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, a systematic approach to summarizing epidemiological data on AA is lacking. OBJECTIVES: To investigate systematically the global, regional and national incidence and prevalence of AA. METHODS: A structured search was conducted using the databases MEDLINE, Embase, Cochrane Library, Web of Science, SciELO and Korean Journal Database from their date of inception to 4 October 2023. Studies that reported the prevalence or incidence of AA were included. We used a Bayesian hierarchical linear mixed model to analyse prevalence estimates. The primary outcomes of our study were the global, regional and national prevalence of physician-diagnosed AA for the overall population, for adults and for children. The incidence data were summarized descriptively. RESULTS: In total, 88 studies from 28 countries were included in the analysis. The reported incidence of AA tended to be higher in adults aged 19-50 years, and this trend was consistent with its estimated prevalence. The reported prevalence in overall populations tended to be higher in men vs. women. The estimated lifetime prevalence rate of AA was 0.10% [95% credible interval (CrI) 0.03-0.39] in the general population worldwide, 0.12% (95% CrI 0.02-0.52) in adults and 0.03% (95% CrI 0.01-0.12) in children. The estimated prevalence of AA was highest in the Asian region and lowest in the African region. CONCLUSIONS: In this study, 48% of the Global Burden of Disease regions had insufficient data on the prevalence or incidence of AA. Further studies are needed to provide epidemiological information on middle- and low-income countries. Our study may serve as a crucial reference in terms of healthcare policy decisions.

Alopecia areata (AA) is a chronic autoimmune disease that can have both a psychological and physical impact on patients. An understanding of in whom, when and where the disease occurs ('epidemiology') is important to evaluate how healthcare resources are used in AA. There is a lack of analysis of epidemiological data in AA. This study investigated the global, regional and national incidence (the number of new cases in a specific time period) and prevalence (the rate of new cases occurring in a specific population over a specific period of time) of AA by reviewing multiple databases. Studies that reported the prevalence or incidence of AA were analysed. The primary outcomes were the global, regional and national prevalence of AA diagnosed by a doctor for the overall population, for adults only and for children only. Altogether, 88 studies from 28 countries were included in the review. The incidence of AA tended to be higher in adults aged 19­50 years, and this trend was consistent with its estimated prevalence. The estimated lifetime prevalence rate (i.e. the proportion of individuals that will be affected) of AA was 0.10% in the general population worldwide, 0.12% in adults and 0.03% in children. The estimated prevalence of AA was highest in the Asian region and lowest in the African region. Overall, we found that 48% of regions did not have enough data on the prevalence or incidence of AA. Further studies are needed to provide epidemiological data on AA, especially in middle- and low-income countries. Our results will help with healthcare policy decisions.

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.

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.

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.

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.

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.