Rationale of decreasing low-density lipoprotein cholesterol below 70 mg/dL in patients with coronary artery disease: A retrospective virtual histology-intravascular ultrasound study

Background: The associations between statin and coronary plaque compositional changes were re­ported according to the use of high dose or not. An evaluation of the impact of low-density lipoprotein cholesterol (LDL-C) < 70 mg/dL by using real world dosages of statin on coronary plaque composition was undertaken. Methods: The study subjects consisted of 61 patients (mean 59.9 years old, 45 males) who underwent percutaneous coronary intervention, baseline and follow-up (F/U; mean 8.4 months) virtual histology- -intravascular ultrasound (VH-IVUS) examination. Change of plaque composition at peri-stent area, which was selected in order to measure the identical site at F/U study, was compared according to the F/U LDL-C level. Results: Body mass index, prevalence of dyslipidemia, baseline total cholesterol and baseline LDL-C were significantly lower in F/U LDL-C < 70 mg/dL group (14 segments in 10 patients) than F/U LDL-C ≥ 70 mg/dL group (79 segments in 51 patients). F/U high-density lipoprotein cholesterol (HDL-C, OR 1.06, 95% CI 1.00–1.11, p = 0.054) and F/U LDL-C < 70 mg/dL (OR 3.43, 95% CI 0.97–12.17, p = 0.056) showed strong tendency of regression of necrotic core volume (NCV) ≥ 10%. In multivariable logis­tic regression analysis, F/U HDL-C (OR 1.07, 95% CI 1.01–1.14, p = 0.020) and F/U LDL-C < 70 mg/dL (OR 8.02, 95% CI 1.58–40.68, p = 0.012) were the independent factors for regression of NCV ≥ 10%. Conclusions: Follow-up LDL-C level < 70 mg/dL with any types of statins and increase of HDL-C were associated with regression of NCV ≥ 10% in patients with coronary artery disease

Contribution of coastal seiches to sediment transport in a microtidal semi-enclosed bay

Moorings and axial surveys using acoustic Doppler current profilers in microtidal Masan Bay were conducted to reveal impacts of coastal seiches on sediment behaviors. The hydrodynamic circulation in the bay was dominated by sluggish tidal and residual currents, with which the coastal seiches with a 1-h period were detected. The coastal seiches velocity (useiche) accounted for approximately 30% of the total velocities, causing back-and-forth water motions along the channel. This was insufficient to resuspend bed sediments without external forcings. Nevertheless, it influenced the suspended sediment concentration (SSC) of turbidity maximum (~40 mg l−1) at the central part of bay, showing SSC anomaly of 8 mg l−1. Although the seiche-induced sediment fluxes were only 1% of the total fluxes due to offsetting effect of bidirectional flows, they reached up to 0.040×10−3 kg m−2 s−1 at each pulse of coastal seiches. Repetitive coastal seiches lifted the sediment particles to the upper layer where they would not have risen if not for seiche vertical motion. However, the distance that the coastal seiches can transport the suspended sediments was too short compared to their transportable amounts. Even if sediment particles within turbidity maximum were advected by coastal seiches, they could not leave the region. This process was intensified toward the land because the useiche slowed down the further as it moved away from the node. As long as the bed sediments were resuspended, the coastal seiches were expected to enhance the potential for water pollution by causing repetitive sediment redistribution

Thermal plasma flow and equivalent circuit analyses on the electrical coupling of a DC-RF hybrid plasma torch

Numerical analyses on the electrical coupling of a DC-RF (direct current – radio frequency) hybrid plasma torch are conducted on the basis of magneto-hydrodynamic flow and equivalent circuit models to find the dependency of coupling efficiency on RF frequency and confinement tube radius. Computations are also carried out for the inductively coupled RF plasma torch to make a comparison between their calculated results. Numerical results reveal that the electrical coupling efficiencies of the RF and DC-RF hybrid plasma torches have a similar dependency on RF frequency with an almost constant difference of slightly higher efficiencies for the hybrid plasma, due to the relatively linear frequency dependency of equivalent circuit parameters as well as the resultant radially expanded DC-RF hybrid plasma toward the confinement tube wall compared with the RF plasma. But it is found that the reduction in the confinement tube radius less than some critical value, for instance 22 mm in this numerical work, possibly causes the coupling efficiency of the hybrid plasma to drastically deteriorate compared with that of the RF plasma. Such poor efficiency of the hybrid torch with relatively small radius is attributed to a significant diminution of the high temperature region upstream between the DC torch exit and the first induction coil segment, which means that the reduced tube radius may lead to an ineffective superposition of DC arc jet and RF plasma. As a result of the reduced high temperature region, the magnetic flux linkage is decreased for the smaller confinement tube, which leads to a drastic decrease in the electrical coupling. As the confinement tube radius becomes smaller, the re-circulation eddies under the DC torch are almost destroyed by a DC arc jet and a stagnation region formed is contracted to the central region. This contracted stagnation region prohibits the convection heat transfer by re-circulation of sheath gas flow from the coil zone to the upper part of the confinement tube, which ultimately results in a significant diminution of the high temperature region in the upstream. The present numerical analyses indicate that a special focus need to be brought into the influences of the DC arc jet on the electrical and thermal flow characteristics of the DC-RF hybrid plasma in determining the torch dimensions for effective conversion of RF power into plasma

Customized Energy Down-Shift using Iridium Complexes for Enhanced Performance of Polymer Solar Cells

