Association Between Macronutrients Intake and Depression in the United States and South Korea

Although the risk for depression appears to be related to daily dietary habits, how the proportion of major macronutrients affects the occurrence of depression remains largely unknown. This study aims to estimate the association between macronutrients (i.e., carbohydrate, protein, fat) and depression through national survey datasets from the United States and South Korea. Association between the prevalence of depression and each macronutrient was measured from 60,935 participants from the National Health and Nutrition Examination Survey (NHANES) and 15,700 participants from the South Korea NHANES (K-NHANES) databases. When the proportion of calories intake by protein increased by 10%, the prevalence of depression was significantly reduced both in the United States [Odds Ratio, OR (95% CI), 0.621 (0.530–0.728)] and South Korea [0.703 (0.397–0.994)]. An association between carbohydrate intake and the prevalence of depression was seen in the United States [1.194 (1.116–1.277)], but not in South Korea. Fat intake was not significantly associated with depression in either country. Subsequent analysis showed that the low protein intake groups had significantly higher risk for depression than the normal protein intake groups in both the United States [1.648 (1.179–2.304)] and South Korea [3.169 (1.598–6.286)]. In the daily diet of macronutrients, the proportion of protein intake is significantly associated with the prevalence of depression. These associations were more prominent in adults with insufficient protein intake, and the pattern of association between macronutrients and depression in Asian American and South Korean populations were similar. Our findings suggest that the proportion of macronutrients intake in everyday life may be related to the occurrence of depression

Association Between Macronutrients Intake and Depression in the United States and South Korea

Although the risk for depression appears to be related to daily dietary habits, how the proportion of major macronutrients affects the occurrence of depression remains largely unknown. This study aims to estimate the association between macronutrients (i.e., carbohydrate, protein, fat) and depression through national survey datasets from the United States and South Korea. Association between the prevalence of depression and each macronutrient was measured from 60,935 participants from the National Health and Nutrition Examination Survey (NHANES) and 15,700 participants from the South Korea NHANES (K-NHANES) databases. When the proportion of calories intake by protein increased by 10%, the prevalence of depression was significantly reduced both in the United States [Odds Ratio, OR (95% CI), 0.621 (0.530–0.728)] and South Korea [0.703 (0.397–0.994)]. An association between carbohydrate intake and the prevalence of depression was seen in the United States [1.194 (1.116–1.277)], but not in South Korea. Fat intake was not significantly associated with depression in either country. Subsequent analysis showed that the low protein intake groups had significantly higher risk for depression than the normal protein intake groups in both the United States [1.648 (1.179–2.304)] and South Korea [3.169 (1.598–6.286)]. In the daily diet of macronutrients, the proportion of protein intake is significantly associated with the prevalence of depression. These associations were more prominent in adults with insufficient protein intake, and the pattern of association between macronutrients and depression in Asian American and South Korean populations were similar. Our findings suggest that the proportion of macronutrients intake in everyday life may be related to the occurrence of depression

Ultraviolet photodepletion spectroscopy of dibenzo-18-crown-6-ether complexes with alkali metal cations

Ultraviolet photodepletion spectra of dibenzo-18-crown-6-ether complexes with alkali metal cations (M+-DB18C6, M = Cs, Rb, K, Na, and Li) were obtained in the gas phase using electrospray ionization quadrupole ion-trap reflectron time-of-flight mass spectrometry. The spectra exhibited a few distinct absorption bands in the wavenumber region of 35450−37800 cm^(−1). The lowest-energy band was tentatively assigned to be the origin of the S_0-S_1 transition, and the second band to a vibronic transition arising from the “benzene breathing” mode in conjunction with symmetric or asymmetric stretching vibration of the bonds between the metal cation and the oxygen atoms in DB18C6. The red shifts of the origin bands were observed in the spectra as the size of the metal cation in M^+-DB18C6 increased from Li^+ to Cs^+. We suggested that these red shifts arose mainly from the decrease in the binding energies of larger-sized metal cations to DB18C6 at the electronic ground state. These size effects of the metal cations on the geometric and electronic structures, and the binding properties of the complexes at the S_0 and S_1 states were further elucidated by theoretical calculations using density functional and time-dependent density functional theories

Identifying Depression in the National Health and Nutrition Examination Survey Data using a Deep Learning Algorithm

Background: As depression is the leading cause of disability worldwide, large-scale surveys have been conducted to establish the occurrence and risk factors of depression. However, accurately estimating epidemiological factors leading up to depression has remained challenging. Deep-learning algorithms can be applied to assess the factors leading up to prevalence and clinical manifestations of depression. Methods: Customized deep-neural-network and machine-learning classifiers were assessed using survey data from 19,725 participants from the NHANES database (from 1999 through 2014) and 4,949 from the South Korea NHANES (K-NHANES) database in 2014. Results: A deep-learning algorithm showed area under the receiver operating characteristic curve (AUCs) of 0.91 and 0.89 for detecting depression in NHANES and K-NHANES, respectively. The deep-learning algorithm trained with serial datasets (NHANES, from 1999 to 2012), predicted the prevalence of depression in the following two years of data (NHANES, 2013 and 2014) with an AUC of 0.92. Machine learning classifiers trained with NHANES could further predict depression in K-NHANES. There, logistic regression had the highest performance (AUC, 0.77) followed by deep learning algorithm (AUC, 0.74). Conclusions: Deep neural-networks managed to identify depression well from other health and demographic factors in both the NHANES and K-NHANES datasets. The deep-learning algorithm was also able to predict depression relatively well on new data set—cross temporally and cross nationally. Further research can delineate the clinical implications of machine learning and deep learning in detecting disease prevalence and progress as well as other risk factors for depression and other mental illnesses

