Challenges and real-world solutions for adoption of holistic skincare routine (cleansing, treatment, moisturization, and photoprotection) in acne, rosacea, atopic dermatitis, and sensitive skin: An expert consensus.

BACKGROUND: While treatment is a definitive therapeutic component in the management of inflammatory skin conditions, adjunctive skin care comprising of appropriate cleansing, moisturization, and photoprotection are just as important. Cleansing, treatment, moisturization, and photoprotection (CTMP) constitute the four major components of holistic skincare routine for dermatological conditions. However, inadequate patient understanding of the condition, limited resources for physicians, and insufficient time for patient education during busy dermatological consultations are the main obstacles to establishing a holistic skincare routine in the real world. AIMS: This study aimed to identify key challenges in the implementation of a holistic skincare routine, and offer practical guidance to physicians to improve adoption in the management of acne, atopic dermatitis, rosacea, and sensitive skin syndrome. METHODS: An expert panel comprising of nine dermatologists from Australia, China, Hong Kong, Taiwan, India, Philippines, Singapore, South Korea, and Thailand convened to develop consensus statements to stimulate real-world adoption of holistic skincare routine in acne, rosacea, atopic dermatitis, and sensitive skin syndrome using the Delphi approach. RESULTS: Consensus was defined as ≥80% of panel rating statement as ≥8 or median rating of ≥8. The final statements were collated to develop consensus recommendations to encourage adoption of holistic skincare routine. CONCLUSION: Promoting patient education on the skin condition, training support staff in patient counseling, and offering physician training opportunities are the key strategies to encourage real-world adoption of CTMP as a holistic skincare routine. The consensus recommendations presented here should be considered in all dermatology patients to accomplish the ultimate goals of improved treatment outcomes and patient satisfaction.

Sparse ordinal discriminant analysis.

Ordinal class labels are frequently observed in classification studies across various fields. In medical science, patients' responses to a drug can be arranged in the natural order, reflecting their recovery postdrug administration. The severity of the disease is often recorded using an ordinal scale, such as cancer grades or tumor stages. We propose a method based on the linear discriminant analysis (LDA) that generates a sparse, low-dimensional discriminant subspace reflecting the class orders. Unlike existing approaches that focus on predictors marginally associated with ordinal labels, our proposed method selects variables that collectively contribute to the ordinal labels. We employ the optimal scoring approach for LDA as a regularization framework, applying an ordinality penalty to the optimal scores and a sparsity penalty to the coefficients for the predictors. We demonstrate the effectiveness of our approach using a glioma dataset, where we predict cancer grades based on gene expression. A simulation study with various settings validates the competitiveness of our classification performance and demonstrates the advantages of our approach in terms of the interpretability of the estimated classifier with respect to the ordinal class labels.

Resampling-based inferences for compositional regression with application to beef cattle microbiomes.

Gut microbiomes are increasingly found to be associated with many health-related characteristics of humans as well as animals. Regression with compositional microbiomes covariates is commonly used to identify important bacterial taxa that are related to various phenotype responses. Often the dimension of microbiome taxa easily exceeds the number of available samples, which creates a serious challenge in the estimation and inference of the model. The sparse log-contrast regression method is useful for such cases as it can yield a model estimate that depends on only a small number of taxa. However, a formal statistical inference procedure for individual regression coefficients has not been properly established yet. We propose a new estimation and inference procedure for linear regression models with extremely low-sample-sized compositional predictors. Under the compositional log-contrast regression framework, the proposed approach consists of two steps. The first step is to screen relevant predictors by fitting a log-contrast model with a sparse penalty. The screened-in variables are used as predictors in the non-sparse log-contrast model in the second step, where each of the regression coefficients is tested using nonparametric, resampling-based methods such as permutation and bootstrap. The performances of the proposed methods are evaluated by a simulation study, which shows they outperform traditional approaches based on normal assumptions or large sample asymptotics. Application to steer microbiome data successfully identifies key bacterial taxa that are related to important cattle quality measures.

Expert consensus on holistic skin care routine: Focus on acne, rosacea, atopic dermatitis, and sensitive skin syndrome.

