Multi-block Analysis of Genomic Data Using Generalized Canonical Correlation Analysis

Recently, there have been many studies in medicine related to genetic analysis. Many genetic studies have been performed to find genes associated with complex diseases. To find out how genes are related to disease, we need to understand not only the simple relationship of genotypes but also the way they are related to phenotype. Multi-block data, which is a summation form of variable sets, is used for enhancing the analysis of the relationships of different blocks. By identifying relationships through a multi-block data form, we can understand the association between the blocks in comprehending the correlation between them. Several statistical analysis methods have been developed to understand the relationship between multi-block data. In this paper, we will use generalized canonical correlation methodology to analyze multi-block data from the Korean Association Resource project, which has a combination of single nucleotide polymorphism blocks, phenotype blocks, and disease blocks

An efficient multivariate feature ranking method for gene selection in high-dimensional microarray data

Classification of microarray data plays a significant role in the diagnosis and prediction of cancer. However, its high-dimensionality (>tens of thousands) compared to the number of observations (<tens of hundreds) may lead to poor classification accuracy. In addition, only a fraction of genes is really important for the classification of a certain cancer, and thus feature selection is very essential in this field. Due to the time and memory burden for processing the high-dimensional data, univariate feature ranking methods are widely-used in gene selection. However, most of them are not that accurate because they only consider the relevance of features to the target without considering the redundancy among features. In this study, we propose a novel multivariate feature ranking method to improve the quality of gene selection and ultimately to improve the accuracy of microarray data classification. The method can be efficiently applied to high-dimensional microarray data. We embedded the formal definition of relevance into a Markov blanket (MB) to create a new feature ranking method. Using a few microarray datasets, we demonstrated the practicability of MB-based feature ranking having high accuracy and good efficiency. The method outperformed commonly-used univariate ranking methods and also yielded the better result even compared with the other multivariate feature ranking method due to the advantage of data efficiency

Joint Application of the Target Trial Causal Framework and Machine Learning Modeling to Optimize Antibiotic Therapy: Use Case on Acute Bacterial Skin and Skin Structure Infections due to Methicillin-resistant Staphylococcus aureus

Bacterial infections are responsible for high mortality worldwide. Antimicrobial resistance underlying the infection, and multifaceted patient's clinical status can hamper the correct choice of antibiotic treatment. Randomized clinical trials provide average treatment effect estimates but are not ideal for risk stratification and optimization of therapeutic choice, i.e., individualized treatment effects (ITE). Here, we leverage large-scale electronic health record data, collected from Southern US academic clinics, to emulate a clinical trial, i.e., 'target trial', and develop a machine learning model of mortality prediction and ITE estimation for patients diagnosed with acute bacterial skin and skin structure infection (ABSSSI) due to methicillin-resistant Staphylococcus aureus (MRSA). ABSSSI-MRSA is a challenging condition with reduced treatment options - vancomycin is the preferred choice, but it has non-negligible side effects. First, we use propensity score matching to emulate the trial and create a treatment randomized (vancomycin vs. other antibiotics) dataset. Next, we use this data to train various machine learning methods (including boosted/LASSO logistic regression, support vector machines, and random forest) and choose the best model in terms of area under the receiver characteristic (AUC) through bootstrap validation. Lastly, we use the models to calculate ITE and identify possible averted deaths by therapy change. The out-of-bag tests indicate that SVM and RF are the most accurate, with AUC of 81% and 78%, respectively, but BLR/LASSO is not far behind (76%). By calculating the counterfactuals using the BLR/LASSO, vancomycin increases the risk of death, but it shows a large variation (odds ratio 1.2, 95% range 0.4-3.8) and the contribution to outcome probability is modest. Instead, the RF exhibits stronger changes in ITE, suggesting more complex treatment heterogeneity.Comment: This is the Proceedings of the KDD workshop on Applied Data Science for Healthcare (DSHealth 2022), which was held on Washington D.C, August 14 202

Enhanced knee joint function due to accelerated rehabilitation exercise after anterior cruciate ligament reconstruction surgery in Korean male high school soccer players.

