Hajizadeh, N. | Search bioRxiv

bioRxiv2018

Bayesian adjustment for trend of colorectal cancer incidence in misclassified registering across Iranian provinces

One of the problems in cancer registry of developing countries is misclassification error. This error leads to overestimation and underestimation of cancer rate in different provinces. The aim of this study is to use Bayesian method to correct for misclassification in registering cancer incidence in neighboring provinces of Iran. Incidence data of colorectal cancer were extracted from Iranian annual of national cancer registration reports 2005 to 2008 And Eighteen of the thirty Iranian provinces were selected to enter the Bayesian model and to correct their misclassification. Always a province with appropriate medical facilities is comparable to its neighbor or neighbors. Between years of 2005 and 2008, on the average, 28% misclassification was estimated between the province of East Azarbaijan and West Azarbayjan, 56% between the province of Fars and Hormozgan, 43% between the province of Isfahan and Charmahal and Bakhtyari, 46% between the province of Isfahan and Lorestan, 58% between the province of Razavi Khorasan and North Khorasan, 50% between the province of Razavi Khorasan and South Khorasan, 74% between the province of Razavi Khorasan and Sistan and Balochestan, 43% between the province of Mazandaran and Golestan, 37% between the province of Tehran and Qazvin, 45% between the province of Tehran and Markazi, 42% between the province of Tehran and Qom, 47% between the province of Tehran and Zanjan. Correcting the regional misclassification and obtaining the correct rates of cancer incidence in different regions is necessary for making cancer control and prevention programs and in healthcare resource allocation.

epidemiology↗

bioRxiv2018

Rapid Therapeutic Recommendations in the Context of a Global Public Health Crisis using Translational Bioinformatics Approaches: A proof-of-concept study using Nipah Virus Infection

We live in a world of emerging new diseases and old diseases resurging in more aggressive forms. Drug development by pharmaceutical companies is a market-driven and costly endeavor, and thus it is often a challenge when drugs are needed for diseases endemic only to certain regions or which affect only a few patients. However, biomedical open data is accessible and reusable for reanalysis and generation of a new hypotheses and discovery. In this study, we leverage biomedical data and tools to analyze available data on Nipah Virus (NiV) infection. NiV infection is an emerging zoonosis that is transmissible to humans and is associated with high mortality rates. In this study, explored the application of computational drug repositioning and chemogenomic enrichment analyses using host transcriptome data to match drugs that could reverse the virus-induced gene signature. We performed analyses using two gene signatures: i) A previously published gene signature (n=34), and ii) a gene signature generated using the characteristic direction method (n= 5,533). Our predictive framework suggests that several drugs including FDA approved therapies like beclometasone, trihexyphenidyl, S-propranolol etc. could modulate the NiV infection induced gene signatures in endothelial cells. A target specific analysis of CXCL10 also suggests the potential application of Eldelumab, an investigative therapy for Crohns disease and ulcerative colitis, as a putative candidate for drug repositioning. To conclude, we also discuss challenges and opportunities in clinical trials (n-of-1 and adaptive trials) for repositioned drugs. Further follow-up studies including biochemical assays and clinical trials are required to identify effective therapies for clinical use. Our proof-of-concept study highlights that translational bioinformatics methods including gene expression analyses and computational drug repositioning could augment epidemiological investigations in the context of an emerging disease with no effective treatment.

microbiology↗