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Worldwide, a large number of cases of harmful mushroom exposure and consumption result in hallucinations, sickness, and death. One contributing factor is that certain poisonous mushrooms closely resemble their edible counterparts, making it difficult for general public collectors to distinguish one from the other. We propose a method to classify mushroom types from field-collection images using a smartphone application based on a convolutional neural network. The application helps people without proper mycology background or training to distinguish poisonous mushrooms from edible ones with which they may be confused. We showed three case studies to classify two-, three-, and five-class models by optimizing their training steps by cross-validation. An android app was developed by transferring the server-based trained model and allowing users to obtain probability scores for the correct genus classification. Our experiments showed that this method could provide sensitivity and specificity of two-, three-, and five-class mushroom models ranging from 89% to 100% using an image from the field with diverse backgrounds and objects.

Pain is constructed through complex interactions among multiple brain systems, but it remains unclear how functional brain networks are reconfigured over time while experiencing pain. Here, we investigated the time-varying changes in the functional brain networks during 20 min capsaicin-induced sustained orofacial pain. In the early stage, the orofacial areas of the primary somatomotor cortex were separated from other areas of the somatosensory cortex and integrated with subcortical and frontoparietal regions, constituting an extended brain network of sustained pain. As pain decreased over time, the subcortical and frontoparietal regions were separated from this brain network and connected to multiple cerebellar regions. Machine-learning models based on these network features showed significant predictions of changes in pain experience across two independent datasets (n = 48 and 74). This study provides new insights into how multiple brain systems dynamically interact to construct and modulate pain experience, advancing our mechanistic understanding of sustained pain.

Identification of predictive neuroimaging markers of pain intensity changes is a crucial issue to better understand macroscopic neural mechanisms of pain. Although a single connection between the medial prefrontal cortex and nucleus accumbens has been suggested as a powerful marker, how the complex interactions on a large-scale brain network can serve as the markers is underexplored. Here, we aimed to identify a set of functional connections predictive of longitudinal changes in pain intensity using large-scale brain networks. We re-analyzed previously published resting-state functional magnetic resonance imaging data of 49 subacute back pain (SBP) patients. We built a network-level model that predicts changes in pain intensity over one year by combining independent component analysis and a penalized regression framework. Connections involving top-down pain modulation, multisensory integration, and mesocorticolimbic circuits were identified as predictive markers for pain intensity changes. Pearson’s correlations between actual and predicted pain scores were r = 0.33–0.72, and group classification results between SBP patients with persisting pain and recovering patients, in terms of area under the curve (AUC), were 0.89/0.75/0.75 for visits four/three/two, thus outperforming the previous work (AUC 0.83/0.73/0.67). This study identified functional connections important for longitudinal changes in pain intensity in SBP patients, providing provisional markers to predict future pain using large-scale brain networks.

Exposure to high and/or repetitive force exertions can lead to musculoskeletal injuries. However, measuring worker force exertion levels is challenging, and existing techniques can be intrusive, interfere with human-machine interface, and/or limited by subjectivity. In this work, computer vision techniques are developed to detect isometric grip exertions using facial videos and wearable photoplethysmogram. Eighteen participants (19-24 years) performed isometric grip exertions at varying levels of maximum voluntary contraction. Novel features that predict forces were identified and extracted from video and photoplethysmogram data. Two experiments with two (High/Low) and three (0%MVC/50%MVC/100%MVC) labels were performed to classify exertions. The Deep Neural Network classifier performed the best with 96% and 87% accuracy for two- and three-level classifications, respectively. This approach was robust to leave subjects out during cross-validation (86% accuracy when 3-subjects were left out) and robust to noise (i.e. 89% accuracy for correctly classifying talking activities as low force exertions).

