Explore the vast range of titles available.

ObjectiveOsteoporosis is hard to detect before it manifests symptoms and complications. In this study, we evaluated machine learning models for identifying individuals with abnormal bone mineral density (BMD) through an analysis of spine X-ray features extracted by deep learning to alert high-risk osteoporosis populations.Materials and methodsWe retrospectively used data obtained from health check-ups including spine X-ray and dual-energy X-ray absorptiometry (DXA). Consecutively, we selected people with normal and abnormal bone mineral density. From the regions of interest of X-ray images, deep convolutional networks were used to generate image features. We designed prediction models for abnormal BMD using the image features trained by machine learning classification algorithms. The performances of each model were evaluated.ResultsFrom 334 participants, 170 images of abnormal (T scores < − 1.0 standard deviations (SD)) and 164 of normal BMD (T scores > = − 1.0 SD) were used for analysis. We found that a combination of feature extraction by VGGnet and classification by random forest based on the maximum balanced classification rate (BCR) yielded the best performance in terms of the area under the curve (AUC) (0.74), accuracy (0.71), sensitivity (0.81), specificity (0.60), BCR (0.70), and F1-score (0.73).ConclusionIn this study, we explored various machine learning algorithms for the prediction of BMD using simple spine X-ray image features extracted by three deep learning algorithms. We identified the combination for the best performance in predicting high-risk populations with abnormal BMD.

The seriousness of the diseases caused by aging have recently gained attention. Alzheimer’s disease (AD), a chronic neurodegenerative disease, accounts for 60–80% of senile dementia cases. Continuous research is being conducted on the cause of Alzheimer’s disease, and it is believed to include complex factors, such as genetic factors, the accumulation of amyloid beta plaques, a tangle of tau protein, oxidative stress, cholinergic dysfunction, neuroinflammation, and cell death. Sinapic acid is a hydroxycinnamic acid found in plant families, such as oranges, grapefruit, cranberry, mustard seeds, and rapeseeds. It exhibits various biological activities, including anti-inflammatory, anti-oxidant, anti-cancer, and anti-depressant effects. Sinapic acid is an acetylcholine esterase inhibitor that can be applied to the treatment of dementia caused by Alzheimer’s disease and Parkinson’s disease. However, electrophysiological studies on the effects of sinapic acid on memory and learning must still be conducted. Therefore, it was confirmed that sinapic acid was effective in long-term potentiation (LTP) using organotypic hippocampal segment tissue. In addition, the effect on scopolamine-induced learning and memory impairment was measured by oral administration of sinapic acid 10 mg/kg/day for 14 days, and behavioral experiments related to short-term and long-term spatial memory and avoidance memory were conducted. Sinapic acid increased the activity of the field excitatory postsynaptic potential (fEPSP) in a dose-dependent manner after TBS, and restored fEPSP activity in the CA1 region suppressed by scopolamine. The scopolamine-induced learning and memory impairment group showed lower results than the control group in the Y-maze, Passive avoidance (PA), and Morris water maze (MWM) experiments. Sinapic acid improved avoidance memory, short and long-term spatial recognition learning, and memory. In addition, sinapic acid weakened the inhibition of the brain-derived neurotrophic factor (BDNF), tropomyosin receptor kinase B (TrkB) and the activation of prostaglandin-endoperoxide synthase 2 (COX-2) and interleukin 1 beta (IL-1β) induced by scopolamine in the hippocampus. These results show that sinapic acid is effective in restoring LTP and cognitive impairment induced by the cholinergic receptor blockade. Moreover, it showed the effect of alleviating the reduction in scopolamine-induced BDNF and TrkB, and alleviated neuroinflammatory effects by inhibiting the increase in COX-2 and IL-1β. Therefore, we showed that sinapic acid has potential as a treatment for neurodegenerative cognitive impairment.

