Detection of COVID-19 epidemic outbreak using machine learning

BackgroundThe coronavirus disease (COVID-19) pandemic has spread rapidly across the world, creating an urgent need for predictive models that can help healthcare providers prepare and respond to outbreaks more quickly and effectively, and ultimately improve patient care. Early detection and warning systems are crucial for preventing and controlling epidemic spread.ObjectiveIn this study, we aimed to propose a machine learning-based method to predict the transmission trend of COVID-19 and a new approach to detect the start time of new outbreaks by analyzing epidemiological data.MethodsWe developed a risk index to measure the change in the transmission trend. We applied machine learning (ML) techniques to predict COVID-19 transmission trends, categorized into three labels: decrease (L0), maintain (L1), and increase (L2). We used Support Vector Machine (SVM), Random Forest (RF), and XGBoost (XGB) as ML models. We employed grid search methods to determine the optimal hyperparameters for these three models. We proposed a new method to detect the start time of new outbreaks based on label 2, which was sustained for at least 14 days (i.e., the duration of maintenance). We compared the performance of different ML models to identify the most accurate approach for outbreak detection. We conducted sensitivity analysis for the duration of maintenance between 7 days and 28 days.ResultsML methods demonstrated high accuracy (over 94%) in estimating the classification of the transmission trends. Our proposed method successfully predicted the start time of new outbreaks, enabling us to detect a total of seven estimated outbreaks, while there were five reported outbreaks between March 2020 and October 2022 in Korea. It means that our method could detect minor outbreaks. Among the ML models, the RF and XGB classifiers exhibited the highest accuracy in outbreak detection.ConclusionThe study highlights the strength of our method in accurately predicting the timing of an outbreak using an interpretable and explainable approach. It could provide a standard for predicting the start time of new outbreaks and detecting future transmission trends. This method can contribute to the development of targeted prevention and control measures and enhance resource management during the pandemic

Body Mass Index Is Associated with Hypercholesterolemia following Thyroid Hormone Withdrawal in Thyroidectomized Patients

Thyroid hormone withdrawal (THW) for postoperative radioiodine adjuvant therapy or diagnostic radioiodine whole body scan in patients with differentiated thyroid cancers results in acute thyroid hormone deficiency and abnormal lipid profiles. To better clarify the clinical pattern of dyslipidemia occurring after THW, we retrospectively analyzed the association between serum total cholesterol level after THW and various clinical factors in a total of 61 patients who underwent total thyroidectomy due to papillary thyroid cancers from January 2010 to March 2012, in Severance Hospital, Seoul, Korea. Preoperative baseline total cholesterol was significantly correlated with post-THW total cholesterol level; however, age, gender, or elevated TSH level after THW itself was not correlated with post-THW total cholesterol level. A significant correlation between preoperative measured BMI and post-THW total cholesterol level was found ( = 0.263, = 0.041). In multiple logistic analysis, BMI was an independent determining factor of post-THW total cholesterol level ( = 0.012)

Familial Correlation and Heritability of Hand Grip Strength in Korean Adults (Korea National Health and Nutrition Examination Survey 2014 to 2019)

Background The onset and progression of sarcopenia are highly variable among individuals owing to genetic and environmental factors. However, there are a limited number of studies measuring the heritability of muscle strength in large numbers of parent-adult offspring pairs. We aimed to investigate the familial correlation and heritability of hand grip strength (HGS) among Korean adults. Methods This family-based cohort study on data from the Korea National Health and Nutrition Examination Survey (2014 to 2019) included 5,004 Koreans aged ≥19 years from 1,527 families. HGS was measured using a digital grip strength dynamometer. Familial correlations of HGS were calculated in different pairs of relatives. Variance component methods were used to estimate heritability. Results The heritability estimate of HGS among Korean adults was 0.154 (standard error, 0.066). Correlation coefficient estimates for HGS between parent-offspring, sibling, and spouse pairs were significant at 0.07, 0.10, and 0.23 (p<0.001, p=0.041, and p<0.001, respectively). The total variance in the HGS phenotype was explained by additive genetic (15.4%), shared environmental (11.0%), and unique environmental (73.6%) influences. The odds of weak HGS significantly increased in the offspring of parents with weak HGS (odds ratio [OR], 1.69–3.10; p=0.027–0.038), especially in daughters (OR, 2.04–4.64; p=0.029–0.034). Conclusion HGS exhibits a familial correlation and significant heritable tendency in Korean adults. Therefore, Asian adults, especially women, who have parents with weak HGS, need to pay special attention to their muscle health with the help of healthy environmental stimuli