A computational approach for identifying pathogenicity islands in prokaryotic genomes

BACKGROUND: Pathogenicity islands (PAIs), distinct genomic segments of pathogens encoding virulence factors, represent a subgroup of genomic islands (GIs) that have been acquired by horizontal gene transfer event. Up to now, computational approaches for identifying PAIs have been focused on the detection of genomic regions which only differ from the rest of the genome in their base composition and codon usage. These approaches often lead to the identification of genomic islands, rather than PAIs. RESULTS: We present a computational method for detecting potential PAIs in complete prokaryotic genomes by combining sequence similarities and abnormalities in genomic composition. We first collected 207 GenBank accessions containing either part or all of the reported PAI loci. In sequenced genomes, strips of PAI-homologs were defined based on the proximity of the homologs of genes in the same PAI accession. An algorithm reminiscent of sequence-assembly procedure was then devised to merge overlapping or adjacent genomic strips into a large genomic region. Among the defined genomic regions, PAI-like regions were identified by the presence of homolog(s) of virulence genes. Also, GIs were postulated by calculating G+C content anomalies and codon usage bias. Of 148 prokaryotic genomes examined, 23 pathogenic and 6 non-pathogenic bacteria contained 77 candidate PAIs that partly or entirely overlap GIs. CONCLUSION: Supporting the validity of our method, included in the list of candidate PAIs were thirty four PAIs previously identified from genome sequencing papers. Furthermore, in some instances, our method was able to detect entire PAIs for those only partial sequences are available. Our method was proven to be an efficient method for demarcating the potential PAIs in our study. Also, the function(s) and origin(s) of a candidate PAI can be inferred by investigating the PAI queries comprising it. Identification and analysis of potential PAIs in prokaryotic genomes will broaden our knowledge on the structure and properties of PAIs and the evolution of bacterial pathogenesis

Effect of Bisphosphonates on Anodized and Heatâ Treated Titanium Surfaces: An Animal Experimental Study

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/141247/1/jper1035.pd

Comparison of pre- and post-processors for ensemble streamflow prediction

This study conducted a broad review of the pre- and post-processor methods for ensemble streamflow prediction using a Korean case study. Categorical forecasts offered by the Korea Meteorogical Administration and deterministic forecasts of a regional climate model called Seoul National University Regional Climate Model(SNURCM) were selected as climate forecast information for the pre-processors and incorporated into Ensemble Streamflow Prediction(ESP) runs with the TANK hydrologic model. The post-processors were then used to minimize a possible error propagated through the streamflow generation. The application results show that use of the post-processor more effectively reduced the uncertainty of the no-processor ESP than use of the pre-processor, especially in dry season

Molecular Detection of Dirofilaria immitis Specific Gene from Infected Dog Blood Sample Using Polymerase Chain Reaction

Background: Dirofilaria immitis, a filarial nematode, is the most important parasite-affecting dogs, causing cardiopulmonary dirofilariasis. Current diagnostic tools for detecting D. immitis include morphological assays, antigen detection, and X-ray. Herein, we developed a method for the molecular detection of D. immitis in blood using polymerase chain reaction (PCR). Methods: The study was conducted at Eulji University, Republic of Korea in 2016. To detect D. immitis-specific gene regions, we aligned the cytochrome c oxidase subunit I (COI) genes of seven filarial nematodes and designed primers targeting the unique region. We used dog glyceraldehyde-3-phosphate dehydrogenase (GAPDH)-targeted primers as the internal control. We conducted PCR-amplified genomic DNA from canine blood samples. The products were confirmed by sequencing. Results: Gene alignment revealed a D. immitis COI-specific gene region, and the activity of designed primers was confirmed by PCR and sequencing. Plasmid DNA made from the PCR products was a positive control. The limit of detection for our method was 50 copies. The D. immitis COI and dog GAPDH genes could be discriminated from blood samples simultaneously. Conclusion: This study provides a method for highly specific and sensitive molecular diagnosis of D. immitis used as a diagnostic and therapeutic tool from the early stage of infection

Chemical Targeting of GAPDH Moonlighting Function in Cancer Cells Reveals Its Role in Tubulin Regulation

SummaryGlycolytic enzymes are attractive anticancer targets. They also carry out numerous, nonglycolytic “moonlighting” functions in cells. In this study, we investigated the anticancer activity of the triazine small molecule, GAPDS, that targets the glycolytic enzyme glyceraldehyde 3-phosphate dehydrogenase (GAPDH). GAPDS showed greater toxicity against cancer cells compared to a known GAPDH enzyme inhibitor. GAPDS also selectively inhibited cell migration and invasion. Our analysis showed that GAPDS treatment reduced GAPDH levels in the cytoplasm, which would modulate the secondary, moonlighting functions of this enzyme. We then used GAPDS as a probe to demonstrate that a moonlighting function of GAPDH is tubulin regulation, which may explain its anti-invasive properties. We also observed that GAPDS has potent anticancer activity in vivo. Our study indicates that strategies to target the secondary functions of anticancer candidates may yield potent therapeutics and useful chemical probes