Pain is constructed through complex interactions among multiple brain systems, but it remains unclear how functional brain network representations are dynamically reconfigured over time while experiencing pain. Here, we investigated the dynamic changes in the functional brain networks during 20-min capsaicin-induced sustained orofacial pain. In the early stage, the orofacial areas of the primary somatomotor cortex were separated from the other primary somatomotor cortices and integrated with subcortical and frontoparietal regions, constituting a brain-wide pain supersystem. As pain decreased over time, the subcortical and frontoparietal regions were separated from this pain supersystem and connected to multiple cerebellar regions. Machine-learning models based on these dynamic network features showed significant predictions of changes in pain experience across two independent datasets (n = 48 and 74). This study provides new insights into how multiple brain systems dynamically interact to construct and modulate pain experience, potentially advancing our mechanistic understanding of chronic pain.

The neurological symptoms of stroke have traditionally provided the foundation for functional mapping of the brain. However, there are many unresolved aspects in our understanding of cerebral activity, especially regarding high-level cognitive functions. This review provides a comprehensive look at the pathophysiology of post-stroke cognitive impairment in light of recent findings from advanced imaging techniques. Combining network neuroscience and clinical neurology, our research focuses on how changes in brain networks correlate with post-stroke cognitive prognosis. More specifically, we first discuss the general consequences of stroke lesions due to damage of canonical resting-state large-scale networks or changes in the composition of the entire brain. We also review emerging methods, such as lesion-network mapping and gradient analysis, used to study the aforementioned events caused by stroke lesions. Lastly, we examine other patient vulnerabilities, such as superimposed amyloid pathology and blood-brain barrier leakage, which potentially lead to different outcomes for the brain network compositions even in the presence of similar stroke lesions. This knowledge will allow a better understanding of the pathophysiology of post-stroke cognitive impairment and provide a theoretical basis for the development of new treatments, such as neuromodulation.

Sustained pain is a major characteristic of clinical pain disorders, but it is difficult to assess in isolation from co-occurring cognitive and emotional features in patients. In this study, we developed a functional magnetic resonance imaging signature based on whole-brain functional connectivity that tracks experimentally induced tonic pain intensity and tested its sensitivity, specificity and generalizability to clinical pain across six studies (total n = 334). The signature displayed high sensitivity and specificity to tonic pain across three independent studies of orofacial tonic pain and aversive taste. It also predicted clinical pain severity and classified patients versus controls in two independent studies of clinical low back pain. Tonic and clinical pain showed similar network-level representations, particularly in somatomotor, frontoparietal and dorsal attention networks. These patterns were distinct from representations of experimental phasic pain. This study identified a brain biomarker for sustained pain with high potential for clinical translation. The functional magnetic resonance imaging connectivity pattern of tonic experimental orofacial pain can be used as a quantitative and unbiased biomarker of clinical pain.

We report an application for the smartphone as an accurate and unbiased reading platform of a lateral flow immunoassays for food safety application. In particular, this report focuses on detection of food-borne bacteria in samples extracted from food matrices such as ground beef and spinach. The lateral flow assay is a widely accepted methodology owing to its on-site results, low-cost analysis, and ease of use with minimum user inputs, even though sensitivity is not quite equivalent to that of standard laboratory equipment. An antibody-antigen relationship is transduced into a color change on a nitrocellulose pad while visual interpretation of this color change can result in uncertainty, particularly near the detection limit of the assay. Employing the high resolution integrated camera, constant illumination from light source, and computing power of a smartphone, we provide an objective and accurate method to determine the bacterial cell concentration in a food matrix based on the regression model from the color intensity of test lines. A 3D-printed sample holder was designed for representative commercial lateral flow assays and an in-house application was developed in Android Studio to solve the inverse problem to provide cell concentration information from the color intensity. Test results with E.coli O157:H7 as a model organism suggests that smartphone-based reader can detect 104-105 CFU/ml from ground beef and spinach food matrices.

