Long-term risk of autoimmune diseases after mRNA-based SARS-CoV2 vaccination in a Korean, nationwide, population-based cohort study.

The long-term association between mRNA-based coronavirus disease 2019 (COVID-19) vaccination and the development of autoimmune connective tissue diseases (AI-CTDs) remains unclear. In this nationwide, population-based cohort study involving 9,258,803 individuals, we aim to determine whether the incidence of AI-CTDs is associated with mRNA vaccination. The study spans over 1 year of observation and further analyses the risk of AI-CTDs by stratifying demographics and vaccination profiles and treating booster vaccination as time-varying covariate. We report that the risk of developing most AI-CTDs did not increase following mRNA vaccination, except for systemic lupus erythematosus with a 1.16-fold risk in vaccinated individuals relative to controls. Comparable results were reported in the stratified analyses for age, sex, mRNA vaccine type, and prior history of non-mRNA vaccination. However, a booster vaccination was associated with an increased risk of some AI-CTDs including alopecia areata, psoriasis, and rheumatoid arthritis. Overall, we conclude that mRNA-based vaccinations are not associated with an increased risk of most AI-CTDs, although further research is needed regarding its potential association with certain conditions.

Lesion-aware cross-phase attention network for renal tumor subtype classification on multi-phase CT scans.

Multi-phase computed tomography (CT) has been widely used for the preoperative diagnosis of kidney cancer due to its non-invasive nature and ability to characterize renal lesions. However, since enhancement patterns of renal lesions across CT phases are different even for the same lesion type, the visual assessment by radiologists suffers from inter-observer variability in clinical practice. Although deep learning-based approaches have been recently explored for differential diagnosis of kidney cancer, they do not explicitly model the relationships between CT phases in the network design, limiting the diagnostic performance. In this paper, we propose a novel lesion-aware cross-phase attention network (LACPANet) that can effectively capture temporal dependencies of renal lesions across CT phases to accurately classify the lesions into five major pathological subtypes from time-series multi-phase CT images. We introduce a 3D inter-phase lesion-aware attention mechanism to learn effective 3D lesion features that are used to estimate attention weights describing the inter-phase relations of the enhancement patterns. We also present a multi-scale attention scheme to capture and aggregate temporal patterns of lesion features at different spatial scales for further improvement. Extensive experiments on multi-phase CT scans of kidney cancer patients from the collected dataset demonstrate that our LACPANet outperforms state-of-the-art approaches in diagnostic accuracy.

Cross-domain Denoising for Low-dose Multi-frame Spiral Computed Tomography.

Computed tomography (CT) has been used worldwide as a non-invasive test to assist in diagnosis. However, the ionizing nature of X-ray exposure raises concerns about potential health risks such as cancer. The desire for lower radiation doses has driven researchers to improve reconstruction quality. Although previous studies on low-dose computed tomography (LDCT) denoising have demonstrated the effectiveness of learning-based methods, most were developed on the simulated data. However, the real-world scenario differs significantly from the simulation domain, especially when using the multi-slice spiral scanner geometry. This paper proposes a two-stage method for the commercially available multi-slice spiral CT scanners that better exploits the complete reconstruction pipeline for LDCT denoising across different domains. Our approach makes good use of the high redundancy of multi-slice projections and the volumetric reconstructions while leveraging the over-smoothing issue in conventional cascaded frameworks caused by aggressive denoising. The dedicated design also provides a more explicit interpretation of the data flow. Extensive experiments on various datasets showed that the proposed method could remove up to 70% of noise without compromised spatial resolution, while subjective evaluations by two experienced radiologists further supported its superior performance against state-of-the-art methods in clinical practice. Code is available at https://github.com/YCL92/TMD-LDCT.

RefQSR: Reference-Based Quantization for Image Super-Resolution Networks.

Single image super-resolution (SISR) aims to reconstruct a high-resolution image from its low-resolution observation. Recent deep learning-based SISR models show high performance at the expense of increased computational costs, limiting their use in resource-constrained environments. As a promising solution for computationally efficient network design, network quantization has been extensively studied. However, existing quantization methods developed for SISR have yet to effectively exploit image self-similarity, which is a new direction for exploration in this study. We introduce a novel method called reference-based quantization for image super-resolution (RefQSR) that applies high-bit quantization to several representative patches and uses them as references for low-bit quantization of the rest of the patches in an image. To this end, we design dedicated patch clustering and reference-based quantization modules and integrate them into existing SISR network quantization methods. The experimental results demonstrate the effectiveness of RefQSR on various SISR networks and quantization methods.

