Behavior, cognition, and future direction of psychiatry

The human behavior and cognition are the two most important functions for human life in the society. The cognitive function is declined in the elderly, but it is not always the case. Fluid intelligence may decline along with aging but crystalized intelligence can be maintained even in the elderly. In addition to neurocognitive disorders (dementia), cognitive function is impaired with various psychiatric disorders and it will be the main target for future psychiatry. Due to the knowledge obtained from the recent development in brain mapping, psychiatrists can perceive and understand the meaning of the psychiatric symptoms based upon the dysfunction of these networks. Subjective experience of the patients should be paid more attention by closer collaboration between psychiatrists/ researchers and patients/ families. Elucidating the brain network representing common sense will be important. Psychiatrists are recommended to expand the range of the frame of their common sense to be able to understand the meaning of the patient behavior

Research Activities in the Department of Nursing

Research activity at the Department of Nursing is overviewed from the point of research topics, the theme of the projects admitted for grant from the Ministry of Education and Science of Japan, and expected research topics, trying to clarify the needs and challenges of the Department from multilateral aspects in future research activities. The Department of Nursing, Aino University is currently divided into the five areas and further into 12 fields. On the other hand, according to the Scientific Research Grant Program (2015 fiscal year), the research topics in nursing science is subdivided into the five areas; a) basic nursing, b) clinical nursing, c) lifelong developmental nursing, d) elderly nursing, and e) community health nursing

The KEGG resource for deciphering the genome

A grand challenge in the post-genomic era is a complete computer representation of the cell and the organism, which will enable computational prediction of higher-level complexity of cellular processes and organism behavior from genomic information. Toward this end we have been developing a knowledge-based approach for network prediction, which is to predict, given a complete set of genes in the genome, the protein interaction networks that are responsible for various cellular processes. KEGG at http://www.genome.ad.jp/kegg/ is the reference knowledge base that integrates current knowledge on molecular interaction networks such as pathways and complexes (PATHWAY database), information about genes and proteins generated by genome projects (GENES/SSDB/KO databases) and information about biochemical compounds and reactions (COMPOUND/GLYCAN/REACTION databases). These three types of database actually represent three graph objects, called the protein network, the gene universe and the chemical universe. New efforts are being made to abstract knowledge, both computationally and manually, about ortholog clusters in the KO (KEGG Orthology) database, and to collect and analyze carbohydrate structures in the GLYCAN database

A liquid biopsy signature for predicting early recurrence in patients with gastric cancer.

BACKGROUND: Gastric cancer (GC) patients who experience recurrence within the first year following surgery (early recurrence [ER]) exhibit worse prognosis. Herein, we established a microRNA-based liquid biopsy assay to predict ER in GC patients. METHODS: A comprehensive biomarker discovery was performed by analysing miRNA expression profiling in 271 primary GC tumours. Thereafter, the expression of these biomarkers was validated in 290 GC cases, which included 218 tissues and 72 pre-treatment sera, from two independent institutions. RESULTS: A panel of 8 miRNAs was identified during the initial biomarker discovery, and this panel could robustly predict ER in a tissue-based clinical cohort (area under the curve [AUC]: 0.81). Furthermore, a model combining the miRNA panel, microsatellite instability (MSI) status and tumour size exhibited superior predictive performance (AUC: 0.86), and was defined as a Prediction of Early Recurrence in GC (PERGC) signature, which was successfully validated in another independent cohort (AUC: 0.82). Finally, the PERGC signature was translated into a liquid biopsy assay (AUC: 0.81), and a multivariate regression analysis revealed this signature to be an independent predictor for ER (odds ratio: 11.20). CONCLUSION: We successfully established a miRNA-based liquid biopsy signature that robustly predicts the risk of ER in GC patients