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LiCl Containing Thermosensitive Formulation Improves Hemostasis, Wound Healing, and Hair Regrowth

REGENERATIVE ENGINEERING AND TRANSLATIONAL MEDICINE 2021年第3期7卷 362-378页

作者： Verma, Yogesh Kumar Verma, Ranjan Sing, Ajay Kumar Gangenahalli, Gurudutta Inst Nucl Med & Allied Sci Div Stem Cell & Gene Therapy Res Delhi 110054 India

Lithium is clinically used for the treatment of the bipolar manic disorder. In this study, we have evaluated its role in the wound healing process on the basis of our previous microarray studies where many genes related to wound healing were upregulated. Formulations containing LiCl, poloxamer 407 (P407), alginate, and serum were prepared so as to make them "active" thermosensitive preparation, which may participate in different phases of wound healing. The formulations were analyzed for their physical properties, hemostasis, healing, hair regrowth, and expression of genes related to wound healing. The formulations showed a thermoreversible property, improved hemostasis in a liver laceration, and improved healing of full-thickness skin wound and hair regrowth in BALB/c mice and Sprague Dawley rat model. Further, we found genes which facilitate repair of the wound, e.g., TGF beta, COX2, and Col1a1, were also upregulated in RT-PCR assay. Particularly, we have detected the potential role of KRTAP10-2 gene in LiCl-induced hair regrowth. These formulations are likely to have application in the treatment of various injuries including accidents, trauma, diabetic wound, radiotherapy, war injuries, etc., where facilitated wound healing is essential. Lay Summary Our studies on microarray data analysis of LiCl-treated cells showed upregulation of wound healing genes. A formulation containing LiCl was evaluated in vitro and in vivo, which facilitated various stages of wound healing. The temperature-dependent phase changing property of this formulation makes it effective at places where the temperature remains subzero. The formulation may be prepared with other ingredients such as analgesics and antibiotics and could be packed as per the requirement such as spray, cream, gel, etc. Future Studies The formulation may further be evaluated in the GLP (Good Laboratory Practice) setting;this may help in the repurposing of LiCl and other constituents for wound healing purpose.

关键词： microarray data analysis Poloxamer LiCl Hemostasis Healing Hair regrowth

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Identification of key candidate genes and biological pathways in neuropathic pain

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COMPUTERS IN BIOLOGY AND MEDICINE 2022年 150卷 106135-106135页

作者： Cui, Chun-Yan Liu, Xiao Peng, Ming-Hui Liu, Qing Zhang, Ying Southwest Med Univ Dept Pain Hosp TCM Luzhou 646000 Sichuan Peoples R China Southwest Med Univ Dept Anesthesiol Hosp TCM Luzhou 646000 Sichuan Peoples R China Hejiang Tradit Chinese Med Hosp Luzhou 646000 Sichuan Peoples R China

Background: Neuropathic pain is a common chronic pain, characterized by spontaneous pain and mechanical allodynia. The incidence of neuropathic pain is on the rise due to infections, higher rates of diabetes and stroke, and increased use of chemotherapy drugs in cancer patients. At present, due to its pathophysiological process and molecular mechanism remaining unclear, there is a lack of effective treatment and prevention methods in clinical practice. Now, we use bioinformatics technology to integrate and filter hub genes that may be related to the pathogenesis of neuropathic pain, and explore their possible molecular mechanism by functional annotation and pathway enrichment analysis. Methods: The expression profiles of GSE24982, GSE2884, GSE2636 and GSE30691 were downloaded from the Gene Expression Omnibus(GEO)database, and these datasets include 93 neuropathic pain Rattus norvegicus and 59 shame controls. After the four datasets were all standardized by quantiles, the differentially expressed genes (DEGs) between NPP Rattus norvegicus and the shame controls were finally identified by the robust rank aggregation (RRA) analysis method. In order to reveal the possible underlying biological function of DEGs, the Gene Ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway enrichment analysis of DEGs were performed. In addition, a Protein-protein Interaction (PPI) network was also established. At the end of our study, a high throughput sequencing dataset GSE117526 was used to corroborate our calculation ***: Through RRA analysis of the above four datasets GSE24982, GSE2884, GSE2636, and GSE30691, we finally obtained 231 DEGs, including 183 up-regulated genes and 47 down-regulated genes. Arranging 231 DEGs in descending order according to |log2 fold change (FC)|, we found that the top 20 key genes include 14 up -regulated genes and 6 down-regulated genes. The most down-regulated hub gene abnormal expressed in NPP was E

