Measuring the Burden of Disease in Korea, 2008-2018

The study aims to examine the current status and differences in the burden of disease in Korea during 2008-2018. We calculated the burden of disease for Koreans from 2008 to 2018 using an incidence-based approach. Disability adjusted life years (DALYs) were expressed in units per 100 000 population by adding years of life lost (YLLs) and years lived with disability (YLDs). DALY calculation results were presented by gender, age group, disease, region, and income level. To explore differences in DALYs by region and income level, we used administrative district and insurance premium information from the National Health Insurance Service claims data. The burden of disease among Koreans showed an increasing trend from 2008 to 2018. By 2017, the burden of disease among men was higher than that among women. Diabetes mellitus, low back pain, and chronic lower respiratory disease were ranked high in the burden of disease; the sum of DALY rates for these diseases accounted for 18.4% of the total burden of disease among Koreans in 2018. The top leading causes associated with a high burden of disease differed slightly according to gender, age group, and income level. In this study, we measured the health status of Koreans and differences in the population health level according to gender, age group, region, and income level. This data can be used as an indicator of health equity, and the results derived from this study can be used to guide community-centered (or customized) health promotion policies and projects, and for setting national health policy goals.

RAHC_GAN: A Data Augmentation Method for Tomato Leaf Disease Recognition

Accurate recognition of tomato diseases is of great significance for agricultural production. Sufficient and insufficient training data of supervised recognition neural network training are symmetry problems. A high precision neural network needs a large number of labeled data, and the difficulty of data sample acquisition is the main challenge to improving the performance of disease recognition. [l.]Moreover, the traditional data augmentation based on geometric transformation can obtain less information, and the generalization is not strong. In order to generate leaves with obvious disease feature and improve the performance of disease recognition, this paper analyzes and solves the problem of insufficient training samples in recognition network training, and proposes a new data augmentation method RAHC_GAN based on GAN, which is used to expand data and identify diseases. First, the proposed hidden variable is used to control the size of the disease area continuously, and the residual attention blocks are used to make the generated adversarial network pay more attention to the disease region in the leaf image, besides, a multi-scale discriminator is used to enrich the detailed texture of the generated image. Then, an expanded data set including original training set images and generated images by RAHC_GAN is established, which is used as the input of four kinds classification networks AlexNet, VGGNet, GoogLeNet and ResNet for performance evaluation. Experimental results show that RAHC_GAN can generate leaves with obvious disease feature, and the generated expanded data set can significantly improve the recognition performance of the classifier. After data augmentation, the recognition effect on the four classifiers is increased by 1.8%, 2.2%, 2.7%, and 0.4% respectively, which are higher than the comparison method. At the same time, the impact of expanded data with different ratio on the recognition performance was evaluated, and the method was extended to apple and grape diseased leaves. The proposed data augmentation method can simulate the distribution of tomato leaf diseases and improve the performance of disease recognition, and it may be extended to solve the problem of insufficient data in other plant research tasks.The tomato leaf data augmented by the traditional data augmentation methods based on geometric transformation usually contain less information, and the generalization is not strong. Therefore, a new data augmentation method, RAHC_GAN, based on generative adversarial networks is proposed in this paper, which is used to expand tomato leaf data and identify diseases. In this method, continuous hidden variables are added at the input of the generator, and the purpose is to continuously control the size of the generated disease area and to supplement the intra class information of the same disease. Additionally, the residual attention block is added to the generator to make it pay more attention to the disease region in the leaf image; a multi-scale discriminator is also used to enrich the detailed texture of the generated image and finally generate leaves with obvious disease features. Then, we use the images generated by RAHC_GAN and the original training images to build an expanded data set, which is used to train four kinds of recognition networks, AlexNet, VGGNet, GoogLeNet, and ResNet, and the performance is evaluated through the test set. Experimental results show that RAHC_GAN can generate leaves with obvious disease features, and the generated expanded data set can significantly improve the recognition performance of the classifier. Furthermore, the results of the apple, grape, and corn data set show that RAHC_GAN can also be used as a method to solve the problem of insufficient data in other plant research tasks.

Disease-, region- and cell type specific diversity of α-synuclein carboxy terminal truncations in synucleinopathies