OBJECTIVE: The number of people with metabolic syndrome is increasing worldwide, and changes in socioenvironmental factors contribute to this increase. Therefore, investigation of changes in metabolic syndrome and its components in South Korea, where rapid socioenvironmental changes have occurred in recent years, would be foundational in setting up an effective strategy for reducing this increasing trend. RESEARCH DESIGN AND METHODS: We compared the prevalence and pattern of metabolic syndrome among participants in the Korean National Health and Nutrition Examination Surveys for 1998, 2001, 2005, and 2007. In each survey, stratified, multistage, probability-sampling designs and weighting adjustments were conducted to represent the entire Korean population. The revised National Cholesterol Education Program criteria were used as the definition of metabolic syndrome. All biochemical parameters were measured in a central laboratory. RESULTS: A total of 6,907 (mean ± SE age 45.0 ± 0.2 years), 4,536 (45.5 ± 0.2), 5,373 (47.1 ± 0.2), and 2,890 (49.9 ± 0.3) Koreans over 20 years of age have participated in the studies in 1998, 2001, 2005, and 2007, respectively. The age-adjusted prevalence of metabolic syndrome increased significantly from 24.9% in 1998, 29.2% in 2001, and 30.4% in 2005 to 31.3% in 2007. Among the five components, the level of low HDL cholesterol increased the most, by 13.8% over the 10 years. Abdominal obesity and hypertriglyceridemia followed, with 8.7 and 4.9% increases, respectively. CONCLUSIONS: Because dyslipidemia and abdominal obesity were major factors in increasing the prevalence of metabolic syndrome in Koreans for the past 10 years, lifestyle interventions should be conducted at the national level to reduce the burden and consequences of metabolic syndrome.

OBJECTIVE: To improve quality and efficiency of care for elderly patients with type 2 diabetes, we introduced elderly-friendly strategies to the clinical decision support system (CDSS)-based ubiquitous healthcare (u-healthcare) service, which is an individualized health management system using advanced medical information technology. RESEARCH DESIGN AND METHODS: We conducted a 6-month randomized, controlled clinical trial involving 144 patients aged >60 years. Participants were randomly assigned to receive routine care (control, n = 48), to the self-monitored blood glucose (SMBG, n = 47) group, or to the u-healthcare group (n = 49). The primary end point was the proportion of patients achieving A1C <7% without hypoglycemia at 6 months. U-healthcare system refers to an individualized medical service in which medical instructions are given through the patient's mobile phone. Patients receive a glucometer with a public switched telephone network-connected cradle that automatically transfers test results to a hospital-based server. Once the data are transferred to the server, an automated system, the CDSS rule engine, generates and sends patient-specific messages by mobile phone. RESULTS: After 6 months of follow-up, the mean A1C level was significantly decreased from 7.8 ± 1.3% to 7.4 ± 1.0% (P < 0.001) in the u-healthcare group and from 7.9 ± 1.0% to 7.7 ± 1.0% (P = 0.020) in the SMBG group, compared with 7.9 ± 0.8% to 7.8 ± 1.0% (P = 0.274) in the control group. The proportion of patients with A1C <7% without hypoglycemia was 30.6% in the u-healthcare group, 23.4% in the SMBG group (23.4%), and 14.0% in the control group (P < 0.05). CONCLUSIONS: The CDSS-based u-healthcare service achieved better glycemic control with less hypoglycemia than SMBG and routine care and may provide effective and safe diabetes management in the elderly diabetic patients.

Aim/Introduction We assess the efficacy of artificial intelligence (AI)‐based, fully automated, volumetric body composition metrics in predicting the risk of diabetes. Materials and Methods This was a cross‐sectional and 10‐year retrospective longitudinal study. The cross‐sectional analysis included health check‐up data of 15,330 subjects with abdominal computed tomography (CT) images between January 1, 2011, and September 30, 2012. Of these, 10,570 subjects with available follow‐up data were included in the longitudinal analyses. The volume of each body segment included in the abdominal CT images was measured using AI‐based image analysis software. Results Visceral fat (VF) proportion and VF/subcutaneous fat (SF) ratio increased with age, and both strongly predicted the presence and risk of developing diabetes. Optimal cut‐offs for VF proportion were 24% for men and 16% for women, while VF/SF ratio values were 1.2 for men and 0.5 for women. The subjects with higher VF/SF ratio and VF proportion were associated with a greater risk of having diabetes (adjusted OR 2.0 [95% CI 1.7–2.4] in men; 2.9 [2.2–3.9] in women). In subjects with normal glucose tolerance, higher VF proportion and VF/SF ratio were associated with higher risk of developing prediabetes or diabetes (adjusted HR 1.3 [95% CI 1.1–1.4] in men; 1.4 [1.2–1.7] in women). These trends were consistently observed across each specified cut‐off value. Conclusions AI‐based volumetric analysis of abdominal CT images can be useful in obtaining body composition data and predicting the risk of diabetes. Artificial intelligence–based volumetric analysis of abdominal CT images is a useful tool in obtaining body composition data and assessing the risk of incident diabetes in clinical practice.

