Associations between depression and anxiety index and frequency of acute exacerbation in chronic obstructive pulmonary disease.

BACKGROUND: Comorbidities of chronic obstructive pulmonary disease (COPD) influence clinical characteristics and prognosis. OBJECTIVES: This study compared the clinical characteristics and exacerbation rate of COPD according to the presence of depression or anxiety. DESIGN: This study used data from The Korea COPD Subgroup Study (KOCOSS) cohort, a nationwide prospective cohort from 54 medical centers, between April 2012 and 2019. METHODS: Depression and anxiety were diagnosed with the Beck Depression Inventory and Beck Anxiety Inventory. Negative binomial regression analysis was performed to analyze the frequency of exacerbations in depressed patients and anxiety. Differences in lung function trajectory according to presence of depression/anxiety were analyzed using a linear mixed model. RESULTS: In all, 2147 patients were enrolled. Depressed patients or anxiety had lower lung function, higher modified Medical Research Council (mMRC) grade, St. George Respiratory Questionnaire (SGRQ) score, and COPD assessment test score, and higher rates of exacerbation in the past year than those without depression/anxiety. Depressed patients had a higher frequency of moderate to severe exacerbations [Incidence Rate Ratio (IRR): 1.57, CI: 1.17-2.11, p = 0.002] and those with anxiety had higher frequencies of moderate to severe (IRR: 1.52, CI: 1.03-2.27, p = 0.038) and severe exacerbations (IRR: 2.13, CI: 1.09-4.15, p = 0.025) during 1-year follow-up compared to those without these comorbidities. The differences in the change in annual forced expiratory volume in 1 seconds (FEV1) over 3 years according to the presence of depression or anxiety were not statistically significant. CONCLUSION: Depressed and anxious patients showed increased respiratory symptoms and exacerbation rate as well as reduced health-related quality of life, whereas there were no significant differences in changes in lung function between groups with and without depression/anxiety.

Encapsulating lithium at the microscale: selective deposition in carbon-doped graphitic carbon nitride spheres.

For stable lithium deposition without dendrites, three-dimensional (3D) porous structure has been intensively investigated. Here, we report the use of carbon-doped graphitic carbon nitride (C-doped g-C3N4) microspheres as a 3D host for lithium to suppress dendrite formation, which is crucial for stable lithium deposition. The C-doped g-C3N4microspheres have a high surface area and porosity, allowing for efficient lithium accommodation with high accessibility. The carbon-doping of the g-C3N4microspheres confers lithiophilic properties, which facilitate the regulation of Li+flux and dense filling of cavities with nucleated lithium, thereby preventing volume expansion and promoting dendrite-free Li deposition. The electrochemical performance was improved with cyclic stability and high Coulombic efficiency over 260 cycles at 1.0 mA cm-2for 1.0 mAh cm-2, and even over 70 cycles at 5.0 mA cm-2for 3.0 mAh cm-2. The use of C-doped g-C3N4microspheres as a 3D Li host shows promising results for stable lithium deposition without dendrite formation.

Association Between Quality of Life Questionnaire at Diagnosis and Survival in Patients With Lung Cancer.

BACKGROUND: Patients with lung cancer experience considerable symptom burden, which can decrease patients' QOL. Our aim was to investigate the association between QOL questionnaire at diagnosis and survival of lung cancer. PATIENTS AND METHODS: This was a multicenter study of lung cancer patients at 7 medical centers of the Catholic University of Korea that responded to a quality of life questionnaire between December 1, 2017 and December 31, 2020. We analyzed 5 functional (physical, role, emotional, cognitive, and social functioning) and nine symptom (fatigue, nausea and vomiting, pain, dyspnea, insomnia, appetite loss, constipation, diarrhea, and financial difficulties) scales and examined their associations with survival. A Cox proportional hazards model was used to evaluate the prognostic value. RESULTS: In total, 1297 lung cancer patients were enrolled. The results of multivariable analysis showed that female, younger age, never smoker, stage I or II cancer, higher physical functioning, and emotional functioning were statistically significant favorable predictors for survival. On subgroup analysis according to early (stage I and II) or advanced (stage III or IV) stage, higher physical functioning and emotional functioning were each found to be favorable prognostic factors for survival. Meanwhile, fatigue, pain, insomnia, and financial difficulties were found to be associated with low scores on the emotional functioning scale; fatigue, pain, dyspnea, and financial difficulties were associated with low scores on the physical functioning scale. CONCLUSION: Assessing the physical functioning and emotional functioning scales of QOL questionnaire items at diagnosis can help clinicians predict the survival of patients with lung cancer.