BACKGROUND: Treatment, cleansing, moisturizing, and photoprotection are four major components of holistic skin care for dermatological conditions. While treatment (T) is recognized as a key component in the management of dermatological conditions, there is a lack of practical guidance on the adjunctive role of cleansing, moisturizing, and photoprotection ("CMP"). Limited patient knowledge, confusion over product selection, and lack of guidance on how to choose and use CMP skin care products (in conjunction with pharmacological therapy) are the main barriers to establishing a holistic skin care routine for dermatological conditions. AIMS: This study aimed to review current clinical evidence, identify gaps, and provide practical guidance on conceptualization and implementation of CMP routine in the management of sensitive skin due to underlying acne, atopic dermatitis, or rosacea, including conditions with idiopathic causes referred to as idiopathic sensitive skin syndrome. METHODS: An expert panel comprising of 10 dermatologists from Australia, China, Hong Kong, Taiwan, India, Indonesia, Philippines, Singapore, South Korea, and Thailand convened to develop consensus statements on holistic skin care in acne, rosacea, atopic dermatitis, and idiopathic sensitive skin syndrome using the Delphi approach. RESULTS: Consensus was defined as ≥80% of panel rating statement as ≥8 or median rating of ≥8. The final statements were collated to develop consensus recommendations on holistic skin care. CONCLUSION: A dermatologist-guided holistic skin care routine is essential to improve patient confidence and reduce confusion over product selection. The consensus recommendations presented here highlight the importance of cleansing, moisturization, and photoprotection in holistic skin care and how it can be utilized as a communication tool for physicians and patients to achieve overall better patient compliance, satisfaction, and treatment outcomes.

Estimation of the case fatality rate based on stratification for the COVID-19 outbreak.

This work is motivated by the recent worldwide pandemic of the novel coronavirus disease (COVID-19). When an epidemiological disease is prevalent, estimating the case fatality rate, the proportion of deaths out of the total cases, accurately and quickly is important as the case fatality rate is one of the crucial indicators of the risk of a disease. In this work, we propose an alternative estimator of the case fatality rate that provides more accurate estimate during an outbreak by reducing the downward bias (underestimation) of the naive CFR, the proportion of deaths out of confirmed cases at each time point, which is the most commonly used estimator due to the simplicity. The proposed estimator is designed to achieve the availability of real-time update by using the commonly reported quantities, the numbers of confirmed, cured, deceased cases, in the computation. To enhance the accuracy, the proposed estimator adapts a stratification, which allows the estimator to use information from heterogeneous strata separately. By the COVID-19 cases of China, South Korea and the United States, we numerically show the proposed stratification-based estimator plays a role of providing an early warning about the severity of a epidemiological disease that estimates the final case fatality rate accurately and shows faster convergence to the final case fatality rate.

Covariate-driven factorization by thresholding for multiblock data.

Multiblock data, where multiple groups of variables from different sources are observed for a common set of subjects, are routinely collected in many areas of science. Methods for joint factorization of such multiblock data are being developed to explore the potentially joint variation structure of the data. While most of the existing work focuses on delineating joint components, shared across all data blocks, from individual components, which is only relevant to a single data block, we propose to model and estimate partially joint components across some, but not all, data blocks. If covariates, with potential multiblock structures, are available, then the components are further modeled to be driven by the covariate information. To estimate such a covariate-driven, block-structured factor model, we propose an iterative algorithm based on thresholding, by transforming the problem of signal segmentation into a grouped variable selection problem. The proposed factorization provides accurate estimation of individual and (partially) joint structures in multiblock data, as confirmed by simulation studies. In the analysis of a real multiblock genomic dataset from the Cancer Genome Atlas project, we demonstrate that the estimated block structures provide straightforward interpretation and facilitate subsequent analyses.

Sparse generalized eigenvalue problem with application to canonical correlation analysis for integrative analysis of methylation and gene expression data.

We present a method for individual and integrative analysis of high dimension, low sample size data that capitalizes on the recurring theme in multivariate analysis of projecting higher dimensional data onto a few meaningful directions that are solutions to a generalized eigenvalue problem. We propose a general framework, called SELP (Sparse Estimation with Linear Programming), with which one can obtain a sparse estimate for a solution vector of a generalized eigenvalue problem. We demonstrate the utility of SELP on canonical correlation analysis for an integrative analysis of methylation and gene expression profiles from a breast cancer study, and we identify some genes known to be associated with breast carcinogenesis, which indicates that the proposed method is capable of generating biologically meaningful insights. Simulation studies suggest that the proposed method performs competitive in comparison with some existing methods in identifying true signals in various underlying covariance structures.

Incorporating covariates into integrated factor analysis of multi-view data.

In modern biomedical research, it is ubiquitous to have multiple data sets measured on the same set of samples from different views (i.e., multi-view data). For example, in genetic studies, multiple genomic data sets at different molecular levels or from different cell types are measured for a common set of individuals to investigate genetic regulation. Integration and reduction of multi-view data have the potential to leverage information in different data sets, and to reduce the magnitude and complexity of data for further statistical analysis and interpretation. In this article, we develop a novel statistical model, called supervised integrated factor analysis (SIFA), for integrative dimension reduction of multi-view data while incorporating auxiliary covariates. The model decomposes data into joint and individual factors, capturing the joint variation across multiple data sets and the individual variation specific to each set, respectively. Moreover, both joint and individual factors are partially informed by auxiliary covariates via nonparametric models. We devise a computationally efficient Expectation-Maximization (EM) algorithm to fit the model under some identifiability conditions. We apply the method to the Genotype-Tissue Expression (GTEx) data, and provide new insights into the variation decomposition of gene expression in multiple tissues. Extensive simulation studies and an additional application to a pediatric growth study demonstrate the advantage of the proposed method over competing methods.