Global, Regional, and National Epidemiology of Alopecia Areata: A Systematic Review and Modelling Study.

BACKGROUND: Alopecia areata (AA) is a chronic autoimmune disease that leads to a high psychiatric, economic, and systemic disease burden. A comprehensive understanding of AA epidemiology is essential for evaluating healthcare source utilization; however, there is a lack of systematic approach for summarizing epidemiologic data on AA. OBJECTIVES: To systematically investigate the global, regional, and national incidence and prevalence of AA. METHODS: A structured search was conducted using the Ovid MEDLINE, EMBASE, Cochrane Library, Web of Science, SciELO, and Korean journal databases from their inception date to October 4, 2023. Studies that reported the prevalence or incidence of AA were included. We used a Bayesian hierarchical linear mixed model to analyse the prevalence estimates. The primary outcomes of our study were the global, regional, and national prevalence of physician-diagnosed AA for overall population, adults, and children. The incidence data were summarised descriptively. RESULTS: In total, 88 studies from 28 countries were included in the analysis. The reported incidence of alopecia areata tended to be higher in adults aged 19-50 years, and this trend was consistent with its estimated prevalence. The reported prevalence in overall population tended to be higher in men compared to in women. The estimated lifetime prevalence of AA was 0.10% (95% credible intervals, 0.03%-0.39%) in the general population worldwide, 0.12% (95% credible intervals, 0.02%-0.52%) in adults, and 0.03% (95% credible intervals, 0.01%-0.12%) in children. The estimated prevalence was highest in the Asian region and lowest in the African region. CONCLUSIONS: In this study, 48% of the total Global Burden of Disease regions had insufficient data reporting the prevalence or incidence of AA. Further studies are needed to provide epidemiological information on middle- and low-income countries. Our study can serve as a crucial reference in terms of healthcare policy decisions.

Role of UPF1-LIN28A interaction during early differentiation of pluripotent stem cells.

UPF1 and LIN28A are RNA-binding proteins involved in post-transcriptional regulation and stem cell differentiation. Most studies on UPF1 and LIN28A have focused on the molecular mechanisms of differentiated cells and stem cell differentiation, respectively. We reveal that LIN28A directly interacts with UPF1 before UPF1-UPF2 complexing, thereby reducing UPF1 phosphorylation and inhibiting nonsense-mediated mRNA decay (NMD). We identify the interacting domains of UPF1 and LIN28A; moreover, we develop a peptide that impairs UPF1-LIN28A interaction and augments NMD efficiency. Transcriptome analysis of human pluripotent stem cells (hPSCs) confirms that the levels of NMD targets are significantly regulated by both UPF1 and LIN28A. Inhibiting the UPF1-LIN28A interaction using a CPP-conjugated peptide promotes spontaneous differentiation by repressing the pluripotency of hPSCs during proliferation. Furthermore, the UPF1-LIN28A interaction specifically regulates transcripts involved in ectodermal differentiation. Our study reveals that transcriptome regulation via the UPF1-LIN28A interaction in hPSCs determines cell fate.

Reversed domain adaptation for nuclei segmentation-based pathological image classification.

Despite the fact that digital pathology has provided a new paradigm for modern medicine, the insufficiency of annotations for training remains a significant challenge. Due to the weak generalization abilities of deep-learning models, their performance is notably constrained in domains without sufficient annotations. Our research aims to enhance the model's generalization ability through domain adaptation, increasing the prediction ability for the target domain data while only using the source domain labels for training. To further enhance classification performance, we introduce nuclei segmentation to provide the classifier with more diagnostically valuable nuclei information. In contrast to the general domain adaptation that generates source-like results in the target domain, we propose a reversed domain adaptation strategy that generates target-like results in the source domain, enabling the classification model to be more robust to inaccurate segmentation results. The proposed reversed unsupervised domain adaptation can effectively reduce the disparities in nuclei segmentation between the source and target domains without any target domain labels, leading to improved image classification performance in the target domain. The whole framework is designed in a unified manner so that the segmentation and classification modules can be trained jointly. Extensive experiments demonstrate that the proposed method significantly improves the classification performance in the target domain and outperforms existing general domain adaptation methods.

Sex Differences Associated with Weekend Catch-Up Sleep and Waist-to-Height-Ratio among South Korean Adults Using Korea National Health and Nutrition Examination Survey 2016-2021 Data.