Classification of early age facial growth pattern and identification of the genetic basis in two Korean populations.

Childhood to adolescence is an accelerated growth period, and genetic features can influence differences of individual growth patterns. In this study, we examined the genetic basis of early age facial growth (EAFG) patterns. Facial shape phenotypes were defined using facial landmark distances, identifying five growth patterns: continued-decrease, decrease-to-increase, constant, increase-to-decrease, and continued-increase. We conducted genome-wide association studies (GWAS) for 10 horizontal and 11 vertical phenotypes. The most significant association for horizontal phenotypes was rs610831 (TRIM29; ß = 0.92, p-value = 1.9 × 10-9) and for vertical phenotypes was rs6898746 (ZSWIM6; ß = 0.1103, p-value = 2.5 × 10-8). It is highly correlated with genes already reported for facial growth. This study is the first to classify and characterize facial growth patterns and related genetic polymorphisms.

Review of Machine Learning Applications Using Retinal Fundus Images.

Automating screening and diagnosis in the medical field saves time and reduces the chances of misdiagnosis while saving on labor and cost for physicians. With the feasibility and development of deep learning methods, machines are now able to interpret complex features in medical data, which leads to rapid advancements in automation. Such efforts have been made in ophthalmology to analyze retinal images and build frameworks based on analysis for the identification of retinopathy and the assessment of its severity. This paper reviews recent state-of-the-art works utilizing the color fundus image taken from one of the imaging modalities used in ophthalmology. Specifically, the deep learning methods of automated screening and diagnosis for diabetic retinopathy (DR), age-related macular degeneration (AMD), and glaucoma are investigated. In addition, the machine learning techniques applied to the retinal vasculature extraction from the fundus image are covered. The challenges in developing these systems are also discussed.

Effect of Highly Periodic Au Nanopatterns on Dendrite Suppression in Lithium Metal Batteries.

Despite the extremely high energy density of the lithium metal, dendritic lithium growth caused by nonuniform lithium deposition can result in low Coulombic efficiency and safety hazards, thereby inhibiting its practical applications. Here, we report a new strategy for adopting a nanopatterned gold (Au) seed on a copper current collector for uniform lithium deposition. We find that Au nanopatterns enhance lithium metal battery performance, which is strongly affected by the feature dimensions of Au nanopatterns (diameter and height). Ex situ scanning electron microscopy images confirm that this can be attributed to the perfectly selective lithium nucleation and uniform growth resulting from the spatial confinement effect. The spatial arrangement of Au dot seeds homogenizes the Li+ flux and electric field, and the size-controlled Au seeds prevent both seed-/substrate-induced agglomeration and interseed-induced lithium growth, leading to uniform lithium deposition. This dendrite-free lithium deposition results in the improvement of electrochemical performance, and the system showed cyclic stability over 300 cycles at 0.5 mA cm-2 and 200 cycles at 1.0 mA cm-2 (1 mA h cm-2) and a high rate capability. This study provides in-depth insights into the more complicated and diverse seed geometry control of seed materials for the development of high-performance lithium metal batteries.

[Comparison between charged aerosol detector and evaporative light scattering detector for analysis of sugar in Zhusheyong Yiqi Fumai and study on accuracy of methods].