Purpose Calcium channel blockers diltiazem and nitrate have been used as selective coronary vasodilators for patients with significant coronary artery spasm (CAS). However, no study has compared the efficacy of diltiazem alone versus diltiazem with nitrate for long-term clinical outcomes in patients with CAS. Materials and methods A total of 2741 consecutive patients without significant coronary artery disease with positive CAS by acetylcholine (Ach) provocation test between November 2004 and May 2014 were enrolled. Significant CAS was defined as a narrowing of >70% by incremental intracoronary injection of 20, 50, and 100 μg of Ach into the left coronary artery. Patients were assigned to either the diltiazem group (n=842) or the dual group (diltiazem with nitrate, n=1899) at physician discretion. To adjust for potential confounders, a propensity score matching (PSM) analysis was performed using the logistic regression model. After PSM analysis, two well-balanced groups (811 pairs, n=1622, C-statistic=0.708) were generated. Results At 5 years, there were similar incidences in primary endpoints, including mortality, myocardial infarction, revascularization, and recurrent angina requiring repeat coronary angiography between the two groups. Diltiazem alone was not an independent predictor for major adverse cardiovascular events or recurrent angina requiring repeat coronary angiography. Conclusion Despite the expected improvement of endothelial function and the relief of CAS, the combination of diltiazem and nitrate treatment was not superior to diltiazem alone in reducing mortality and cardiovascular events up to 5 years in patients with significant CAS.

Purpose: Intravascular optical coherence tomography (IV-OCT) is a high-resolution imaging method used to visualize the microstructure of arterial walls in vivo. IV-OCT enables the clinician to clearly observe and accurately measure stent apposition and neointimal coverage of coronary stents, which are associated with side effects such as in-stent thrombosis. In this study, the authors present an algorithm for quantifying stent apposition and neointimal coverage by automatically detecting lumen contours and stent struts in IV-OCT images. Methods: The algorithm utilizes OCT intensity images and their first and second gradient images along the axial direction to detect lumen contours and stent strut candidates. These stent strut candidates are classified into true and false stent struts based on their features, using an artificial neural network with one hidden layer and ten nodes. After segmentation, either the protrusion distance (PD) or neointimal thickness (NT) for each strut is measured automatically. In randomly selected image sets covering a large variety of clinical scenarios, the results of the algorithm were compared to those of manual segmentation by IV-OCT readers. Results: Stent strut detection showed a 96.5% positive predictive value and a 92.9% true positive rate. In addition, case-by-case validation also showed comparable accuracy for most cases. High correlation coefficients (R > 0.99) were observed for PD and NT between the algorithmic and the manual results, showing little bias (0.20 and 0.46 μm, respectively) and a narrow range of limits of agreement (36 and 54 μm, respectively). In addition, the algorithm worked well in various clinical scenarios and even in cases with a low level of stent malapposition and neointimal coverage. Conclusions: The presented automatic algorithm enables robust and fast detection of lumen contours and stent struts and provides quantitative measurements of PD and NT. In addition, the algorithm was validated using various clinical cases to demonstrate its reliability. Therefore, this technique can be effectively utilized for clinical trials on stent-related side effects, including in-stent thrombosis and in-stent restenosis.

Background and purpose Similar-sized stroke lesions at similar locations can have different prognoses in clinical practice. Lesion-network mapping elucidates network-level effects of lesions that cause specific neurologic symptoms and signs, and also provides a group-level understanding. This study visualized the effects of stroke lesions on the functional brain networks of individual patients. Methods We enrolled patients with ischemic stroke who were hospitalized within 1 week of the stroke occurrence. Resting-state functional magnetic resonance imaging was performed 3 months after the index stroke. For image preprocessing, acute stroke lesions were visually delineated based on diffusion-weighted images obtained at admission, and the lesion mask was drawn using MRIcron software. Correlation matrices were calculated from 280 brain regions using the Brainnetome Atlas, and connectograms were visualized using in-house MATLAB code. Results We found characteristic differences in connectograms between pairs of patients who had comparable splenial, frontal cortical, cerebellar, and thalamocapsular lesions. Two representative patients with bilateral thalamic infarctions showed significant differences in their reconstructed connectograms. The cognitive function had recovered well at 3 months after stroke occurrence in patients with well-maintained interhemispheric and intrahemispheric connectivities. Conclusions This pilot study has visualized the effects of stroke lesions on the functional brain networks of individual patients. Consideration of the neurobiologic mechanisms underlying the differences between their connectograms has yielded new hypotheses about differences in the effects of stroke lesions.

