Insurance Industry and E-Business

亀井利明教授古稀記念特

Development of tricuspid regurgitation late after left-sided valve surgery: a single-center experience with long-term echocardiographic examinations

OBJECTIVES: This study sought to investigate the incidence and identify the predictors of significant tricuspid regurgitation (TR) development long after left-sided valve surgery. METHODS: Of 615 patients who underwent surgery for left-sided valve disease between 1992 and 1995, 335 patients without significant TR who completed at least 5 years of clinical and echocardiographic follow-up were enrolled. Late significant TR development was assessed by echocardiography with a mean follow-up duration of 11.6 +/- 2.1 years. RESULTS: Significant late TR was found in 90 patients (26.9%). Patients with late TR showed an advanced age (47.6 +/- 13.4 vs 44.3 +/- 13.2 years, P = .04), a higher prevalence of preoperative atrial fibrillation (83.3 vs 46.5%, P < .001), a greater left atrial dimension (56.9 +/- 13.2 vs 52.4 +/- 11.5 mm, P = .006), and a higher prevalence of prior valve surgery (40.0 vs 25.3%, P = .01). In addition, late TR occurred more frequently in patients who had undergone mitral valve surgery than in those who did not (93.3 vs 72.2%, P < .001). However, multivariate analysis showed that the presence of preoperative atrial fibrillation (odds ratio 5.37; 95% CI 2.71-10.65; P < .001) was the only independent factor of late TR development. Patients who developed late TR had a lower event-free survival rate than those who did not (P = .03). CONCLUSIONS: The development of significant TR long after left-sided valve surgery is not uncommon with an estimated incidence of 27% and is closely associated with a poor prognosis. The presence of preoperative atrial fibrillation was identified as the only independent predictor of the development of late TR

Genetic Drivers of Heterogeneity in Type 2 Diabetes Pathophysiology

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P \u3c 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care

Genetic drivers of heterogeneity in type 2 diabetes pathophysiology

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P &lt; 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care.</p

Fault Detection of a Roller-Bearing System through the EMD of a Wavelet Denoised Signal

This paper investigates fault detection of a roller bearing system using a wavelet denoising scheme and proper orthogonal value (POV) of an intrinsic mode function (IMF) covariance matrix. The IMF of the bearing vibration signal is obtained through empirical mode decomposition (EMD). The signal screening process in the wavelet domain eliminates noise-corrupted portions that may lead to inaccurate prognosis of bearing conditions. We segmented the denoised bearing signal into several intervals, and decomposed each of them into IMFs. The first IMF of each segment is collected to become a covariance matrix for calculating the POV. We show that covariance matrices from healthy and damaged bearings exhibit different POV profiles, which can be a damage-sensitive feature. We also illustrate the conventional approach of feature extraction, of observing the kurtosis value of the measured signal, to compare the functionality of the proposed technique. The study demonstrates the feasibility of wavelet-based de-noising, and shows through laboratory experiments that tracking the proper orthogonal values of the covariance matrix of the IMF can be an effective and reliable measure for monitoring bearing fault

Genome sequencing and protein domain annotations of Korean Hanwoo cattle identify Hanwoo-specific immunity-related and other novel genes

Abstract Background Identification of genetic mechanisms and idiosyncrasies at the breed-level can provide valuable information for potential use in evolutionary studies, medical applications, and breeding of selective traits. Here, we analyzed genomic data collected from 136 Korean Native cattle, known as Hanwoo, using advanced statistical methods. Results Results revealed Hanwoo-specific protein domains which were largely characterized by immunoglobulin function. Furthermore, domain interactions of novel Hanwoo-specific genes reveal additional links to immunity. Novel Hanwoo-specific genes linked to muscle and other functions were identified, including protein domains with functions related to energy, fat storage, and muscle function that may provide insight into the mechanisms behind Hanwoo cattle’s uniquely high percentage of intramuscular fat and fat marbling. Conclusion The identification of Hanwoo-specific genes linked to immunity are potentially useful for future medical research and selective breeding. The significant genomic variations identified here can crucially identify genetic novelties that are arising from useful adaptations. These results will allow future researchers to compare and classify breeds, identify important genetic markers, and develop breeding strategies to further improve significant traits

Acceptance and Commitment Therapy for Destructive Experiential Avoidance (ACT-DEA): A Feasibility Study

Background: This study is a preliminary study on an acceptance and commitment therapy (ACT) program that mitigates destructive experiential avoidance (DEA) behaviors, including self-harm behavior and addiction; Methods: Twenty participants aged 15&ndash;25 years who had confirmed DEA behavior within the last month participated in a total of six sessions of ACT. Demographic characteristics, history of psychiatric illness, and TYPES and patterns of DEA behavior were confirmed in the baseline survey. The severity of clinical symptoms, frequency of DEA behavior and impulsivity, characteristics of experiential avoidance (EA) behavior, depression, and quality of life (QOL) were measured before and after the program for comparative statistical tests using the intention-to-treat method. Furthermore, the severity of clinical symptoms was evaluated after each program, along with the frequency of DEA behavior and trends in impulsivity, which were investigated based on the behavior log; Results: After the ACT program, both the frequency of DEA behavior and impulsivity and the severity of clinical symptoms, depression, and anxiety decreased significantly. Furthermore, among the EA characteristics, pain aversion, distraction and inhibition, and delayed behavior significantly improved. Moreover, the overall QOL, psychological and social relationships, and QOL regarding the environment also improved; Conclusions: The results of this feasibility study demonstrate the potential of the ACT program as an effective intervention in DEA behavior. The results of this study may be used as preliminary data for future large-scale randomized studies