This study was conducted on Korean male high school soccer players who underwent anterior cruciate ligament reconstruction (ACLR) to identify the effects of an accelerated rehabilitation exercise (ARE) program on knee joint isometric strength, thigh circumference, Lysholm score, and active balance agility. We assigned eight test participants each to a physical therapy group (PTG) and an accelerated rehabilitation exercise group (AREG), and compared differences between the groups. Both the PTG and AREG showed significant increases in 30° away and 60° toward isometric strength after treatment. In addition, significant differences were observed in these strength tests between the two groups. Both groups also showed significant increases in thigh circumference, Lysholm score, and active balance agility after treatment, but no significant differences were observed between the two groups. We conclude that the ARE treatment was more effective for improving isometric strength of the knee joint than that of physical therapy, and that an active rehabilitation exercise program after ACLR had positive effects on recovery performance of patients with an ACL injury and their return to the playing field

Joint semiparametric kernel network regression

Variable selection and graphical modeling play essential roles in highly correlated and high-dimensional (HCHD) data analysis. Variable selection methods have been developed under both parametric and nonparametric model settings. However, variable selection for nonadditive, nonparametric regression with high-dimensional variables is challenging due to complications in modeling unknown dependence structures among HCHD variables. Gaussian graphical models are a popular and useful tool for investigating the conditional dependence between variables via estimating sparse precision matrices. For a given class of interest, the estimated precision matrices can be mapped onto networks for visualization. However, the limitation of Gaussian graphical models is that they are only applicable to discretized response variables and for the case when (Formula presented.), where (Formula presented.) is the number of variables and (Formula presented.) is the sample size. They are necessary to develop a joint method for variable selection and graphical modeling. To the best of our knowledge, the methods for simultaneously selecting variable selection and estimating networks among variables in the semiparametric regression settings are quite limited. Hence, in this paper, we develop a joint semiparametric kernel network regression method to solve this limitation and to provide a connection between them. Our approach is a unified and integrated method that can simultaneously identify important variables and build a network among those variables. We developed our approach under a semiparametric kernel machine regression framework, which can allow for nonlinear or nonadditive associations and complicated interactions among the variables. The advantages of our approach are that it can (1) simultaneously select variables and build a network among HCHD variables under a regression setting; (2) model unknown and complicated interactions among the variables and estimate the network among these variables; (3) allow for any form of semiparametric model, including non-additive, nonparametric model; and (4) provide an interpretable network that considers important variables and a response variable. We demonstrate our approach using a simulation study and real application on genetic pathway-based analysis

Effect of tetrabromobisphenol A (TBBPA) on early implantation using the three-dimensional spheroid model with human endometrial cell line, Ishikawa

Abstract Background Tetrabromobisphenol A (TBBPA) can be characterized as an endocrine-disrupting chemical (EDCs). It has been widely used as a brominated flame retardant in industrial products. EDCs have effects on female reproduction leading to issues, such as infertility, hormone imbalance, and endometriosis. In Korea, the problems of infertility and decreasing birth rate are of significant concern. Exposure to EDCs might have a harmful effect on female fertility by mediating a decrease endometrial receptivity. This study aimed to investigate the effects of TBBPA on infertility, particularly on early implantation events in the uterine endometrium. Human endometrial adenocarcinoma and trophoblastic cell lines were used in this study. The cytotoxicity of TBBPA on Ishikawa cells and Jeg-3 cells was measured using the Cell Counting Kit-8 assay. The mRNA expression was analyzed by reverse transcription-quantitative polymerase chain reaction, and protein levels were measured by western blotting. The attachment rate was analyzed using an attachment assay, and the outgrowth area was measured using an outgrowth assay. Results The mRNA expression of interleukin (IL)-6, IL-1β, tumor necrosis factor-α, and leukemia inhibitory factor was significantly increased upon treatment of Ishikawa cells by TBBPA. Moreover, the outgrowth area in the TBBPA group was significantly decreased compared to that in the control. In contrast, TBBPA had a minor effect on protein levels and attachment rates. Conclusions In this study, TBBPA induced an inflammatory milieu in mRNA expression. An increase in inflammation-related cytokines in the endometrium can disrupt embryo implantation. TBBPA disrupted the outgrowth of spheroids in the endometrium; however, the protein levels and attachment rate were comparable to those in the control group. The effect of TBBPA on implantation events should be elucidated further

Intelligent pH indicator film composed of agar/potato starch and anthocyanin extracts from purple sweet potato.

