1.Development, Reliability and Validity of Child-Neglect Scale
Shi-chang, YANG ; Ya-lin, ZHANG ; Ai-ling, DU
Journal of Applied Clinical Pediatrics 2009;24(16):1293-1296
reliability, content validity, construct validity, and criterion - related validity of the CNS are entirely in accordance with the psychometric demands.
2.Development,Reliability and Validity of Child-Neglect Scale
shi-chang, YANG ; ya-lin, ZHANG ; ai-ling, DU
Journal of Applied Clinical Pediatrics 2006;0(16):-
Objective To develop a child neglect scale with Chinese culture background to assess the status of the neglected children in China,and examine the reliability and validity of the child-neglect scale(CNS).Methods Considering the cultural background of China,an item pool was established by revising items in correlative literatures and scales.Then,the first draft of the CNS was improved by reserving the effective items well graded by professional experts.A total of 871 students from 2 junior high schools and a vocational and technical college were involved in the study.Those students were surveyed with Parental Rearing Patterns scale and child neglect scale.Exploratory and confirmatory factor analysis were applied to the development and evaluation of the structure of the scale.Results The findings were as follows:the general Cronbach′s Alpha reliability was 0.85,the split-half reliability was 0.81,the test-retest reliability was 0.90. The CNS was made of the 4 sub-scales which was safe neglect scale,physical neglect scale,communion neglect scale and affection neglect scale.the general Cronbach′s Alpha reliability of the child neglect scale was 0.79-0.85,the split-half reliability was 0.64-0.81,and the test-retest reliability was 0.82-0.90.The item loadings of the neglect scale were over 0.30.The confirmatory factor analysis indicated that the ratio of Chi-square to degrees of freedom were 1.766,the goodness of fit index was 0.917,the Tucker-Lewis index was 0.916,and the root mean square error of approximation was 0.047.Criterion-related validity studies indicated that the scores of the CNS were significantly correlated with the rearing patterns as well(r=0.049,-0.465 P
3.Development of HTS model on SERT inhibitors combined biological screening model with HTVS.
Rui ZHAO ; Jian-song FANG ; Ai-lin LIU ; Guan-hua DU
Acta Pharmaceutica Sinica 2015;50(9):1116-1121
In order to improve the efficiency of drug screening on serotonin transporter (SERT) inhibitors, a high-throughput screening (HTS) model is established in RBL-2H3 cells. The RBL-2H3 cells are very similar to the serotonin genetic neuro, in modulation of post-receptor mechanisms and transduction pathway of SERT reactivated. Depending on a fluorescence substrate ASP+ used in detection method of inhibitor rates, it's convenient, quick, accurate and effective, not making the environmental biohazard compared with radioactive experiments. Furthermore, biological screening model combined with computer aided virtual screening technique describing high-throughput virtual screening (HTVS). Bayesian classification method and molecular fingerprint similarity were applied to virtual screening technique, for screening compounds in compound library. Some compounds have been found, and then validated further by biological screening model. Combination of HTS and HTVS improves the efficiency of screening SERT inhibitors.
Animals
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Bayes Theorem
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Cell Line
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Drug Evaluation, Preclinical
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High-Throughput Screening Assays
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Models, Biological
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Rats
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Serotonin Plasma Membrane Transport Proteins
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metabolism
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Serotonin Uptake Inhibitors
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pharmacology
7.Progress on the application of artificial intelligence technology in ligand-based and receptor structure-based drug screening
Run-zhe LIU ; Jun-ke SONG ; Ai-lin LIU ; Guan-hua DU
Acta Pharmaceutica Sinica 2021;56(8):2136-2145
Artificial intelligence technology is being widely applied in drug screening. This paper introduces the characteristics of artificial intelligence, and summarizes the application and progress of artificial intelligence technology especially deep learning in drug screening, from ligand-based and receptor structure-based aspects. This paper also introduces how to apply artificial intelligence to drug design from these two aspects. Finally, we discuss the main limitations, challenges, and prospects of artificial intelligence technology in the field of drug screening.