Intraplaque hemorrhages (IPH) accelerate plaque destabilization via oxidative stress, free cholesterol secretion, macrophage accumulation, and necrotic core expansion. Macrophages in these hemoglobin-exposed segments differentiate into a distinct phenotype characterized by the expression of CD163

Multidirectional coronary artery fistulas (CAFs) are rare in patients with tetralogy of Fallot (TOF). We report an adult patient who underwent open-heart surgery for TOF 24 years before the discussed presentation. Coronary angiogram and cardiac computed tomography revealed multidirectional CAFs originating from the left main coronary artery and draining to the left atrium, pulmonary artery and subphrenic artery. The patient also showed additional congenital anomalies, such as persistent left superior vena cava and right-sided aortic arch.

Primary cardiac lymphoma (PCL) is an extremely rare and fatal neoplasm of the heart. Traditionally, it is defined as lymphoma involving the heart or pericardium. PCL has a poor prognosis because of the diagnostic difficulty and its location. We present the case of a 48-year-old man who presented with pericardial effusion and diffuse cardiac wall thickening. We first suspected infiltrative heart disease. However, even after performing a biopsy, we could not establish an accurate diagnosis. After 20 months, primary cardiac diffuse large B cell lymphoma (DLBCL) was diagnosed by cervical lymph node biopsy. In this case, after chemotherapy, the DLBCL lesions, including cardiac wall thickening, improved. The treatment outcome suggests that the diagnosis was diffuse infiltrative PCL with delayed extracardiac involvement.

The aim of this study was to assess the clinical and echocardiographic predictors for the recurrence of persistent atrial fibrillation (AF) after ablation during a long-term period.A total of 130 patients with persistent AF who had undergone radiofrequency catheter ablation (RFCA) were enrolled. We analyzed the relation between clinical parameters, echocardiographic parameters, and AF recurrences at 6 months, 1 year, and 2 years after ablation.During the 2-year follow-up, AF recurred in 61 patients (46.6%). In the 6 month follow-up, AF recurrence was associated only with total ablation time only. However, during the 1-year and 2-year follow-up periods, the presence of hypertension, impaired left atrial (LA) emptying fraction (eF) (≤ 20%), decreased LA appendage (LAA) emptying velocity (≤ 20 cm/sec), and LAAeF (≤ 20%) were correlated with AF recurrence (odds ratio [OR] = 1.87, 2.45, 1.93, and 2.15 respectively, P = 0.016, 0.004, 0.029, and 0.004 respectively). Among these factors, impaired LAeF was the only independent predictor of AF recurrence in multivariate analysis (OR = 2.81, P = 0.012).In patients with persistent AF who had undergone RFCA, the best predictor of AF recurrence after ablation varied according to the follow-up period. Diminished LA function was the only predictor of recurrence in the 2-year follow-up. Pre-procedural assessment of LA function might be helpful in selecting those patients who would benefit from RFCA.

The noise prediction of motor driving system is one of the most important parts in EV/HEV, as the number of power electronic devices increases. This paper describes the mechanism of noise making process and proposes a simulation model of motor driving system for the prediction of the conducted noise. Theoretical calculations and model based simulations were carried out. DOD-dependent-battery parameters were extracted by AC analysis, and an inverter model including dynamic diode was used. Furthermore, 2-D EM tool was used for the motor modeling and was combined with the circuit models of battery and inverter. The simulated voltages, currents and spectrums in the motor driving system showed qualitatively meaningful results, suggesting the validness of the suggested modeling methods.