关键词： Neuropathic pain Hub genes Robust rank aggregation microarray data analysis Protein-protein interaction (PPI) network analysis

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Using prior knowledge in the inference of gene association networks

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APPLIED INTELLIGENCE 2020年第11期50卷 3882-3893页

作者： Neponnuceno-Channorro, Isabel A. Neponnuceno, Juan A. Luis Galvan-Rojas, Jose Vega-Marquez, Belen Rubio-Escudero, Cristina Univ Seville Dept Lenguajes & Sistemas Informat Seville Spain

Traditional computational techniques are recently being improved with the use of prior biological knowledge from open-access repositories in the area of gene expression data analysis. In this work, we propose the use of prior knowledge as heuristic in an inference method of gene-gene associations from gene expression profiles. In this paper, we use Gene Ontology, which is an open-access ontology where genes are annotated using their biological functionality, as a source of prior knowledge together with a gene pairwise Gene-Ontology-based measure. The performance of our proposal has been compared to other benchmark methods for the inference of gene networks, outperforming in some cases and obtaining similar and competitive results in others, but with the advantage of providing simple and interpretable models, which is a desired feature for the Artificial Intelligence Health related models as stated by the European Union.

关键词： Gene-gene association networks Ontology Semantic similarity measure Information fusion microarray data analysis

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Survival genes expression analysis following ionizing radiation to LiCl treated KG1a cells

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INTERNATIONAL JOURNAL OF RADIATION BIOLOGY 2020年第5期96卷 671-688页

作者： Verma, Yogesh Kumar Singh, Ajay Kumar Gurudutta, Gangenahalli Ugraiah DRDO Div Stem Cell & Gene Therapy Res INMAS Delhi 110054 India

Purpose: Lithium chloride (LiCl) is clinically used for manic disorders. Its role has been shown in improving cell survival by decreasing Bax and p53 expression and increasing Bcl-2 concentration in the cell. This potential of LiCl is responsible for reducing irradiated cell death. In this study, we have explored the role of LiCl as a radioprotectant affecting survival genes. Materials and methods: To find out the cellular response upon LiCl pretreatment to radiation-exposed KG1a cells;viability, clonogenic assay and microarray studies were performed. This was followed by the detection of transcription factor binding motif in coregulated genes. These results were confirmed by reverse transcription-polymerase chain reaction (RT-PCR) and chromatin immunoprecipitation (CHIP). Results: LiCl improved irradiated KG1a cell survival and its clonogenicity at 2 mM concentration (clinically used). microarray data analysis showed differential expression of cell-protecting genes playing an important role in apoptosis, cell cycle, adhesion and inflammation, etc. The coregulation analysis revealed genes involved in bile acid biosynthesis were also affected by LiCl treatment, these genes are likely to be responsible for radiation-induced gastrointestinal (GI) syndrome through bile production. Conclusions: This is the first study with respect to global genetic expression upon LiCl treatment to radiation-exposed cells. Our results suggest considering repurposing of LiCl as a protective agent for radiation injury.

关键词： microarray data analysis cell survival clustering coregulated expression network analysis bile acid synthesis

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A New Locally Weighted K-Means for Cancer-Aided microarray data analysis

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JOURNAL OF MEDICAL SYSTEMS 2012年第1期36卷 S43-S49页

作者： Iam-On, Natthakan Boongoen, Tossapon Mae Fah Luang Univ Sch Informat Technol Chiang Rai 57100 Thailand Royal Thai Air Force Acad Dept Math & Comp Sci Bangkok 10220 Thailand

Cancer has been identified as the leading cause of death. It is predicted that around 20-26 million people will be diagnosed with cancer by 2020. With this alarming rate, there is an urgent need for a more effective methodology to understand, prevent and cure cancer. microarray technology provides a useful basis of achieving this goal, with cluster analysis of gene expression data leading to the discrimination of patients, identification of possible tumor subtypes and individualized treatment. Amongst clustering techniques, k-means is normally chosen for its simplicity and efficiency. However, it does not account for the different importance of data attributes. This paper presents a new locally weighted extension of k-means, which has proven more accurate across many published datasets than the original and other extensions found in the literature.

