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BMC Medical Informatics and Decision Making (1)
Biomagnetic Research and Technology (1)
PLoS ONE (1)
Honda, Hiroyuki (3)
Kato, Ryuji (3)
Agata, Hideki (1)
Hakata, Toshiyuki (1)
Izawa, Hideo (1)
Kagami, Hideaki (1)
Kawase, Mitsuo (1)
Kiyota, Yasujiro (1)
Kobayashi, Takeshi (1)
Matsuoka, Fumiko (1)
Morino, Tomio (1)
Motoyama, Jun (1)
Murohara, Toyoaki (1)
Niwa, Kosuke (1)
Pelacho, Beatriz (1)
Shiono, Hirofumi (1)
Takase, Tomokazu (1)
Takeuchi, Ichiro (1)
Tanimura, Daisuke (1)
Ushida, Yasunori (1)
Yamashita, Noriyuki (1)
Yasui, Kenji (1)
Yoshida, Tsutomu (1)
Yoshida, Yasuko (1)
Year of Publication
Morphology-Based Prediction of Osteogenic Differentiation Potential of Human Mesenchymal Stem Cells
Human bone marrow mesenchymal stem cells (hBMSCs) are widely used cell source for clinical bone regeneration. Achieving the greatest therapeutic effect is dependent on the osteogenic differentiation potential of the stem cells to be implanted. However, there are still no practical methods to characterize such potential non-invasively or previously. Monitoring cellular morphology is a practical and non-invasive approach for evaluating osteogenic potential. Unfortunately, such image-based approaches had been historically qualitative and requiring experienced interpretation. By combining the non-invasive attributes of microscopy with the latest technology allowing higher throughput and quantitative imaging metrics, we studied the applicability of morphometric features to quantitatively predict cellular osteogenic potential. We applied computational machine learning, combining cell morphology features with their corresponding biochemical osteogenic assay results, to develop prediction model of osteogenic differentiation. Using a dataset of 9,990 images automatically acquired by BioStation CT during osteogenic differentiation culture of hBMSCs, 666 morphometric features were extracted as parameters. Two commonly used osteogenic markers, alkaline phosphatase (ALP) activity and calcium deposition were measured experimentally, and used as the true biological differentiation status to validate the prediction accuracy. Using time-course morphological features throughout differentiation culture, the prediction results highly correlated with the experimentally defined differentiation marker values (R>0.89 for both marker predictions). The clinical applicability of our morphology-based prediction was further examined with two scenarios: one using only historical cell images and the other using both historical images together with the patient's own cell images to predict a new patient's cellular potential. The prediction accuracy was found to be greatly enhanced by incorporation of patients' own cell features in the modeling, indicating the practical strategy for clinical usage. Consequently, our results provide strong evidence for the feasibility of using a quantitative time series of phase-contrast cellular morphology for non-invasive cell quality prediction in regenerative medicine.
Combinational risk factors of metabolic syndrome identified by fuzzy neural network analysis of health-check data
BMC Medical Informatics and Decision Making
Lifestyle-related diseases represented by metabolic syndrome develop as results of complex interaction. By using health check-up data from two large studies collected during a long-term follow-up, we searched for risk factors associated with the development of metabolic syndrome.
In our original study, we selected 77 case subjects who developed metabolic syndrome during the follow-up and 152 healthy control subjects who were free of lifestyle-related risk components from among 1803 Japanese male employees. In a replication study, we selected 2196 case subjects and 2196 healthy control subjects from among 31343 other Japanese male employees. By means of a bioinformatics approach using a fuzzy neural network (FNN), we searched any significant combinations that are associated with MetS. To ensure that the risk combination selected by FNN analysis was statistically reliable, we performed logistic regression analysis including adjustment.
We selected a combination of an elevated level of γ-glutamyltranspeptidase (γ-GTP) and an elevated white blood cell (WBC) count as the most significant combination of risk factors for the development of metabolic syndrome. The FNN also identified the same tendency in a replication study. The clinical characteristics of γ-GTP level and WBC count were statistically significant even after adjustment, confirming that the results obtained from the fuzzy neural network are reasonable. Correlation ratio showed that an elevated level of γ-GTP is associated with habitual drinking of alcohol and a high WBC count is associated with habitual smoking.
This result obtained by fuzzy neural network analysis of health check-up data from large long-term studies can be useful in providing a personalized novel diagnostic and therapeutic method involving the γ-GTP level and the WBC count.
Data mining; Combinational risk factor; Fuzzy neural network; Glutamyltranspeptidase; Lifestyle disease; Personalized diagnostic method; White blood cell
Size dependent heat generation of magnetite nanoparticles under AC magnetic field for cancer therapy
Biomagnetic Research and Technology
We have developed magnetic cationic liposomes (MCLs) that contained magnetic nanoparticles as heating mediator for applying them to local hyperthermia. The heating performance of the MCLs is significantly affected by the property of the incorporated magnetite nanoparticles. We estimated heating capacity of magnetite nanoparticles by measuring its specific absorption rate (SAR) against irradiation of the alternating magnetic field (AMF).
Magnetite nanoparticles which have various specific-surface-area (SSA) are dispersed in the sample tubes, subjected to various AMF and studied SAR.
Heat generation of magnetite particles under variable AMF conditions was summarized by the SSA. There were two maximum SAR values locally between 12 m2/g to 190 m2/g of the SSA in all ranges of applied AMF frequency and those values increased followed by the intensity of AMF power. One of the maximum values was observed at approximately 90 m2/g of the SSA particles and the other was observed at approximately 120 m2/g of the SSA particles. A boundary value of the SAR for heat generation was observed around 110 m2/g of SSA particles and the effects of the AMF power were different on both hand. Smaller SSA particles showed strong correlation of the SAR value to the intensity of the AMF power though larger SSA particles showed weaker correlation.
Those results suggest that two maximum SAR value stand for the heating mechanism of magnetite nanoparticles represented by hysteresis loss and relaxation loss.
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