8.Network pharmacology: new guidelines for drug discovery.
Acta Pharmaceutica Sinica 2010;45(12):1472-1477
The development of new drug is not only the main driving force for the development of pharmaceutical industry, but also plays a very important role in the social development. However, with the increasing demands, new drug development is facing great difficulties in recent years. The hypothesis of highly selective single-target is meeting the challenges because of its limitations. Network pharmacology has been one of the new strategies for new drug discovery based on single-target drug research in recent years. This paper focused on the basis of network pharmacology and its research progress, discussed its development direction and application prospects, and analyzed its limitations and problems as well. The application of network pharmacology in new drug development is discussed by comparing its guidelines with those of traditional Chinese medicine theory and Effective Components Group hypothesis of Chinese medicines.
Algorithms
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Animals
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Computational Biology
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methods
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Drug Delivery Systems
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methods
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Drug Discovery
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methods
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Drug Interactions
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Humans
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Medicine, Chinese Traditional
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methods
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Software
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Systems Biology
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methods
9.Effects of polydatin on learning and memory and Cdk5 kinase activity in the hippocampus of rats with chronic alcoholism.
Xin-juan LI ; Yan ZHANG ; Chun-yang XU ; Shuang LI ; Ai-lin DU ; Li-bin ZHANG ; Rui-ling ZHANG
Chinese Journal of Applied Physiology 2015;31(2):117-120
OBJECTIVETo observe the effects of polydatin on learning and memory and cyclin-dependent kinase 5 (Cdk5) kinase activity in the hippocampus of rats with chronic alcoholism.
METHODSForty rats were randomly divided into 4 groups: control group, chronic alcoholism group, low and high polydatin group. The rat chronic alcoholism model was established by ethanol 3.0 g/(kg · d) (intragastric administration). The abstinence scoring was used to evaluate the rats withdrawal symptoms; cognitive function was measured by Morris water maze experiment; Cdk5 protein expression in the hippocampus was detected by immunofluorescence; Cdk5 kinase activity in the hippocampus was detected by liquid scintillation counting method.
RESULTSThe abstinence score, escape latency, Cdk5 kinase activity in chronic alcoholism group rats were significantly higher than those of control group (P < 0.05). The abstinence score, escape latency in high polydatin group rats were significantly lower than those of chronic alcoholism group (P < 0.05); Cdk5 kinase activity in high and low polydatin group rats was significantly lower than that of chronic alcoholism group( P < 0.05); immunofluorescence showed that the Cdk5 positive cells of chronic alcoholism group were significantly increased compared with control group (P < 0.05), and the Cdk5 positive cells of polydatin groups were significantly decreased compared with chronic alcoholism group ( P < 0.05).
CONCLUSIONPolydatin-reduced the chronic alcoholism damage may interrelate with regulation of Cdk5 kinase activity.
Alcoholism ; physiopathology ; Animals ; Cyclin-Dependent Kinase 5 ; metabolism ; Drugs, Chinese Herbal ; pharmacology ; Glucosides ; pharmacology ; Hippocampus ; drug effects ; enzymology ; Learning ; drug effects ; Memory ; drug effects ; Rats ; Stilbenes ; pharmacology
10.Research advance in the drug target prediction based on chemoinformatics.
Jian-song FANG ; Ai-lin LIU ; Guan-hua DU
Acta Pharmaceutica Sinica 2014;49(10):1357-1364
The emerging of network pharmacology and polypharmacology forces the scientists to recognize and explore new mechanisms of existing drugs. The drug target prediction can play a key significance on the elucidation of the molecular mechanism of drugs and drug reposition. In this paper, we systematically review the existing approaches to the prediction of biological targets of small molecule based on chemoinformatics, including ligand-based prediction, receptor-based prediction and data mining-based prediction. We also depict the strength of these methods as well as their applications, and put forward their developing direction.
Computational Biology
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Data Mining
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Drug Delivery Systems
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Drug Design
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Ligands