The purpose of our study is to figure out the transitions of the cryptocurrency market due to the outbreak of COVID-19 through network analysis, and we studied the complexity of the market from different perspectives. To construct a cryptocurrency network, we first apply a mutual information method to the daily log return values of 102 digital currencies from January 1, 2019, to December 31, 2020, and also apply a correlation coefficient method for comparison. Based on these two methods, we construct networks by applying the minimum spanning tree and the planar maximally filtered graph. Furthermore, we study the statistical and topological properties of these networks. Numerical results demonstrate that the degree distribution follows the power-law and the graphs after the COVID-19 outbreak have noticeable differences in network measurements compared to before. Moreover, the results of graphs constructed by each method are different in topological and statistical properties and the network’s behavior. In particular, during the post-COVID-19 period, it can be seen that Ethereum and Qtum are the most influential cryptocurrencies in both methods. Our results provide insight and expectations for investors in terms of sharing information about cryptocurrencies amid the uncertainty posed by the COVID-19 pandemic.

Objective: This study aimed to elucidate the effects of cooking conditions on the physicochemical and sensory characteristics of dry- and wet-aged beef strip loins.Methods: Dry- and wet-aged beef aged for 28 days were cooked using different cooking methods (grilling or oven roasting)×cooking temperatures (150°C or 230°C), and their pH, 2-thiobarbituric acid reactive substances (TBARS), volatile compounds, and color were measured.Results: Cooking conditions did not affect pH; however, grilling resulted in lower TBARS but higher cooking doneness at the dry-aged beef surface compared to oven roasting (p< 0.05). In descriptive sensory analysis, the roasted flavor of dry-aged beef was significantly stronger when grill-cooked compared to oven roasting. Dry-aged beef grill-cooked at 150°C presented a higher intensity of cheesy flavor, and that grilled at 230°C showed a greater intensity of roasted flavor compared to wet-aged beef at the same condition, respectively.Conclusion: Grilling may be effective for enhancing the unique flavor in dry-aged beef.

Glucagon-Like Peptide-1 Gene Therapy in Obese Diabetic Mice Results in Long-Term Cure of Diabetes by Improving Insulin Sensitivity and Reducing Hepatic Gluconeogenesis Young-Sun Lee , Seungjin Shin , Toshikatsu Shigihara , Eunsil Hahm , Meng-Ju Liu , Jaeseok Han , Ji-Won Yoon and Hee-Sook Jun From the Rosalind Franklin Comprehensive Diabetes Center, Department of Pathology, Chicago Medical School, North Chicago, Illinois Address correspondence and reprint requests to Hee-Sook Jun, PhD, Rosalind Franklin Comprehensive Diabetes Center, Chicago Medical School, 3333 Green Bay Rd., North Chicago, IL 60064. E-mail: hee-sook.jeon{at}rosalindfranklin.edu Abstract Long-term treatment with glucagon-like peptide (GLP)-1 or its analog can improve insulin sensitivity. However, continuous administration is required due to its short half-life. We hypothesized that continuous production of therapeutic levels of GLP-1 in vivo by a gene therapy strategy may remit hyperglycemia and maintain prolonged normoglycemia. We produced a recombinant adenovirus expressing GLP-1 (rAd-GLP-1) under the cytomegalovirus promoter, intravenously injected it into diabetic ob/ob mice, and investigated the effect of this treatment on remission of diabetes, as well as the mechanisms involved. rAd-GLP-1–treated diabetic ob/ob mice became normoglycemic 4 days after treatment, remained normoglycemic over 60 days, and had reduced body weight gain. Glucose tolerance tests found that exogenous glucose was cleared normally. rAd-GLP-1–treated diabetic ob/ob mice showed improved β-cell function, evidenced by glucose-responsive insulin release, and increased insulin sensitivity, evidenced by improved insulin tolerance and increased insulin-stimulated glucose uptake in adipocytes. rAd-GLP-1 treatment increased basal levels of insulin receptor substrate (IRS)-1 in the liver and activation of IRS-1 and protein kinase C by insulin in liver and muscle; increased Akt activation was only observed in muscle. rAd-GLP-1 treatment reduced hepatic glucose production and hepatic expression of phosphoenolpyruvate carboxykinase, glucose-6-phosphatase, and fatty acid synthase in ob/ob mice. Taken together, these results show that a single administration of rAd-GLP-1 results in the long-term remission of diabetes in ob/ob mice by improving insulin sensitivity through restoration of insulin signaling and reducing hepatic gluconeogenesis. FAS, fatty acid synthase FFA, free fatty acid G6Pase, glucose-6-phosphatase GAPDH, glyceraldehyde 3-phosphate dehydrogenase GLP, glucagon-like peptide IRS, insulin receptor substrate PEPCK, phosphoenolpyruvate carboxykinase PKC, protein kinase C rAd-βgal, recombinant adenovirus expressing β-galactosidase rAd-GLP-1, recombinant adenovirus expressing GLP-1 Footnotes Published ahead of print at http://diabetes.diabetesjournals.org on 16 March 2007. DOI: 10.2337/db06-1182. J.-W.Y. is deceased. The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked “advertisement” in accordance with 18 U.S.C. Section 1734 solely to indicate this fact. Accepted March 2, 2007. Received August 23, 2006. DIABETES