In the EV/HEV motor driving system, one of the most important elements that should be taken into account would be a battery that drives the whole system, because the rest of the circuit elements except battery have been relatively well established in the power electronics area. Because of the high frequency power harmonic noises inherent in the circuit, exact voltage fluctuation analysis of the driving circuit at the design stage has been emphasized quite a lot so far. This paper describes the analysis of motor driving circuit with novel battery model employed: as a result, the fluctuated battery voltage for a given DOD (Depth of Discharge) could be calculated. A filter design procedure that takes into consideration of the battery characteristics is also described for a fixed DOD. The effectiveness of the designed filter was confirmed by simulation, which was performed by using MATLAB, Ansys' Simplorer, and Agilent Technology's ADS (Advanced Design System). EV (Electric Vehicle) or HEV (Hybrid-Electric Vehicle) basically employs power electronic circuit to charge battery as well as to drive a motor, and the high voltage in the circuit, typically in the range of hundreds volts, needs to be chopped to control the driving circuits, and as a result it is inevitable to produce high frequency harmonic voltage/ current components inside. Therefore one of the most important things the HEV motor circuit designer needs to consider is the prediction of the electromagnetic compatibility of the driving circuit at the design stage. This is crucial to avoid unnecessary trial and errors after fabrication of the whole driving circuit only to find that the circuit needs to be revised to satisfy EMC standards such as IEC/CISPR and ISO. Therefore, precise analysis of the motor driving circuit is mandatory in the EV/HEV development process at the design stage. The main elements of power electronic circuit in the HEV are such as a high voltage battery, IGBT (Insulated Gate Bipolar Transistor) inverter to control motor through PWM (Pulse Width Modulation) algorithm, BLDC (Brushless DC) motor, and so on. Since the other elements except the battery have been common in the classical driving circuit simulation, an emphasis is put on the battery circuit modeling, which enables to simulate the driving circuit at various DOD levels. For this purpose, battery circuit model includes not only a simple DC model in time domain, but also complex R-C elements in frequency domain, coming from charge transfer resistance and double layer capacitors in the battery. Since the R and C in the battery model depend on the DOD level of the battery, one needs to take into consideration the frequency-dependent battery impedance as well as the load condition to design the filters in the motor driving circuit. (1)(2) This paper is organized as follows. In the next chapter, battery model which is used in the analysis of motor driving system is introduced. In chapter 3 all the circuit elements, such as battery, inverter, and filters, are integrated to form the whole circuit, and brief filter design procedure is described. Chapter 4 demonstrates the simulated results of the voltage fluctuations and the conducted noise, and the effectiveness of the filter is also shown. Finally, this paper ends with a conclusion and further works in the last section.

Background Coronary artery spasm ( CAS ) is a well‐known endothelial dysfunction, and a major cause of vasospastic angina ( VSA ). The renin–angiotensin system ( RAS ) is known to be closely associated with endothelial function. However, there are only a few studies that investigated the impact of RAS inhibitor on long‐term clinical outcomes in VSA patients. Methods and Results A total of 3349 patients with no significant coronary artery disease, diagnosed with CAS by acetylcholine provocation test were enrolled for this study. Significant CAS was defined as having ≥70% narrowing of the artery after incremental injections of 20, 50, and 100 μg of acetylcholine into the left coronary artery. Patients were divided into 2 groups according to whether the prescription included RAS inhibitor or not ( RAS inhibitor group: n=666, non‐ RAS inhibitor group; n=2683). To adjust for any potential confounders that could cause bias, propensity score matching ( PSM ) analysis was performed using a logistic regression model. After PSM analysis, 2 matched groups (524 pairs, n=1048 patients, C‐statistic=0.845) were generated and their baseline characteristics were balanced. During the 5‐year clinical follow‐up, the RAS inhibitor group showed a lower incidence of recurrent angina (8.7% versus 14.1%, P =0.027), total death (0.0% versus 1.3%, P =0.045), and total major adverse cardiovascular events (1.0% versus 4.1%, P =0.026) than the non‐ RAS inhibitor group. Conclusions Chronic RAS inhibitor therapy was associated with lower incidence of cardiovascular events in VSA patients in the 5‐year clinical follow‐up.

