Methods in molecular biology
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Duchenne muscular dystrophy (DMD) is a devastating X-linked muscle disorder affecting many children. The disease is caused by the lack of dystrophin production and characterized by muscle wasting. The most common causes of death are respiratory failure and heart failure. ⋯ Here, we present methodologies to systemically inject PMOs into humanized DMD model mice and determine levels of dystrophin restoration via Western blotting. Using a tris-acetate gradient SDS gel and semi-dry transfer with three buffers, including the Concentrated Anode Buffer, Anode Buffer, and Cathode Buffer, less than 1% normal levels of dystrophin expression are easily detectable. This method is fast, easy, and sensitive enough for the detection of dystrophin from both cultured muscle cells and muscle biopsy samples.
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The number of studies published in the biomedical literature has dramatically increased over the last few decades. This massive proliferation of literature makes clinical medicine increasingly complex, and information from multiple studies is often needed to inform a particular clinical decision. However, available studies often vary in their design, methodological quality, and population studied, and may define the research question of interest quite differently. ⋯ In addition, since even highly cited trials may be challenged over time, clinical decision-making requires ongoing reconciliation of studies which provide different answers to the same question. Because it is often impractical for readers to track down and review all the primary studies, systematic reviews and meta-analyses are an important source of evidence on the diagnosis, prognosis and treatment of any given disease. This chapter summarizes methods for conducting and reading systematic reviews and meta-analyses, as well as describes potential advantages and disadvantages of these publications.
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The intention-to-treat analysis is the gold standard for evaluating the efficacy in a randomized controlled trial. However, when non-adherence to randomized treatments is high the actual treatment effect may be underestimated. ⋯ These analyses may include censoring at the time of co-interventions associated with stopping treatment, lag censoring which allows an additional period after discontinuation of study treatment to account for residual treatment effects, inverse probability of censoring weights (IPCW), accelerated failure time models, and contamination adjusted intent-to-treat analysis. These methods are particularly useful in assessing the "prescribed efficacy" of the study treatment, which can aid clinical decision-making .
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DNA methylation is a transgenerational stable epigenetic modification able to regulate gene expression and genome stability. The analysis of DNA methylation by genome-wide bisulfite sequencing become the main genomic approach to study epigenetics in many organisms; leading to standardization of the alignment and methylation call procedures. ⋯ Therefore, in this chapter we propose a computational workflow for the analysis, visualization, and interpretation of data obtained from alignment of whole genome bisulfite sequencing of plant samples. Using almost exclusively the R working environment we will examine in depth how to tackle some plant-related issues during epigenetic analysis.
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With rapid advances in experimental instruments and protocols, imaging and sequencing data are being generated at an unprecedented rate contributing significantly to the current and coming big biomedical data. Meanwhile, unprecedented advances in computational infrastructure and analysis algorithms are realizing image-based digital diagnosis not only in radiology and cardiology but also oncology and other diseases. Machine learning methods, especially deep learning techniques, are already and broadly implemented in diverse technological and industrial sectors, but their applications in healthcare are just starting. ⋯ Moreover, the applications of genomics data are realizing the potential for personalized medicine, making diagnosis, treatment, monitoring, and prognosis more accurate. In this chapter, we discuss machine learning methods readily available for digital pathology applications, new prospects of integrating spatial genomics data on tissues with tissue morphology, and frontier approaches to combining genomics data with pathological imaging data. We present perspectives on how artificial intelligence can be synergized with molecular genomics and imaging to make breakthroughs in biomedical and translational research for computer-aided applications.