Synucleinopathies, including Parkinson’s disease (PD), Lewy body dementia (LBD), Alzheimer’s disease with amygdala restricted Lewy bodies (AD/ALB), and multiple system atrophy (MSA) comprise a spectrum of neurodegenerative disorders characterized by the presence of distinct pathological α-synuclein (αSyn) inclusions. Experimental and pathological studies support the notion that αSyn aggregates contribute to cellular demise and dysfunction with disease progression associated with a prion-like spread of αSyn aggregates via conformational templating. The initiating event(s) and factors that contribute to diverse forms of synucleinopathies remain poorly understood. A major post-translational modification of αSyn associated with pathological inclusions is a diverse array of specific truncations within the carboxy terminal region. While these modifications have been shown experimentally to induce and promote αSyn aggregation, little is known about their disease-, region- and cell type specific distribution. To this end, we generated a series of monoclonal antibodies specific to neo-epitopes in αSyn truncated after residues 103, 115, 119, 122, 125, and 129. Immunocytochemical investigations using these new tools revealed striking differences in the αSyn truncation pattern between different synucleinopathies, brain regions and specific cellular populations. In LBD, neuronal inclusions in the substantia nigra and amygdala were positive for αSyn cleaved after residues 103, 119, 122, and 125, but not 115. In contrast, in the same patients' brain αSyn cleaved at residue 115, as well as 103, 119 and 122 were abundant in the dorsal motor nucleus of the vagus. In patients with AD/ALB, these modifications were only weakly or not detected in amygdala αSyn inclusions. αSyn truncated at residues 103, 115, 119, and 125 was readily present in MSA glial cytoplasmic inclusions, but 122 cleaved αSyn was only weakly or not present. Conversely, MSA neuronal pathology in the pontine nuclei was strongly reactive to the αSyn x-122 neo-epitope but did not display any reactivity for αSyn 103 cleavage. These studies demonstrate significant disease-, region- and cell type specific differences in carboxy terminal αSyn processing associated with pathological inclusions that likely contributes to their distinct strain-like prion properties and promotes the diversity displayed in the degrees of these insidious diseases.

Development of a Structure-based Computational Simulation to Optimize the Blocking Efficacy of Pro-antibodies

Background: The on-target toxicity of monoclonal antibodies (Abs) is mainly due to the fact that Abs cannot distinguish target antigens (Ags) expressed in disease regions from those in normal tissues during systemic administration. In order to overcome this issue, we “copied” an autologous Ab hinge as an “Ab lock” and “pasted” it on the binding site of the Ab by connecting a protease substrate and linker in between to generate a pro-Ab, which can be specifically activated in the disease region to enhance Ab selectivity and reduce side effects. Previously, we reported that 70% of pro-Abs can achieve more than 100-fold blocking ability compared to the parental Abs. However, 30% of pro-Abs do not have such efficient blocking ability. This is because the same Ab lock linker cannot be applied to every Ab due to the differences in the complementarity-determining region (CDR) loops. Here we designed a method which uses structure-based computational simulation (MSCS) to optimize the blocking ability of the Ab lock for all Ab drugs. MSCS can precisely adjust the amino acid composition of the linker between the Ab lock and Ab drug with the assistance of molecular simulation.Results: We selected αPD-1, αIL-1β, αCTLA-4 and αTNFα Ab as models and attached the Ab lock with various linkers (L1 to L7) to form pro-Abs by MSCS, respectively. The resulting cover rates of the Ab lock with various linkers compared to the Ab drug were in the range 28.33%-42.33%. The recombinant pro-Abs were generated by MSCS prediction in order to verify the application of molecular simulation for pro-Ab development. The binding kinetics effective concentration (EC-50) for αPD-1 (200-250-fold), αIL-1β (152-186-fold), αCTLA-4 (68-150-fold) and αTNFα Ab (20-123-fold) were presented as the blocking ability of pro-Ab compared to the Ab drug. Further, there was a positive correlation between cover rate and blocking ability of all pro-Ab candidates. Conclusions: The results suggested that MSCS was able to predict the Ab lock linker most suitable for application to αPD-1, αIL-1β, αCTLA-4 and αTNFα Ab to form pro-Abs efficiently. The success of MSCS in optimizing the pro-Ab can aid the development of next-generation pro-Ab drugs to significantly improve Ab-based therapies and thus patients’ quality of life.

Ofloxacin-Containing Multidrug Therapy in Ambulatory Leprosy Patients: A Case Series

Background Standard dapsone and clofazimine-containing multidrug therapy (MDT) for leprosy is limited by drug tolerability, which poses treatment adherence barriers. Although ofloxacin-based regimens are promising alternatives, current efficacy and safety data are limited, particularly outside of endemic areas. We evaluated treatment outcomes in patients with leprosy receiving ofloxacin-containing MDT (OMDT) at our center. Methods We performed a retrospective chart review of patients treated for leprosy at our center over an 8-year period (2011-2019). Primary outcomes evaluated were clinical cure rate, occurrence of leprosy reactions, antibiotic-related adverse events, and treatment adherence. Analyses were descriptive; however, data were stratified by age, sex, spectrum of disease, region of origin, and treatment regimen, and odds ratios were reported to assess associations with adverse outcomes. Results Over the enrolment period, 26 patients were treated with OMDT ( n = 19 multibacillary, n = 7 paucibacillary), and none were treated with clofazimine-based standard MDT. At the time of analysis, 23 patients (88%) had completed their course of treatment, and all were clinically cured, while 3 (12%) were still on treatment. Eighteen patients (69%) experienced either ENL ( n = 7, 27%), type 1 reactions ( n = 7, 27%), or both ( n = 4, 15%). No patients stopped ofloxacin due to adverse drug effects, and there were no cases of allergic hypersensitivity, tendinopathy or rupture, or C. difficile colitis. Conclusions We demonstrate a high cure rate and tolerability of OMDT in this small case series over an 8-year period, suggesting its viability as an alternative to standard clofazimine-containing MDT.

Loss of region-specific glial homeostatic signature in prion diseases

Background Chronic neuroinflammation is recognized as a major neuropathological hallmark in a broad spectrum of neurodegenerative diseases including Alzheimer’s, Parkinson’s, Frontal Temporal Dementia, Amyotrophic Lateral Sclerosis, and prion diseases. Both microglia and astrocytes exhibit region-specific homeostatic transcriptional identities, which under chronic neurodegeneration, transform into reactive phenotypes in a region- and disease-specific manner. Little is known about region-specific identity of glia in prion diseases. The current study was designed to determine whether the region-specific homeostatic signature of glia changes with the progression of prion diseases, and whether these changes occur in a region-dependent or universal manner. Also of interest was whether different prion strains give rise to different reactive phenotypes. Methods To answer these questions, we analyzed gene expression in thalamus, cortex, hypothalamus and hippocampus of mice infected with 22L and ME7 prion strains using Nanostring Neuroinflammation panel at subclinical, early clinical and advanced stages of the disease. Results We found that at the preclinical stage of the disease, region-specific homeostatic identities were preserved. However, with the appearance of clinical signs, region-specific signatures were partially lost and replaced with a neuroinflammation signature. While the same sets of genes were activated by both prion strains, the timing of neuroinflammation and the degree of activation in different brain regions was strain-specific. Changes in astrocyte function scored at the top of activated pathways. Moreover, clustering analysis suggested that the astrocyte function pathway responded to prion infection prior to activated microglia or neuron and neurotransmission pathways. Conclusions The current work established neuroinflammation gene expression signature associated with prion diseases. Our results illustrate that with the disease progression, the region-specific homeostatic transcriptome signatures are replaced by region-independent neuroinflammation signature, which was common for prion strains with different cell tropism. The prion-associated neuroinflammation signature identified in the current study overlapped only partially with the microglia degenerative phenotype and the disease-associated microglia phenotype reported for animal models of other neurodegenerative diseases.

Loss of region-specific glial homeostatic signature in prion diseases

BackgroundChronic neuroinflammation is recognized as a major neuropathological hallmark in a broad spectrum of neurodegenerative diseases including Alzheimer’s, Parkinson’s, Frontal Temporal Dementia, Amyotrophic Lateral Sclerosis, and prion diseases. Both microglia and astrocytes exhibit region-specific homeostatic transcriptional identities, which under chronic neurodegeneration, transform into reactive phenotypes in a region- and disease-specific manner. Little is known about region-specific identity of glia in prion diseases. The current study was designed to determine whether the region-specific homeostatic signature of glia changes with the progression of prion diseases, and whether these changes occur in a region-dependent or universal manner. Also of interest was whether different prion strains give rise to different reactive phenotypes.MethodsTo answer these questions, we analyzed gene expression in thalamus, cortex, hypothalamus and hippocampus of mice infected with 22L and ME7 prion strains using Nanostring Neuroinflammation panel at subclinical, early clinical and advanced stages of the disease.ResultsWe found that at the preclinical stage of the disease, region-specific homeostatic identities were preserved. However, with the appearance of clinical signs, region-specific signatures were partially lost and replaced with a neuroinflammation signature. While the same sets of genes were activated by both prion strains, the timing of neuroinflammation and the degree of activation in different brain regions was strain-specific. Changes in astrocyte function scored at the top of activated pathways. Moreover, clustering analysis suggested that the astrocyte function pathway responded to prion infection prior to activated microglia or neuron and neurotransmission pathways.ConclusionsThe current work established neuroinflammation gene expression signature associated with prion diseases. Our results illustrate that with the disease progression, the region-specific homeostatic transcriptome signatures are replaced by region-independent neuroinflammation signature, which was common for prion strains with different cell tropism. The prion-associated neuroinflammation signature identified in the current study overlapped only partially with the microglia degenerative phenotype and the disease-associated microglia phenotype reported for animal models of other neurodegenerative diseases.

Centroid Distance Neighbourhood Features and Genetic Algorithm Optimization for Leaf Disease Detection

Leaf disease detection algorithm using Centroid Distance Neighbourhood Features (CDNF) and Genetic Algorithm (GA) optimization is presented in this paper. This method initially segment the disease affected regions from the leaf. The disease affected region is applied for identifying the best feature points using SURF (Speeded Up Robust Feature) algorithm. From a single SURF point four features are extracted by forming a 5×5 neighbourhood across the SURF feature point. The feature extracted using Centroid Distance Neighbour (CDN) is optimized using genetic algorithm to find best features that are able to classify multiple diseases. During testing phase, the disease region is identified and features points are selected using the SURF points. The features are extracted using the CDN and the necessary features that are optimized by genetic algorithm are sorted out as test features. The test features are classified from the trained features using K-Nearest Neighbour (KNN) algorithm. Performance of the proposed leaf disease detection algorithm is evaluated using metrics such as specificity, sensitivity and accuracy. Experimental results shows that the proposed leaf detection algorithm outperforms the state of-the-art methods and it can be used in real time disease detection