A major obstacle to improving prognoses in ovarian cancer is the lack of effective screening methods for early detection. Circulating microRNAs (miRNAs) have been recognized as promising biomarkers that could lead to clinical applications. Here, to develop an optimal detection method, we use microarrays to obtain comprehensive miRNA profiles from 4046 serum samples, including 428 patients with ovarian tumors. A diagnostic model based on expression levels of ten miRNAs is constructed in the discovery set. Validation in an independent cohort reveals that the model is very accurate (sensitivity, 0.99; specificity, 1.00), and the diagnostic accuracy is maintained even in early-stage ovarian cancers. Furthermore, we construct two additional models, each using 9–10 serum miRNAs, aimed at discriminating ovarian cancers from the other types of solid tumors or benign ovarian tumors. Our findings provide robust evidence that the serum miRNA profile represents a promising diagnostic biomarker for ovarian cancer.

Background and Purpose— Numerous studies have shown that circulating microRNAs (miRNAs) can be used as noninvasive biomarkers of various diseases. This study aimed to identify serum miRNAs that predict the risk of stroke. Methods— The cases were individuals who had been diagnosed with cerebrovascular disorder by brain imaging. The controls were individuals with no history of stroke who had undergone a medical checkup. Serum miRNA profiling was performed for all participants using microarray analysis. Samples were divided into discovery, training, and validation sets. In the discovery set, which consisted of control samples only, serum miRNAs that correlated with the predicted risk of stroke, as calculated using 7 clinical risk factors, were identified by Pearson correlation analysis. In the training set, a discriminant model between cases and controls was constructed using the identified miRNAs, Fisher linear discrimination model with leave-one-out cross-validation and DeLong test. In the validation set, the predictive accuracy of the constructed model was calculated. Results— First, in 1523 control samples (discovery set), we identified 10 miRNAs that correlated with a predicted risk of stroke. Second, in 45 controls and 87 cases (training set), we identified 7 of 10 miRNAs that significantly associated with cerebrovascular disorder (miR-1228-5p, miR-1268a, miR-1268b, miR-4433b-3p, miR-6090, miR-6752-5p, and miR-6803-5p). Third, a 3-miRNA combination model (miR-1268b, miR-4433b-3p, and miR-6803-5p) was constructed in the training set with a sensitivity of 84%, a specificity of 98%, and an area under the receiver operating characteristic curve of 0.95 (95% CI, 0.92–0.98). Finally, in 45 controls and 86 cases (validation set), the 3-miRNA model achieved a sensitivity of 80%, a specificity of 82%, and an area under the receiver operating characteristic of 0.89 (95% CI, 0.83–0.95) for cerebrovascular disorder. Conclusions— We identified 7 serum miRNAs that could predict the risk of cerebrovascular disorder before the onset of stroke.

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Abstract-The demand for ubiquitous healthcare monitoring has been increasingly raised for prevention of lifestyle-related diseases, acute life support or chronic therapies for inpatients and/or outpatients having chronic disorder and home medical care. From these view points, we developed a non-conscious healthcare monitoring system without any attachment of biological sensors and operations of devices, and an ambulatory postural changes and activities monitoring system. Furthermore in this study, in order to investigate those applicability to the ubiquitous healthcare monitoring, we have developed a new healthcare monitoring system combined with the non-conscious and the ambulatory measurements developed by us. In patients with chronic cardiovascular disease or stroke, the daily health conditions such as pulse, respiration, activities and so on, could be continuously measured in the hospital, the rehabilitation room and subject's own home, using the present system. The results demonstrated that the system appears useful for the ubiquitous healthcare monitoring not only at medical facility, but also during daily living at home.

Aim: The purpose of this study was to clarify the mechanism of the antibacterial action of two high potential and natural food additives, epigallocatechin gallate (EGCg) and theaflavin-3,3 0 -digallate (TF3), on Clostridium perfringens. Methods and Results: Minimal inhibitory concentrations were determined by the serial dilution method. Afterwards, the cells were treated with 250 or 1000 mg l À1 of EGCg and 125 or 500 mg l À1 of TF3 and morphological changes were observed and cell sizes were also measured under fluorescence microscopy. Our results showed that TF3 had a twice stronger antibacterial activity than EGCg against C. perfringens. Phase-contrast and fluorescence microscopy confirmed that the bacterial cells elongated without DNA segregation and septum formation in the presence of 250 mg l À1 EGCg. While in the higher concentration of EGCg and TF3, cell growth was suppressed. Bacterial cells reached to around 12 lm after the 24 h incubation with 250 mg l À1 EGCg, but the cells were shorter than the control at 1000 mg l À1 of EGCg. After washing and incubating the elongated cells in fresh medium, DNA segregated at 2 h of incubation. The average cell length decreased gradually and reached the normal size at 8 h. Conclusion: It seems that EGCg at a low concentration affected the proteins involved in the septum formation, DNA segregation and cell division. Furthermore, the high concentration of EGCg and TF3 seemed to cause stronger cellular damage to C. perfringens. Significance and Impact of the Study: These polyphenols are widely distributed in all higher plants especially in tea plants, and people tend to use natural food additives rather than synthetic ones. EGCg and TF3, as natural food additives, can prevent C. perfringens food poisoning along with other potential health benefits.