The global surge in obesity rates is closely linked to the rise in sleep deprivation and prevalence of sleep disorders. This study aimed to investigate the association between weekend catch-up sleep (CUS) and obesity among Korean adults. Using multiple logistic regression analysis, we analyzed the data of 6790 adults aged >19 years obtained from the Korea National Health and Nutrition Examination Survey 2016-2021. In the subgroup analysis, we conducted multiple logistic regression analysis to determine the association between weekend CUS and obesity, stratified by sex. Women were significantly more likely to be obese than men (odds ratio (OR) = 0.53, 95% confidence interval (CI) = 0.46-0.61). Obesity was associated with 1 ≤ weekend CUS < 2 (OR = 0.86, 95% CI = 0.75-0.99) but not with weekend CUS ≤ 0. Compared to men, women had a lower obesity risk when engaging in weekend supplementary sleep that was 1 ≤ weekend CUS < 2 (OR = 0.78, 95% CI = 0.63-0.97). Our findings revealed that weekend CUS was associated with obesity. Our findings suggest that weekend CUS may offer a form of biological protection against obesity, and they contribute to a better understanding of this association and may serve as a basis for better obesity management.

Pretransplant BKV-IgG serostatus and BKV-specific ELISPOT assays to predict BKV infection after kidney transplantation.

Introduction: Polyomavirus (BKV) infection can lead to major complications and damage to the graft in kidney transplant recipients (KTRs). We investigated whether pretransplant BK serostatus and BK-specific cell-mediated immunity (CMI) predicts post-transplant BK infection. Methods: A total of 93 donor-recipient pairs who underwent kidney transplantation (KT) and 44 healthy controls were examined. Assessment of donor and recipient BKV serostatus and BKV-CMI in recipients was performed prior to transplantation using BKV-IgG ELISA and BKV-specific IFN-g ELISPOT assays against five BK viral antigens (LT, St, VP1, VP2, and VP3). BK viremia was diagnosed when blood BKV-DNA of 104 copies/mL or more was detected during follow-up periods. Results: Anti-BKV IgG antibody was detected in 74 (79.6%) of 93 KTRs and in 68 (73.1%) of 93 KT donors. A greater percentage of KTRs who received allograft from donors with high levels of anti-BKV IgG had posttransplant BK viremia (+) than KTRs from donors with low anti-BKV IgG (25.5% [12/47] vs. 4.3% [2/46], respectively; P = 0.007). Pretransplant total BKV-ELISPOT results were lower in BK viremia (+) patients than in patients without viremia (-) 20.5 [range 9.9-63.6] vs. 72.0 [43.2 - 110.8]; P = 0. 027). The sensitivity and specificity of the total BKV-ELISPOT assay (cut-off ≤ 53 spots/3×105 cells) for prediction of posttransplant BK viremia were 71.4 (95% CI: 41.9-91.6) and 54.4 (42.8-65.7), respectively. The combination of high donor BKV-IgG, low recipient BKV-IgG, and low total BKV-ELISPOT results improved specificity to 91.1%. Discussion: Our study highlights the importance of pretransplant BKV-IgG serostatus and BKV-specific CMI in predicting posttransplant BKV infection in KTRs. The combination of high donor BKV-IgG, low recipient BKV-IgG, and low total BKV-ELISPOT results predicted BK viremia after KT. Pretransplant identification of patients at highrisk for BK viremia could enable timely interventions and improve clinical outcomes of KTRs.

Deep Learning-Based Evaluation of Ultrasound Images for Benign Skin Tumors.

In this study, a combined convolutional neural network for the diagnosis of three benign skin tumors was designed, and its effectiveness was verified through quantitative and statistical analysis. To this end, 698 sonographic images were taken and diagnosed at the Department of Dermatology at Severance Hospital in Seoul, Korea, between 10 November 2017 and 17 January 2020. Through an empirical process, a convolutional neural network combining two structures, which consist of a residual structure and an attention-gated structure, was designed. Five-fold cross-validation was applied, and the train set for each fold was augmented by the Fast AutoAugment technique. As a result of training, for three benign skin tumors, an average accuracy of 95.87%, an average sensitivity of 90.10%, and an average specificity of 96.23% were derived. Also, through statistical analysis using a class activation map and physicians' findings, it was found that the judgment criteria of physicians and the trained combined convolutional neural network were similar. This study suggests that the model designed and trained in this study can be a diagnostic aid to assist physicians and enable more efficient and accurate diagnoses.

