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Background Since Middle East respiratory syndrome (MERS) infection was first reported in 2012, many studies have analysed its transmissibility and severity. However, the methodology and results of these studies have varied, and there has been no systematic review of MERS. This study reviews the characteristics and associated risk factors of MERS. Method We searched international (PubMed, ScienceDirect, Cochrane) and Korean databases (DBpia, KISS) for English- or Korean-language articles using the terms “MERS” and “Middle East respiratory syndrome”. Only human studies with > 20 participants were analysed to exclude studies with low representation. Epidemiologic studies with information on transmissibility and severity of MERS as well as studies containing MERS risk factors were included. Result A total of 59 studies were included. Most studies from Saudi Arabia reported higher mortality (22–69.2%) than those from South Korea (20.4%). While the R 0 value in Saudi Arabia was < 1 in all but one study, in South Korea, the R 0 value was 2.5–8.09 in the early stage and decreased to < 1 in the later stage. The incubation period was 4.5–5.2 days in Saudi Arabia and 6–7.8 days in South Korea. Duration from onset was 4–10 days to confirmation, 2.9–5.3 days to hospitalization, 11–17 days to death, and 14–20 days to discharge. Older age and concomitant disease were the most common factors related to MERS infection, severity, and mortality. Conclusion The transmissibility and severity of MERS differed by outbreak region and patient characteristics. Further studies assessing the risk of MERS should consider these factors.

ObjectivesTo evaluate radiomics studies according to radiomics quality score (RQS) and Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) to provide objective measurement of radiomics research.Materials and methodsPubMed and Embase were searched for studies published in high clinical imaging journals until December 2018 using the terms “radiomics” and “radiogenomics.” Studies were scored against the items in the RQS and TRIPOD guidelines. Subgroup analyses were performed for journal type (clinical vs. imaging), intended use (diagnostic vs. prognostic), and imaging modality (CT vs. MRI), and articles were compared using Fisher’s exact test and Mann-Whitney analysis.ResultsSeventy-seven articles were included. The mean RQS score was 26.1% of the maximum (9.4 out of 36). The RQS was low in demonstration of clinical utility (19.5%), test-retest analysis (6.5%), prospective study (3.9%), and open science (3.9%). None of the studies conducted a phantom or cost-effectiveness analysis. The adherence rate for TRIPOD was 57.8% (mean) and was particularly low in reporting title (2.6%), stating study objective in abstract and introduction (7.8% and 16.9%), blind assessment of outcome (14.3%), sample size (6.5%), and missing data (11.7%) categories. Studies in clinical journals scored higher and more frequently adopted external validation than imaging journals.ConclusionsThe overall scientific quality and reporting of radiomics studies is insufficient. Scientific improvements need to be made to feature reproducibility, analysis of clinical utility, and open science categories. Reporting of study objectives, blind assessment, sample size, and missing data is deemed to be necessary.Key Points• The overall scientific quality and reporting of radiomics studies is insufficient.• The RQS was low in demonstration of clinical utility, test-retest analysis, prospective study, and open science.• Room for improvement was shown in TRIPOD in stating study objective in abstract and introduction, blind assessment of outcome, sample size, and missing data categories.

Background The effect of temperature and humidity on the incidence of influenza may differ by climate region. In addition, the effect of diurnal temperature range on influenza incidence is unclear, according to previous study findings. Objectives The aim of this study was to analyze the effects of temperature, humidity, and diurnal temperature range on the incidence of influenza in Seoul, Republic of Korea, which is located in a temperate region. Methods We used Korean National Health insurance data to assess the weekly influenza incidence between 2010 and 2016, and used meteorological data from Seoul. To investigate the effect of temperature, relative humidity, and diurnal temperature range levels on influenza incidence, we used a distributed lag non‐linear model. Results The risk of influenza incidence was significantly increased with low daily temperatures of 0‐5°C and low (30%–40%) or high (70%) relative humidity. We found a positive significant association between diurnal temperature range and influenza incidence in this study. Conclusions Influenza incidence increased with low temperature and low/high humidity in a temperate region. Influenza incidence also increased with high diurnal temperature range, after considering temperature and humidity.