Non-Alcoholic Fatty Liver Disease with Sarcopenia and Carotid Plaque Progression Risk in Patients with Type 2 Diabetes Mellitus

Background We aimed to evaluate whether non-alcoholic fatty liver disease (NAFLD) with or without sarcopenia is associated with progression of carotid atherosclerosis in patients with type 2 diabetes mellitus (T2DM). Methods We investigated 852 T2DM patients who underwent abdominal ultrasonography, bioelectrical impedance analysis, and carotid artery ultrasonography at baseline and repeated carotid ultrasonography after 6 to 8 years. NAFLD was confirmed by abdominal ultrasonography, and sarcopenia was defined as a sex-specific skeletal muscle mass index (SMI) value <2 standard deviations below the mean for healthy young adults. SMI was calculated by dividing the sum of appendicular skeletal mass by body weight. We investigated the association between NAFLD with or without sarcopenia and the progression of carotid atherosclerosis. Results Of the 852 patients, 333 (39.1%) were classified as NAFLD without sarcopenia, 66 (7.7%) were classified as sarcopenia without NAFLD, and 123 (14.4%) had NAFLD with sarcopenia at baseline. After 6 to 8 years, patients with both NAFLD and sarcopenia had a higher risk of atherosclerosis progression (adjusted odds ratio, 2.20; P<0.009) than controls without NAFLD and sarcopenia. When a subgroup analysis was performed on only patients with NAFLD, female sex, absence of central obesity, and non-obesity were significant factors related to increased risk of plaque progression risk in sarcopenic patients. Conclusion NAFLD with sarcopenia was significantly associated with the progression of carotid atherosclerosis in T2DM patients

Transparent and Flexible Copper Iodide Resistive Memories Processed with a Dissolution-Recrystallization Solution Technique

Copyright © 2022 American Chemical Society. This study explores a class of resistive memory candidates -simple binary halides -and demonstrates their efficacy in switching between high-and low-resistive states. Herein, copper halide, particularly copper iodide (CuI), is investigated for its resistive switching efficacy when sandwiched between indium tin oxide (ITO) and silver electrodes on flexible polyethylene terephthalate (PET) substrates. CuI is deposited on ITO-coated PET using an innovative dissolution-recrystallization technique, in which a deposition temperature of 80 & DEG;C is sufficient to eliminate the carrier solvent-acetonitrile-and impart considerable densification of CuI for effective memory characteristics. The PET/ ITO/CuI is transparent (> 90%), and the PET//ITO/CuI/Ag devices display states of notably low-and high-resistive states with a ratio of more than 10 within a voltage biasing range of -2.5 to +2.5 V. Additionally, the devices exhibit similar resistive states under bending stress. Halides (in particular, CuI) are, thus, introduced as a class of active materials for transparent and flexible resistive memories.11Nsciescopu

Association of Metabolomic Change and Treatment Response in Patients with Non-Alcoholic Fatty Liver Disease

