Electro-acupuncture for post-stroke cognitive impairment: A protocol for systematic review and meta-analysis.

BACKGROUND: A considerable number of stroke survivors suffered from cognitive impairment, and more than one third of stroke survivors are affected at 3 and 12 months after the stroke. Although the published systematic reviews suggest that acupuncture can help improve post-stroke cognitive dysfunction, the power of the results is low due to study limitations. Therefore, this review is necessary to analyze the effect of acupuncture on cognitive impairment after stroke and to provide evidence for cognitive impairment in stroke. METHODS: This study will be carried out in strict accordance with the Cochrane Handbook for Systematic Reviews of Interventions. According to the pre-established search strategy (PICOS framework), all the literature will be obtained from online databases including Cochrane Central Register of Controlled Trials in the Cochrane Library, Medline (via PubMed), EMBASE (via embase.com), CINAHL (via EBSCOhost), China National Knowledge Infrastructure database, WanFang Database, Chinese Science and Technology Periodical Database, and Sino-Med Database from inception until December 31, 2021 with no language limitations. Two reviewers will screen the records and include quality studies according to inclusion criteria independently. The data needed will be extracted independently by 2 authors according to a table of data extraction. Any inconsistencies in literature screening and data collection will be resolved to reach a consensus via discussion with a third author. Risk of bias for each study will be assessed using risk of bias tool. RevMan5.3 will be used to analyze the data. Heterogeneity will be identified and measured by Chi2. Subgroup analyses and sensitivity analysis will be carried out. Grading of Recommendations Assessment, Development and Evaluation will be used to evaluate the evidence for each outcome. CONCLUSION: The results of this study will provide a theoretical basis for the clinical use of electro-acupuncture to treat cognitive dysfunction after stroke. UNIQUE INPLASY NUMBER: INPLASY202210038.

Multiple-to-multiple path analysis model.

One-to-multiple path analysis model describes the regulation mechanism of multiple independent variables to one dependent variable by dividing the correlation coefficient and the determination coefficient. How to analyse more complex regulation mechanisms of multiple independent variables to multiple dependent variables? Similarly, according to multiple-to-multiple linear regression analysis, multiple-to-multiple path analysis model was proposed in this paper and it demonstrated more complex regulation mechanisms among multiple independent variables and multiple dependent variables by dividing the generalized determination coefficient. Differently, three other types of paths were generated in multiple-to-multiple path analysis model in that the correlation among multiple dependent variables was considered. Then, the decision coefficient of each independent variable was constructed for dependent variables system, and its hypothesis testing statistics were given. Finally, the research example of the wheat breeding rules in arid area demonstrated that the multiple-to-multiple path analysis considering more correlation information can get better results.

A decision analysis model for KEGG pathway analysis.

BACKGROUND: The knowledge base-driven pathway analysis is becoming the first choice for many investigators, in that it not only can reduce the complexity of functional analysis by grouping thousands of genes into just several hundred pathways, but also can increase the explanatory power for the experiment by identifying active pathways in different conditions. However, current approaches are designed to analyze a biological system assuming that each pathway is independent of the other pathways. RESULTS: A decision analysis model is developed in this article that accounts for dependence among pathways in time-course experiments and multiple treatments experiments. This model introduces a decision coefficient-a designed index, to identify the most relevant pathways in a given experiment by taking into account not only the direct determination factor of each Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway itself, but also the indirect determination factors from its related pathways. Meanwhile, the direct and indirect determination factors of each pathway are employed to demonstrate the regulation mechanisms among KEGG pathways, and the sign of decision coefficient can be used to preliminarily estimate the impact direction of each KEGG pathway. The simulation study of decision analysis demonstrated the application of decision analysis model for KEGG pathway analysis. CONCLUSIONS: A microarray dataset from bovine mammary tissue over entire lactation cycle was used to further illustrate our strategy. The results showed that the decision analysis model can provide the promising and more biologically meaningful results. Therefore, the decision analysis model is an initial attempt of optimizing pathway analysis methodology.

KEGG-PATH: Kyoto encyclopedia of genes and genomes-based pathway analysis using a path analysis model.