关键词： Subspace clustering Attribute weighting Cancer microarray data analysis

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PreCLAS: An Evolutionary Tool for Unsupervised Feature Selection 15th

PreCLAS: An Evolutionary Tool for Unsupervised Feature Selec...

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15th International Conference on Hybrid Artificial Intelligence Systems (HAIS)

作者： Carballido, Jessica A. Ponzoni, Ignacio Cecchini, Rocio L. Univ Nacl Sur Dept Comp Sci & Engn Inst Comp Sci & Engn UNS CONICET Bahia Blanca Buenos Aires Argentina

ISBN: (纸本)9783030617059;9783030617042

Several research areas are being faced with data matrices that are not suitable to be managed with traditional clustering, regression, or classification strategies. For example, biological so-called omic problems present models with thousands or millions of rows and less than a hundred columns. This matrix structure hinders the successful progress of traditional data analysis methods and thus needs some means for reducing the number of rows. This article presents an unsupervised approach called PreCLAS for preprocessing matrices with dimension problems to obtain data that are apt for clustering and classification strategies. The PreCLAS was implemented as an unsupervised strategy that aims at finding a submatrix with a drastically reduced number of rows, preferring those rows that together present some group structure. Experimentation was carried out in two stages. First, to assess its functionality, a benchmark dataset was studied in a clustering context. Then, a microarray dataset with genomic information was analyzed, and the PreCLAS was used to select informative genes in the context of classification strategies. Experimentation showed that the new method performs successfully at drastically reducing the number of rows of a matrix, smartly performing unsupervised feature selection for both classification and clustering problems.

关键词： Clustering tendency Classification strategies Evolutionary algorithm Unsupervised feature selection microarray data analysis

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microarray US: A user-friendly graphical interface to Bioconductor tools that enables accurate microarray data analysis and expedites comprehensive functional analysis of microarray results

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BMC Research Notes 2012年第1期5卷 1-6页

作者： Dai, Yilin Guo, Ling Li, Meng Chen, Yi-Bu Department of Mathematical Sciences Michigan Technological University Houghton MI 49934 1400 Townsend Drive United States Bioinformatics Service Program Norris Medical Library University of Southern California Los Angeles CA 91007 2003 Zonal Ave United States

Background: microarray data analysis presents a significant challenge to researchers who are unable to use the powerful Bioconductor and its numerous tools due to their lack of knowledge of R language. Among the few existing software programs that offer a graphic user interface to Bioconductor packages, none have implemented a comprehensive strategy to address the accuracy and reliability issue of microarray data analysis due to the well known probe design problems associated with many widely used microarray chips. There is also a lack of tools that would expedite the functional analysis of microarray results. Findings: We present microarray ? US, an R-based graphical user interface that implements over a dozen popular Bioconductor packages to offer researchers a streamlined workflow for routine differential microarray expression data analysis without the need to learn R language. In order to enable a more accurate analysis and interpretation of microarray data, we incorporated the latest custom probe re-definition and re-annotation for Affymetrix and Illumina chips. A versatile microarray results output utility tool was also implemented for easy and fast generation of input files for over 20 of the most widely used functional analysis software programs. Conclusion: Coupled with a well-designed user interface, microarray ? US leverages cutting edge Bioconductor packages for researchers with no knowledge in R language. It also enables a more reliable and accurate microarray data analysis and expedites downstream functional analysis of microarray results. © 2012 Dai et al.;licensee BioMed Central Ltd.

关键词： Gene expression microarray data analysis Probe reannotation

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SugarArray: A User-centred-designed Platform for the analysis of Lectin and Glycan microarrays 13

SugarArray: A User-centred-designed Platform for the Analysi...

