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"Kim, Jun"
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Influence of lesion and disease subsets on the diagnostic performance of the quantitative flow ratio in real-world patients
The quantitative flow ratio (QFR) is a novel angiography-based computational method assessing functional ischemia caused by coronary stenosis. This study aimed to evaluate the diagnostic performance of quantitative flow ratio (QFR) in patients with angina and acute myocardial infarction (AMI) and to identify the conditions with low diagnostic performance. We assessed the QFR for 1077 vessels under fractional flow ratio (FFR) evaluation in 915 patients with angina and AMI. The diagnostic accuracies of the QFR for identifying an FFR ≤ 0.8 were 95.98% (95% confidence interval [CI] 94.52 to 97.14%) for the angina group and 92.42% (95% CI 86.51 to 96.31%) for the AMI group. The diagnostic accuracy of the QFR in the borderline FFR zones (> 0.75, ≤ 0.85) (91.23% [95% CI 88.25 to 93.66%]) was significantly lower than that in others (difference: 4.32; p = 0.001). The condition accompanying both AMI and the borderline FFR zone showed the lowest QFR diagnostic accuracy in our data (83.93% [95% CI 71.67 to 92.38]). The diagnostic accuracy was reduced for tandem lesions (p = 0.04, not correcting for multiple testing). Our study found that the QFR method yielded a high overall diagnostic performance in real-world patients. However, low diagnostic accuracy has been observed in borderline FFR zones with AMI, and the hybrid FFR approach needs to be considered.
Journal Article
High-performance p-channel transistors with transparent Zn doped-CuI
‘Ideal’ transparent
p
-type semiconductors are required for the integration of high-performance thin-film transistors (TFTs) and circuits. Although CuI has recently attracted attention owing to its excellent opto-electrical properties, solution processability, and low-temperature synthesis, the uncontrolled copper vacancy generation and subsequent excessive hole doping hinder its use as a semiconductor material in TFT devices. In this study, we propose a doping approach through soft chemical solution process and transparent
p
-type Zn-doped CuI semiconductor for high-performance TFTs and circuits. The optimised TFTs annealed at 80 °C exhibit a high hole mobility of over 5 cm
2
V
−1
s
−1
and high on/off current ratio of ~10
7
with good operational stability and reproducibility. The CuI:Zn semiconductors show intrinsic advantages for next-generation TFT applications and wider applications in optoelectronics and energy conversion/storage devices. This study paves the way for the realisation of transparent, flexible, and large-area integrated circuits combined with
n
-type metal-oxide semiconductor.
Designing efficient thin-film transistors and circuits based on transparent p-type semiconductors remains a challenge. Here, the authors propose a solution-based doping approach to realize high performance transparent inorganic p-type semiconductors (Zn-doped CuI) by spin coating at 80 C with good operational stability.
Journal Article
Deep Learning for Automated Detection of Cyst and Tumors of the Jaw in Panoramic Radiographs
Patients with odontogenic cysts and tumors may have to undergo serious surgery unless the lesion is properly detected at the early stage. The purpose of this study is to evaluate the diagnostic performance of the real-time object detecting deep convolutional neural network You Only Look Once (YOLO) v2—a deep learning algorithm that can both detect and classify an object at the same time—on panoramic radiographs. In this study, 1602 lesions on panoramic radiographs taken from 2010 to 2019 at Yonsei University Dental Hospital were selected as a database. Images were classified and labeled into four categories: dentigerous cysts, odontogenic keratocyst, ameloblastoma, and no lesion. Comparative analysis among three groups (YOLO, oral and maxillofacial surgeons, and general practitioners) was done in terms of precision, recall, accuracy, and F1 score. While YOLO ranked highest among the three groups (precision = 0.707, recall = 0.680), the performance differences between the machine and clinicians were statistically insignificant. The results of this study indicate the usefulness of auto-detecting convolutional networks in certain pathology detection and thus morbidity prevention in the field of oral and maxillofacial surgery.
Journal Article
Object Detection and Classification Based on YOLO-V5 with Improved Maritime Dataset
SMD (Singapore Maritime Dataset) is a public dataset with annotated videos, and it is almost unique in the training of deep neural networks (DNN) for the recognition of maritime objects. However, there are noisy labels and imprecisely located bounding boxes in the ground truth of the SMD. In this paper, for the benchmark of DNN algorithms, we correct the annotations of the SMD dataset and present an improved version, which we coined SMD-Plus. We also propose augmentation techniques designed especially for the SMD-Plus. More specifically, an online transformation of training images via Copy & Paste is applied to solve the class-imbalance problem in the training dataset. Furthermore, the mix-up technique is adopted in addition to the basic augmentation techniques for YOLO-V5. Experimental results show that the detection and classification performance of the modified YOLO-V5 with the SMD-Plus has improved in comparison to the original YOLO-V5. The ground truth of the SMD-Plus and our experimental results are available for download.
Journal Article
Current status and applications of genome-scale metabolic models
Genome-scale metabolic models (GEMs) computationally describe gene-protein-reaction associations for entire metabolic genes in an organism, and can be simulated to predict metabolic fluxes for various systems-level metabolic studies. Since the first GEM for
Haemophilus influenzae
was reported in 1999, advances have been made to develop and simulate GEMs for an increasing number of organisms across bacteria, archaea, and eukarya. Here, we review current reconstructed GEMs and discuss their applications, including strain development for chemicals and materials production, drug targeting in pathogens, prediction of enzyme functions, pan-reactome analysis, modeling interactions among multiple cells or organisms, and understanding human diseases.