Background: An association between low muscle mass and nonalcoholic fatty liver disease (NAFLD) has been suggested. We investigated this relationship using controlled attenuation parameter (CAP). Methods: A retrospective cohort of subjects had liver FibroScan® (Echosens, Paris, France) and bioelectrical impedance analyses during health screening exams. Low muscle mass was defined based on appendicular skeletal muscle mass/body weight ratios of one (class I) or two (class II) standard deviations below the sex-specific mean for healthy young adults. Results: Among 960 subjects (58.1 years; 67.4% male), 344 (45.8%, class I) and 110 (11.5%, class II) had low muscle mass. After adjusting for traditional metabolic risk factors, hepatic steatosis, defined as a CAP ≥ 248 dB/m, was associated with low muscle mass (class I, odds ratio (OR): 1.96, 95% confidence interval (CI): 1.38–2.78; class II, OR: 3.33, 95% CI: 1.77–6.26). A dose-dependent association between the grade of steatosis and low muscle mass was also found (class I, OR: 1.88, for CAP ≥ 248, <302; OR: 2.19, in CAP ≥ 302; class II, OR: 2.33, for CAP ≥ 248, <302; OR: 6.17, in CAP ≥ 302). High liver stiffness was also significantly associated with an increased risk of low muscle mass (class I, OR: 1.97, 95% CI: 1.31–2.95; class II, OR: 2.96, 95% CI: 1.51–5.78). Conclusion: Hepatic steatosis is independently associated with low muscle mass in a dose-dependent manner. The association between hepatic steatosis and low muscle mass suggests that particular attention should be given to subjects with NAFLD for an adequate assessment of muscle mass.

Severe acute liver failure, even when transient, must be treated by transplantation and lifelong immune suppression. Treatment could be improved by bioartificial liver (BAL) support, but this approach is hindered by a shortage of human hepatocytes. To generate an alternative source of cells for BAL support, we differentiated mouse embryonic stem (ES) cells into hepatocytes by coculture with a combination of human liver nonparenchymal cell lines and fibroblast growth factor-2, human activin-A and hepatocyte growth factor. Functional hepatocytes were isolated using albumin promoter–based cell sorting. ES cell–derived hepatocytes expressed liver-specific genes, secreted albumin and metabolized ammonia, lidocaine and diazepam. Treatment of 90% hepatectomized mice with a subcutaneously implanted BAL seeded with ES cell–derived hepatocytes or primary hepatocytes improved liver function and prolonged survival, whereas treatment with a BAL seeded with control cells did not. After functioning in the BAL, ES cell–derived hepatocytes developed characteristics nearly identical to those of primary hepatocytes.