Evaporative light scattering detector(ELSD) and charged aerosol detector(CAD) methods were established in this study for the content determination of four kinds of sugars in Zhusheyong Yiqi Fumai(YQFM), and the factors affecting the accuracy of CAD methods were discussed. HPLC-ELSD chromatographic separation was performed on a Shodex Asahipak NH2 P-50 column with acetonitrile-water(75∶25)as the mobile phase, with a flow rate of 0.8 mL·min~(-1), drift tube temperature of 80 ℃. The analysis by HPLC-CAD was performed on the same column with acetonitrile-water as mobile phase for gradient elution, with a flow rate of 0.8 mL·min~(-1), a neb temperature of 45 ℃, and power function(PF) of 1.3. The samples of YQFM were detected by ELSD and CAD respectively. It was found that YQFM was composed of fructose, glucose, sucrose and maltose. The linear relationship of the two methods was good, and the recoveries, reproducibility and stability of these four kinds of sugars measured by the two methods satisfied the requirements of methodology. Both CAD and ELSD detectors were accurate and reliable in detecting saccharides components in YQFM. In addition, it was revealed in this study for the first time that the PF parameter of CAD had an important influence on the accuracy of sugar determination and acted as the key parameter of CAD method. It was also found that for CAD, a non-linear detector, there was no significant difference between the results of linear regression and logarithmic regression.

Automatic Facial Recognition System Assisted-facial Asymmetry Scale Using Facial Landmarks.

OBJECTIVES: This study aimed to demonstrate the application of our automated facial recognition system to measure facial nerve function and compare its effectiveness with other conventional systems and provide a preliminary evaluation of deep learning-facial grading systems. STUDY DESIGN: Retrospective, observational. SETTING: Tertiary referral center, hospital. PATIENTS: Facial photos taken from 128 patients with facial paralysis and two persons with no history of facial palsy were analyzed. INTERVENTION: Diagnostic. MAIN OUTCOME MEASURES: Correlation with Sunnybrook (SB) and House-Brackmann (HB) grading scales. RESULTS: Our results had good reliability and correlation with other grading systems (r = 0.905 and 0.783 for Sunnybrook and HB grading scales, respectively), while being less time-consuming than Sunnybrook grading scale. CONCLUSIONS: Our objective method shows good correlation with both Sunnybrook and HB grading systems. Furthermore, this system could be developed into an application for use with a variety of electronic devices, including smartphones and tablets.

Luminescence Quenching Behavior of Hydrothermally Grown YVO4:Eu3+ Nanophosphor Excited under Low Temperature and Vacuum Ultra Violet Discharge.

The luminescence quenching behavior and energy transfer process in hydrothermally grown Eu3+-doped YVO4 nanophosphors were studied using low temperature photoluminescence spectroscopy. The luminescence efficiency of nanophosphor is dependent on the acidity of its solution media and the post annealing condition after hydrothermal processing. The overall results suggest that the abnormal luminescence behavior of Eu3+-doped nanocrystalline YVO4 under low temperature photoexcitation is due to the incorporated non-radiative hydroxyl groups often encountered in hydrothermal synthesis as well as to the inefficient energy transfer to luminescent ions from vanadate groups.

Adaptive 3D Model-Based Facial Expression Synthesis and Pose Frontalization.

Facial expressions are one of the important non-verbal ways used to understand human emotions during communication. Thus, acquiring and reproducing facial expressions is helpful in analyzing human emotional states. However, owing to complex and subtle facial muscle movements, facial expression modeling from images with face poses is difficult to achieve. To handle this issue, we present a method for acquiring facial expressions from a non-frontal single photograph using a 3D-aided approach. In addition, we propose a contour-fitting method that improves the modeling accuracy by automatically rearranging 3D contour landmarks corresponding to fixed 2D image landmarks. The acquired facial expression input can be parametrically manipulated to create various facial expressions through a blendshape or expression transfer based on the FACS (Facial Action Coding System). To achieve a realistic facial expression synthesis, we propose an exemplar-texture wrinkle synthesis method that extracts and synthesizes appropriate expression wrinkles according to the target expression. To do so, we constructed a wrinkle table of various facial expressions from 400 people. As one of the applications, we proved that the expression-pose synthesis method is suitable for expression-invariant face recognition through a quantitative evaluation, and showed the effectiveness based on a qualitative evaluation. We expect our system to be a benefit to various fields such as face recognition, HCI, and data augmentation for deep learning.

Systemic and Local Metabolic Alterations in Sleep-Deprivation-Induced Stress: A Multiplatform Mass-Spectrometry-Based Lipidomics and Metabolomics Approach.