Intravascular optical coherence tomography (IV-OCT) is a high-resolution imaging method used to visualize the microstructure of arterial walls in vivo. IV-OCT enables the clinician to clearly observe and accurately measure stent apposition and neointimal coverage of coronary stents, which are associated with side effects such as in-stent thrombosis. In this study, the authors present an algorithm for quantifying stent apposition and neointimal coverage by automatically detecting lumen contours and stent struts in IV-OCT images.The algorithm utilizes OCT intensity images and their first and second gradient images along the axial direction to detect lumen contours and stent strut candidates. These stent strut candidates are classified into true and false stent struts based on their features, using an artificial neural network with one hidden layer and ten nodes. After segmentation, either the protrusion distance (PD) or neointimal thickness (NT) for each strut is measured automatically. In randomly selected image sets covering a large variety of clinical scenarios, the results of the algorithm were compared to those of manual segmentation by IV-OCT readers.Stent strut detection showed a 96.5% positive predictive value and a 92.9% true positive rate. In addition, case-by-case validation also showed comparable accuracy for most cases. High correlation coefficients (R0.99) were observed for PD and NT between the algorithmic and the manual results, showing little bias (0.20 and 0.46 μm, respectively) and a narrow range of limits of agreement (36 and 54 μm, respectively). In addition, the algorithm worked well in various clinical scenarios and even in cases with a low level of stent malapposition and neointimal coverage.The presented automatic algorithm enables robust and fast detection of lumen contours and stent struts and provides quantitative measurements of PD and NT. In addition, the algorithm was validated using various clinical cases to demonstrate its reliability. Therefore, this technique can be effectively utilized for clinical trials on stent-related side effects, including in-stent thrombosis and in-stent restenosis.

Intravascular optical coherence tomography (IV-OCT) is a high-resolution imaging method used to visualize the internal structures of walls of coronary arteries in vivo. However, accurate characterization of atherosclerotic plaques with gray-scale IV-OCT images is often limited by various intrinsic artifacts. In this study, we present an algorithm for characterizing lipid-rich plaques with a spectroscopic OCT technique based on a Gaussian center of mass (GCOM) metric. The GCOM metric, which reflects the absorbance properties of lipids, was validated using a lipid phantom. In addition, the proposed characterization method was successfully demonstrated in vivo using an atherosclerotic rabbit model and was found to have a sensitivity and specificity of 94.3% and 76.7% for lipid classification, respectively.

Macrophages mediate atheroma expansion and disruption, and denote high-risk arterial plaques. Therefore, they are substantially gaining importance as a diagnostic imaging target for the detection of rupture-prone plaques. Here, we developed an injectable near-infrared fluorescence (NIRF) probe by chemically conjugating thiolated glycol chitosan with cholesteryl chloroformate, NIRF dye (cyanine 5.5 or 7), and maleimide-polyethylene glycol-mannose as mannose receptor binding ligands to specifically target a subset of macrophages abundant in high-risk plaques. This probe showed high affinity to mannose receptors, low toxicity, and allowed the direct visualization of plaque macrophages in murine carotid atheroma. After the scale-up of the MMR-NIRF probe, the administration of the probe facilitated in vivo intravascular imaging of plaque inflammation in coronary-sized vessels of atheromatous rabbits using a custom-built dual-modal optical coherence tomography (OCT)-NIRF catheter-based imaging system. This novel imaging approach represents a potential imaging strategy enabling the identification of high-risk plaques in vivo and holds promise for future clinical implications.

Background Despite intensive anti-hypertensive treatment, overall control rates of only 30 ~ 50 % have been reported in patients with hypertension (HTN). However, clinical significance and angiographic characteristics of patients with uncontrolled HTN following Below-the-knee arteries (BTK) interventions in patients with critical limb ischemia (CLI) are not clarified yet as compared to those with controlled HTN. Methods A total 165 consecutive hypertensive patients with BTK lesions from August 2004 to November 2012 were enrolled for this study. Uncontrolled HTN was defined as a blood pressure of > 140 mmHg systolic and 90 mmHg diastolic under anti-hypertensive treatment. A total of 112 patients (67.8 %) had uncontrolled HTN. We compared the clinical and angiographic characteristics of patients with uncontrolled HTN following BTK interventions to those with controlled HTN at 12-month follow-up. Results The baseline characteristics are well balanced between the two groups. At 12 months, there was no difference in the incidence of mortality, target lesion revascularization (TLR), target extremity revascularization (TER), and limb salvage rate in both groups. However, amputation rates were higher in patients with controlled HTN (33.9 vs. 19.6 %, P = 0.045). Conclusion Regardless of blood pressure control, HTN itself was an independent risk factor for BTK lesions, suggesting more intensive medical therapy with close clinical follow up will be required for all BTK patients with HTN.