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13th International Joint Conference on Biomedical Engineering Systems and Technologies

作者： Sucre, Aurora Pazos, Raquel Reichardt, Niels-Christian Garin-Muga, Alba Biodonostia eHlth Grp Donostia San Sebastian 20014 Spain Vicomtech eHlth & Biomed Applicat Donostia San Sebastian 20014 Spain Glycotechnol Lab CIC BiomaGUNE Paseo Miramon 182 San Sebastian 20014 Spain CIBER BBN Paseo Miramon 182 San Sebastian 20014 Spain

ISBN: (纸本)9789897583988

Glycan and lectin microarrays are two arising technologies, very important to the glycomics field. Glycomics is the science that focuses on defining the structures and functions of carbohydrates in nature. These microarrays provide information regarding the interactions between specific carbohydrates and proteins, and it has many applications in clinical and research settings. Nevertheless, the availability of analytical software for these types of arrays is very limited, so researchers usually perform data processing and analytical pipelines manually, which is very time consuming and prone to error. SugarArray was born as a user-friendly and intuitive stand-alone solution that process the intensity data generated from glycan or lectin array studies, and displays the results to the user in an understandable manner The solution also allows the users to manage the data as needed, create data plots and automatically generate reports. This tool was intended to simplify the processing steps of the analytical pipeline, so the users can focus on what really matters: understanding the results.

关键词： Glycomics Lectin microarray Glycan microarray microarray data analysis data Visualization

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Gene selection for enhanced classification on microarray data using a weighted k-NN based algorithm

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INTELLIGENT data analysis 2019年第1期23卷 241-253页

作者： Ventura-Molina, Elias Alarcon-Paredes, Antonio Aldape-Perez, Mario Yanez-Marquez, Cornelio Adolfo Alonso, Gustavo Inst Politecn Nacl Ctr Invest Computac Av Juan de Dios Batiz Ciudad De Mexico 07738 Mexico Univ Autonoma Guerrero Fac Ingn Av Lazaro Cardenas S-NCiudad Univ Zona Sur Chilpancingo Guerrero 39087 Mexico Inst Politecn Nacl Ctr Innovac & Desarrollo Tecnol Computo Av Juan de Dios Batiz Ciudad De Mexico 07700 Mexico

Feature selection is a common solution to microarray analysis. Previous approaches either select features based on classical statistical tests that can be tuned up with a classifier, or using regularization penalties incorporated in the cost function. Here we propose to use a feature ranking and weighting scheme instead, which combines statistical techniques with a weighted k-NN classifier using a modified forward selection procedure. We demonstrate that classification accuracy of our proposal outperforms existing methods on a range of public microarray gene expression datasets. The proposed method is also compared to state-of-the-art feature selection algorithms by means of the Friedman test. Although a bunch of feature selection techniques has been used for genomic data, the experimental results show the classification superiority of our method on most of the present gene expression datasets.

关键词： Computational genomics microarray data analysis feature selection feature ranking feature weighting k-nearest neighbors

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Speeding up the discovery of combinations of differentially expressed genes for disease prediction and classification

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COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2019年 170卷 69-80页

作者： Khamesipour, Alireza Kagaris, Dimitri Southern Illinois Univ ECE Dept Carbondale IL 62901 USA

Background and objective: Finding combinations (i.e., pairs, or more generally, q-tuples with q >= 2) of genes whose behavior as a group differs significantly between two classes has received a lot of attention in the quest for the discovery of simple, accurate, and easily interpretable decision rules for disease classification and prediction. For example, the Top Scoring Pair (TSP) method seeks to find pairs of genes so that the probability of the reversal of the relative ranking of the expression levels of the genes in the two classes is maximized. The computational cost of finding a q-tuple of genes that scores highest under a given metric is O(G(q)), where G is the total number of genes. This cost is often problematic or prohibitive in practice (even for q = 2), as the number of genes G is often in the order of tens of thousands. Methods: In this paper, we show that this computational cost can be significantly reduced by excluding from consideration genes whose behavior is almost identical in the two classes and therefore their inclusion in any q-tuple is rather non-informative. Our criterion for the exclusion of genes is supported by a statistically robust metric, the Area Under the Curve (AUC) of the corresponding Receiver Operating Characteristic (ROC) curve. By filtering out genes whose AUC value is below a user-chosen threshold, as determined by a procedure that we describe in the paper, dramatic reductions in the run times are obtained while maintaining the same classification accuracy. Results: We have experimentally verified the gains of this approach on several case studies involving ovarian, colon, leukemia, breast and prostate cancers, and diffuse large b-cell lymphoma. Conclusions: The proposed method is not only faster (for example, we observed an average 78.65% reduction over the run time of TSP) while maintaining the same classification accuracy, but it can even result in better classification accuracy due to its inherent ability to avoid the so-c

关键词： microarray data analysis Gene expression Top Scoring Pair (TSP) Computational cost Cancer prediction

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