Indian Journal of Dermatology 

About: Indian Journal of Dermatology is an academic journal published by Medknow. The journal publishes majorly in the area(s): Medicine & Dermatology. It has an ISSN identifier of 0019-5154. It is also open access. Over the lifetime, 3621 publications have been published receiving 30365 citations.

Aloe vera: a short review

Abstract: Aloe vera is a natural product that is now a day frequently used in the field of cosmetology. Though there are various indications for its use, controlled trials are needed to determine its real efficacy. The aloe vera plant, its properties, mechanism of action and clinical uses are briefly reviewed in this article.

Methodology Series Module 3: Cross-sectional Studies.

Abstract: Cross-sectional study design is a type of observational study design. In a cross-sectional study, the investigator measures the outcome and the exposures in the study participants at the same time. Unlike in case-control studies (participants selected based on the outcome status) or cohort studies (participants selected based on the exposure status), the participants in a cross-sectional study are just selected based on the inclusion and exclusion criteria set for the study. Once the participants have been selected for the study, the investigator follows the study to assess the exposure and the outcomes. Cross-sectional designs are used for population-based surveys and to assess the prevalence of diseases in clinic-based samples. These studies can usually be conducted relatively faster and are inexpensive. They may be conducted either before planning a cohort study or a baseline in a cohort study. These types of designs will give us information about the prevalence of outcomes or exposures; this information will be useful for designing the cohort study. However, since this is a 1-time measurement of exposure and outcome, it is difficult to derive causal relationships from cross-sectional analysis. We can estimate the prevalence of disease in cross-sectional studies. Furthermore, we will also be able to estimate the odds ratios to study the association between exposure and the outcomes in this design.

Oral lichen planus: An update on etiology, pathogenesis, clinical presentation, diagnosis and management

Abstract: The mouth is a mirror of health or disease, a sentinel or early warning system. The oral cavity might well be thought as a window to the body because oral manifestations accompany many systemic diseases. In many instances, oral involvement precedes the appearance of other symptoms or lesions at other locations. Oral lichen planus (OLP) is a chronic mucocutaneous disorder of stratified squamous epithelium of uncertain etiology that affects oral and genital mucous membranes, skin, nails, and scalp. LP is estimated to affect 0.5% to 2.0% of the general population. This disease has most often been reported in middle-aged patients with 30-60 years of age and is more common in females than in males. The disease seems to be mediated by an antigen-specific mechanism, activating cytotoxic T cells, and non-specific mechanisms like mast cell degranulation and matrix metalloproteinase activation. A proper understanding of the pathogenesis, clinical presentation, diagnosis of the disease becomes important for providing the right treatment. This article discusses the prevalence, etiology, clinical features, oral manifestations, diagnosis, complications and treatment of oral LP.

The Great Indian Epidemic of Superficial Dermatophytosis: An Appraisal.

Abstract: We would like to admit that if we were purists, it would prove to be a difficult task to choose between the terms “epidemic” and “hyperendemic” to describe the current alarming situation of increased incidence as well as the prevalence of superficial dermatophytosis in India. For both terms, it would be essential to have comparative epidemiological data of the past and the present, and sadly, we are lacking in both. There is a dire need for well-designed studies as well as more solid evidence for various issues pertaining to the dermatophytosis scenario in India.[1]

Biostatistics series module 3: Comparing groups: Numerical variables

Abstract: Numerical data that are normally distributed can be analyzed with parametric tests, that is, tests which are based on the parameters that define a normal distribution curve. If the distribution is uncertain, the data can be plotted as a normal probability plot and visually inspected, or tested for normality using one of a number of goodness of fit tests, such as the Kolmogorov-Smirnov test. The widely used Student's t-test has three variants. The one-sample t-test is used to assess if a sample mean (as an estimate of the population mean) differs significantly from a given population mean. The means of two independent samples may be compared for a statistically significant difference by the unpaired or independent samples t-test. If the data sets are related in some way, their means may be compared by the paired or dependent samples t-test. The t-test should not be used to compare the means of more than two groups. Although it is possible to compare groups in pairs, when there are more than two groups, this will increase the probability of a Type I error. The one-way analysis of variance (ANOVA) is employed to compare the means of three or more independent data sets that are normally distributed. Multiple measurements from the same set of subjects cannot be treated as separate, unrelated data sets. Comparison of means in such a situation requires repeated measures ANOVA. It is to be noted that while a multiple group comparison test such as ANOVA can point to a significant difference, it does not identify exactly between which two groups the difference lies. To do this, multiple group comparison needs to be followed up by an appropriate post hoc test. An example is the Tukey's honestly significant difference test following ANOVA. If the assumptions for parametric tests are not met, there are nonparametric alternatives for comparing data sets. These include Mann-Whitney U-test as the nonparametric counterpart of the unpaired Student's t-test, Wilcoxon signed-rank test as the counterpart of the paired Student's t-test, Kruskal-Wallis test as the nonparametric equivalent of ANOVA and the Friedman's test as the counterpart of repeated measures ANOVA.