Visualization of Minute Mechanical-Excitation/Relaxation Wave-front Propagation in Myocardial Tissue

Unlike the case of skeletal muscle, the direction of myocardial contraction does not coincide with the direction of work necessary to eject the intraventricular blood, contributing to great complexity of the wall deformation sequence of cardiac contraction. The advent of advanced techniques (CT^1^, MRI^2,3^, SPECT^4^, echocardiology^5-9^, electrocardiography^10^, and magnetocardiography^11,12^) has enabled to the evaluation of cardiac function and disorders by the measurement of blood flow, pressure, electrical reaction process, and other factors. However, complexity of the contraction sequence is still not fully understood because the dynamic mechanical excitation process, which directly correlates with contraction, cannot be accurately measured based on these electro-magnetic phenomena. Here, developing and using a noninvasive novel imaging modality with high temporal and spatial resolutions^13-17^, we show that the propagation of the mechanical wave-front occurs at the beginning of each cardiac contraction and relaxation sequence for the first time. The former occurs about 60 ms prior to the ordinarily accepted onset time of the contraction (R-wave of the electrocardiogram). From the apical side of the interventricular septum, close to the terminal of the Purkinje fibers (specialized to carry contraction impulses), a minute velocity component with an amplitude of several tenth micrometers is generated and propagates sequentially to the entire left ventricle, that is, it propagates from the apex to the base of the posterior wall, and then from the base to the apex of the septum, with a propagation speed of 3-9 m/s. The latter occurs at the end of the first heart sound at the apical side and propagates to the base side with a speed of 0.6 m/s. These physiological findings, unlike the widely accepted myocardial excitation process, have potential for accurate assessment of myocardial tissue damage in coronary disease and cardiomyopathy. This dynamic measurement modality is also applicable to various tissues in biology

Regional Differences in Phase Velocity of Pulsive Wave Propagating Along the Heart Wall

科研費報告書収録論文(課題番号:17206043/研究代表者:金井浩/粘弾性特性に関する断層像の非侵襲的描出による心筋拡張特性の体系的解明

In vivo viscoelasticity estimation of myocardium

科研費報告書収録論文(課題番号:17206043/研究代表者:金井浩/粘弾性特性に関する断層像の非侵襲的描出による心筋拡張特性の体系的解明

Ultrasonic Imaging of Propagation of Electric Excitation in Heart Wall

科研費報告書収録論文(課題番号:17206043/研究代表者:金井浩/粘弾性特性に関する断層像の非侵襲的描出による心筋拡張特性の体系的解明

Visualization of Propagation of Pulse Vibration along the Heart Wall and Imaging of its Propagation Speed

科研費報告書収録論文(課題番号:17206043/研究代表者:金井浩/粘弾性特性に関する断層像の非侵襲的描出による心筋拡張特性の体系的解明

Distribution of a brain-specific extracellular matrix protein in developing and adult zebrafish

A monoclonal antibody (IgG) that recognizes a 53-kDa zebrafishnext brain protein was isolated and used to characterize the distribution of this protein in zebrafish.next (1) The antigen was found only in the brain and not in any other tissues such as muscle, dermis and cartilage. Within the brain, the antibody recognized extracellular matrix (ECM) outside neuronal cells. (2) Digestion by hyaluronidase released the antigen from brain tissue, and the monoclonal antibody staining was also decreased by the digestion by hyaluronidase. (3) The pattern of antigen distribution is not perineuronal, as the density of the antigen at the periphery of the cells was practically identical to that of the empty intercellular spaces. Therefore, this monoclonal antibody does not recognize the perineuronal glycocortex. (4) The antigen is distributed only in limited areas of the brain, namely in the periphery of the forebrain, the hypothalamus, the optic tectum, the interpeduncular nucleus, the cerebellum and the ventricular rim of the medulla. In the optic tectum, the antibody strongly stained the most superficial layer, and in the cerebellum, it stained the molecular but not the granular layer. These patterns of distribution are very different from those of other typical brain ECM proteins and suggest that this protein may play quite distinct roles in brain development and maintenance.</p

Implication of Tryptophan 2,3-Dioxygenase and its Novel Variants in the Hippocampus and Cerebellum During the Developing and Adult Brain