School of Molecular Sciences(Chemistry)For the higher performance of polymer solar cells (PSCs), many researchers tried to develop new polymers that can absorb broader range of spectrum. However, there are some limits to absorb broader range with single donor. Therefore, multi donor systems and energy transfer systems have been researched. With two different donors it is easier to enhance absorption range. As a result, multi donor and energy transfer was successful to increase performance. However, the existing systems are applying polymer-polymer systems. When two different polymers are mixed, the compatibility between two polymers is critical to morphology of blend film. Also, in polymer-polymer energy transfer, the boundary between charge transfer and energy transfer is unclear. Therefore, for the first time, we developed customized iridium (Ir(III)) complexes, with Ir(III) complex incorporated into the active materials poly(thieno[3,4-b]-thiophene/benzodithiophene) (PTB7, amorphous) or poly(3-hexylthiophene) (P3HT, high crystalline) as energy donor additives. The Ir(III) complex with the 2-phenyl quinolone ligand energy donor increased the power conversion efficiency of the corresponding devices by approximately 20%. The enhancements are attributed to the improved molecular compatibility and energy level between the Ir(III) complex and the active materials, long F??rster resonance energy transfer radius, and high energy down-shift efficiency. Overall, we reveal Ir(III) complex additives for amorphous and highly crystalline polymer active materialsthese additives would enable efficient energy transfer in polymer solar cells, while retaining the desirable active layer morphology, thereby resulting in improved light absorption and conversion.ope

In-stent restenosis-prone coronary plaque composition: A retrospective virtual histology-intravascular ultrasound study

  Background: The mechanism of in-stent restenosis (ISR) is multifactorial, which includes biological, mechanical and technical factors. This study hypothesized that increased inflammatory reaction, which is known to be an important atherosclerotic process, at a culprit lesion may lead to higher restenosis rates. Methods: The study population consisted of 241 patients who had undergone percutaneous coronary intervention with virtual histology-intravascular ultrasound (VH-IVUS) and a 9-month follow-up coronary angiography. Compared herein is the coronary plaque composition between patients with ISR and those without ISR. Results: Patients with ISR (n = 27) were likely to be older (66.2 ± 9.5 years vs. 58.7 ± 11.7 years, p = 0.002) and have higher levels of high-sensitivity C-reactive protein (hs-CRP, 1.60 ± 3.59 mg/dL vs. 0.31 ± 0.76 mg/dL, p < 0.001) than those without ISR (n = 214). VH-IVUS examination showed that percent necrotic core volume (14.3 ± 8.7% vs. 19.5 ± 9.1%, p = 0.005) was higher in those without ISR than those with ISR. Multivariate analysis revealed that hs-CRP (odds ratio [OR] 3.334, 95% con­fidence interval [CI] 1.158–9.596, p = 0.026) and age (OR 3.557, 95% CI 1.242–10.192, p = 0.018) were associated with ISR. Conclusions: This study suggests that ISR is not associated with baseline coronary plaque composition but is associated with old age and increased expression of the inflammatory marker of hs-CRP. (Cardiol J 2018; 25, 1: 7–13

Automating Rey Complex Figure Test scoring using a deep learning-based approach: a potential large-scale screening tool for cognitive decline

Background The Rey Complex Figure Test (RCFT) has been widely used to evaluate the neurocognitive functions in various clinical groups with a broad range of ages. However, despite its usefulness, the scoring method is as complex as the figure. Such a complicated scoring system can lead to the risk of reducing the extent of agreement among raters. Although several attempts have been made to use RCFT in clinical settings in a digitalized format, little attention has been given to develop direct automatic scoring that is comparable to experienced psychologists. Therefore, we aimed to develop an artificial intelligence (AI) scoring system for RCFT using a deep learning (DL) algorithm and confirmed its validity. Methods A total of 6680 subjects were enrolled in the Gwangju Alzheimers and Related Dementia cohort registry, Korea, from January 2015 to June 2021. We obtained 20,040 scanned images using three images per subject (copy, immediate recall, and delayed recall) and scores rated by 32 experienced psychologists. We trained the automated scoring system using the DenseNet architecture. To increase the model performance, we improved the quality of training data by re-examining some images with poor results (mean absolute error (MAE) ≥ 5 [points]) and re-trained our model. Finally, we conducted an external validation with 150 images scored by five experienced psychologists. Results For fivefold cross-validation, our first model obtained MAE = 1.24 [points] and R-squared (R2 ) = 0.977. However, after evaluating and updating the model, the performance of the final model was improved (MAE = 0.95 [points], R2 = 0.986). Predicted scores among cognitively normal, mild cognitive impairment, and dementia were significantly different. For the 150 independent test sets, the MAE and R2 between AI and average scores by five human experts were 0.64 [points] and 0.994, respectively. Conclusion We concluded that there was no fundamental difference between the rating scores of experienced psychologists and those of our AI scoring system. We expect that our AI psychologist will be able to contribute to screen the early stages of Alzheimers disease pathology in medical checkup centers or large-scale community-based research institutes in a faster and cost-effective way.This research was supported by the Technology Innovation Program (20022810, Development and Demonstration of a Digital System for the evaluation of geriatric Cognitive impairment) funded By the Ministry of Trade, Industry & Energy(MOTIE, Korea), by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2020R1F1A1052932), by the Healthcare AI Convergence Research & Development Program through the National IT Industry Promotion Agency of Korea (NIPA) funded by the Ministry of Science and ICT(No.1711120216), by the KBRI basic research program through the Korea Brain Research Institute funded by the Ministry of Science and ICT (22-BR-03–05), and by the Korea National Institute of Health research project (project No. 2021-ER1007-01)