Soluble receptors are soluble forms of receptors found in the extracellular space. They have emerged as pivotal regulators of cellular signaling and disease pathogenesis. This review emphasizes their significance in cancer as diagnostic/prognostic markers and potential therapeutic targets. We provide an overview of the mechanisms by which soluble receptors are generated along with their functions. By exploring their involvement in cancer progression, metastasis, and immune evasion, we highlight the importance of soluble receptors, particularly soluble cytokine receptors and immune checkpoints, in the tumor microenvironment. Although current research has illustrated the emerging clinical relevance of soluble receptors, their therapeutic applications remain underexplored. As the landscape of cancer treatment evolves, understanding and targeting soluble receptors might pave the way for novel strategies for cancer diagnosis, prognosis, and therapy. Decoding soluble receptors: the future of cancer prognosis and immune evasion Soluble receptors, a kind of cellular receptor that exist in a dissolvable form and can boost or interrupt cellular signaling pathways, are the focus of this study. Abnormal amounts of these receptors are associated with the severity of many diseases, including cancer. This research by Eun-Ji Park and Chang-Woo Lee delves into the role of soluble receptors in cancer, specifically soluble cytokine receptors and soluble immune checkpoints. Their study involved a thorough review of existing literature and data on these soluble receptors. They discovered that high levels of soluble receptors are found in the blood of cancer patients, suggesting their potential use as minimally invasive indicators for early cancer detection and prognosis. The authors also suggest that blocking these soluble receptors could enhance the effectiveness of current cancer treatments. This summary was initially drafted using artificial intelligence, then revised and fact-checked by the author.

ObjectivesTo determine whether diffusion- and perfusion-weighted MRI–based radiomics features can improve prediction of isocitrate dehydrogenase (IDH) mutation and tumor aggressiveness in lower grade gliomas (LGGs)MethodsRadiomics features (n = 6472) were extracted from multiparametric MRI including conventional MRI, apparent diffusion coefficient (ADC), and normalized cerebral blood volume, acquired on 127 LGG patients with determined IDH mutation status and grade (WHO II or III). Radiomics models were constructed using machine learning–based feature selection and generalized linear model classifiers. Segmentation stability was calculated between two readers using concordance correlation coefficients (CCCs). Diagnostic performance to predict IDH mutation and tumor grade was compared between the multiparametric and conventional MRI radiomics models using the area under the receiver operating characteristics curve (AUC). The models were tested using a temporally independent validation set (n = 28).ResultsThe multiparametric MRI radiomics model was optimized with a random forest feature selector, with segmentation stability of a CCC threshold of 0.8. For IDH mutation, multiparametric MR radiomics showed similar performance (AUC 0.795) to the conventional radiomics model (AUC 0.729). In tumor grading, multiparametric model with ADC features showed higher performance (AUC 0.932) than the conventional model (AUC 0.555). The independent validation set showed the same trend with AUCs of 0.747 for IDH prediction and 0.819 for tumor grading with multiparametric MRI radiomics model.ConclusionMultiparametric MRI radiomics model showed improved diagnostic performance in tumor grading and comparable diagnostic performance in IDH mutation status, with ADC features playing a significant role.Key Points• The multiparametric MRI radiomics model was comparable with conventional MRI radiomics model in predicting IDH mutation.• The multiparametric MRI radiomics model outperformed conventional MRI in glioma grading.• Apparent diffusion coefficient played an important role in glioma grading and predicting IDH mutation status using radiomics.

The recent introduction of various high-dimensional modeling methods, such as radiomics and deep learning, has created a much greater diversity in modeling approaches for survival prediction (or, more generally, time-to-event prediction). The newness of the recent modeling approaches and unfamiliarity with the model outputs may confuse some researchers and practitioners about the evaluation of the performance of such models. Methodological literacy to critically appraise the performance evaluation of the models and, ideally, the ability to conduct such an evaluation would be needed for those who want to develop models or apply them in practice. This article intends to provide intuitive, conceptual, and practical explanations of the statistical methods for evaluating the performance of survival prediction models with minimal usage of mathematical descriptions. It covers from conventional to deep learning methods, and emphasis has been placed on recent modeling approaches. This review article includes straightforward explanations of C indices (Harrell's C index, etc.), time-dependent receiver operating characteristic curve analysis, calibration plot, other methods for evaluating the calibration performance, and Brier score.