Tryptophan 2,3-dioxygenase (TDO) is a first and rate-limiting enzyme for the kynurenine pathway of tryptophan metabolism. Using Tdo−/− mice, we have recently shown that TDO plays a pivotal role in systemic tryptophan metabolism and brain serotonin synthesis as well as emotional status and adult neurogenesis. However, the expression of TDO in the brain has not yet been well characterized, in contrast to its predominant expression in the liver. To further examine the possible role of local TDO in the brain, we quantified the levels of tdo mRNA in various nervous tissues, using Northern blot and quantitative real-time RT-PCR. Higher levels of tdo mRNA expression were detected in the cerebellum and hippocampus. We also identified two novel variants of the tdo gene, termed tdo variant1 and variant2, in the brain. Similar to the known TDO form (TDO full-form), tetramer formation and enzymatic activity were obtained when these variant forms were expressed in vitro. While quantitative real-time RT-PCR revealed that the tissue distribution of these variants was similar to that of tdo full-form, the expression patterns of these variants during early postnatal development in the hippocampus and cerebellum differed. Our findings indicate that in addition to hepatic TDO, TDO and its variants in the brain might function in the developing and adult nervous system. Given the previously reported associations of tdo gene polymorphisms in the patients with autism and Tourette syndrome, the expression of TDO in the brain suggests the possible influence of TDO on psychiatric status. Potential functions of TDOs in the cerebellum, hippocampus and cerebral cortex under physiological and pathological conditions are discussed

Transcutaneous Measurement and SpectrumAnalysis of Heart Wall Vibrations

科研費報告書収録論文(課題番号:08555096・基盤研究(B)(2)・H8～H9/研究代表者:金井, 浩/心筋の早期診断を可能とする心臓壁微小振動の超音波計測及び解析装置の開発

Study on Application of Static Magnetic Field for Adjuvant Arthritis Rats

In order to examine the effectiveness of the application of static magnetic field (SMF) on pain relief, we performed a study on rats with adjuvant arthritis (AA). Sixty female Sprague–Dawley (SD) rats (age: 6 weeks, body weight: approximately 160 g) were divided into three groups [SMF-treated AA rats (Group I), non-SMF-treated AA rats (Group II) and control rats (Group III)]. The SD rats were injected in the left hind leg with 0.6 mg/0.05 ml Mycobacterium butyrium to induce AA. The rats were bred for 6 months as chronic pain model. Thereafter, the AA rats were or were not exposed to SMF for 12 weeks. We assessed the changes in the tail surface temperature, locomotor activity, serum inflammatory marker and bone mineral density (BMD) using thermography, a metabolism measuring system and the dual-energy X-ray absorptiometry (DEXA) method, respectively. The tail surface temperature, locomotor activity and femoral BMD of the SMF-exposed AA rats were significantly higher than those of the non-SMF-exposed AA rats, and the serum inflammatory marker was significantly lower. These findings suggest that the pain relief effects are primarily due to the increased blood circulation caused by the rise in the tail surface temperature. Moreover, the pain relief effects increased with activity and BMD of the AA rats

Smoothness Analysis of Adversarial Training

Deep neural networks are vulnerable to adversarial attacks. Recent studies about adversarial robustness focus on the loss landscape in the parameter space since it is related to optimization and generalization performance. These studies conclude that the difficulty of adversarial training is caused by the non-smoothness of the loss function: i.e., its gradient is not Lipschitz continuous. However, this analysis ignores the dependence of adversarial attacks on model parameters. Since adversarial attacks are optimized for models, they should depend on the parameters. Considering this dependence, we analyze the smoothness of the loss function of adversarial training using the optimal attacks for the model parameter in more detail. We reveal that the constraint of adversarial attacks is one cause of the non-smoothness and that the smoothness depends on the types of the constraints. Specifically, the $L_\infty$ constraint can cause non-smoothness more than the $L_2$ constraint. Moreover, our analysis implies that if we flatten the loss function with respect to input data, the Lipschitz constant of the gradient of adversarial loss tends to increase. To address the non-smoothness, we show that EntropySGD smoothens the non-smooth loss and improves the performance of adversarial training.Comment: 22 pages, 7 figures. In V3, we add the results of EntropySGD for adversarial trainin