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1.  Label-Free Morphology-Based Prediction of Multiple Differentiation Potentials of Human Mesenchymal Stem Cells for Early Evaluation of Intact Cells 
PLoS ONE  2014;9(4):e93952.
Precise quantification of cellular potential of stem cells, such as human bone marrow–derived mesenchymal stem cells (hBMSCs), is important for achieving stable and effective outcomes in clinical stem cell therapy. Here, we report a method for image-based prediction of the multiple differentiation potentials of hBMSCs. This method has four major advantages: (1) the cells used for potential prediction are fully intact, and therefore directly usable for clinical applications; (2) predictions of potentials are generated before differentiation cultures are initiated; (3) prediction of multiple potentials can be provided simultaneously for each sample; and (4) predictions of potentials yield quantitative values that correlate strongly with the experimental data. Our results show that the collapse of hBMSC differentiation potentials, triggered by in vitro expansion, can be quantitatively predicted far in advance by predicting multiple potentials, multi-lineage differentiation potentials (osteogenic, adipogenic, and chondrogenic) and population doubling potential using morphological features apparent during the first 4 days of expansion culture. In order to understand how such morphological features can be effective for advance predictions, we measured gene-expression profiles of the same early undifferentiated cells. Both senescence-related genes (p16 and p21) and cytoskeleton-related genes (PTK2, CD146, and CD49) already correlated to the decrease of potentials at this stage. To objectively compare the performance of morphology and gene expression for such early prediction, we tested a range of models using various combinations of features. Such comparison of predictive performances revealed that morphological features performed better overall than gene-expression profiles, balancing the predictive accuracy with the effort required for model construction. This benchmark list of various prediction models not only identifies the best morphological feature conversion method for objective potential prediction, but should also allow clinicians to choose the most practical morphology-based prediction method for their own purposes.
doi:10.1371/journal.pone.0093952
PMCID: PMC3976343  PMID: 24705458
2.  Morphology-Based Prediction of Osteogenic Differentiation Potential of Human Mesenchymal Stem Cells 
PLoS ONE  2013;8(2):e55082.
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.
doi:10.1371/journal.pone.0055082
PMCID: PMC3578868  PMID: 23437049
3.  Combinational risk factors of metabolic syndrome identified by fuzzy neural network analysis of health-check data 
Background
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.
Methods
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.
Results
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.
Conclusions
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.
doi:10.1186/1472-6947-12-80
PMCID: PMC3469424  PMID: 22853735
Data mining; Combinational risk factor; Fuzzy neural network; Glutamyltranspeptidase; Lifestyle disease; Personalized diagnostic method; White blood cell
4.  Size dependent heat generation of magnetite nanoparticles under AC magnetic field for cancer therapy 
Background
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).
Method
Magnetite nanoparticles which have various specific-surface-area (SSA) are dispersed in the sample tubes, subjected to various AMF and studied SAR.
Result
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.
Conclusion
Those results suggest that two maximum SAR value stand for the heating mechanism of magnetite nanoparticles represented by hysteresis loss and relaxation loss.
doi:10.1186/1477-044X-6-4
PMCID: PMC2579422  PMID: 18928573
5.  Analysing the substrate multispecificity of a proton-coupled oligopeptide transporter using a dipeptide library 
Nature Communications  2013;4:2502.
Peptide uptake systems that involve members of the proton-coupled oligopeptide transporter (POT) family are conserved across all organisms. POT proteins have characteristic substrate multispecificity, with which one transporter can recognize as many as 8,400 types of di/tripeptides and certain peptide-like drugs. Here we characterize the substrate multispecificity of Ptr2p, a major peptide transporter of Saccharomyces cerevisiae, using a dipeptide library. The affinities (Ki) of di/tripeptides toward Ptr2p show a wide distribution range from 48 mM to 0.020 mM. This substrate multispecificity indicates that POT family members have an important role in the preferential uptake of vital amino acids. In addition, we successfully establish high performance ligand affinity prediction models (97% accuracy) using our comprehensive dipeptide screening data in conjunction with simple property indices for describing ligand molecules. Our results provide an important clue to the development of highly absorbable peptides and their derivatives including peptide-like drugs.
Proton-coupled oligopeptide transporters (POTs) can recognize and mediate the uptake of up to 8,400 di/tripeptides or peptide-like drugs. Ito et al. comprehensively map the substrate specificity of the yeast POT Ptr2p, and use this information to construct models for the prediction of ligand affinity.
doi:10.1038/ncomms3502
PMCID: PMC3791473  PMID: 24060756

Results 1-5 (5)