Subclinical vascular inflammation in subjects with normal weight obesity and its association with body Fat: an 18 F-FDG-PET/CT study

BACKGROUND: Although body mass index (BMI) is the most widely accepted parameter for defining obesity, recent studies have indicated a unique set of patients who exhibit normal BMI and excess body fat (BF), which is termed as normal weight obesity (NWO). Increased BF is an established risk factor for atherosclerosis. However, it is unclear whether NWO subjects already have a higher degree of vascular inflammation compared to normal weight lean (NWL) subjects; moreover, the association of BF with vascular inflammation in normal weight subjects is largely unknown. METHODS: NWO and NWL subjects (n = 82 in each group) without any history of significant vascular disease were identified from a 3-year database of consecutively recruited patients undergoing (18) F-fluorodeoxyglucose positron emission tomography/computed tomography ((18) F-FDG-PET/CT) at a self-referred Healthcare Promotion Program. The degree of subclinical vascular inflammation was evaluated using the mean and maximum target-to-background ratios (TBRmean and TBRmax) of the carotid artery, which were measured by (18) F-FDG-PET/CT (a noninvasive tool for assessing vascular inflammation). RESULTS: We found that metabolically dysregulation was greater in NWO subjects than in NWL subjects, with a significantly higher blood pressure, higher fasting glucose level, and worse lipid profile. Moreover, NWO subjects exhibited higher TBR than NWL subjects (TBRmean: 1.33 ± 0.16 versus 1.45 ± 0.19, p < 0.001; TBRmax: 1.52 ± 0.23 versus 1.67 ± 0.25, p < 0.001). TBR was significantly associated with total BF (TBRmean: r = 0.267, p = 0.001; TBRmax: r = 0.289, p < 0.001), age (TBRmean: r = 0.170, p = 0.029; TBRmax: r = 0.165, p = 0.035), BMI (TBRmean: r = 0.184, p = 0.018; TBRmax: r = 0.206, p = 0.008), and fasting glucose level (TBRmean: r = 0.157, p = 0.044; TBRmax: r = 0.182, p = 0.020). In multiple linear regression analysis, BF was an independent determinant of TBRmean and TBRmax, after adjusting for age, BMI, and fasting glucose level (TBRmean: regression coefficient = 0.020, p = 0.008; TBRmax: regression coefficient = 0.028, p = 0.005). Compared to NWL, NWO was also independently associated with elevated TBRmax values, after adjusting for confounding factors (odds ratio = 2.887, 95% confidence interval 1.206–6.914, p = 0.017). CONCLUSIONS: NWO is associated with a higher degree of subclinical vascular inflammation, of which BF is a major contributing factor. These results warrant investigations for subclinical atherosclerosis in NWO patients

DRAINCLoG: Detecting Rogue Accounts with Illegally-obtained NFTs using Classifiers Learned on Graphs

As Non-Fungible Tokens (NFTs) continue to grow in popularity, NFT users have become targets of phishing attacks by cybercriminals, called \textit{NFT drainers}. Over the last year, \$100 million worth of NFTs were stolen by drainers, and their presence remains a serious threat to the NFT trading space. However, no work has yet comprehensively investigated the behaviors of drainers in the NFT ecosystem. In this paper, we present the first study on the trading behavior of NFT drainers and introduce the first dedicated NFT drainer detection system. We collect 127M NFT transaction data from the Ethereum blockchain and 1,135 drainer accounts from five sources for the year 2022. We find that drainers exhibit significantly different transactional and social contexts from those of regular users. With these insights, we design \textit{DRAINCLoG}, an automatic drainer detection system utilizing Graph Neural Networks. This system effectively captures the multifaceted web of interactions within the NFT space through two distinct graphs: the NFT-User graph for transaction contexts and the User graph for social contexts. Evaluations using real-world NFT transaction data underscore the robustness and precision of our model. Additionally, we analyze the security of \textit{DRAINCLoG} under a wide variety of evasion attacks.Comment: To appear in NDSS 202

Targeting the stress support network regulated by autophagy and senescence for cancer treatment

Autophagy and cellular senescence are two potent tumor suppressive mechanisms activated by various cellular stresses, including the expression of activated oncogenes. However, emerging evidence has also indicated their pro-tumorigenic activities, strengthening the case for the complexity of tumorigenesis. More specifically, tumorigenesis is a systemic process emanating from the combined accumulation of changes in the tumor support pathways, many of which cannot cause cancer on their own but might still provide excellent therapeutic targets for cancer treatment. In this review, we discuss the dual roles of autophagy and senescence during tumorigenesis, with a specific focus on the stress support networks in cancer cells modulated by these processes. A deeper understanding of such context-dependent roles may help to enhance the effectiveness of cancer therapies targeting autophagy and senescence, while limiting their potential side effects. This will steer and accelerate the pace of research and drug development for cancer treatment.N

Translation and validation of the Korean version of PROMIS® pediatric and parent proxy measures for emotional distress

This study was funded by Seoul National University (Grant Number: 810–20160011)

Statistical modeling of health space based on metabolic stress and oxidative stress scores

Abstract Background Health space (HS) is a statistical way of visualizing individuals health status in multi-dimensional space. In this study, we propose a novel HS in two-dimensional space based on scores of metabolic stress and of oxidative stress. Methods These scores were derived from three statistical models: logistic regression model, logistic mixed effect model, and proportional odds model. HSs were developed using Korea National Health And Nutrition Examination Survey data with 32,140 samples. To evaluate and compare the performance of the HSs, we also developed the Health Space Index (HSI) which is a quantitative performance measure based on the approximate 95% confidence ellipses of HS. Results Through simulation studies, we confirmed that HS from the proportional odds model showed highest power in discriminating health status of individual (subject). Further validation studies were conducted using two independent cohort datasets: a health examination dataset from Ewha-Boramae cohort with 862 samples and a population-based cohort from the Korea association resource project with 3,199 samples. Conclusions These validation studies using two independent datasets successfully demonstrated the usefulness of the proposed HS

Estimation of the NiCu Cycle Strength and Its Impact on Type I X-Ray Bursts

Type I X-ray bursts (XRBs) are powered by thermonuclear burning on proton-rich unstable nuclides. The construction of burst models with accurate knowledge of nuclear physics is required to properly interpret burst observations. Numerous studies that have investigated the sensitivities of burst models to nuclear inputs have commonly extracted the strength of the NiCu cycle in the rp process, determined by the Cu-59(p,alpha)Ni-56 and Cu-59(p,gamma)Zn-60 thermonuclear reaction rates, as critical in the determination of reaction flow in the burst. In this study, the strength of the cycle at the XRB temperature range was estimated based on published experimental data. The nuclear properties of the compound nucleus Zn-60 were evaluated for the Cu-59(p,alpha)Ni-56 and Cu-59(p,gamma)Zn-60 reaction rate calculations. Monte Carlo rate calculations were conducted to include the large uncertainties of nuclear properties in the calculations. In the current work, a weak NiCu cycle is expected, whereas the rates adopted by the previous studies suggest a strong NiCu cycle. Model simulations were performed with the new rates to assess the impact on Type I XRBs. The results show that the estimated cycle strength does not strongly influence the model predictions of the burst light curve or synthesized abundances