitro by the whole bone marrow adherent screening method. The morphological observation, cell surface markers (CD29, CD45, CD90) detection, cell proliferation, induced differentiation experimental detection were performed to evaluate the biological activity of Passage 1, 2, 3, 4 populations (P1, P2, P3, P4) OVX-rBMSCs. The results showed that whole bone marrow adherent culture method isolated and differentially subcultured OVX-The morphology of P4 OVX-rBMSCs was identical fibroblast-like and had the characteristics of ultrastructure of stem cells. The CD29 positive cells rate, CD90 positive cells rate, cell proliferation index, and the osteogenic, adipogenic, chondrogenic differentiation capacities of P4 OVX-rBMSCs were significantly better than those of other populations (P < 0.05). OVX-rBMSCs purity and biological activity were gradually optimized with the passaged, and among them P4 cells were superior to all the other populations. Based on these results, we report that the P4 OVX-rBMSCs model developed in this study can be used to develop a new and effective medical method for osteoporotic drug screening.
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.