Histopathological correlations of CT-based radiomics imaging biomarkers in native kidney biopsy.

Background: Kidney biopsy is the standard of care for the diagnosis of various kidney diseases. In particular, chronic histopathologic lesions, such as interstitial fibrosis and tubular atrophy, can provide prognostic information regarding chronic kidney disease progression. In this study, we aimed to evaluate historadiological correlations between CT-based radiomic features and chronic histologic changes in native kidney biopsies and to construct and validate a radiomics-based prediction model for chronicity grade.

Single-cell analysis of human PBMCs in healthy and type 2 diabetes populations: dysregulated immune networks in type 2 diabetes unveiled through single-cell profiling.

Abnormalities in glucose metabolism that precede the onset of type 2 diabetes (T2D) activate immune cells, leading to elevated inflammatory factors and chronic inflammation. However, no single-cell RNA sequencing (scRNA-seq) studies have characterized the properties and networks of individual immune cells in T2D. Here, we analyzed peripheral blood mononuclear cells (PBMCs) from non-diabetes and T2D patients by scRNA-seq.

Human-Unrecognizable Differential Private Noised Image Generation Method.

Differential privacy has emerged as a practical technique for privacy-preserving deep learning. However, recent studies on privacy attacks have demonstrated vulnerabilities in the existing differential privacy implementations for deep models. While encryption-based methods offer robust security, their computational overheads are often prohibitive.

Bypassing Heaven's Gate Technique Using Black-Box Testing.

In recent years, the number and sophistication of malware attacks on computer systems have increased significantly. One technique employed by malware authors to evade detection and analysis, known as Heaven's Gate, enables 64-bit code to run within a 32-bit process. Heaven's Gate exploits a feature in the operating system that allows the transition from a 32-bit mode to a 64-bit mode during execution, enabling the malware to evade detection by security software designed to monitor only 32-bit processes.

Deep learning and clustering approaches for dental implant size classification based on periapical radiographs.

This study investigated two artificial intelligence (AI) methods for automatically classifying dental implant diameter and length based on periapical radiographs. The first method, deep learning (DL), involved utilizing the pre-trained VGG16 model and adjusting the fine-tuning degree to analyze image data obtained from periapical radiographs. The second method, clustering analysis, was accomplished by analyzing the implant-specific feature vector derived from three key points coordinates of the dental implant using the k-means++ algorithm and adjusting the weight of the feature vector.

AIROGS: Artificial Intelligence for Robust Glaucoma Screening Challenge.

The early detection of glaucoma is essential in preventing visual impairment. Artificial intelligence (AI) can be used to analyze color fundus photographs (CFPs) in a cost-effective manner, making glaucoma screening more accessible. While AI models for glaucoma screening from CFPs have shown promising results in laboratory settings, their performance decreases significantly in real-world scenarios due to the presence of out-of-distribution and low-quality images.

Clinical relevance of deep learning models in predicting the onset timing of cancer pain exacerbation.

Cancer pain is a challenging clinical problem that is encountered in the management of cancer pain. We aimed to investigate the clinical relevance of deep learning models that predict the onset of cancer pain exacerbation in hospitalized patients. We defined cancer pain exacerbation (CPE) as the pain with a numerical rating scale (NRS) score of ≥ 4.

Deep learning model for distinguishing Mayo endoscopic subscore 0 and 1 in patients with ulcerative colitis.

The aim of this study was to address the issue of differentiating between Mayo endoscopic subscore (MES) 0 and MES 1 using a deep learning model. A dataset of 492 ulcerative colitis (UC) patients who demonstrated MES improvement between January 2018 and December 2019 at Samsung Medical Center was utilized. Specifically, two representative images of the colon and rectum were selected from each patient, resulting in a total of 984 images for analysis.

Fluidic self-assembly for MicroLED displays by controlled viscosity.

