• Journal of Radiopharmaceuticals and Molecular Probes
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• v.6 no.2
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• pp.116-130
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• 2020

The aging society is globally one of biggest issue because it is related with various degenerative brain disease such as dementia, Parkinson's disease, Alzheimer's disease, multiple sclerosis, and cerebrovascular disease. These diseases are characterized by misfolded-protein aggregation; another pathological trait is "neuroinflammation". In physiological state, the resting microglia cells are activated and it removes abnormal synapses and cell membrane debris to maintain the homeostasis. In pathological state, however, microglia undergo morphological change form 'resting' to 'activated amoeboid phenotype' and the microglia cells are accumulated by neuronal damage, the inflammatory reactions induced nerve metamorphosis with a variety of neurotoxic factors including cytokines, chemokines, and reactive oxygen species. Thus, the activated microglia cell with various receptors (TSPO, COX, CR, P2XR, etc.) was perceived as important biomarkers for imaging the inflammatory progression. In this review, we would like to introduce the current status of the development of radiotracers that can image activated microglia.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.5
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• pp.2273-2278
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• 2014

Purpose: The purpose of our study was to explore the molecular mechanisms in the process of oral squamous cells carcinoma (OSCC) development. Method: We downloaded the affymetrix microarray data GSE31853 and identified differentially expressed genes (DEGs) between OSCC and normal tissues. Then Gene Ontology (GO) and Protein-Protein interaction (PPI) networks analysis was conducted to investigate the DEGs at the function level. Results: A total 372 DEGs with logFCI >1 and P value < 0.05 were obtained, including NNMT, BAX, MMP9 and VEGF. The enriched GO terms mainly were associated with the nucleoplasm, response to DNA damage stimuli and DNA repair. PPI network analysis indicated that GMNN and TSPO were significant hub proteins and steroid biosynthesis and synthesis and degradation of ketone bodies were significantly dysregulated pathways. Conclusion: It is concluded that the genes and pathways identified in our work may play critical roles in OSCC development. Our data provides a comprehensive perspective to understand mechanisms underlying OSCC and the significant genes (proteins) and pathways may be targets for therapy in the future.

• Journal of Bone Metabolism
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• v.25 no.4
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• pp.251-266
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• 2018

Background: The causal networks among genes that are commonly expressed in osteoblasts and during bone metastasis (BM) of breast cancer (BC) are not well understood. Here, we developed a machine learning method to obtain a plausible causal network of genes that are commonly expressed during BM and in osteoblasts in BC. Methods: We selected BC genes that are commonly expressed during BM and in osteoblasts from the Gene Expression Omnibus database. Bayesian Network Inference with Java Objects (Banjo) was used to obtain the Bayesian network. Genes registered as BC related genes were included as candidate genes in the implementation of Banjo. Next, we obtained the Bayesian structure and assessed the prediction rate for BM, conditional independence among nodes, and causality among nodes. Furthermore, we reported the maximum relative risks (RRs) of combined gene expression of the genes in the model. Results: We mechanistically identified 33 significantly related and plausibly involved genes in the development of BC BM. Further model evaluations showed that 16 genes were enough for a model to be statistically significant in terms of maximum likelihood of the causal Bayesian networks (CBNs) and for correct prediction of BM of BC. Maximum RRs of combined gene expression patterns showed that the expression levels of UBIAD1, HEBP1, BTNL8, TSPO, PSAT1, and ZFP36L2 significantly affected development of BM from BC. Conclusions: The CBN structure can be used as a reasonable inference network for accurately predicting BM in BC.