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.
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.
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。
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.
Systematic reviews can provide important evidence support for clinical practice and health decision-making. In this process, literature screening and data extraction are extensively time-consuming procedures. Natural language processing (NLP), as one of the research directions of computer science and artificial intelligence, can accelerate the process of literature screening and data extraction in systematic reviews. This paper introduced the requirements of systematic reviews for rapid literature screening and data extraction, the development of NLP and types of machine learning; and systematically collated the NLP tools for the title and abstract screening, full-text screening and data extraction in systematic reviews; and discussed the problems in the application of NLP tools in the field of systematic reviews and proposed a prospect for its future development.
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.
We aim to screen out the active components that may have therapeutic effect on coronavirus disease 2019 (COVID-19) from the severe and critical cases’ prescriptions in the “Coronavirus Disease 2019 Diagnosis and Treatment Plan (Trial Ninth Edition)” issued by the National Health Commission of the People’s Republic of China and explain its mechanism through the interactions with proteins. The ETCM database and SwissADME database were used to screen the active components contained in 25 traditional Chinese medicines in 3 prescriptions, and the PDB database was used to obtain the crystal structures of 4 proteins of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Molecular docking was performed using Autodock Vina and molecular dynamics simulations were performed using GROMACS. Binding energy results showed that 44 active ingredients including xambioona, gancaonin L, cynaroside, and baicalin showed good binding affinity with multiple targets of SARS-CoV-2, while molecular dynamics simulations analysis showed that xambioona bound more tightly to the nucleocapsid protein of SARS-CoV-2 and exerted a potent inhibitory effect. Modern technical methods are used to study the active components of traditional Chinese medicine and show that xambioona is an effective inhibitor of SARS-CoV-2 nucleocapsid protein, which provides a theoretical basis for the development of new anti-SARS-CoV-2 drugs and their treatment methods.
Hypertension is the primary disease that endangers human health. A convenient and accurate blood pressure measurement method can help to prevent the hypertension. This paper proposed a continuous blood pressure measurement method based on facial video signal. Firstly, color distortion filtering and independent component analysis were used to extract the video pulse wave of the region of interest in the facial video signal, and the multi-dimensional feature extraction of the pulse wave was preformed based on the time-frequency domain and physiological principles; Secondly, an integrated feature selection method was designed to extract the universal optimal feature subset; After that, we compared the single person blood pressure measurement models established by Elman neural network based on particle swarm optimization, support vector machine (SVM) and deep belief network; Finally, we used SVM algorithm to build a general blood pressure prediction model, which was compared and evaluated with the real blood pressure value. The experimental results showed that the blood pressure measurement results based on facial video were in good agreement with the standard blood pressure values. Comparing the estimated blood pressure from the video with standard blood pressure value, the mean absolute error (MAE) of systolic blood pressure was 4.9 mm Hg with a standard deviation (STD) of 5.9 mm Hg, and the MAE of diastolic blood pressure was 4.6 mm Hg with a STD of 5.0 mm Hg, which met the AAMI standards. The non-contact blood pressure measurement method based on video stream proposed in this paper can be used for blood pressure measurement.
In order to solve the current problems in medical equipment maintenance, this study proposed an intelligent fault diagnosis method for medical equipment based on long short term memory network(LSTM). Firstly, in the case of no circuit drawings and unknown circuit board signal direction, the symptom phenomenon and port electrical signal of 7 different fault categories were collected, and the feature coding, normalization, fusion and screening were preprocessed. Then, the intelligent fault diagnosis model was built based on LSTM, and the fused and screened multi-modal features were used to carry out the fault diagnosis classification and identification experiment. The results were compared with those using port electrical signal, symptom phenomenon and the fusion of the two types. In addition, the fault diagnosis algorithm was compared with BP neural network (BPNN), recurrent neural network (RNN) and convolution neural network (CNN). The results show that based on the fused and screened multi-modal features, the average classification accuracy of LSTM algorithm model reaches 0.970 9, which is higher than that of using port electrical signal alone, symptom phenomenon alone or the fusion of the two types. It also has higher accuracy than BPNN, RNN and CNN, which provides a relatively feasible new idea for intelligent fault diagnosis of similar equipment.