The dynamic impact approach (DIA) represents an alternative to overrepresentation analysis (ORA) for functional analysis of time-course experiments or those involving multiple treatments. The DIA can be used to estimate the biological impact of the differentially expressed genes (DEGs) associated with particular biological functions, for example, as represented by the Kyoto encyclopedia of genes and genomes (KEGG) annotations. However, the DIA does not take into account the correlated dependence structure of the KEGG pathway hierarchy. We have developed herein a path analysis model (KEGG-PATH) to subdivide the total effect of each KEGG pathway into the direct effect and indirect effect by taking into account not only each KEGG pathway itself, but also the correlation with its related pathways. In addition, this work also attempts to preliminarily estimate the impact direction of each KEGG pathway by a gradient analysis method from principal component analysis (PCA). As a result, the advantage of the KEGG-PATH model is demonstrated through the functional analysis of the bovine mammary transcriptome during lactation.

[Comparative analysis on content and distribution of CpG sites in milk production traits and mastitis-related genes in dairy cattle].

DNA methylation is a major part of epigenetics. DNA methylation on the CpG sites in gene promoter and the first exon often represses gene expression, but demethylation activates gene expression. Previous research has shown that a negative correlation was found between mastitis index (somatic cell count, SCC) and milk production traits in Holsteins. The content and distribution of CpG dinucleotide sites in different regions of the candidate genes related to milk production traits and mastitis were studied in the present study. The regions contained promoter (2000 bp upstream of transcriptional start site), exon 1, and 2000 bp downstream of transcriptional end site. The CpG number of promoter and exon 1 in the mastitis-related genes was significantly less than that of the milk production-associated genes. However, the CpG number of 2000 bp downstream of the genes for the two traits was not significantly different. Two new index quantified CpG characterizations were proposed. One is the CpG distance, which can measure the distribution of CpG. The other is the conditional probability p(G|C), which is used to quantify the probability of CpG in a nucleotide sequence along with C. The two indexes of promoter and exon 1 in the two types of genes and their statistic analysis were carried out. This study sets the basis for DNA methylation regulation of milk production traits- and mastitis-related genes.

Protein sequence analysis based on hydropathy profile of amino acids.

Biology sequence comparison is a fundamental task in computational biology. According to the hydropathy profile of amino acids, a protein sequence is taken as a string with three letters. Three curves of the new protein sequence were defined to describe the protein sequence. A new method to analyze the similarity/dissimilarity of protein sequence was proposed based on the conditional probability of the protein sequence. Finally, the protein sequences of ND6 (NADH dehydrogenase subunit 6) protein of eight species were taken as an example to illustrate the new approach. The results demonstrated that the method is convenient and efficient.

[A new alignment-free sequence analysis based on the distribution of K-tuple].

Based on the distribution of K-tuple in complete genome, a method without doing sequence alignment to infer difference of biological sequence is proposed in this paper. The method can be used to measure the difference of distribution on K-tuple between the native DNA sequences and the corresponding randomized ones. Applied to construct phylogenetic trees of the complete mitochondrial genomes of 26 species of placental mammals, with K increasing, it yields phylogenetic trees of which the classification effect increasingly matches the result widely recognised by the biological field. The final results show that the phylogenetic trees built by this method is more reasonable than by other alignment-free sequence methods.

[Maximum entropy principle and population genetic equilibrium].

A general mathematic model of population genetic equilibrium was constructed based on the maximum entropy principle. We proved that the maximum entropy probability distribution was equivalent to the Hardy-Weinberg equilibrium law. A population reached genetic equilibrium when the genotype entropy of the population reached the maximal possible value. In information theory, the entropy or the information content is used to measure the uncertainty of a system. In population genetics, we can use entropy to measure the uncertainty of the genotype of a population. The agreement of the maximum entropy principle and the hardy-Weinberg equilibrium law indicated that random crossing is an irreversible process, which increases the genotype entropy of the population, while inbreeding and selection decrease the genotype entropy of the population. In animal or plant breeding, we often use selection and/or inbreeding to decrease the entropy of a population, and use intercrossing to increase the entropy of the population. In this point of view, breeding is actually regulating the entropy of population. By applying the basic principle of informatics in population genetics, we revealed the biological significance of the genotype entropy and demonstrated that we can work over population genetic problems with the principles and methods of informatics and cybernetics.