This paper presents an equivalent circuit model of a LIPB (Li-Ion Polymer battery) for Hybrid Electric Vehicles (HEVs). The proposed equivalent circuit can be used to predict the charging/discharging characteristics in time domain as well as the impedance characteristic analysis in frequency domain. Based on these features, a one-cell model is established as a function of Depth of Discharge (DoD), and a 48-cell model for a battery pack was also established. It was confirmed by experiment that the proposed model predict the discharging and impedance (AC) characteristics quite accurately at different constant current levels. To check the usefulness of the proposed circuit, the model was used to simulate a motor driving circuit with an Insulated Gate Bipolar Transistor (IGBT) inverter and Brushless DC (BLDC) motor, and it is confirmed that the model can calculate the battery voltage fluctuation in time domain at different DoDs.

In this paper, we propose a design method that the crosstalk of probe connector pins satisfy the limitation of -30 dB. The parameters(inductance and capacitance) were extracted in the grid-structured probe connector pin system, and it is shown that the new parameters are easily calculated with increasing ground pin numbers using the previously calculated parameters. In addition, the crosstalk reduction algorithm by employing more grounds around the signal pin has been suggested, and it is confirmed that the suggested method is quite effective especially for the reduction of inductive couplings. Finally, we suggested the correlation between the pitch and the length of the pins to satisfy the crosstalk limitation of -30 dB with the given number of ground pins, which will be quite useful when design a probe connector pin system.

In order to present that the electromagnetic compatibility standards following the frequency goes up which is based automotive electronics, in this paper, a hybrid/electric vehicle battery which reflects the frequency of the equivalent circuit model is introduced. By using this circuit modeling, the impedance characteristics can be analysed and an analyze of battery one cell is finished. Using this model, each different from the discharging situation, the discharge characteristic curve could be led. Basic theoretical approaches and measuring results through MATLAB and experimental validation of the EIS measurement equipment was used.

Introduction: Intraplaque hemorrhages (IPHs) are associated with rupture-prone plaques. Within IPHs, macrophages differentiate into a specific phenotype characterized by the expression of mannose receptors. The purpose of this study was to investigate the feasibility of intravascular optical coherence tomography (OCT)-near infrared fluorescence (NIRF) structural-molecular imaging combined with a NIRF probe targeting mannose receptors to estimate IPHs in vivo. Methods and Results: IPH was induced by injecting centrifuged autologous erythrocytes through a micro-infusion catheter into rabbit aortic plaques. Normal chow diet was continued for an additional 6 weeks for macrophage differentiation. 48 hours after intravenous injection of our newly synthesized NIRF labeled nanoprobe (ex/em 750/773 nm) targeting macrophage mannose receptors at a dose of 10 mg/kg, OCT-NIRF imaging demonstrated highly enhanced NIRF signals in IPH lesions characterized by black-dotted neovessels surrounded with heterogenous OCT signals (asterisk), suggesting the association of mannose receptor positive macrophages with intraplaque hemorrhagic transformation (Figure). FM and histologic findings from the corresponding sister sections corroborated the in vivo imagings, which demonstrated the co-localization of NIRF signals with macrophage mannose receptors and hemorrhagic compositions. Conclusions: OCT-NIRF imaging with a NIRF probe targeting mannose receptors could provide a promising novel strategy to estimate IPH-associated macrophages and detect high-risk plaques.

