A co-occurrence framework conceptualized for bridging the gap between basic science, clinical research and clinical practices

The intellectual impulsiveness of man to understand the unknown and the continual need of the society to improve healthcare have encouraged extensive investigation on numerous and diverse cause-and-effect relationships. The nature of this endeavor, however, renders the inability of investigator at all levels to escape beyond the narrow conceptual boundary described by an early French philosopher as the vicious cycle. To enjoy the theoretically plausible benefits of refined labor division, data-driven healthcare management, and real-time evidence-based practices, it must first be acknowledged that co-occurrence is better than cause-and-effect in explaining how an observation takes place at a particular time. This paper details a co-occurrence framework, and discusses its implications for the global healthcare system

Detecting mutations in mixed sample sequencing data using empirical Bayes

We develop statistically based methods to detect single nucleotide DNA mutations in next generation sequencing data. Sequencing generates counts of the number of times each base was observed at hundreds of thousands to billions of genome positions in each sample. Using these counts to detect mutations is challenging because mutations may have very low prevalence and sequencing error rates vary dramatically by genome position. The discreteness of sequencing data also creates a difficult multiple testing problem: current false discovery rate methods are designed for continuous data, and work poorly, if at all, on discrete data. We show that a simple randomization technique lets us use continuous false discovery rate methods on discrete data. Our approach is a useful way to estimate false discovery rates for any collection of discrete test statistics, and is hence not limited to sequencing data. We then use an empirical Bayes model to capture different sources of variation in sequencing error rates. The resulting method outperforms existing detection approaches on example data sets.Comment: Published in at http://dx.doi.org/10.1214/12-AOAS538 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Therapeutic evaluation of homeopathic treatment for canine oral papillomatosis

Aim: A study was conducted to evaluate the ameliorative potential of homeopathic drugs in combination (Sulfur 30C, Thuja 30C, Graphites 30C, and Psorinum 30C) in 16 dogs affected with oral papillomatosis which was not undergone any previous treatment. Materials and Methods: Dogs affected with oral papillomatosis, which have not undergone any initial treatment and fed with a regular diet. Dogs (total=16) were randomly divided into two groups, namely, homeopathic treatment group (n=8) and placebo control group (n=8). Random number table was used for allocation. Homeopathic combination of drugs and placebo drug (distilled water) was administered orally twice daily for 15 days. Clinical evaluation in both groups of dogs was performed by the same investigator throughout the period of study (12 months). Dogs were clinically scored for oral lesions on days 0, 5, 7, 10, 15, 20, 25, 30, 45, 60, 90, 120, and 150 after initiation of treatment. Results: The homeopathic treatment group showed early recovery with a significant reduction in oral lesions reflected by clinical score (p<0.001) in comparison to placebo-treated group. Oral papillomatous lesions regressed in the homeopathic group between 7 and 15 days, whereas regression of papilloma in the placebo group occurred between 90 and 150 days. The homeopathic treated group was observed for 12 months post-treatment period and no recurrence of oral papilloma was observed. Conclusion: The current study proves that the combination of homeopathy drugs aids in fastening the regression of canine oral papilloma and proved to be safe and cost-effective