Genetic Analysis of Cardiacβ Myosin Heavy Chain(MHC)Gene in Seven Families with Hypertrophic Cardiomyopathy in Japan

The purpose of this study was to identify the presence of either mutation or polymorphism in the cardiac β myosin heavy chain (MHC) gene of the Japanese who had familial hypertrophic cardiomyopathy (FHCM). We analyzed exons 3-25 of the cardiac MHC gene in seven unrelated Japanese families (17 affected and 10 unaffected individual with HCM), using the polymerase chain reaction (PCR)-single strand conformation polymorphism (SSCP) analysis. Our study showed that affected members of one family (proband; I.I.) had an identical pattern of aberrantly migrating band of exon 21.Similarly we found polymorphism and probable point mutation located on exon 3 of one patient with sporadic HCM (Pt;T.M.). Both proband;I. I. and Pt; T.M., developed lethal congestive heart failure with left ventricular (LV) dilatation as confirmed by autopsy. This suggest that PCR-SSCP analysis is an useful tool for clinical screening of HCM

A Case of Asymptomatic Left Ventricular Dysfunction during the Treatment of Metastatic Breast Cancer with Trastuzumab

We report a case of 29-year-old Japanese female with a history of resection of primary carcinoma of the right breast. After a partial response by chemotherapy with pirarubicin and docetaxel for lung metastasis, the patient developed multiple brain and bone metastases. As the degree of overexpression of human epidermal growth factor receptor-2 (HER2) was (2+), trastuzumab was administered in combination with paclitaxel. Asymptomatic left ventricular (LV) systolic dysfunction evaluated by echocardiography was observed ten weeks after the beginning of the treatment. After two weeks of discontinuation of the therapy, however, LV function showed rapid recovery and the resumed use of trastuzumab did not cause further cardiac deterioration. The patient died of sudden respiratory failure due to cerebral herniation and not to heart failure

Negatively charged low-density lipoprotein is associated with atherogenic risk in hypertensive patients

Negatively charged low-density lipoprotein (LDL), generated via multiple processes such as oxidation, acetylation, or glycosylation, plays a key role in the initiation and progression of atherosclerosis and related diseases. Anion-exchange high-performance liquid chromatography (AE-HPLC) can subfractionate LDL into LDL-1, LDL-2, and LDL-3 based on LDL particle charge, but the clinical significance of LDL subfractions has not yet been elucidated. The aim of this study was to determine the clinical significance of these fractions with particular regard to atherogenic risk in hypertensive patients. Ninety-eight patients with essential hypertension (age 67.0 ± 10.7 years; 54 males) were enrolled in the present study. The relationships between LDL subfractions and atherogenic risk factors, including lipid profiles, blood pressure and plasma 8-isoprostane as a marker of oxidative stress, were examined. LDL-1 levels were significantly and negatively correlated with body mass index (r = -0.384, p < 0.001), systolic blood pressure (r = -0.457, p < 0.001), non-high-density lipoprotein cholesterol levels (r = -0.457, p < 0.001) and 8-isoprostane levels (r = -0.415, p < 0.001). LDL-3, which is the most negatively charged fraction of total LDL, was significantly and positively correlated with these parameters (r = 0.267, 0.481, 0.357, and 0.337, respectively). LDL-1 levels were significantly lower (p < 0.001), and LDL-2 and LDL-3 levels were significantly higher (each p < 0.001) in patients with poorly controlled hypertension than in patients with well-controlled hypertension. In addition, an increase in the total number of traditional risk factors at time of study participation, but not previous diagnosis, was associated with a decrease in LDL-1 levels and increases in LDL-2 and LDL-3 levels. These data suggest that LDL subfractions are associated with multiple atherogenic risk factors and that treatment to modify these risk factors could result in Negatively charged low-density lipoprotein (LDL), generated via multiple processes such as oxidation, acetylation, or glycosylation, plays a key role in the initiation and progression of atherosclerosis and related diseases. Anion-exchange high-performance liquid chromatography (AE-HPLC) can subfractionate LDL into LDL-1, LDL-2, and LDL-3 based on LDL particle charge, but the clinical significance of LDL subfractions has not yet been elucidated. The aim of this study was to determine the clinical significance of these fractions with particular regard to atherogenic risk in hypertensive patients. Ninety-eight patients with essential hypertension (age 67.0 ± 10.7 years; 54 males) were enrolled in the present study. The relationships between LDL subfractions and atherogenic risk factors, including lipid profiles, blood pressure and plasma 8-isoprostane as a marker of oxidative stress, were examined. LDL-1 levels were significantly and negatively correlated with body mass index (r = −0.384, p < 0.001), systolic blood pressure (r = −0.457, p < 0.001), non-high-density lipoprotein cholesterol levels (r = −0.457, p < 0.001) and 8-isoprostane levels (r = −0.415, p < 0.001). LDL-3, which is the most negatively charged fraction of total LDL, was significantly and positively correlated with these parameters (r = 0.267, 0.481, 0.357, and 0.337, respectively). LDL-1 levels were significantly lower (p < 0.001), and LDL-2 and LDL-3 levels were significantly higher (each p < 0.001) in patients with poorly controlled hypertension than in patients with well-controlled hypertension. In addition, an increase in the total number of traditional risk factors at time of study participation, but not previous diagnosis, was associated with a decrease in LDL-1 levels and increases in LDL-2 and LDL-3 levels. These data suggest that LDL subfractions are associated with multiple atherogenic risk factors and that treatment to modify these risk factors could result in changes in LDL subfraction levels. In conclusion, LDL subfractions isolated by AE-HPLC may represent a marker of atherogenic risk in patients with hypertension