Displays in which arrays of microscopic 'particles', or chiplets, of inorganic light-emitting diodes (LEDs) constitute the pixels, termed MicroLED displays, have received considerable attention because they can potentially outperform commercially available displays based on organic LEDs in terms of power consumption, colour saturation, brightness and stability and without image burn-in issues. To manufacture these displays, LED chiplets must be epitaxially grown on separate wafers for maximum device performance and then transferred onto the display substrate. Given that the number of LEDs needed for transfer is tremendous-for example, more than 24 million chiplets smaller than 100 μm are required for a 50-inch, ultra-high-definition display-a technique capable of assembling tens of millions of individual LEDs at low cost and high throughput is needed to commercialize MicroLED displays.

Validation of resonance Raman spectroscopy-measured skin carotenoid status as a biomarker for fruit and vegetable intake in Korean adults.

Blood carotenoid concentration measurement is considered the gold standard for fruit and vegetable (F&V) intake estimation; however, this method is invasive and expensive. Recently, skin carotenoid status (SCS) measured by optical sensors has been evaluated as a promising parameter for F&V intake estimation. In this cross-sectional study, we aimed to validate the utility of resonance Raman spectroscopy (RRS)-assessed SCS as a biomarker of F&V intake in Korean adults.

Concurrent self-assembly of RGB microLEDs for next-generation displays.

MicroLED displays have been in the spotlight as the next-generation displays owing to their various advantages, including long lifetime and high brightness compared with organic light-emitting diode (OLED) displays. As a result, microLED technology is being commercialized for large-screen displays such as digital signage and active R&D programmes are being carried out for other applications, such as augmented reality, flexible displays and biological imaging. However, substantial obstacles in transfer technology, namely, high throughput, high yield and production scalability up to Generation 10+ (2,940 × 3,370 mm) glass sizes, need to be overcome so that microLEDs can enter mainstream product markets and compete with liquid-crystal displays and OLED displays.

Task-Specific Adaptive Differential Privacy Method for Structured Data.

Data are needed to train machine learning (ML) algorithms, and in many cases often include private datasets that contain sensitive information. To preserve the privacy of data used while training ML algorithms, computer scientists have widely deployed anonymization techniques. These anonymization techniques have been widely used but are not foolproof.

Deep learning approach to detection of colonoscopic information from unstructured reports.

Background: Colorectal cancer is a leading cause of cancer deaths. Several screening tests, such as colonoscopy, can be used to find polyps or colorectal cancer. Colonoscopy reports are often written in unstructured narrative text.

Outcomes of Endoscopic Resection for Early Gastric Cancer in Very Elderly Patients: A Nationwide Population-Based Study.

Background/aims: Few studies have investigated the long-term outcomes of endoscopic resection for early gastric cancer (EGC) in very elderly patients. The aim of this study was to determine the appropriate treatment strategy and identify the risk factors for mortality in these patients.

Methods: Patients with EGC who underwent endoscopic resection from 2006 to 2017 were identified using National Health Insurance Data and divided into three age groups: very elderly (≥85 years), elderly (65 to 84 years), and non-elderly (≤64 years).

Associations of Cardiorespiratory Fitness With Estimated Remnant Cholesterol and Non-High-Density Lipoprotein Cholesterol in Healthy Men.

Remnant cholesterol (RC) and non-high-density lipoprotein cholesterol (non-HDL-C) may contribute to the residual risk for atherosclerotic cardiovascular disease. High cardiorespiratory fitness (CRF) is associated with favorable traditional lipid profiles, but its relation with RC and non-HDL-C remains unclear. We analyzed cross-sectional data on 4,613 healthy men (mean age 49 years).

Clustering Approach for Detecting Multiple Types of Adversarial Examples.

With intentional feature perturbations to a deep learning model, the adversary generates an adversarial example to deceive the deep learning model. As an adversarial example has recently been considered in the most severe problem of deep learning technology, its defense methods have been actively studied. Such effective defense methods against adversarial examples are categorized into one of the three architectures: (1) model retraining architecture; (2) input transformation architecture; and (3) adversarial example detection architecture.

Joint Associations of Obesity and Cardiorespiratory Fitness With Coronary Artery Calcium Composition: IS THERE EVIDENCE FOR FAT-BUT-FIT?