Nonalcoholic fatty liver disease (NAFLD), the most common cause of chronic liver disease, ranges from simple hepatic steatosis to nonalcoholic steatohepatitis (NASH), which is a more aggressive form characterized by hepatocyte injury, inflammation, and fibrosis. Increasing evidence suggests that NASH is a risk factor for hepatocellular carcinoma (HCC), which is the fifth most common cancer worldwide and the second most common cause of cancer-related death. Recent studies support a strong mechanistic link between the NASH microenvironment and HCC development. The liver has a large capacity to remove circulating pathogens and gut-derived microbial compounds. Thus, the liver is a central player in immunoregulation. Altered immune responses are tightly associated with the development of NASH and HCC. The objective of this study was to differentiate the roles of specific immune cell subsets in NASH and HCC pathogenesis.Liver disease: Immune cells in disease progressionClarifying the role of specific cells in the immune system in the transition from non-alcoholic fatty liver disease (NAFLD) to liver cancer will help to understand disease progression and may open avenues towards new preventive and therapeutic strategies. NAFLD is the most common chronic liver disease. Growing evidence suggests that its most aggressive form, non-alcoholic steatohepatitis (NASH), can promote the development of liver cancer, the second most common cause of cancer deaths worldwide. Chang-Woo Lee and colleagues at Sungkyunkwan University, Suwon, South Korea review the immunological distinction between NASH and liver cancer, focusing on the levels and activities of six key types of immune system cells. Chronic inflammation mediated by the immune system can create conditions for NAFLD, NASH and liver cancer to develop and worsen.

We evaluated the diagnostic performance and generalizability of traditional machine learning and deep learning models for distinguishing glioblastoma from single brain metastasis using radiomics. The training and external validation cohorts comprised 166 (109 glioblastomas and 57 metastases) and 82 (50 glioblastomas and 32 metastases) patients, respectively. Two-hundred-and-sixty-five radiomic features were extracted from semiautomatically segmented regions on contrast-enhancing and peritumoral T2 hyperintense masks and used as input data. For each of a deep neural network (DNN) and seven traditional machine learning classifiers combined with one of five feature selection methods, hyperparameters were optimized through tenfold cross-validation in the training cohort. The diagnostic performance of the optimized models and two neuroradiologists was tested in the validation cohort for distinguishing glioblastoma from metastasis. In the external validation, DNN showed the highest diagnostic performance, with an area under receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy of 0.956 (95% confidence interval [CI], 0.918–0.990), 90.6% (95% CI, 80.5–100), 88.0% (95% CI, 79.0–97.0), and 89.0% (95% CI, 82.3–95.8), respectively, compared to the best-performing traditional machine learning model (adaptive boosting combined with tree-based feature selection; AUC, 0.890 (95% CI, 0.823–0.947)) and human readers (AUC, 0.774 [95% CI, 0.685–0.852] and 0.904 [95% CI, 0.852–0.951]). The results demonstrated deep learning using radiomic features can be useful for distinguishing glioblastoma from metastasis with good generalizability.

Changes in the DNA damage response (DDR) and cellular metabolism are two important factors that allow cancer cells to proliferate. DDR is a set of events in which DNA damage is recognized, DNA repair factors are recruited to the site of damage, the lesion is repaired, and cellular responses associated with the damage are processed. In cancer, DDR is commonly dysregulated, and the enzymes associated with DDR are prone to changes in ubiquitination. Additionally, cellular metabolism, especially glycolysis, is upregulated in cancer cells, and enzymes in this metabolic pathway are modulated by ubiquitination. The ubiquitin–proteasome system (UPS), particularly E3 ligases, act as a bridge between cellular metabolism and DDR since they regulate the enzymes associated with the two processes. Hence, the E3 ligases with high substrate specificity are considered potential therapeutic targets for treating cancer. A number of small molecule inhibitors designed to target different components of the UPS have been developed, and several have been tested in clinical trials for human use. In this review, we discuss the role of ubiquitination on overall cellular metabolism and DDR and confirm the link between them through the E3 ligases NEDD4, APC/CCDH1, FBXW7, and Pellino1. In addition, we present an overview of the clinically important small molecule inhibitors and implications for their practical use.

Background Many countries are trying to integrate traditional and complementary medicine (T&CM) into their health care systems. However, it is not easy to integrate T&CM within a given health care system. This study aims to draw policy outcomes and lessons from the case of Malaysia, which has been making efforts for over 20 years to integrate various types of T&CM into the national health care system (NHS). Methods Documents were searched in major databases and websites using words such as Malaysia and T&CM, and additional documents were secured using snowballing techniques. Data were classified and organized according to the World Health Organization health systems framework. Results Malaysia has focused on managing the safety and quality of T&CM, and to that end it has been institutionalized by enacting specialized laws rather than by applying existing medical law directly. Malaysia was able to institutionalize T&CM by adopting a step-by-step approach that considered the appropriateness of administrative policies and measures. Conclusions Malaysia's experiences in implementing its T&CM policies will raise practical implications for countries struggling to integrate their existing T&CM into the NHS and utilize it for universal health coverage.