There have been several reports showing that the statin use is associated with new-onset diabetes mellitus (DM). The aim of the present study was to evaluate the impact of chronic statin use on development of new-onset DM in a series of Asian population. The patients were retrospectively enrolled using the electronic database of Korea University Guro Hospital from January 2004 to February 2010. A total of 10,994 patients without a history of diabetes were analyzed. Baseline lipid profiles, fasting glucose, Hemoglobin (Hb) A1c, and glucose tolerance tests were measured in all patients before statin treatment. Included patients had HbA1c ≤ 5.7% and fasting glucose level ≤ 100 (mg/dl). The patients were divided into 2 groups according to the use of statins (the statin group, n = 2,324 patients and the nonstatin group, n = 8,670 patients). To adjust baseline potential confounders, a propensity score-matched analysis was performed using logistic regression model. After propensity score matching, 2 propensity-matched groups (1,699 pairs, n = 3,398, C statistic = 0.859) were generated and analyzed. After propensity score matching, baseline characteristics of both groups were balanced except that the statin group was older and had higher rate of coronary artery disease compared with the nonstatin group. During a 3-year follow-up, the statin group had higher incidence of new-onset DM compared with the nonstatin group (hazard ratio 1.99, 95% CI 1.36 to 2.92, p0.001), but the statin group showed lower incidence of major adverse cerebral-cardiovascular events compared with the nonstatin group (hazard ratio 0.40, 95% CI 0.19 to 0.85, p0.001). In the present study, although the use of statins was associated with higher rate of new-onset DM, it markedly improved 3-year cardiovascular outcomes in Asian population.

Introduction Serum uric acid (UA) has been known to have a positive association with blood pressure (BP). However, the relationship between serum UA and BP in different age groups is unclear. Methods A total of 45,098 Koreans who underwent health examinations at Korea Association of Health Promotion with no history of taking drugs related with UA and/or BP were analyzed for determining the relationship between serum UA and BP. Results In men

Peripheral arterial disease (PAD) is associated with poor outcomes. We assessed the clinical outcomes of diabetic versus non-diabetic patients with PAD who underwent peripheral transluminal angioplasty (PTA).The outcomes of 239 consecutive patients with symptomatic PAD who underwent PTA were analyzed. Restenosis and clinical outcomes were assessed at a follow-up of 2 years.Diabetic patients had a higher percentage of wound as the initial diagnosis for PTA (72.7% vs. 14.2%; P.001), chronic kidney disease (26.7% vs. 6.3%; P.01), need for dialysis (19.3% vs. 3.1%; P.01), and coronary artery disease (67.6% vs. 50.7%; P=.02). Infrapopliteal PTA was more commonly performed in the diabetic group (70.4% vs. 25.3%; P.001). Diabetic patients had lower rates of angiographic follow-up at 8 months (38.6% vs. 60.3%; P.01). Diabetic patients had higher binary restenosis (54.4% vs. 31.5%; P=.02) and had a trend toward a higher incidence of total occlusion (34.0% vs. 19.5%; P=.08). At 2-year follow-up, the amputation rate was higher in the diabetic group (24.4% vs. 1.5%; P.001) despite PTA.Diabetic patients more frequently presented with critical limb ischemia compared with non-diabetic patients and had higher rates of restenosis and amputation at 2 years following standard PTA. Improved therapies are needed for this high-risk group of patients.

Recently, it was reported that statins are associated with lower rates of major adverse cardiovascular events and amputations in critical limb ischemia (CLI) patients (pts). However, the impact of statin administration duration on clinical outcomes for pts undergoing percutaneous transluminal

There are limited data comparing the safety and efficacy of newer generation Platinum Chromium Everolimus-eluting Stent (PtCr-EES, Promus ElementTM, Boston Scientific) with Cobalt Chromium EES (CoCr-EES, PromusTM, Boston Scientific and XienceTM, Abbott Vascular) in a series of Asian population. A

Hypertension (HTN) and its organ damage such as left ventricular hypertrophy (LVH) can lead to worsening heart failure, cardiovascular morbidity and mortality. However, there are limited data regarding the impact of LVH in HTN patients (pts) who underwent successful percutaneous coronary