Introduction: The purpose of this study was to examine the individual and joint associations of obesity and cardiorespiratory fitness (CRF) with indices of coronary artery calcification (CAC) in 2090 middle-aged men.

Methods: Obesity was defined as a body mass index (BMI) ≥25 kg/m2 and a waist circumference (WC) ≥90 cm. Cardiorespiratory fitness was operationally defined as peak oxygen uptake (V˙o2peak) directly measured using gas analysis.

Prediction Algorithms for Blood Pressure Based on Pulse Wave Velocity Using Health Checkup Data in Healthy Korean Men: Algorithm Development and Validation.

Background: Pulse transit time and pulse wave velocity (PWV) are related to blood pressure (BP), and there were continuous attempts to use these to predict BP through wearable devices. However, previous studies were conducted on a small scale and could not confirm the relative importance of each variable in predicting BP.

Objective: This study aims to predict systolic blood pressure and diastolic blood pressure based on PWV and to evaluate the relative importance of each clinical variable used in BP prediction models.

Is Homomorphic Encryption-Based Deep Learning Secure Enough?

As the amount of data collected and analyzed by machine learning technology increases, data that can identify individuals is also being collected in large quantities. In particular, as deep learning technology-which requires a large amount of analysis data-is activated in various service fields, the possibility of exposing sensitive information of users increases, and the user privacy problem is growing more than ever. As a solution to this user's data privacy problem, homomorphic encryption technology, which is an encryption technology that supports arithmetic operations using encrypted data, has been applied to various field including finance and health care in recent years.

The Relationship Between Breast Density Change During Menopause and the Risk of Breast Cancer in Korean Women.

Background: The aim of this study was to investigate the relationship between changes in breast density during menopause and breast cancer risk.

Methods: This study was a retrospective, longitudinal cohort study for women over 30 years of age who had undergone breast mammography serially at baseline and postmenopause during regular health checkups at Samsung Medical Center. None of the participants had been diagnosed with breast cancer at baseline.

COVID-19 Vaccination-Associated Lymphadenopathy on FDG PET/CT: Distinctive Features in Adenovirus-Vectored Vaccine.

Purpose: This study investigated 18F-FDG PET/CT features of adenovirus-vectored vaccination against COVID-19 in healthy subjects.

Patients And Methods: Thirty-one health care workers had been vaccinated Vaxzevria and underwent FDG PET/CT as an optional test for a cancer screening program. Size and FDG uptake of the hypermetabolic lymph nodes were measured.

Deep learning for detection and segmentation of artefact and disease instances in gastrointestinal endoscopy.

The Endoscopy Computer Vision Challenge (EndoCV) is a crowd-sourcing initiative to address eminent problems in developing reliable computer aided detection and diagnosis endoscopy systems and suggest a pathway for clinical translation of technologies. Whilst endoscopy is a widely used diagnostic and treatment tool for hollow-organs, there are several core challenges often faced by endoscopists, mainly: 1) presence of multi-class artefacts that hinder their visual interpretation, and 2) difficulty in identifying subtle precancerous precursors and cancer abnormalities. Artefacts often affect the robustness of deep learning methods applied to the gastrointestinal tract organs as they can be confused with tissue of interest.

Enhancement in external quantum efficiency of AlGaInP red μ-LED using chemical solution treatment process.

To investigate the effects of their surface recovery and optical properties, extremely small sized (12 µm × 12 µm mesa area) red AlGaInP micro light emitting diodes ([Formula: see text] LED) were fabricated using a diluted hydrofluoric acid (HF) surface etch treatment. After the chemical treatment, the external quantum efficiencies (EQEs) of [Formula: see text]-LED at low and high injection current regions have been improved by 35.48% and 12.

Robust Indoor Localization Methods Using Random Forest-Based Filter against MAC Spoofing Attack.

With the development of wireless networks and mobile devices, interest on indoor localization systems (ILSs) has increased. In particular, Wi-Fi-based ILSs are widely used because of the good prediction accuracy without additional hardware. However, as the prediction accuracy decreases in environments with natural noise, some studies were conducted to remove it.