Socioeconomic Costs of Overweight and Obesity in Korean Adults

This study was conducted to estimate the socioeconomic costs of overweight and obesity in a sample of Korean adults aged 20 yr and older in 2005. The socioeconomic costs of overweight and obesity include direct costs (inpatient care, outpatient care and medication) and indirect costs (loss of productivity due to premature deaths and inpatient care, time costs, traffic costs and nursing fees). Hypertension, diabetes mellitus, dyslipidemia, ischemic heart disease, stroke, colon cancer and osteoarthritis were selected as obesity-related diseases. The population attributable fraction (PAF) of obesity was calculated from national representative data of Korea such as the National Health Insurance Corporation (NHIC) cohort data and the 2005 Korea National Health and Nutrition Examination Survey (KNHANES) data. Direct costs of overweight and obesity were estimated at approximately U$1,081 million equivalent (men: U$497 million, women: U$584 million) and indirect costs were estimated at approximately U$706 million (men: U$527 million, women: U$178 million). The estimated total socioeconomic costs of overweight and obesity were approximately U$1,787 million (men: U$1,081 million, women: U$706 million). These total costs represented about 0.22% of the gross domestic product (GDP) and 3.7% of the national health care expenditures in 2005. We found the socioeconomic costs of overweight and obesity in Korean adults aged 20 yr and older are substantial. In order to control the socioeconomic burden attributable to overweight and obesity, effective national strategies for prevention and management of obesity should be established and implemented

Identifying and extracting bark key features of 42 tree species using convolutional neural networks and class activation mapping

© 2022, The Author(s).The significance of automatic plant identification has already been recognized by academia and industry. There were several attempts to utilize leaves and flowers for identification; however, bark also could be beneficial, especially for trees, due to its consistency throughout the seasons and its easy accessibility, even in high crown conditions. Previous studies regarding bark identification have mostly contributed quantitatively to increasing classification accuracy. However, ever since computer vision algorithms surpassed the identification ability of humans, an open question arises as to how machines successfully interpret and unravel the complicated patterns of barks. Here, we trained two convolutional neural networks (CNNs) with distinct architectures using a large-scale bark image dataset and applied class activation mapping (CAM) aggregation to investigate diagnostic keys for identifying each species. CNNs could identify the barks of 42 species with > 90% accuracy, and the overall accuracies showed a small difference between the two models. Diagnostic keys matched with salient shapes, which were also easily recognized by human eyes, and were typified as blisters, horizontal and vertical stripes, lenticels of various shapes, and vertical crevices and clefts. The two models exhibited disparate quality in the diagnostic features: the old and less complex model showed more general and well-matching patterns, while the better-performing model with much deeper layers indicated local patterns less relevant to barks. CNNs were also capable of predicting untrained species by 41.98% and 48.67% within the correct genus and family, respectively. Our methodologies and findings are potentially applicable to identify and visualize crucial traits of other plant organs.Y

Peripheral blood transcriptomic clusters uncovered immune phenotypes of asthma

Background Transcriptomic analysis has been used to elucidate the complex pathogenesis of heterogeneous disease and may also contribute to identify potential therapeutic targets by delineating the hub genes. This study aimed to investigate whether blood transcriptomic clustering can distinguish clinical and immune phenotypes of asthmatics, and microbiome in asthmatics. Methods Transcriptomic expression of peripheral blood mononuclear cells (PBMCs) from 47 asthmatics and 21 non-asthmatics was measured using RNA sequencing. A hierarchical clustering algorithm was used to classify asthmatics. Differentially expressed genes, clinical phenotypes, immune phenotypes, and microbiome of each transcriptomic cluster were assessed. Results In asthmatics, three distinct transcriptomic clusters with numerously different transcriptomic expressions were identified. The proportion of severe asthmatics was highest in cluster 3 as 73.3%, followed by cluster 2 (45.5%) and cluster 1 (28.6%). While cluster 1 represented clinically non-severe T2 asthma, cluster 3 tended to include severe non-T2 asthma. Cluster 2 had features of both T2 and non-T2 asthmatics characterized by the highest serum IgE level and neutrophil-dominant sputum cell population. Compared to non-asthmatics, cluster 1 showed higher CCL23 and IL1RL1 expression while the expression of TREML4 was suppressed in cluster 3. CTSD and ALDH2 showed a significant positive linear relationship across three clusters in the order of cluster 1 to 3. No significant differences in the diversities of lung and gut microbiomes were observed among transcriptomic clusters of asthmatics and non-asthmatics. However, our study has limitations in that small sample size data were analyzed with unmeasured confounding factors and causal relationships or function pathways were not verified. Conclusions Genetic clustering based on the blood transcriptome may provide novel immunological insight, which can be biomarkers of asthma immune phenotypes. Trial registration Retrospectively registeredN

5-Day repeated inhalation and 28-day post-exposure study of graphene

<div><p></p><p>Graphene has recently been attracting increasing attention due to its unique electronic and chemical properties and many potential applications in such fields as semiconductors, energy storage, flexible electronics, biosensors and medical imaging. However, the toxicity of graphene in the case of human exposure has not yet been clarified. Thus, a 5-day repeated inhalation toxicity study of graphene was conducted using a nose-only inhalation system for male Sprague-Dawley rats. A total of three groups (20 rats per group) were compared: (1) control (ambient air), (2) low concentration (0.68 ± 0.14 mg/m³ graphene) and (3) high concentration (3.86 ± 0.94 mg/m³ graphene). The rats were exposed to graphene for 6 h/day for 5 days, followed by recovery for 1, 3, 7 or 28 days. The bioaccumulation and macrophage ingestion of the graphene were evaluated in the rat lungs. The exposure to graphene did not change the body weights or organ weights of the rats after the 5-day exposure and during the recovery period. No statistically significant difference was observed in the levels of lactate dehydrogenase, protein and albumin between the exposed and control groups. However, graphene ingestion by alveolar macrophages was observed in the exposed groups. Therefore, these results suggest that the 5-day repeated exposure to graphene only had a minimal toxic effect at the concentrations and time points used in this study.</p></div