International audienceA new colorimetric pH indicator film was developed using agar, potato starch, and natural dyes extracted from purple sweet potato, Ipomoea batatas. Both agar and potato starch are solid matrices used to immobilize natural dyes, anthocyanins. The ultraviolet-visible (UV-vis) spectrum of anthocyanin extract solutions and agar/potato starch films with anthocyanins showed color variations to different pH values (pH 2.0-10.0). Fourier transform infrared (FT-IR) and UV-vis region spectra showed compatibility between agar, starch, and anthocyanin extracts. Color variations of pH indicator films were measured by a colorimeter after immersion in different pH buffers. An application test was conducted for potential use as a meat spoilage sensor. The pH indicator films showed pH changes and spoilage point of pork samples, changing from red to green. Therefore, the developed pH indicator films could be used as a diagnostic tool for the detection of food spoilage

Development of examination objectives based on nursing competency for the Korean Nursing Licensing Examination: a validity study

Purpose This study aimed to develop the examination objectives based on nursing competency of the Korean Nursing Licensing Examination. Methods This is a validity study to develop the examination objectives based on nursing competency. Data were collected in December 2021. We reviewed the literature related to changing nurse roles and on the learning objectives for the Korea Medical Licensing Examination and other health personnel licensing examinations. Thereafter, we created a draft of the nursing problems list for examination objectives based on the literature review, and the content validity was evaluated by experts. A final draft of the examination objectives is presented and discussed. Results A total of 4 domains, 12 classes, and 85 nursing problems for the Korean Nursing Licensing Examination were developed. They included the essentials of objectives, related factors, evaluation goals, related activity statements, related clients, related settings, and specific outcomes. Conclusion This study developed a draft of the examination objectives based on clinical competency that were related to the clinical situations of nurses and comprised appropriate test items for the licensing examination. Above results may be able to provide fundamental data for item development that reflects future nursing practices

Differences in Aortic Valve and Left Ventricular Parameters Related to the Severity of Myocardial Fibrosis in Patients with Severe Aortic Valve Stenosis

<div><p>Objective</p><p>This study investigated the morphological and functional characteristics of the aortic valve and the left ventricular (LV) systolic functional parameters and myocardial mass related to the severity of myocardial fibrosis (MF) in patients with severe aortic valve stenosis (AS).</p><p>Materials and Methods</p><p>We retrospectively enrolled 81 patients (48 men; mean age: 59±12 years) with severe AS who underwent transthoracic echocardiography (TTE), cardiac computed tomography (CCT), and cardiovascular magnetic resonance (CMR) within 1 month and subsequent aortic valve surgery. Degree of MF was determined on delayed contrast-enhanced CMR with visual sub-segmental analysis-based quantification and was classified into three groups (no, mild, and severe) for identifying the differences in LV function and characteristics of the aortic valve. One-way ANOVA, Chi-square test or Fisher’s exact test were used to compare variables of the three groups. Univariate multinomial logistic regression analysis was performed to determine the association between the severity of MF and variables on imaging modalities.</p><p>Results</p><p>Of 81 patients, 34 (42%) had MF (mild, n = 18; severe, n = 16). Aortic valve calcium volume score on CCT, aortic valve area, LV mass index, LV end-diastolic volume index on CMR, presence of mild aortic regurgitation (AR), transaortic mean pressure gradient, and peak velocity on TTE were significantly different among the three groups and were associated with severity of MF on a univariate multinomial logistic regression analysis. Aortic valve calcium grade was different (<i>p</i> = 0.008) among the three groups but not associated with severity of MF (<i>p</i> = 0.375).</p><p>Conclusions</p><p>A multi-imaging approach shows that severe AS with MF is significantly associated with more severe calcific AS, higher LV end-diastolic volume, higher LV mass, and higher prevalence of mild AR.</p></div