Non-alcoholic fatty liver disease (NAFLD) is the major cause of chronic liver disease, yet cost-effective and non-invasive diagnostic tools to monitor the severity of the disease are lacking. We aimed to investigate the metabolomic changes in NAFLD associated with therapeutic responses. It was conducted in 63 patients with NAFLD who received either ezetimibe plus rosuvastatin or rosuvastatin monotherapy. The treatment response was determined by MRI performed at baseline and week 24. The metabolites were measured at baseline and week 12. In the combination group, a relative decrease in xanthine was associated with a good response to liver fat decrease, while a relative increase in choline was associated with a good response to liver stiffness. In the monotherapy group, the relative decreases in triglyceride (TG) 20:5_36:2, TG 18:1_38:6, acetylcarnitine (C2), fatty acid (FA) 18:2, FA 18:1, and docosahexaenoic acid were associated with a decrease in liver fat, while hexosylceramide (d18:2/16:0) and hippuric acid were associated with a decrease in liver stiffness. Models using the metabolite changes showed an AUC of &gt;0.75 in receiver operating curve analysis for predicting an improvement in liver fat and stiffness. This approach revealed the physiological impact of drugs, suggesting the mechanism underlying the development of this disease

Data_Sheet_1_Detection of COVID-19 epidemic outbreak using machine learning.pdf

BackgroundThe coronavirus disease (COVID-19) pandemic has spread rapidly across the world, creating an urgent need for predictive models that can help healthcare providers prepare and respond to outbreaks more quickly and effectively, and ultimately improve patient care. Early detection and warning systems are crucial for preventing and controlling epidemic spread.ObjectiveIn this study, we aimed to propose a machine learning-based method to predict the transmission trend of COVID-19 and a new approach to detect the start time of new outbreaks by analyzing epidemiological data.MethodsWe developed a risk index to measure the change in the transmission trend. We applied machine learning (ML) techniques to predict COVID-19 transmission trends, categorized into three labels: decrease (L0), maintain (L1), and increase (L2). We used Support Vector Machine (SVM), Random Forest (RF), and XGBoost (XGB) as ML models. We employed grid search methods to determine the optimal hyperparameters for these three models. We proposed a new method to detect the start time of new outbreaks based on label 2, which was sustained for at least 14 days (i.e., the duration of maintenance). We compared the performance of different ML models to identify the most accurate approach for outbreak detection. We conducted sensitivity analysis for the duration of maintenance between 7 days and 28 days.ResultsML methods demonstrated high accuracy (over 94%) in estimating the classification of the transmission trends. Our proposed method successfully predicted the start time of new outbreaks, enabling us to detect a total of seven estimated outbreaks, while there were five reported outbreaks between March 2020 and October 2022 in Korea. It means that our method could detect minor outbreaks. Among the ML models, the RF and XGB classifiers exhibited the highest accuracy in outbreak detection.ConclusionThe study highlights the strength of our method in accurately predicting the timing of an outbreak using an interpretable and explainable approach. It could provide a standard for predicting the start time of new outbreaks and detecting future transmission trends. This method can contribute to the development of targeted prevention and control measures and enhance resource management during the pandemic.</p

Effect of Ezetimibe on Glucose Metabolism and Inflammatory Markers in Adipose Tissue

Despite numerous studies, the effects of ezetimibe on glucose metabolism are poorly understood. Here, we aimed to investigate the effects of ezetimibe on glucose metabolism and the expression of inflammatory markers. Thirteen rats were randomly assigned to an ezetimibe (n = 6) or control group (n = 7). The control group received a high fat diet (HFD; 60 Kcal%), whereas the ezetimibe group received an HFD (60 Kcal%) containing 160 mg/kg of ezetimibe. After 14 weeks, adipose and liver tissues, along with plasma, were collected and comparatively analyzed. The effects of combination therapy with ezetimibe and statins on glucose metabolism were investigated over a 1-year period using data from patients with hyperlipidemia. Several indices of glucose metabolism partially improved in the ezetimibe group. The sizes of adipocytes and the accumulation of pro-inflammatory cytokines were reduced in the ezetimibe group. Ezetimibe treatment induced anti-inflammatory cytokines and fatty acid oxidation in adipocytes and reduced serum levels of free fatty acids. Clinical data analysis revealed that statin monotherapy significantly increased insulin resistance. However, combination therapy with ezetimibe and statins did not increase insulin resistance. In conclusion, ezetimibe was found to reduce the sizes of adipocytes in visceral fat and serum levels of free fatty acids, to induce fatty acid oxidation, to improve adipocytic inflammation, and to partially improve glycemic index values