CONSUMPTION OF ANTIBIOTICS AS SELF-MEDICATION FROM OVER-THE-COUNTER PURCHASE: AN EMPIRICAL STUDY ARUNKUMAR

Objective: The primary objective of this study is to find the reasons behind the practice of self-medication (SM) by the people with over-the-counter (OTC) drugs which are usually available in all medical stores. Methods: This article presents an empirical view of SM practice with OTC drugs. The research design of the study is descriptive, and the population of the study is customers who buy OTC drugs. The target population of this research is the common public who are all having the possibility to consume OTC drugs ever. The sampling technique used for this study is a systematic random sampling, and the sample size is 144. An instrument used for collecting data is a self-administered questionnaire and personal interview with the pharmacists. The data were analyzed using descriptive statistics. Results: The study results that most of the OTC customers consider SM is not a good practice, even though they practice SM of antibiotics in certain circumstances, due to reasons such as time-saving, convenience, cost saving, avoid waiting time to consult a doctor, easy and quick availability of antibiotics in neighbourhood drug stores, etc. Conclusion: The study concludes the reasons behind the SM practice and some remedies to overcome OTC drug-related problems from SM.Â

Learning preferences for large scale multi-label problems

Despite that the majority of machine learning approaches aim to solve binary classification problems, several real-world applications require specialized algorithms able to handle many different classes, as in the case of single-label multi-class and multi-label classification problems. The Label Ranking framework is a generalization of the above mentioned settings, which aims to map instances from the input space to a total order over the set of possible labels. However, generally these algorithms are more complex than binary ones, and their application on large-scale datasets could be untractable. The main contribution of this work is the proposal of a novel general online preference-based label ranking framework. The proposed framework is able to solve binary, multi-class, multi-label and ranking problems. A comparison with other baselines has been performed, showing effectiveness and efficiency in a real-world large-scale multi-label task

Mining association rules for label ranking

Lecture Notes in Computer Science Volume 6635, 2011.Recently, a number of learning algorithms have been adapted for label ranking, including instance-based and tree-based methods. In this paper, we continue this line of work by proposing an adaptation of association rules for label ranking based on the APRIORI algorithm. Given that the original APRIORI algorithm does not aim to obtain predictive models, two changes were needed for this achievement. The adaptation essentially consists of using variations of the support and confidence measures based on ranking similarity functions that are suitable for label ranking. Additionally we propose a simple greedy method to select the parameters of the algorithm. We also adapt the method to make a prediction from the possibly con icting consequents of the rules that apply to an example. Despite having made our adaptation from a very simple variant of association rules for classification, partial results clearly show that the method is making valid predictions. Additionally, they show that it competes well with state-of-the-art label ranking algorithms.This work was partially supported by project Rank! (PTDC/EIA/81178/2006) from FCT and Palco AdI project Palco3.0 financed by QREN and Fundo Europeu de Desenvolvimento Regional (FEDER). We thank the anonymous referees for useful comments

Exceptional Preferences Mining

Exceptional Preferences Mining (EPM) is a crossover between two subfields of datamining: local pattern mining and preference learning. EPM can be seen as a local pattern mining task that finds subsets of observations where the preference relations between subsets of the labels significantly deviate from the norm; a variant of Subgroup Discovery, with rankings as the (complex) target concept. We employ three quality measures that highlight subgroups featuring exceptional preferences, where the focus of what constitutes 'exceptional' varies with the quality measure: the first gauges exceptional overall ranking behavior, the second indicates whether a particular label stands out from the rest, and the third highlights subgroups featuring unusual pairwise label ranking behavior. As proof of concept, we explore five datasets. The results confirm that the new task EPM can deliver interesting knowledge. The results also illustrate how the visualization of the preferences in a Preference Matrix can aid in interpreting exceptional preference subgroups

Pan-cancer analysis of whole genomes

Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale(1-3). Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4-5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter(4); identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation(5,6); analyses timings and patterns of tumour evolution(7); describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity(8,9); and evaluates a range of more-specialized features of cancer genomes(8,10-18).Peer reviewe

Inferring structural variant cancer cell fraction

Abstract: We present SVclone, a computational method for inferring the cancer cell fraction of structural variant (SV) breakpoints from whole-genome sequencing data. SVclone accurately determines the variant allele frequencies of both SV breakends, then simultaneously estimates the cancer cell fraction and SV copy number. We assess performance using in silico mixtures of real samples, at known proportions, created from two clonal metastases from the same patient. We find that SVclone’s performance is comparable to single-nucleotide variant-based methods, despite having an order of magnitude fewer data points. As part of the Pan-Cancer Analysis of Whole Genomes (PCAWG) consortium, which aggregated whole-genome sequencing data from 2658 cancers across 38 tumour types, we use SVclone to reveal a subset of liver, ovarian and pancreatic cancers with subclonally enriched copy-number neutral rearrangements that show decreased overall survival. SVclone enables improved characterisation of SV intra-tumour heterogeneity

Comprehensive analysis of chromothripsis in 2,658 human cancers using whole-genome sequencing

Chromothripsis is a mutational phenomenon characterized by massive, clustered genomic rearrangements that occurs in cancer and other diseases. Recent studies in selected cancer types have suggested that chromothripsis may be more common than initially inferred from low-resolution copy-number data. Here, as part of the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA), we analyze patterns of chromothripsis across 2,658 tumors from 38 cancer types using whole-genome sequencing data. We find that chromothripsis events are pervasive across cancers, with a frequency of more than 50% in several cancer types. Whereas canonical chromothripsis profiles display oscillations between two copy-number states, a considerable fraction of events involve multiple chromosomes and additional structural alterations. In addition to non-homologous end joining, we detect signatures of replication-associated processes and templated insertions. Chromothripsis contributes to oncogene amplification and to inactivation of genes such as mismatch-repair-related genes. These findings show that chromothripsis is a major process that drives genome evolution in human cancer

Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

Funder: NCI U24CA211006Abstract: The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts