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Recently, interest and advances in artificial intelligence (AI) including deep learning for medical images have surged. As imaging plays a major role in the assessment of pulmonary diseases, various AI algorithms have been developed for chest imaging. Some of these have been approved by governments and are now commercially available in the marketplace. In the field of chest radiology, there are various tasks and purposes that are suitable for AI: initial evaluation/triage of certain diseases, detection and diagnosis, quantitative assessment of disease severity and monitoring, and prediction for decision support. While AI is a powerful technology that can be applied to medical imaging and is expected to improve our current clinical practice, some obstacles must be addressed for the successful implementation of AI in workflows. Understanding and becoming familiar with the current status and potential clinical applications of AI in chest imaging, as well as remaining challenges, would be essential for radiologists and clinicians in the era of AI. This review introduces the potential clinical applications of AI in chest imaging and also discusses the challenges for the implementation of AI in daily clinical practice and future directions in chest imaging.

Objective: This study aimed to identify the genetic diversity and population structure of Mongolian horse populations according to the province of residence (Khentii, KTP; Uvs, USP; Omnogovi and Dundgovi, GOP; Khovsgol, KGP) using 14 microsatellite (MS) markers.Methods: A total of 269 whole blood samples were obtained from the four populations (KTP, USP, GOP, KGP) geographically distinct provinces. Multiplex polymerase chain reaction (PCR) was conducted using 14 MS markers (AHT4, ASB2, ASB17, ASB23, CA425, HMS1, HMS2, HMS3, HMS6, HMS7, HTG4, HTG6, HTG7, and VHL20), as recommended by the International Society for Animal Genetics. Capillary electrophoresis was conducted using the amplified PCR products, alleles were determined. Alleles were used for statistical analysis of genetic variability, Nei’s DA genetic distance, principal coordinate analysis (PCoA), factorial corresponding analysis (FCA), and population structure.Results: On average, the number of alleles, expected heterozygosity (HExp), observed heterozygosity (HObs), and polymorphic information content among all populations were 11.43, 0.772, 0.757, and 0.737, respectively. In the PCoA and FCA, GOP, and KGP were genetically distinct from other populations, and the KTP and USP showed a close relationship. The two clusters identified using Nei’s DA genetic distance analysis and population structure highlighted the presence of structurally clear genetic separation.Conclusion: Overall, the results of this study suggest that genetic diversity between KTP and USP was low, and that between GOP and KGP was high. It is thought that these results will help in the effective preservation and improvement of Mongolian horses through genetic diversity analysis and phylogenetic relationships.

PDF file - 43K, Chloroquine and 6-AN treatment stimulate IL-6 production in Tsc2-null MEFs.

PDF file - 98K, Autophagy and pentose phosphate pathway inhibition suppresses proliferation of Tsc2-null cystadenoma 105K cells.

The mammalian target of rapamycin complex 1 (mTORC1) is hyperactive in many human cancers and in tuberous sclerosis complex (TSC). Autophagy, a key mTORC1-targeted process, is a critical determinant of metabolic homeostasis. Metabolomic profiling was performed to elucidate the cellular consequences of autophagy dysregulation under conditions of hyperactive mTORC1. It was discovered that TSC2-null cells have distinctive autophagy-dependent pentose phosphate pathway (PPP) alterations. This was accompanied by enhanced glucose uptake and utilization, decreased mitochondrial oxygen consumption, and increased mitochondrial reactive oxygen species (ROS) production. Importantly, these findings revealed that the PPP is a key autophagy-dependent compensatory metabolic mechanism. Furthermore, PPP inhibition with 6-aminonicotinamide (6-AN) in combination with autophagy inhibition suppressed proliferation and prompted the activation of NF-κB and CASP1 in TSC2-deficient, but not TSC2-proficient cells. These data demonstrate that TSC2-deficient cells can be therapeutically targeted, without mTORC1 inhibitors, by focusing on their metabolic vulnerabilities.Implications: This study provides proof-of-concept that therapeutic targeting of diseases with hyperactive mTORC1 can be achieved without the application of mTORC1 inhibitors. Mol Cancer Res; 12(1); 48–57. ©2013 AACR.

PDF file - 42K, Parthenolide rescues proliferation in Tsc2-/- MEFs.

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PDF file - 35K, Chloroquine treatment induces ROS production.

PDF file - 55K, Effect of chloroquine on the metabolome of Tsc2+/+ MEFs.

PDF file - 204K, mTORC1 signaling upon chloroquine/6-AN treatment and effect of mTORC1 inhibition on the proliferation of Tsc2-/- cells treated with the dual drug combination.

PDF file - 34K, Effect of chloroquine on the metabolome of Tsc2+/+ MEFs.

PDF file - 61K, Effect of chloroquine in Tsc2-deficient models.

XLSX file - 144K, Metabolomics data (raw and processed).

Background Despite the increasing number of older adults as the population ages, there is a lack of frailty prevention guidelines for community-dwelling older adults. The Korean Frailty and Aging Cohort Study conducted this systematic review of literature on contributors to frailty and developed guidelines on the primary prevention of frailty in community-dwelling older adults. Methods This study updated a previous systematic review of contributors to frailty by adding the most recent articles. Based on this updated systematic review, experts in geriatrics and gerontology developed guidelines for preventing frailty using the Delphi method. Results These guidelines categorized the recommendations into physical activity, resilience, oral health, management of non-communicable diseases, involvement in society, smoking cessation, and eating various kinds of food. Conclusion Unlike previous frailty-related guidelines, this study developed evidence-based frailty prevention guidelines based on a systematic review. The guidelines are expected to contribute to the healthy aging of community-dwelling older adults by the primary prevention of frailty.

Objective. To unify the style of computed tomography (CT) images from multiple sources, we propose a novel multi-domain image translation network to convert CT images from different scan parameters and manufacturers by simply changing a routing vector. Approach. Unlike the existing multi-domain translation techniques, our method is based on a shared encoder and a routable decoder architecture to maximize the expressivity and conditioning power of the network. Main results. Experimental results show that the proposed CT image conversion can minimize the variation of image characteristics caused by imaging parameters, reconstruction algorithms, and hardware designs. Quantitative results and clinical evaluation from radiologists also show that our method can provide accurate translation results. Significance. Quantitative evaluation of CT images from multi-site or longitudinal studies has been a difficult problem due to the image variation depending on CT scan parameters and manufacturers. The proposed method can be utilized to address this for the quantitative analysis of multi-domain CT images.

To apply radiomics analysis for overall survival prediction in chronic obstructive pulmonary disease (COPD), and evaluate the performance of the radiomics signature (RS). This study included 344 patients from the Korean Obstructive Lung Disease (KOLD) cohort. External validation was performed on a cohort of 112 patients. In total, 525 chest CT-based radiomics features were semi-automatically extracted. The five most useful features for survival prediction were selected by least absolute shrinkage and selection operation (LASSO) Cox regression analysis and used to generate a RS. The ability of the RS for classifying COPD patients into high or low mortality risk groups was evaluated with the Kaplan-Meier survival analysis and Cox proportional hazards regression analysis. The five features remaining after the LASSO analysis were %LAA−950, AWT_Pi10_6th, AWT_Pi10_heterogeneity, %WA_heterogeneity, and VA18mm. The RS demonstrated a C-index of 0.774 in the discovery group and 0.805 in the validation group. Patients with a RS greater than 1.053 were classified into the high-risk group and demonstrated worse overall survival than those in the low-risk group in both the discovery (log-rank test, < 0.001; hazard ratio [HR], 5.265) and validation groups (log-rank test, < 0.001; HR, 5.223). For both groups, RS was significantly associated with overall survival after adjustments for patient age and body mass index. A radiomics approach for survival prediction and risk stratification in COPD patients is feasible, and the constructed radiomics model demonstrated acceptable performance. The RS derived from chest CT data of COPD patients was able to effectively identify those at increased risk of mortality. • A total of 525 chest CT-based radiomics features were extracted and the five radiomics features of %LAA −950 , AWT_Pi10_6 th , AWT_Pi10_heterogeneity, %WA_heterogeneity, and VA 18mm were selected to generate a radiomics model. • A radiomics model for predicting survival of COPD patients demonstrated reliable performance with a C-index of 0.774 in the discovery group and 0.805 in the validation group. • Radiomics approach was able to effectively identify COPD patients with an increased risk of mortality, and patients assigned to the high-risk group demonstrated worse overall survival in both the discovery and validation groups.

Tumor necrosis factor receptor-associated protein 1 (TRAP1) is a mitochondrial homolog of HSP90 chaperones. It plays an important role in protection against oxidative stress and apoptosis by regulating reactive oxidative species (ROS). Recent studies have suggested TRAP1 as a regulator of metabolism and mitochondrial homeostasis in diverse pathological states, including cancer. To further elucidate the mechanistic role of TRAP1 in regulating tumor cell survival, we used gamitrinib-triphenylphosphonium (G-TPP) to inhibit TRAP1 signaling pathways in colon cancer. Inhibition of TRAP1 disrupted redox homeostasis and increased ER stress in colon cancer. To confirm the G-TPP effects of TRAP1 inhibition in colon cancer, we performed TRAP1 CRISPR/Cas9 to deplete TRAP1 expression in colon cancer cell line CT26. TRAP1-depleted cells were found to acquire several distinct phenotypes: induction of reactive oxidative species (ROS) level, reduction of cell growth, and lower pH value of culture medium. In addition, proteomic data showed that depletion of TRAP1 induced significant metabolism alteration in CT26 cells. Using seahorse assay to access mitochondrial respiration and glycolysis, we found that TRAP1-depleted cells were insensitive to Oxidative Phosphorylation (OXPHOS) inhibitor treatment and significantly increased extracellular acidification rate (ECAR) compared to parental cells. Furthermore, higher glucose consumption rate and higher lactic acid level of culture medium were observed in TRAP1-depleted cells. On the other hand, Glycolytic inhibitor (2DG) led to excessive ROS production, reduction of glucose uptake and exacerbated cell death in TRAP1-depleted cell compared to parental cells. Taken together, depletion of TRAP1 induces mitochondrial dysfunction and modulates metabolic switch toward aerobic glycolysis in TRAP1-depleted colon cancer cells. Dual inhibition of TRAP1 and glycolytic signaling may increase therapeutic efficiency through induction of excessive ROS production in colon cancer. Citation Format: Hong-Yuan Tsai, Miao-Hsueh Chen, Herath Lakmini Senavirathna, Jihye Yun, Sheng Pan, Ru Chen. Synergistically suppressive effects by combination of glycolytic inhibitor with TRAP1-depletion in colorectal cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 273.

To develop and validate a model using radiomics features from apparent diffusion coefficient (ADC) map to diagnose local tumor recurrence in head and neck squamous cell carcinoma (HNSCC).This retrospective study included 285 patients (mean age ± standard deviation, 62 ± 12 years; 220 male, 77.2%), including 215 for training (n = 161) and internal validation (n = 54) and 70 others for external validation, with newly developed contrast-enhancing lesions at the primary cancer site on the surveillance MRI following definitive treatment of HNSCC between January 2014 and October 2019. Of the 215 and 70 patients, 127 and 34, respectively, had local tumor recurrence. Radiomics models using radiomics scores were created separately for T2-weighted imaging (T2WI), contrast-enhanced T1-weighted imaging (CE-T1WI), and ADC maps using non-zero coefficients from the least absolute shrinkage and selection operator in the training set. Receiver operating characteristic (ROC) analysis was used to evaluate the diagnostic performance of each radiomics score and known clinical parameter (age, sex, and clinical stage) in the internal and external validation sets.Five radiomics features from T2WI, six from CE-T1WI, and nine from ADC maps were selected and used to develop the respective radiomics models. The area under ROC curve (AUROC) of ADC radiomics score was 0.76 (95% confidence interval [CI], 0.62-0.89) and 0.77 (95% CI, 0.65-0.88) in the internal and external validation sets, respectively. These were significantly higher than the AUROC values of T2WI (0.53 [95% CI, 0.40-0.67],The radiomics model using ADC maps exhibited higher diagnostic performance than those of the radiomics models using T2WI or CE-T1WI and clinical parameters in the diagnosis of local tumor recurrence in HNSCC following definitive treatment.

We aimed to evaluate the performance of a fully automated quantitative software in detecting interstitial lung abnormalities (ILA) according to the Fleischner Society guidelines on routine chest CT compared with radiologists' visual analysis.This retrospective single-centre study included participants with ILA findings and 1:2 matched controls who underwent routine chest CT using various CT protocols for health screening. Two thoracic radiologists independently reviewed the CT images using the Fleischner Society guidelines. We developed a fully automated quantitative tool for detecting ILA by modifying deep learning-based quantification of interstitial lung disease and evaluated its performance using the radiologists' consensus for ILA as a reference standard.A total of 336 participants (mean age, 70.5 ± 6.1 years; M:F = 282:54) were included. Inter-reader agreements were substantial for the presence of ILA (weighted κ, 0.74) and fair for its subtypes (weighted κ, 0.38). The quantification system for identifying ILA using a threshold of 5 % in at least one zone showed 67.6 % sensitivity, 93.3 % specificity, and 90.5 % accuracy. Eight of 20 (40 %) false positives identified by the system were underestimated by readers for ILA extent. Contrast-enhancement in a certain vendor and suboptimal inspiration caused a true false-positive on the system (all P 0.05). The best cut-off value of abnormality extent detecting ILA on the system was 3.6 % (sensitivity, 84.8 %; specificity 92.4 %).Inter-reader agreement was substantial for ILA but only fair for its subtypes. Applying an automated quantification system in routine clinical practice may aid the objective identification of ILA.

There is a significant discrepancy between the actual choice for initial treatment option for hepatocellular carcinoma (HCC) and recommendations from the currently used BCLC staging system. We develop a machine learning-based clinical decision support system (CDSS) for recommending initial treatment option in HCC and predicting overall survival (OS). From hospital records of 1,021 consecutive patients with HCC treated at a single centre in Korea between January 2010 and October 2010, we collected information on 61 pretreatment variables, initial treatment, and survival status. Twenty pretreatment key variables were finally selected. We developed the CDSS from the derivation set (N = 813) using random forest method and validated it in the validation set (N = 208). Among the 1,021 patients (mean age: 56.9 years), 81.8% were male and 77.0% had positive hepatitis B BCLC stages 0, A, B, C, and D were observed in 13.4%, 26.0%, 18.0%, 36.6%, and 6.3% of patients, respectively. The six multi-step classifier model was developed for treatment decision in a hierarchical manner, and showed good performance with 81.0% of accuracy for radiofrequency ablation (RFA) or resection versus not, 88.4% for RFA versus resection, and 76.8% for TACE or not. We also developed seven survival prediction models for each treatment option. Our newly developed HCC-CDSS model showed good performance in terms of treatment recommendation and OS prediction and may be used as a guidance in deciding the initial treatment option for HCC.

The main objective of this study was to find the relationship between oral health status and grip strength in older adults living in the community. Low handgrip strength was associated with using full dentures and remaining 0–9 teeth in older men. Low grip strength may serve as an important indicator of poor oral health, especially among men in late life. Poor oral conditions in older adults are not limited to oral problems, which lead to physical problems. Prior research insufficiently has probed the relationship between oral health and physical function. This study investigated the relationship between oral health status and grip strength in older adults living in the community. This study used data from the 2014–2018 Korean National Health and Nutrition Survey. Oral health status was assessed by the type of dental prosthesis, the number of teeth. Grip strength was determined as the highest value among the three consecutive measurements of the dominant hand. The covariates included age, income, education, alcohol drinking, smoking, body mass index, sedentary time, comorbidity and number of caries teeth. Multiple logistic regression analysis was performed to examine the association between oral health status and grip strength. Data on 6,437 older adults (men 2766; women 3671) were analyzed. The mean age was 72.9 ± 0.1 years. In the crude model of logistic regression analysis, both men and women had an association between full denture use and low grip strength compared to high grip strength. After controlling for covariates, the remaining 0–9 teeth was associated with low grip strength compared to high grip strength in men [odds ratio (OR) = 1.39, 95% confidence interval (CI) = 1.03–1.88]. The use of full dentures was also associated with low grip strength compared to high grip strength in men (OR = 1.47, 95% CI = 1.09–1.98). No significant associations were found in women. Low handgrip strength was associated with using full dentures and remaining 0–9 teeth in older men. Low grip strength, one of the key characteristics of sarcopenia and frailty, may serve as an important indicator of poor oral health, especially among men in late life.

Description, Neutralization of CTRP3 opposes degenerative tendon matrix remodeling during tendinopathy., Tendinopathy, the most common disorder affecting tendons, is characterized by chronic disorganization of the tendon matrix, which leads to tendon tear and rupture. The goal was to identify a rational molecular target whose blockade can serve as a potential therapeutic intervention for tendinopathy. We identified C1q/TNF-related protein-3 (CTRP3) as a markedly up-regulated cytokine in human and rodent tendinopathy. Overexpression of CTRP3 enhanced the progression of tendinopathy by accumulating cartilaginous proteoglycans and degenerating collagenous fibers in the mouse tendon, whereas CTRP3 knockdown suppressed the tendinopathy pathogenesis. Functional blockade of CTRP3 using a neutralizing antibody ameliorated overuse-induced tendinopathy of the Achilles and rotator cuff tendons. Mechanistically, CTRP3 elicited a transcriptomic pattern that stimulates abnormal differentiation of tendon stem/progenitor cells and ectopic chondrification as an effect linked to activation of Akt signaling. Collectively, we reveal an essential role for CTRP3 in tendinopathy and propose a potential therapeutic strategy for the treatment of tendinopathy.

Background Evaluation of interstitial lung disease (ILD) at CT is a challenging task that requires experience and is subject to substantial interreader variability. Purpose To investigate whether a proposed content-based image retrieval (CBIR) of similar chest CT images by using deep learning can aid in the diagnosis of ILD by readers with different levels of experience. Materials and Methods This retrospective study included patients with confirmed ILD after multidisciplinary discussion and available CT images identified between January 2000 and December 2015. Database was composed of four disease classes: usual interstitial pneumonia (UIP), nonspecific interstitial pneumonia (NSIP), cryptogenic organizing pneumonia, and chronic hypersensitivity pneumonitis. Eighty patients were selected as queries from the database. The proposed CBIR retrieved the top three similar CT images with diagnosis from the database by comparing the extent and distribution of different regional disease patterns quantified by a deep learning algorithm. Eight readers with varying experience interpreted the query CT images and provided their most probable diagnosis in two reading sessions 2 weeks apart, before and after applying CBIR. Diagnostic accuracy was analyzed by using McNemar test and generalized estimating equation, and interreader agreement was analyzed by using Fleiss κ. Results A total of 288 patients were included (mean age, 58 years ± 11 [standard deviation]; 145 women). After applying CBIR, the overall diagnostic accuracy improved in all readers (before CBIR, 46.1% [95% CI: 37.1, 55.3]; after CBIR, 60.9% [95% CI: 51.8, 69.3]

Heterogeneous clinical manifestations and progression of chronic obstructive pulmonary disease (COPD) affect patient health risk assessment, stratification, and management. Pulmonary function tests are used to diagnose and classify the severity of COPD, but they cannot fully represent the type or range of pathophysiologic abnormalities of the disease. To evaluate whether deep radiomics from chest computed tomography (CT) images can predict mortality in patients with COPD, we designed a convolutional neural network (CNN) model for extracting representative features from CT images and then performed random survival forest to predict survival in COPD patients. We trained CNN-based binary classifier based on six-minute walk distance results (> 440 m or not) and extracted high-throughput image features (i.e., deep radiomics) directly from the last fully connected layer of it. The various sizes of fully connected layers and combinations of deep features were experimented using a discovery cohort with 344 patients from the Korean Obstructive Lung Disease cohort and an external validation cohort with 102 patients from Penang General Hospital in Malaysia. In the integrative analysis of discovery and external validation cohorts, with combining 256 deep features from the coronal slice of the vertebral body and two sagittal slices of the left/right lung, deep radiomics for survival prediction achieved concordance indices of 0.8008 (95% CI, 0.7642–0.8373) and 0.7156 (95% CI, 0.7024–0.7288), respectively. Deep radiomics from CT images could be used to predict mortality in COPD patients.

Objectives This study aimed to evaluate the association between dental implants and cognitive function in community-dwelling older adults. Methods Data were collected from the baseline survey (2016-2017) of the Korean Frailty and Aging Cohort Study. The study sample comprised 1115 community-dwelling people aged 70 years to 84 years who had 0-19 natural teeth. Dental implants and natural teeth were identified by panoramic radiography, while the cognitive function was assessed by the Korean version of the Mini-Mental State Examination (MMSE-KC). The association between dental implants and cognitive function was analyzed by multiple linear regression. Sensitivity analysis was performed to test for potential bias. Results The mean number of natural teeth in the study population was 9.50 (standard deviation [SD], 6.42), and the mean MMSE-KC score was 24.93 (SD, 3.55). In the simple univariate analysis, tooth replacement, age, sex, smoking status, alcohol consumption, body mass index, osteoporosis, number of natural teeth, periodontitis, chewing discomfort, tooth-brushing frequency, education level, monthly household income, participation in economic activity, living alone, and marital status had a significant impact on the association. After adjusting for confounders, the association between dental implants and cognitive function remained significant (B, 0.85; standard error, 0.40; pl0.05). Age, body mass index, periodontitis, tooth-brushing frequency, and education level were also significantly associated with cognitive function. The results of the sensitivity analyses were consistent with those of the primary analysis. Conclusions Dental implants were associated with cognitive function in older adults living in the community. Dental implants as tooth replacements may play a role in preserving cognitive function.

Lung lobe segmentation in chest CT has been used for the analysis of lung functions and surgical planning. However, accurate lobe segmentation is difficult as 80% of patients have incomplete and/or fake fissures. Furthermore, lung diseases such as chronic obstructive pulmonary disease (COPD) can increase the difficulty of differentiating the lobar fissures. Lobar fissures have similar intensities to those of the vessels and airway wall, which could lead to segmentation error in automated segmentation. In this study, a fully automated lung lobe segmentation method with 3D U-Net was developed and validated with internal and external datasets. The volumetric chest CT scans of 196 normal and mild-to-moderate COPD patients from three centers were obtained. Each scan was segmented using a conventional image processing method and manually corrected by an expert thoracic radiologist to create gold standards. The lobe regions in the CT images were then segmented using a 3D U-Net architecture with a deep convolutional neural network (CNN) using separate training, validation, and test datasets. In addition, 40 independent external CT images were used to evaluate the model. The segmentation results for both the conventional and deep learning methods were compared quantitatively to the gold standards using four accuracy metrics including the Dice similarity coefficient (DSC), Jaccard similarity coefficient (JSC), mean surface distance (MSD), and Hausdorff surface distance (HSD). In internal validation, the segmentation method achieved high accuracy for the DSC, JSC, MSD, and HSD (0.97 ± 0.02, 0.94 ± 0.03, 0.69 ± 0.36, and 17.12 ± 11.07, respectively). In external validation, high accuracy was also obtained for the DSC, JSC, MSD, and HSD (0.96 ± 0.02, 0.92 ± 0.04, 1.31 ± 0.56, and 27.89 ± 7.50, respectively). This method took 6.49 ± 1.19 s and 8.61 ± 1.08 s for lobe segmentation of the left and right lungs, respectively. Although various automatic lung lobe segmentation methods have been developed, it is difficult to develop a robust segmentation method. However, the deep learning–based 3D U-Net method showed reasonable segmentation accuracy and computational time. In addition, this method could be adapted and applied to severe lung diseases in a clinical workflow.

A sweetener's not-so-sweet effects Obesity increases an individual's risk of developing many types of cancer, including colorectal cancer. One of the factors driving the rise in obesity rates is thought to be the use of high-fructose corn syrup (HFCS) as a sweetener in soft drinks. Goncalves et al. found that ingestion of HFCS promotes the growth of intestinal cancer even in the absence of obesity in mouse tumor models. An enzyme in tumors (ketohexokinase) converts fructose to fructose-1-phosphate, which alters tumor cell metabolism and leads to enhanced cell growth. Whether a similar process occurs in humans remains to be seen. Science , this issue p. 1345

We propose a novel airway segmentation method in volumetric chest computed tomography (CT) and evaluate its performance on multiple datasets. The segmentation is performed voxel-by-voxel by a 2.5D convolutional neural net (2.5D CNN) trained in a supervised manner. To enhance the accuracy of the segmented airway tree, we simultaneously took three adjacent slices in each of the orthogonal directions including axial, sagittal, and coronal and fine-tuned the parameters that influence the tree length and the number of leakage. The gold standard of airway segmentation was generated by a semi-automated method using AVIEW™. The 2.5D CNN was trained and evaluated on a subset of inspiratory thoracic CT scans taken from the Korean obstructive lung disease study, which includes normal subjects and chronic obstructive pulmonary disease patients. The reliability and further practicality of our proposed method was demonstrated in multiple datasets. In eight test datasets collected by the same imaging protocol, the percentage detected tree length, false positive rate, and Dice similarity coefficient of our method were 92.16%, 7.74%, and 0.8997 ± 0.0892, respectively. In 20 test datasets of the EXACT’09 challenge, the percentage detected tree length was 60.1% and the false positive rate was 4.56%. Our fully automated (end-to-end) segmentation method could be applied in radiologic practice.

The endoscopic features between herpes simplex virus (HSV) and cytomegalovirus (CMV) esophagitis overlap significantly, and hence the differential diagnosis between HSV and CMV esophagitis is sometimes difficult. Therefore, we developed a machine-learning-based classifier to discriminate between CMV and HSV esophagitis. We analyzed 87 patients with HSV esophagitis and 63 patients with CMV esophagitis and developed a machine-learning-based artificial intelligence (AI) system using a total of 666 endoscopic images with HSV esophagitis and 416 endoscopic images with CMV esophagitis. In the five repeated five-fold cross-validations based on the hue–saturation–brightness color model, logistic regression with a least absolute shrinkage and selection operation showed the best performance (sensitivity, specificity, positive predictive value, negative predictive value, accuracy, and area under the receiver operating characteristic curve: 100%, 100%, 100%, 100%, 100%, and 1.0, respectively). Previous history of transplantation was included in classifiers as a clinical factor; the lower the performance of these classifiers, the greater the effect of including this clinical factor. Our machine-learning-based AI system for differential diagnosis between HSV and CMV esophagitis showed high accuracy, which could help clinicians with diagnoses.

ESG management has become the most prominent topic this year as the worldwide business environment of companies changes. ESG management is not an option to raise a company’s reputation but a factor that significantly affects corporate sustainability and corporate value in the long run as well as can be seen as an essential requirement for a company’s survival. As this trend is essential in evaluating corporate sustainability, ESG management is an inevitable global issue. This study aims to develop an index for ESG, a key indicator of corporate sustainability, targeting 49 companies that issue ‘sustainability reports’ among the top 150 asset-ranked companies. In addition, companies were divided into high and low groups based on the ESG index, and each group attempted to study the effect of economic responsibility among corporate social responsibility on financial performance. As a result, out of 49 companies, the high group was divided into 17 companies, and the low group was split into 14 companies. It was found that only the low group had a significantly positive (+) effect on the relationship between corporate economic responsibility and financial performance. This study can be used as a reference data for ESG research by developing an ESG index related to corporate sustainability. It is meaningful to confirm the management paradigm for corporate social responsibility and the importance of financial performance through comparison.

Obesity causes a wide range of metabolic diseases including diabetes, cardiovascular disease, and kidney disease. Thus, plenty of studies have attempted to discover naturally derived compounds displaying anti-obesity effects. In this study, we evaluated the inhibitory effects of morolic acid 3-O-caffeate (MAOC), extracted from Betula schmidtii, on adipogenesis. Treatment of 3T3-L1 cells with MAOC during adipogenesis significantly reduced lipid accumulation and decreased the expression of adiponectin, a marker of mature adipocytes. Moreover, the treatment with MAOC only during the early phase (day 0–2) sufficiently inhibited adipogenesis, comparable with the inhibitory effects observed following MAOC treatment during the whole processes of adipogenesis. In the early phase of adipogenesis, the expression level of Wnt6, which inhibits adipogenesis, increased by MAOC treatment in 3T3-L1 cells. To identify the gene regulatory mechanism, we assessed alterations in histone modifications upon MAOC treatment. Both global and local levels on the Wnt6 promoter region of histone H3 lysine 4 trimethylation, an active transcriptional histone marker, increased markedly by MAOC treatment in 3T3-L1 cells. Our findings identified an epigenetic event associated with inhibition of adipocyte generation by MAOC, suggesting its potential as an efficient therapeutic compound to cure obesity and metabolic diseases.

Since the global outbreak of SARS-CoV-2 (COVID-19), infections of diverse human organs along with multiple symptoms continue to be reported. However, the susceptibility of the brain to SARS-CoV-2, and the mechanisms underlying neurological infection are still elusive. Here, we utilized human embryonic stem cell-derived brain organoids and monolayer cortical neurons to investigate infection of brain with pseudotyped SARS-CoV-2 viral particles. Spike-containing SARS-CoV-2 pseudovirus infected neural layers within brain organoids. The expression of ACE2, a host cell receptor for SARS-CoV-2, was sustained during the development of brain organoids, especially in the somas of mature neurons, while remaining rare in neural stem cells. However, pseudotyped SARS-CoV-2 was observed in the axon of neurons, which lack ACE2. Neural infectivity of SARS-CoV-2 pseudovirus did not increase in proportion to viral load, but only 10% of neurons were infected. Our findings demonstrate that brain organoids provide a useful model for investigating SARS-CoV-2 entry into the human brain and elucidating the susceptibility of the brain to SARS-CoV-2.

BACKGROUND Generative adversarial network (GAN)–based synthetic images can be viable solutions to current supervised deep learning challenges. However, generating highly realistic images is a prerequisite for these approaches. OBJECTIVE The aim of this study was to investigate and validate the unsupervised synthesis of highly realistic body computed tomography (CT) images by using a progressive growing GAN (PGGAN) trained to learn the probability distribution of normal data. METHODS We trained the PGGAN by using 11,755 body CT scans. Ten radiologists (4 radiologists with 10 years of experience [Group III]) evaluated the results in a binary approach by using an independent validation set of 300 images (150 real and 150 synthetic) to judge the authenticity of each image. RESULTS The mean accuracy of the 10 readers in the entire image set was higher than random guessing (1781/3000, 59.4% vs 1500/3000, 50.0%, respectively; PP=.29). The accuracy between the 3 reader groups with different experience levels was not significantly different (Group I, 696/1200, 58.0%; Group II, 726/1200, 60.5%; and Group III, 359/600, 59.8%; P=.36). Interreader agreements were poor (κ=0.11) for the entire image set. In subgroup analysis, the discrepancies between real and synthetic CT images occurred mainly in the thoracoabdominal junction and in the anatomical details. CONCLUSIONS The GAN can synthesize highly realistic high-resolution body CT images that are indistinguishable from real images; however, it has limitations in generating body images of the thoracoabdominal junction and lacks accuracy in the anatomical details.

Deep learning-based automatic segmentation (DLAS) helps the reproducibility of radiomics features, but its effect on radiomics modeling is unknown. We therefore evaluated whether DLAS can robustly extract anatomical and physiological MRI features, thereby assisting in the accurate assessment of treatment response in glioblastoma patients. A DLAS model was trained on 238 glioblastomas and validated on an independent set of 98 pre- and 86 post-treatment glioblastomas from two tertiary hospitals. A total of 1618 radiomics features from contrast-enhanced T1-weighted images (CE-T1w) and histogram features from apparent diffusion coefficient (ADC) and cerebral blood volume (CBV) mapping were extracted. The diagnostic performance of radiomics features and ADC and CBV parameters for identifying treatment response was tested using area under the curve (AUC) from receiver operating characteristics analysis. Feature reproducibility was tested using a 0.80 cutoff for concordance correlation coefficients. Reproducibility was excellent for ADC and CBV features (ICC, 0.82–0.99) and first-order features (pre- and post-treatment, 100% and 94.1% remained), but lower for texture (79.0% and 69.1% remained) and wavelet-transformed (81.8% and 74.9% remained) features of CE-T1w. DLAS-based radiomics showed similar performance to human-performed segmentations in internal validation (AUC, 0.81 [95% CI, 0.64–0.99] vs. AUC, 0.81 [0.60–1.00], p = 0.80), but slightly lower performance in external validation (AUC, 0.78 [0.61–0.95] vs. AUC, 0.65 [0.46–0.84], p = 0.23). DLAS-based feature extraction showed high reproducibility for first-order features from anatomical and physiological MRI, and comparable diagnostic performance to human manual segmentations in the identification of pseudoprogression, supporting the utility of DLAS in quantitative MRI analysis. • Deep learning-based automatic segmentation (DLAS) enables fast and robust feature extraction from diffusion- and perfusion-weighted MRI. • DLAS showed high reproducibility in first-order feature extraction from anatomical, diffusion, and perfusion MRI across two centers. • DLAS-based radiomics features showed comparable diagnostic accuracy to manual segmentations in post-treatment glioblastoma.

To compare image quality and radiation dose between dual-energy subtraction (DES)–based bone suppression images (D-BSIs) and software-based bone suppression images (S-BSIs). Chest radiographs (CXRs) of forty adult patients were obtained with the two X-ray devices, one with DES and one with bone suppression software. Three image quality metrics (relative mean absolute error (RMAE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM)) between original CXR and BSI for each of D-BSI and S-SBI groups were calculated for each bone and soft tissue areas. Two readers rated the visual image quality for original CXR and BSI for each of D-BSI and S-SBI groups. The dose area product (DAP) values were recorded. Paired t test was used to compare the image quality and DAP values between D-BSI and S-BSI groups. In bone areas, S-BSIs had better SSIM values than D-BSI (94.57 vs. 87.77) but worse RMAE and PSNR values (0.50 vs. 0.20; 20.93 vs. 34.37) (all p

Early prevention of sarcopenia could be an important strategy for muscle retention, but most studies have focused on subjects aged 65 or older. Therefore, in this study we investigated the effects of leucine-enriched protein supplementation on muscle condition in a sample including late middle-aged adults. A 12-week intervention was performed for 120 healthy community-dwelling adults by providing either leucine-enriched protein supplement [protein 20g(casein 50%+ whey 40%+ soy 10%, total leucine 3000 mg), vitamin D 800IU(20 ug), calcium 300 mg, fat 1.1 g, carbohydrate 2.5 g] or isocaloric carbohydrate supplement twice per day. Appendicular skeletal muscle mass (ASM) and lean body mass (LBM) were measured by dual-energy X-ray absorptiometry. A total of 111 participants completed the study, with a dropout rate of 9.2%. LBM normalized by body weight (LBM/Wt) was significantly increased (p < 0.001) in the intervention group (0 wk: 63.38 ± 0.85 vs. 12 wk 63.68 ± 0.83 in the intervention group; 0 wk: 63.85 ± 0.82 vs. 12 wk: 63.29 ± 0.81 in the control group). In subgroup analyses, significant differences remained only in subjects between 50 and 64 years of age. We concluded that leucine-enriched protein supplementation can have beneficial effects by preventing muscle loss, mainly for late middle-aged adults.

Background Cognitive function and physical frailty are known to be closely related. Among older adults with dementia, those who wear dentures have a higher mortality rate than those who do not wear them. This suggests the possibility that oral health may affect the cognitive-frailty relationship. This study aims to investigate whether the number of teeth present, acts as a moderating variable in the cognitive function-frailty relationship. Methods Data were obtained from the cross-sectional baseline study of the Korean Frailty Aging Cohort Study (2016–2017). Cognitive function was assessed using the Mini-Mental State Examination. Frailty score was based on the Cardiovascular Health Study Index. Oral condition was evaluated by the number of teeth present and analyzed using categories of 0-9 teeth, 10-19 teeth, and ≥20. The moderation effect was analyzed using the ordinary least squares (OLS) regression. Results Data on 2,310 older adults (1,110 men; mean age 75.9 ± 3.9 years) was analyzed. Adjusting for age, sex, income, education, alcohol drinking, body mass index, and number of comorbidities, cognitive function and frailty showed a negative association (B=-.030, p = .011). In the 10-19 teeth category, compared to the 0-9 teeth category, a negative association with frailty was found (B=-.152, p = .026). A significant interaction effect between the number of teeth and cognitive function was detected (p = .007). Conclusion The number of teeth may modify the degree of the association between cognitive function and frailty. For effective frailty management of older persons, cognitive function management and oral management should be considered and performed together.

The frequency of extreme weather phenomena such as heat wave and cold wave has increased recently, and the intensity of weather has been strengthened, resulting in human and physical damage. The Republic of Korea has been working to reduce damage since 2018 by including heat and cold waves in natural disasters. The Korea Meteorological Administration (KMA) also provides impact-based forecasts, which requires research that suits local characteristics. In this study, weather observation data related to the summer heat wave in Busan, Ulsan and South Gyeongsang Province was analyzed to determine the weather conditions for the heat wave. In addition, in relation to the heat wave impact-based forecast that was provided regularly in 2019, the heat threshold was applied by comparing the current status of the heat-related patients with the maximum temperature, the number of consecutive days of the heat wave and the current status of the heat-related patients. The impacts of heat waves in different fields were analyzed, including livestock waste, fisheries food damage, and heat damage by crops. The cold wave also analyzed the number of days of cold wave in Busan, Ulsan, and South Gyeongsang Province by comparing the lowest temperature with the current status of cold-related patients. The impacts of cold weather conditions such as wind direction, wind speed and the number of consecutive days of the cold wave were also analyzed. Further, for regular provision of cold wave impact-based forecast to be implemented in 2020, the impacts of each cold wave vulnerable areas suitable for Busan, Ulsan, and South Gyeongsang Province were analyzed and referred to when applying cold wave thresholds.

We investigated the ability of machine-learning classifiers on radiomics from pre-treatment multiparametric magnetic resonance imaging (MRI) to accurately predict human papillomavirus (HPV) status in patients with oropharyngeal squamous cell carcinoma (OPSCC). This retrospective study collected data of 60 patients (48 HPV-positive and 12 HPV-negative) with newly diagnosed histopathologically proved OPSCC, who underwent head and neck MRIs consisting of axial T1WI, T2WI, CE-T1WI, and apparent diffusion coefficient (ADC) maps from diffusion-weighted imaging (DWI). The median age was 59 years (the range being 35 to 85 years), and 83.3% of patients were male. The imaging data were randomised into a training set (32 HPV-positive and 8 HPV-negative OPSCC) and a test set (16 HPV-positive and 4 HPV-negative OPSCC) in each fold. 1618 quantitative features were extracted from manually delineated regions-of-interest of primary tumour and one definite lymph node in each sequence. After feature selection by using the least absolute shrinkage and selection operator (LASSO), three different machine-learning classifiers (logistic regression, random forest, and XG boost) were trained and compared in the setting of various combinations between four sequences. The highest diagnostic accuracies were achieved when using all sequences, and the difference was significant only when the combination did not include the ADC map. Using all sequences, logistic regression and the random forest classifier yielded higher accuracy compared with the that of the XG boost classifier, with mean area under curve (AUC) values of 0.77, 0.76, and 0.71, respectively. The machine-learning classifier of non-invasive and quantitative radiomics signature could guide the classification of the HPV status.

To apply radiomics analysis for overall survival prediction in chronic obstructive pulmonary disease (COPD), and evaluate the performance of the radiomics signature (RS).This study included 344 patients from the Korean Obstructive Lung Disease (KOLD) cohort. External validation was performed on a cohort of 112 patients. In total, 525 chest CT-based radiomics features were semi-automatically extracted. The five most useful features for survival prediction were selected by least absolute shrinkage and selection operation (LASSO) Cox regression analysis and used to generate a RS. The ability of the RS for classifying COPD patients into high or low mortality risk groups was evaluated with the Kaplan-Meier survival analysis and Cox proportional hazards regression analysis.The five features remaining after the LASSO analysis were %LAAA radiomics approach for survival prediction and risk stratification in COPD patients is feasible, and the constructed radiomics model demonstrated acceptable performance. The RS derived from chest CT data of COPD patients was able to effectively identify those at increased risk of mortality.• A total of 525 chest CT-based radiomics features were extracted and the five radiomics features of %LAA

Poor oral conditions in older adults are not limited to oral problems, which lead to physical problems. Prior research insufficiently has probed the relationship between oral health and physical function. This study investigated the relationship between oral health status and grip strength in older adults living in the community.This study used data from the 2014-2018 Korean National Health and Nutrition Survey. Oral health status was assessed by the type of dental prosthesis, the number of teeth. Grip strength was determined as the highest value among the three consecutive measurements of the dominant hand. The covariates included age, income, education, alcohol drinking, smoking, body mass index, sedentary time, comorbidity and number of caries teeth. Multiple logistic regression analysis was performed to examine the association between oral health status and grip strength.Data on 6,437 older adults (men 2766; women 3671) were analyzed. The mean age was 72.9 ± 0.1 years. In the crude model of logistic regression analysis, both men and women had an association between full denture use and low grip strength compared to high grip strength. After controlling for covariates, the remaining 0-9 teeth was associated with low grip strength compared to high grip strength in men [odds ratio (OR) = 1.39, 95% confidence interval (CI) = 1.03-1.88]. The use of full dentures was also associated with low grip strength compared to high grip strength in men (OR = 1.47, 95% CI = 1.09-1.98). No significant associations were found in women.Low handgrip strength was associated with using full dentures and remaining 0-9 teeth in older men. Low grip strength, one of the key characteristics of sarcopenia and frailty, may serve as an important indicator of poor oral health, especially among men in late life.

Chemo-mechanical caries removal methods are known to be more effective compared with conventional methods in pain reduction. Carie-care™, a chemo-mechanical caries removal agent, was introduced in 2010 but a systematic review of its efficacy has not yet been performed. The purpose of this study was to investigate the effectiveness of Carie-care™ on the outcomes of treatment of caries in children and adolescents. The primary outcome was pain while the secondary outcomes included complete caries removal (CCR), time, need for local anesthesia and behavioral response changes. A Comprehensive literature search was performed in PubMed, EMBASE, and the Cochrane Library up to 30 September 2018. The following keywords were used in the search: 'chemo-mechanical caries removal agent', 'dental caries', 'Carie-care', 'chemo-mechanical caries removal', 'chemo-mechanical caries excavation', other related keywords, and their combinations. From 942 studies identified, 16 were analyzed. Finally, 4 studies met the eligibility criteria and 260 teeth in 120 children and adolescents were included in this review. This review showed that Carie-care™ reduces pain during caries treatment but requires a longer time for effective treatment than conventional methods. Local anesthesia was not required in the Chemo-mechanical caries removal (CMCR) group. In addition, dental anxiety decreased compared to the control group, and co-operation was more positive. Therefore, it may be a useful alternative to conventional methods in children and adolescents, but further verification through additional studies is needed.