RESEAT: Recurrent Self-Attention Network for Multi-Regional Influenza Forecasting.

Early forecasting of influenza is an important task for public health to reduce losses due to influenza. Various deep learning-based models for multi-regional influenza forecasting have been proposed to forecast future influenza occurrences in multiple regions. While they only use historical data for forecasting, temporal and regional patterns need to be jointly considered for better accuracy. Basic deep learning models such as recurrent neural networks and graph neural networks have limited ability to model both patterns together. A more recent approach uses an attention mechanism or its variant, self-attention. Although these mechanisms can model regional interrelationships, in state-of-the-art models, they consider accumulated regional interrelationships based on attention values that are calculated only once for all of the input data. This limitation makes it difficult to effectively model the regional interrelationships that change dynamically during that period. Therefore, in this article, we propose a recurrent self-attention network (RESEAT) for various multi-regional forecasting tasks such as influenza and electrical load forecasting. The model can learn regional interrelationships over the entire period of the input data using self-attention, and it recurrently connects the attention weights using message passing. We demonstrate through extensive experiments that the proposed model outperforms other state-of-the-art forecasting models in terms of the forecasting accuracy for influenza and COVID-19. We also describe how to visualize regional interrelationships and analyze the sensitivity of hyperparameters to forecasting accuracy.

A Phase 2a, Randomized, Double-Blind, Placebo-Controlled Study to Assess the Efficacy and Safety of ALS-L1023 in Non-Alcoholic Fatty Liver Disease.

Preclinical data have shown that the herbal extract, ALS-L1023, from Melissa officinalis reduces visceral fat and hepatic steatosis. We aimed to assess the safety and efficacy of ALS-L1023 as the treatment of non-alcoholic fatty liver disease (NAFLD). We conducted a 24-week randomized, double-blind, placebo-controlled 2a study in patients with NAFLD (MRI-proton density fat fraction [MRI-PDFF] ≥ 8% and liver fibrosis ≥ 2.5 kPa on MR elastography [MRE]) in Korea. Patients were randomly assigned to 1800 mg ALS-L1023 (n = 19), 1200 mg ALS-L1023 (n = 21), or placebo (n = 17) groups. Efficacy endpoints included changes in liver fat on MRI-PDFF, liver stiffness on MRE, and liver enzymes. For the full analysis set, a relative hepatic fat reduction from baseline was significant in the 1800 mg ALS-L1023 group (-15.0%, p = 0.03). There was a significant reduction in liver stiffness from baseline in the 1200 mg ALS-L1023 group (-10.7%, p = 0.03). Serum alanine aminotransferase decreased by -12.4% in the 1800 mg ALS-L1023 group, -29.8% in the 1200 mg ALS-L1023 group, and -4.9% in the placebo group. ALS-L1023 was well tolerated and there were no differences in the incidence of adverse events among the study groups. ALS-L1023 could reduce hepatic fat content in patients with NAFLD.

Monocular depth estimation network with single-pixel depth guidance.

Due to the scale ambiguity problem, the performance of monocular depth estimation (MDE) is inherently restricted. Multi-camera systems, especially those equipped with active depth cameras, have addressed this problem at the expense of increased hardware costs and space. In this Letter, we adopt a similar but cost-effective solution using only single-pixel depth guidance with a single-photon avalanche diode. To this end, we design a single-pixel guidance module (SPGM) that combines the global information from the single-pixel depth guidance with the spatial information from the image at the feature level. By integrating SPGMs into an MDE network, we introduce PhoMoNet, the first, to the best of our knowledge, end-to-end MDE network with single-pixel depth guidance. Experimental results show the effectiveness and superiority of PhoMoNet over state-of-the-art MDE networks on synthetic and real-world datasets.

Microparticles as Viral RNA Carriers from Stool for Stable and Sensitive Surveillance.