Journal Article
The role of TDP-43 propagation in neurodegenerative diseases: integrating insights from clinical and experimental studies
TAR DNA-binding protein 43 (TDP-43) is a highly conserved nuclear RNA/DNA-binding protein involved in the regulation of RNA processing. The accumulation of TDP-43 aggregates in the central nervous system is a common feature of many neurodegenerative diseases, such as amyotrophic lateral sclerosis (ALS), frontotemporal dementia (FTD), Alzheimer’s disease (AD), and limbic predominant age-related TDP-43 encephalopathy (LATE). Accumulating evidence suggests that prion-like spreading of aberrant protein aggregates composed of tau, amyloid-β, and α-synuclein is involved in the progression of neurodegenerative diseases such as AD and PD. Similar to those of prion-like proteins, pathological aggregates of TDP-43 can be transferred from cell-to-cell in a seed-dependent and self-templating manner. Here, we review clinical and experimental studies supporting the prion-like spreading of misfolded TDP-43 and discuss the molecular mechanisms underlying the propagation of these pathological aggregated proteins. The idea that misfolded TDP-43 spreads in a prion-like manner between cells may guide novel therapeutic strategies for TDP-43-associated neurodegenerative diseases.Neurodegenerative disorders: Spread of misfolded protein aggregatesFurther research is needed to determine how an aggregate-forming protein common to several neurodegenerative disorders propagates throughout the brain. Many neurodegenerative conditions involve aggregates created by ‘prion-like’ proteins, misfolded proteins that can confer their abnormal structure on neighboring healthy proteins, resulting in aggregates which spread rather like an infection. Hyung-Jun Kim at the Korea Brain Research Institute in Daegu, South Korea, and co-workers reviewed current understanding of the transactive response DNA-binding protein 43 (TDP-43), an aggregate-forming protein implicated in disorders such as Alzheimer’s disease and frontotemporal dementia. Growing evidence suggests that TDP-43 may spread in a prion-like fashion. TDP-43 is implicated in the onset of Alzheimer’s, and the spread of misfolded TDP-43 aggregates is closely tied to disease severity. More research is needed into how TDP-43 propagates in different tissues and central nervous system cells.
Journal Article
Ciclopirox inhibits Hepatitis B Virus secretion by blocking capsid assembly
Chronic hepatitis B virus (HBV) infection can cause cirrhosis and hepatocellular carcinoma and is therefore a serious public health problem. Infected patients are currently treated with nucleoside/nucleotide analogs and interferon α, but this approach is not curative. Here, we screen 978 FDA-approved compounds for their ability to inhibit HBV replication in HBV-expressing HepG2.2.15 cells. We find that ciclopirox, a synthetic antifungal agent, strongly inhibits HBV replication in cells and in mice by blocking HBV capsid assembly. The crystal structure of the HBV core protein and ciclopirox complex reveals a unique binding mode at dimer-dimer interfaces. Ciclopirox synergizes with nucleoside/nucleotide analogs to prevent HBV replication in cells and in a humanized liver mouse model. Therefore, orally-administered ciclopirox may provide a novel opportunity to combat chronic HBV infection by blocking HBV capsid assembly.
Current treatments for chronic hepatitis B virus (HBV) infection are not curative. Here, the authors show that an antifungal drug, ciclopirox, inhibits HBV capsid assembly and synergizes with nucleoside/nucleotide analogs to prevent HBV replication in cells and in a humanized liver mouse model.
Journal Article
Monolithic 3D integration of 2D materials-based electronics towards ultimate edge computing solutions
Three-dimensional (3D) hetero-integration technology is poised to revolutionize the field of electronics by stacking functional layers vertically, thereby creating novel 3D circuity architectures with high integration density and unparalleled multifunctionality. However, the conventional 3D integration technique involves complex wafer processing and intricate interlayer wiring. Here we demonstrate monolithic 3D integration of two-dimensional, material-based artificial intelligence (AI)-processing hardware with ultimate integrability and multifunctionality. A total of six layers of transistor and memristor arrays were vertically integrated into a 3D nanosystem to perform AI tasks, by peeling and stacking of AI processing layers made from bottom-up synthesized two-dimensional materials. This fully monolithic-3D-integrated AI system substantially reduces processing time, voltage drops, latency and footprint due to its densely packed AI processing layers with dense interlayer connectivity. The successful demonstration of this monolithic-3D-integrated AI system will not only provide a material-level solution for hetero-integration of electronics, but also pave the way for unprecedented multifunctional computing hardware with ultimate parallelism.Monolithic 3D integration of electronics based on fully 2D materials is demonstrated in the performance of artificial intelligence tasks.
Journal Article
The Role of Prebiotics in Modulating Gut Microbiota: Implications for Human Health
The human gut microbiota, an intricate ecosystem within the gastrointestinal tract, plays a pivotal role in health and disease. Prebiotics, non-digestible food ingredients that beneficially affect the host by selectively stimulating the growth and/or activity of beneficial microorganisms, have emerged as a key modulator of this complex microbial community. This review article explores the evolution of the prebiotic concept, delineates various types of prebiotics, including fructans, galactooligosaccharides, xylooligosaccharides, chitooligosaccharides, lactulose, resistant starch, and polyphenols, and elucidates their impact on the gut microbiota composition. We delve into the mechanisms through which prebiotics exert their effects, particularly focusing on producing short-chain fatty acids and modulating the gut microbiota towards a health-promoting composition. The implications of prebiotics on human health are extensively reviewed, focusing on conditions such as obesity, inflammatory bowel disease, immune function, and mental health. The review further discusses the emerging concept of synbiotics—combinations of prebiotics and probiotics that synergistically enhance gut health—and highlights the market potential of prebiotics in response to a growing demand for functional foods. By consolidating current knowledge and identifying areas for future research, this review aims to enhance understanding of prebiotics’ role in health and disease, underscoring their importance in maintaining a healthy gut microbiome and overall well-being.
Journal Article