Culprit segments identified by optical coherence tomography in patients with acute myocardial infarction: Two case reports

The high resolution of optical coherence tomography (OCT) provides detailed information about coronary plaque morphology, which enables the mechanism of acute myocardial infarction to be evaluated. We describe two patients with acute myocardial infarction in whom culprit segments were identified by OCT, but not by either coronary angiography or intravascular ultrasound

Impact of Gender on In-hospital Mortality in Patients with Acute Myocardial Infarction in Nagasaki

Acute myocardial infarction (AMI) is one of the leading causes of death in Japan. Immediate reperfusion therapy, includingcoronary intervention, improves patient prognosis. Despite this, females are said to be more prone to poor prognosis. A regional AMI registry in Nagasaki prefecture has been instituted recently that will evaluate whether female gender might predict short-term in-hospital death. Seventeen regional AMI centers enrolled all AMI patients from September 2014 through March 2016. A propensity score (PS) was derived using logistic regression to model the probability of females as a total function of the potential confounding covariates. Two types of PS techniques were used: PS matching and PS stratification. The consistency of in-hospital death was determined between PS matched patients of both genders. Based on PS, patients were ranked and stratified into five groups for the PS stratification. Out of 996 patients, 67 (6.7%) died during hospitalization: 31 (10.4%) out of 298 females and 36 (5.2%) out of 698 males (p < 0.0025). The proportion of cardiac and non-cardiac related death was almost same between genders (25 and 6 in female, 29 and 7 in male, respectively). Among 196 PS matched patients, there was a consistency between genders regarding in-hospital deaths (McNemar test, p = 0.6698). The 717 propensity scored patients had no significant differences between genders among propensity quintiles (Cochran-Mantel-Heanszel test, p = 0.7117). We found that gender alone is not an indicator of short-term in-hospital death in acute myocardial infarction patients

Eth2Vec: Learning contract-wide code representations for vulnerability detection on Ethereum smart contracts

Ethereum smart contracts are computer programs that are deployed and executed on the Ethereum blockchain to enforce agreements among untrusting parties. Being the most prominent platform that supports smart contracts, Ethereum has been targeted by many attacks and plagued by security incidents. Consequently, many smart contract vulnerabilities have been discovered in the past decade. To detect and prevent such vulnerabilities, different security analysis tools, including static and dynamic analysis tools, have been created, but their performance decreases drastically when codes to be analyzed are constantly being rewritten. In this paper, we propose Eth2Vec, a machine-learning-based static analysis tool that detects smart contract vulnerabilities. Eth2Vec maintains its robustness against code rewrites; i.e., it can detect vulnerabilities even in rewritten codes. Other machine-learning-based static analysis tools require features, which analysts create manually, as inputs. In contrast, Eth2Vec uses a neural network for language processing to automatically learn the features of vulnerable contracts. In doing so, Eth2Vec can detect vulnerabilities in smart contracts by comparing the similarities between the codes of a target contract and those of the learned contracts. We performed experiments with existing open databases, such as Etherscan, and Eth2Vec was able to outperform a recent model based on support vector machine in terms of well-known metrics, i.e., precision, recall, and F1-score

Eth2Vec: Learning Contract-Wide Code Representations for Vulnerability Detection on Ethereum Smart Contracts

Ethereum smart contracts are programs that run on the Ethereum blockchain, and many smart contract vulnerabilities have been discovered in the past decade. Many security analysis tools have been created to detect such vulnerabilities, but their performance decreases drastically when codes to be analyzed are being rewritten. In this paper, we propose Eth2Vec, a machine-learning-based static analysis tool for vulnerability detection, with robustness against code rewrites in smart contracts. Existing machine-learning-based static analysis tools for vulnerability detection need features, which analysts create manually, as inputs. In contrast, Eth2Vec automatically learns features of vulnerable Ethereum Virtual Machine (EVM) bytecodes with tacit knowledge through a neural network for language processing. Therefore, Eth2Vec can detect vulnerabilities in smart contracts by comparing the code similarity between target EVM bytecodes and the EVM bytecodes it already learned. We conducted experiments with existing open databases, such as Etherscan, and our results show that Eth2Vec outperforms the existing work in terms of well-known metrics, i.e., precision, recall, and F1-score. Moreover, Eth2Vec can detect vulnerabilities even in rewritten codes

Serum Adiponectin and Leptin in a Patient with Cushings Syndrome Before and After Adrenalectomy

We measured the serum adiponectin and leptin concentrations before and after successful removal of a left adrenal adenoma in a 46-year-old woman with Cushings syndrome. The serum adiponectin level was 6.0 μg/ml before the operation and rose to 8.1 μg/ml after adrenalectomy. However, the serum leptin level was markedly high (24.8 ng/ml) before the operation and decreased to within the normal range (6.0 ng/ml) 6 months after adrenalectomy, concomitant with weight reduction and normalization of the serum cortisol level