Sleep deprivation (SD) is known to be associated with metabolic disorders and chronic diseases. Complex metabolic alterations induced by SD at omics scale and the associated biomarker candidates have been proposed. However, in vivo systemic and local metabolic shift patterns of the metabolome and lipidome in acute and chronic partial SD models remain to be elucidated. In the present study, the serum, hypothalamus, and hippocampus CA1 of sleep-deprived rats (SD rats) from acute and chronic sleep restriction models were analyzed using three different omics platforms for the discovery and mechanistic assessment of systemic and local SD-induced dysregulated metabolites. We found a similar pattern of systemic metabolome alterations between two models, for which the area under the curve (AUC) of receiver operating characteristic curves was AUC = 0.847 and 0.930 with the pseudotargeted and untargeted metabolomics approach, respectively. However, SD-induced systemic lipidome alterations were significantly different and appeared to be model-dependent (AUC = 0.374). Comprehensive pathway analysis of the altered lipidome and metabolome in the hypothalamus indicated the abnormal behavior of eight metabolic and lipid metabolic pathways. The metabolic alterations of the hippocampus CA1 was subtle in two SD models. Collectively, these results extend our understanding of the quality of sleep and suggest metabolic targets in developing diagnostic biomarkers for better SD control.

A systematic review on metabolomics-based diagnostic biomarker discovery and validation in pancreatic cancer.

INTRODUCTION: Metabolomics is an emerging approach for early detection of cancer. Along with the development of metabolomics, high-throughput technologies and statistical learning, the integration of multiple biomarkers has significantly improved clinical diagnosis and management for patients. OBJECTIVES: In this study, we conducted a systematic review to examine recent advancements in the oncometabolomics-based diagnostic biomarker discovery and validation in pancreatic cancer. METHODS: PubMed, Scopus, and Web of Science were searched for relevant studies published before September 2017. We examined the study designs, the metabolomics approaches, and the reporting methodological quality following PRISMA statement. RESULTS AND CONCLUSION: The included 25 studies primarily focused on the identification rather than the validation of predictive capacity of potential biomarkers. The sample size ranged from 10 to 8760. External validation of the biomarker panels was observed in nine studies. The diagnostic area under the curve ranged from 0.68 to 1.00 (sensitivity: 0.43-1.00, specificity: 0.73-1.00). The effects of patients' bio-parameters on metabolome alterations in a context-dependent manner have not been thoroughly elucidated. The most reported candidates were glutamic acid and histidine in seven studies, and glutamine and isoleucine in five studies, leading to the predominant enrichment of amino acid-related pathways. Notably, 46 metabolites were estimated in at least two studies. Specific challenges and potential pitfalls to provide better insights into future research directions were thoroughly discussed. Our investigation suggests that metabolomics is a robust approach that will improve the diagnostic assessment of pancreatic cancer. Further studies are warranted to validate their validity in multi-clinical settings.

Inhibitory effect of cinnamon powder on pathogen growth in laboratory media and oriental-style rice cakes (sulgidduk).

There has been an increasing interest in the use of natural plant materials as alternative food preservatives. We examined the antimicrobial effects of natural plant materials used as additives against foodborne pathogens in laboratory media and Sulgidduk, oriental-style rice cakes. Cinnamon, mugwort, and garlic powder solutions (3%) were tested for their antimicrobial activities against pathogens in laboratory media. Sulgidduk prepared with different amounts of cinnamon powder (1, 3, and 6%) was inoculated with a Staphylococcus aureus or Bacillus cereus cocktail. The samples were air or vacuum packaged and stored at 22 ± 1°C for 72 h, and microbial growth was determined. Cinnamon powder showed more inhibitory properties against pathogens such as Salmonella enterica serovar Typhimurium, Escherichia coli O157:H7, Listeria monocytogenes, S. aureus, and B. cereus than did mugwort or garlic powder. The populations of S. aureus and B. cereus in Sulgidduk containing cinnamon powder were significantly lower than in the control during storage time. Different packaging methods did not result in a significant difference in pathogen growth. In a sensory evaluation, Sulgidduk containing 1 and 3% cinnamon powder did not significantly differ from the control sample in any of the attributes tested other than flavor. These results indicate that natural plant materials such as cinnamon powder could be used as food additives to improve the microbiological stability of rice cakes.