Respiratory motion prediction and prospective correction for free-breathing arterial spin-labeled perfusion MRI of the kidneys.

PURPOSE: Respiratory motion prediction using an artificial neural network (ANN) was integrated with pseudocontinuous arterial spin labeling (pCASL) MRI to allow free-breathing perfusion measurements in the kidney. In this study, we evaluated the performance of the ANN to accurately predict the location of the kidneys during image acquisition. METHODS: A pencil-beam navigator was integrated with a pCASL sequence to measure lung/diaphragm motion during ANN training and the pCASL transit delay. The ANN algorithm ran concurrently in the background to predict organ location during the 0.7-s 15-slice acquisition based on the navigator data. The predictions were supplied to the pulse sequence to prospectively adjust the axial slice acquisition to match the predicted organ location. Additional navigators were acquired immediately after the multislice acquisition to assess the performance and accuracy of the ANN. The technique was tested in eight healthy volunteers. RESULTS: The root-mean-square error (RMSE) and mean absolute error (MAE) for the eight volunteers were 1.91 ± 0.17 mm and 1.43 ± 0.17 mm, respectively, for the ANN. The RMSE increased with transit delay. The MAE typically increased from the first to last prediction in the image acquisition. The overshoot was 23.58% ± 3.05% using the target prediction accuracy of ± 1 mm. CONCLUSION: Respiratory motion prediction with prospective motion correction was successfully demonstrated for free-breathing perfusion MRI of the kidney. The method serves as an alternative to multiple breathholds and requires minimal effort from the patient.

A statistical approach to set classification by feature selection with applications to classification of histopathology images.

Set classification problems arise when classification tasks are based on sets of observations as opposed to individual observations. In set classification, a classification rule is trained with N sets of observations, where each set is labeled with class information, and the prediction of a class label is performed also with a set of observations. Data sets for set classification appear, for example, in diagnostics of disease based on multiple cell nucleus images from a single tissue. Relevant statistical models for set classification are introduced, which motivate a set classification framework based on context-free feature extraction. By understanding a set of observations as an empirical distribution, we employ a data-driven method to choose those features which contain information on location and major variation. In particular, the method of principal component analysis is used to extract the features of major variation. Multidimensional scaling is used to represent features as vector-valued points on which conventional classifiers can be applied. The proposed set classification approaches achieve better classification results than competing methods in a number of simulated data examples. The benefits of our method are demonstrated in an analysis of histopathology images of cell nuclei related to liver cancer.

Analysis of principal nested spheres.

A general framework for a novel non-geodesic decomposition of high-dimensional spheres or high-dimensional shape spaces for planar landmarks is discussed. The decomposition, principal nested spheres, leads to a sequence of submanifolds with decreasing intrinsic dimensions, which can be interpreted as an analogue of principal component analysis. In a number of real datasets, an apparent one-dimensional mode of variation curving through more than one geodesic component is captured in the one-dimensional component of principal nested spheres. While analysis of principal nested spheres provides an intuitive and flexible decomposition of the high-dimensional sphere, an interesting special case of the analysis results in finding principal geodesics, similar to those from previous approaches to manifold principal component analysis. An adaptation of our method to Kendall's shape space is discussed, and a computational algorithm for fitting principal nested spheres is proposed. The result provides a coordinate system to visualize the data structure and an intuitive summary of principal modes of variation, as exemplified by several datasets.

Design, synthesis, and discovery of novel trans-stilbene analogues as potent and selective human cytochrome P450 1B1 inhibitors.

A series of trans-stilbene derivatives containing a 3,5-dimethoxyphenyl moiety were prepared through a new efficient solution phase synthetic pathway, and their inhibitory activities were evaluated on human cytochrome P450s (CYP) 1A1, 1A2, and 1B1 to find a potent and selective CYP1B1 inhibitor. We found that a substituent at the 2-position of the stilbene skeleton plays a very important role in discriminating between CYP1As and CYP1B1. Among the compounds tested, the most selective and potent CYP1B1 inhibitor was 2,3',4,5'-tetramethoxystilbene. Compound 7j, 2-[2-(3,5-dimethoxy-phenyl)vinyl]thiophene, showed greater inhibitory activities but had a lower selectivity toward all of the CYP1s tested.