Oral Cannabidiol for Seborrheic Dermatitis in Patients With Parkinson Disease: Randomized Clinical Trial

BACKGROUND

Seborrheic dermatitis (SD) affects 18.6%-59% of persons with Parkinson disease (PD), and recent studies provide evidence that oral cannabidiol (CBD) therapy could reduce sebum production in addition to improving motor and psychiatric symptoms in PD. Therefore, oral CBD could be useful for improving symptoms of both commonly co-occurring conditions.

OBJECTIVE

This study investigates whether oral CBD therapy is associated with a decrease in SD severity in PD.

METHODS

Facial photographs were collected as a component of a randomized (1:1 CBD vs placebo), parallel, double-blind, placebo-controlled trial assessing the efficacy of a short-term 2.5 mg per kg per day oral sesame solution CBD-rich cannabis extract (formulated to 100 mg/mL CBD and 3.3 mg/mL THC) for reducing motor symptoms in PD. Participants took 1.25 mg per kg per day each morning for 4 ±1 days and then twice daily for 10 ±4 days. Reviewers analyzed the photographs independently and provided a severity ranking based on the Seborrheic Dermatitis Area and Severity Index (SEDASI) scale. Baseline demographic and disease characteristics, as well as posttreatment SEDASI averages and the presence of SD, were analyzed with 2-tailed t tests and Pearson χ2 tests. SEDASI was analyzed with longitudinal regression, and SD was analyzed with generalized estimating equations.

RESULTS

A total of 27 participants received a placebo and 26 received CBD for 16 days. SD severity was low in both groups at baseline, and there was no treatment effect. The risk ratio for patients receiving CBD, post versus pre, was 0.69 (95% CI 0.41-1.18; P=.15), compared to 1.20 (95% CI 0.88-1.65; P=.26) for the patients receiving the placebo. The within-group pre-post change was not statistically significant for either group, but they differed from each other (P=.07) because there was an estimated improvement for the CBD group and an estimated worsening for the placebo group.

CONCLUSIONS

This study does not provide solid evidence that oral CBD therapy reduces the presence of SD among patients with PD. While this study was sufficiently powered to detect the primary outcome (efficacy of CBD on PD motor symptoms), it was underpowered for the secondary outcomes of detecting changes in the presence and severity of SD. Multiple mechanisms exist through which CBD can exert beneficial effects on SD pathogenesis. Larger studies, including participants with increased disease severity and longer treatment periods, may better elucidate treatment effects and are needed to determine CBD's true efficacy for affecting SD severity.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03582137; https://clinicaltrials.gov/ct2/show/NCT03582137.

Predicting the Severity of Adverse Events on Osteoporosis Drugs Using Attribute Weighted Logistic Regression

Osteoporosis is a serious bone disease that affects many people worldwide. Various drugs have been used to treat osteoporosis. However, these drugs may cause severe adverse events in patients. Adverse drug events are harmful reactions caused by drug usage and remain one of the leading causes of death in many countries. Predicting serious adverse drug reactions in the early stages can help save patients' lives and reduce healthcare costs. Classification methods are commonly used to predict the severity of adverse events. These methods usually assume independence among attributes, which may not be practical in real-world applications. In this paper, a new attribute weighted logistic regression is proposed to predict the severity of adverse drug events. Our method relaxes the assumption of independence among the attributes. An evaluation was performed on osteoporosis data obtained from the United States Food and Drug Administration databases. The results showed that our method achieved a higher recognition performance and outperformed baseline methods in predicting the severity of adverse drug events.

Genomic Scan of Male Fertility Restoration Genes in a 'Gülzow' Type Hybrid Breeding System of Rye (Secale cereale L.)

Efficient and stable restoration of male fertility (Rf) is a prerequisite for large-scale hybrid seed production but remains an inherent issue in the predominant fertility control system of rye (Secale cereale L.). The ‘Gülzow’ (G)-type cytoplasmic male sterility (CMS) system in hybrid rye breeding exhibits a superior Rf. While having received little scientific attention, one major G-type Rf gene has been identified on 4RL (Rfg1) and two minor genes on 3R (Rfg2) and 6R (Rfg3) chromosomes. Here, we report a comprehensive investigation of the genetics underlying restoration of male fertility in a large G-type CMS breeding system using recent advents in rye genomic resources. This includes: (I) genome-wide association studies (GWAS) on G-type germplasm; (II) GWAS on a biparental mapping population; and (III) an RNA sequence study to investigate the expression of genes residing in Rf-associated regions in G-type rye hybrids. Our findings provide compelling evidence of a novel major G-type non-PPR Rf gene on the 3RL chromosome belonging to the mitochondrial transcription termination factor gene family. We provisionally denote the identified novel Rf gene on 3RL RfNOS1. The discovery made in this study is distinct from known P- and C-type systems in rye as well as recognized CMS systems in barley (Hordeum vulgare L.) and wheat (Triticum aestivum L.). We believe this study constitutes a stepping stone towards understanding the restoration of male fertility in the G-type CMS system and potential resources for addressing the inherent issues of the P-type system.

