Rakesh Aggarwal

Prediction of Essential Proteins in Prokaryotes by Incorporating Various Physico-chemical Features into the General form of Chou’s Pseudo Amino Acid Composition

Prediction of essential proteins of a pathogenic organism is the key for the potential drug target identification, because inhibition of these would be fatal for the pathogen. Identification of these proteins requires the use of complex experimental techniques which are quite expensive and time consuming. We implemented Support Vector Machine algorithm to develop a classifier model for in silico prediction of prokaryotic essential proteins based on the physico-chemical properties of the amino acid sequences. This classifier was designed based on a set of 10 physico-chemical descriptor vectors (DVs) and 4 hybrid DVs calculated from amino acid sequences using PROFEAT and PseAAC servers. The classifier was trained using data sets consisting of 500 known essential and 500 non-essential proteins (n=1,000) and evaluated using an external validation set consisting of 3,462 essential proteins and 5,538 non-essential proteins (n=9,000). The performances of individual DV sets were evaluated. DV set 13, which is the combination of composition, transition and distribution descriptor set and hybrid autocorrelation descriptor set, provided accuracy of 91.2% in 10-fold cross-validation of the training set and an accuracy of 89.7% in external validation set and of 91.8% and 88.1% using a different yeast protein dataset. Our result indicates that this classification model can be used for identification of novel prokaryotic essential proteins.

Recurrent duodenal ulcer haemorrhage: a pharmacoeconomic comparison of various management strategies

Background: Duodenal ulcer (DU) bleeding has a 7 – 13% mortality rate and bleeding often recurs. Prevention of recurrence is, therefore, an important goal. Eradication of Helicobacter pylori or maintenance treatment with a proton pump inhibitor (PPI) may reduce recurrent DU bleeding. Economic comparison of these options is sparse.Methods: After the control of index bleeding with endotherapy and drugs, three strategies were evaluated: empirical treatment for possible H. pylori infection followed by a PPI for 2 months; test for H. pylori, eradication if positive, maintenance PPI if negative; maintenance PPI alone. Probability and direct cost data were obtained from a Medline search and Indian hospitals, respectively. Cost-minimisation, cost-utility, one- and two-way sensitivity analyses and threshold values were evaluated.Results: Treatment of H. pylori, particularly empirical, was the preferred strategy and dominated maintenance treatment with PPI. The test-and-treat strategy was better than the empirical treatment strategy only when the probabilities of H. pylori eradication, ulcer healing following eradication and of frequency of H. pylori infection in bleeding DU were less than 58, 73 and 58%, respectively.Conclusions: Eradication of H. pylori is preferred in preventing recurrent bleeding from DU.

Autoimmune polyendocrine syndrome type 1 in an Indian cohort: a longitudinal study

Objective Autoimmune polyendocrine syndrome type 1 (APS1) is a rare autosomal recessive disorder characterized by progressive organ-specific autoimmunity. There is scant information on APS1 in ethnic groups other than European Caucasians. We studied clinical aspects and autoimmune regulator (AIRE) gene mutations in a cohort of Indian APS1 patients. Design Twenty-three patients (19 families) from six referral centres in India, diagnosed between 1996 and 2016, were followed for [median (range)] 4 (0.2–19) years. Methods Clinical features, mortality, organ-specific autoantibodies and AIRE gene mutations were studied. Results Patients varied widely in their age of presentation [3.5 (0.1–17) years] and number of clinical manifestations [5 (2–11)]. Despite genetic heterogeneity, the frequencies of the major APS1 components (mucocutaneous candidiasis: 96%; hypoparathyroidism: 91%; primary adrenal insufficiency: 55%) were similar to reports in European series. In contrast, primary hypothyroidism (23%) occurred more frequently and at an early age, while kerato-conjunctivitis, urticarial rash and autoimmune hepatitis were uncommon (9% each). Six (26%) patients died at a young age [5.8 (3–23) years] due to septicaemia, hepatic failure and adrenal/hypocalcaemic crisis from non-compliance/unexplained cause. Interferon-α and/or interleukin-22 antibodies were elevated in all 19 patients tested, including an asymptomatic infant. Eleven AIRE mutations were detected, the most common being p.C322fsX372 (haplotype frequency 37%). Four mutations were novel, while six others were previously described in European Caucasians. Conclusions Indian APS1 patients exhibited considerable genetic heterogeneity and had highly variable clinical features. While the frequency of major manifestations was similar to that of European Caucasians, other features showed significant differences. A high mortality at a young age was observed.

Common pitfalls in statistical analysis: Absolute risk reduction, relative risk reduction, and number needed to treat.

In the previous article in this series on common pitfalls in statistical analysis, we looked at the difference between risk and odds. Risk, which refers to the probability of occurrence of an event or outcome, can be defined in absolute or relative terms. Understanding what these measures represent is essential for the accurate interpretation of study results.

Common pitfalls in statistical analysis: The use of correlation techniques

Correlation is a statistical technique which shows whether and how strongly two continuous variables are related. In this article, which is the eighth part in a series on ′Common pitfalls in Statistical Analysis′, we look at the interpretation of the correlation coefficient and examine various situations in which the use of technique of correlation may be inappropriate.

Common pitfalls in statistical analysis: Linear regression analysis

In a previous article in this series, we explained correlation analysis which describes the strength of relationship between two continuous variables. In this article, we deal with linear regression analysis which predicts the value of one continuous variable from another. We also discuss the assumptions and pitfalls associated with this analysis.

Common pitfalls in statistical analysis: Measures of agreement

Agreement between measurements refers to the degree of concordance between two (or more) sets of measurements. Statistical methods to test agreement are used to assess inter-rater variability or to decide whether one technique for measuring a variable can substitute another. In this article, we look at statistical measures of agreement for different types of data and discuss the differences between these and those for assessing correlation.

Common pitfalls in statistical analysis: Odds versus risk.

In biomedical research, we are often interested in quantifying the relationship between an exposure and an outcome. “Odds” and “Risk” are the most common terms which are used as measures of association between variables. In this article, which is the fourth in the series of common pitfalls in statistical analysis, we explain the meaning of risk and odds and the difference between the two.