Since its discovery, polymerase chain reaction (PCR) has emerged as an important technology for the diagnosis and identification of infectious diseases. It is a highly sensitive and reliable nucleic acids (NA) detection tool for various sample types. However, stool, which carries the most abundant micro-organisms and physiological byproducts, remains to be the trickiest clinical specimen for molecular detection of pathogens. Herein, we demonstrate the novel application of hydrogel microparticles as carriers of viral RNA from stool samples without prior RNA purification for real-time polymerase chain reaction (qPCR). In each microparticle of primer-incorporated network (PIN) as a self-sufficient reaction compartment, immobilized reverse transcription (RT) primers capture the viral RNA by hybridization and directly initiate RT of RNA to generate a pool of complementary DNA (PIN-cDNA pool). Through a simple operation with a portable thermostat device, a PIN-cDNA pool for influenza A virus (IAV) was obtained in 20 min. The PIN-cDNA pools can be stored at room temperature, or directly used to deliver cDNA templates for qPCR. The viral cDNA templates were freely released in the subsequent qPCR to allow amplification efficiency of over 91%. The assay displayed good linearity, repeatability, and comparable limit of detection (LoD) with a commercialized viral RNA purification kit. As a proof of concept, this technology carries a huge potential for onsite application to improve human and animal infectious disease surveillance activities using stool samples without the need for a laboratory or centrifuge for sample preparation.

Ultrafast Real-Time PCR in Photothermal Microparticles.

As the turnaround time of diagnosis becomes important, there is an increasing demand for rapid, point-of-care testing (POCT) based on polymerase chain reaction (PCR), the most reliable diagnostic tool. Although optical components in real-time PCR (qPCR) have quickly become compact and economical, conventional PCR instruments still require bulky thermal systems, making it difficult to meet emerging needs. Photonic PCR, which utilizes photothermal nanomaterials as heating elements, is a promising platform for POCT as it reduces power consumption and process time. Here, we develop a photonic qPCR platform using hydrogel microparticles. Microparticles consisting of hydrogel matrixes containing photothermal nanomaterials and primers are dubbed photothermal primer-immobilized networks (pPINs). Reduced graphene oxide is selected as the most suitable photothermal nanomaterial to generate heat in pPIN due to its superior light-to-heat conversion efficiency. The photothermal reaction volume of 100 nL (predefined by the pPIN dimensions) provides fast heating and cooling rates of 22.0 ± 3.0 and 23.5 ± 2.6 °C s-1, respectively, enabling ultrafast qPCR within 5 min only with optical components. The microparticle-based photonic qPCR facilitates multiplex assays by loading multiple encoded pPIN microparticles in a single reaction. As a proof of concept, four-plex pPIN qPCR for bacterial discrimination are successfully demonstrated.

Risk-aware survival time prediction from whole slide pathological images.

Deep-learning-based survival prediction can assist doctors by providing additional information for diagnosis by estimating the risk or time of death. The former focuses on ranking deaths among patients based on the Cox model, whereas the latter directly predicts the survival time of each patient. However, it is observed that survival time prediction for the patients, particularly with close observation times, possibly has incorrect orders, leading to low prediction accuracy. Therefore, in this paper, we present a whole slide image (WSI)-based survival time prediction method that takes advantage of both the risk as well as time prediction. Specifically, we propose to combine these two approaches by extracting the risk prediction features and using them as guides for the survival time prediction. Considering the high resolution of WSIs, we extract tumor patches from WSIs using a pre-trained tumor classifier and apply the graph convolutional network to aggregate information across these patches effectively. Extensive experiments demonstrate that the proposed method significantly improves the time prediction accuracy when compared with direct prediction of the survival times without guidance and outperforms existing methods.

A Unified Multi-Phase CT Synthesis and Classification Framework for Kidney Cancer Diagnosis With Incomplete Data.

Multi-phase computed tomography (CT) is widely adopted for the diagnosis of kidney cancer due to the complementary information among phases. However, the complete set of multi-phase CT is often not available in practical clinical applications. In recent years, there have been some studies to generate the missing modality image from the available data. Nevertheless, the generated images are not guaranteed to be effective for the diagnosis task. In this paper, we propose a unified framework for kidney cancer diagnosis with incomplete multi-phase CT, which simultaneously recovers missing CT images and classifies cancer subtypes using the completed set of images. The advantage of our framework is that it encourages a synthesis model to explicitly learn to generate missing CT phases that are helpful for classifying cancer subtypes. We further incorporate lesion segmentation network into our framework to exploit lesion-level features for effective cancer classification in the whole CT volumes. The proposed framework is based on fully 3D convolutional neural networks to jointly optimize both synthesis and classification of 3D CT volumes. Extensive experiments on both in-house and external datasets demonstrate the effectiveness of our framework for the diagnosis with incomplete data compared with state-of-the-art baselines. In particular, cancer subtype classification using the completed CT data by our method achieves higher performance than the classification using the given incomplete data.