Introduction to Research Statistical Analysis: An Overview of the Basics

Description This article covers many statistical ideas essential to research statistical analysis. Sample size is explained through the concepts of statistical significance level and power. Variable types and definitions are included to clarify necessities for how the analysis will be interpreted. Categorical and quantitative variable types are defined, as well as response and predictor variables. Statistical tests described include t-tests, ANOVA and chi-square tests. Multiple regression is also explored for both logistic and linear regression. Finally, the most common statistics produced by these methods are explored.

A test of homogeneity of distributions when observations are subject to measurement errors

When the observed data are contaminated with errors, the standard two-sample testing approaches that ignore measurement errors may produce misleading results, including a higher type-I error rate than the nominal level. To tackle this inconsistency, a nonparametric test is proposed for testing equality of two distributions when the observed contaminated data follow the classical additive measurement error model. The proposed test takes into account the presence of errors in the observed data, and the test statistic is defined in terms of the (deconvoluted) characteristic functions of the latent variables. Proposed method is applicable to a wide range of scenarios as no parametric restrictions are imposed either on the distribution of the underlying latent variables or on the distribution of the measurement errors. Asymptotic null distribution of the test statistic is derived, which is given by an integral of a squared Gaussian process with a complicated covariance structure. For data-based calibration of the test, a new nonparametric Bootstrap method is developed under the two-sample measurement error framework and its validity is established. Finite sample performance of the proposed test is investigated through simulation studies, and the results show superior performance of the proposed method than the standard tests that exhibit inconsistent behavior. Finally, the proposed method was applied to real data sets from the National Health and Nutrition Examination Survey. An R package MEtest is available through CRAN.

Do MCAT scores predict USMLE scores? An analysis on 5 years of medical student data

INTRODUCTION

The purpose of this study was to determine the associations and predictive values of Medical College Admission Test (MCAT) component and composite scores prior to 2015 with U.S. Medical Licensure Exam (USMLE) Step 1 and Step 2 Clinical Knowledge (CK) scores, with a focus on whether students scoring low on the MCAT were particularly likely to continue to score low on the USMLE exams.

METHOD

Multiple linear regression, correlation, and chi-square analyses were performed to determine the relationship between MCAT component and composite scores and USMLE Step 1 and Step 2 CK scores from five graduating classes (2011-2015) at the University of Minnesota Medical School (N=1,065).

RESULTS

The multiple linear regression analyses were both significant (p<0.001). The three MCAT component scores together explained 17.7% of the variance in Step 1 scores (p<0.001) and 12.0% of the variance in Step 2 CK scores (p<0.001). In the chi-square analyses, significant, albeit weak associations were observed between almost all MCAT component scores and USMLE scores (Cramer's V ranged from 0.05 to 0.24).

DISCUSSION

Each of the MCAT component scores was significantly associated with USMLE Step 1 and Step 2 CK scores, although the effect size was small. Being in the top or bottom scoring range of the MCAT exam was predictive of being in the top or bottom scoring range of the USMLE exams, although the strengths of the associations were weak to moderate. These results indicate that MCAT scores are predictive of student performance on the USMLE exams, but, given the small effect sizes, should be considered as part of the holistic view of the student.

[Clinical research XVII. χ(2) test, from the expected to the observed]

When you want to show if there is a statistical association or differences between categorical variables, it is recommended to use the χ(2) test. This nonparametric test is one of the most used in clinical research; it contrasts nominal or ordinal qualitative variables that are observed in clinical practice. This test calculates the p value that determines whether differences between groups are real or due to chance. The χ(2) test is the basis of other tests to analyze qualitative ordinal variables as χ(2) for linear trend, which compares three groups with two outcomes or McNemar test, which contrasts two related samples (a before and afterward comparison) or Mantel-Haenszel χ(2), which controls for potential confounding variables. When using small samples, where the expected results are less than 5, Fisher's exact test should be used. These tests are the most widely used in the medical literature; however, they do not give us the magnitude or the direction of the event and a proper interpretation that requires clinical judgment is needed.

Statistical recognition of random and regular phyllotactic patterns

AIMS

A statistical method used in ecology is adapted to characterize the degree of order in phyllotactic systems.

SCOPE

The test consists of subdividing a planar projection of the stem apical meristem into 16 sectors and counting the number of primordia appearing in each. By dividing the sum of squared deviations by the mean number of primordia per sector the chi-square (chi2) is obtained. When there are a total number of 20 primordia, if the chi2 is less than 6.26, the phyllotaxis is spiral; if it is between 6.26 and 27.5 the phyllotaxis is random; and if it is greater than 27.5, the phyllotaxis is distichous or whorled (level of significance alpha = 5 %). It is also possible to remove one or more sectors. If there are k sectors, the two critical values delimiting the random zone will be found in a chi2 table for k - 1 degrees of freedom.

CONCLUSIONS

The method is applied to the analysis of sho mutants described by Itoh et al. in 2000 (Plant Cell 12: 2161-2174). The results obtained are in agreement with the theoretical analysis showing that a whorled or spiral phyllotactic system may contain a certain number of randomly distributed elements without losing its regular global structure.