Physician network position and patient outcomes following implantable cardioverter defibrillator therapy

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/150523/1/hesr13151.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/150523/2/hesr13151_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/150523/3/hesr13151-sup-0001-AuthorMatrix.pd

Analyzing Clustered Data: Why and How to Account for Multiple Observations Nested within a Study Participant?

A conventional study design among medical and biological experimentalists involves col- lecting multiple measurements from a study subject. For example, experiments utilizing mouse models in neuroscience often involve collecting multiple neuron measurements per mouse to increase the number of observations without requiring a large number of mice. This leads to a form of statistical dependence referred to as clustering. Inappropriate analy- ses of clustered data have resulted in several recent critiques of neuroscience research that suggest the bar for statistical analyses within the field is set too low. We compare naïve ana- lytical approaches to marginal, fixed-effect, and mixed-effect models and provide guidelines for when each of these models is most appropriate based on study design. We demonstrate the influence of clustering on a between-mouse treatment effect, a within-mouse treatment effect, and an interaction effect between the two. Our analyses demonstrate that these sta- tistical approaches can give substantially different results, primarily when the analyses include a between-mouse treatment effect. In a novel analysis from a neuroscience per- spective, we also refine the mixed-effect approach through the inclusion of an aggregate mouse-level counterpart to a within-mouse (neuron level) treatment as an additional predic- tor by adapting an advanced modeling technique that has been used in social science research and show that this yields more informative results. Based on these findings, we emphasize the importance of appropriate analyses of clustered data, and we aim for this work to serve as a resource for when one is deciding which approach will work best for a given study

Modeling peer effect modification by network strength: The diffusion of implantable cardioverter defibrillators in the US hospital network

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154422/1/sim8466.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154422/2/sim8466_am.pd

PEGylation of Polyethylenimine Lowers Acute Toxicity while Retaining Anti-Biofilm and β-Lactam Potentiation Properties against Antibiotic-Resistant Pathogens

Bacterial biofilms, often impenetrable to antibiotic medications, are a leading cause of poor wound healing. The prognosis is worse for wounds with biofilms of antimicrobial-resistant (AMR) bacteria, such as methicillin-resistant Staphylococcus aureus (MRSA), methicillin-resistant S. epidermidis (MRSE), and multi-drug resistant Pseudomonas aeruginosa (MDR-PA). Resistance hinders initial treatment of standard-of-care antibiotics. The persistence of MRSA, MRSE, and/or MDR-PA often allows acute infections to become chronic wound infections. The water-soluble hydrophilic properties of low-molecular-weight (600 Da) branched polyethylenimine (600 Da BPEI) enable easy drug delivery to directly attack AMR and biofilms in the wound environment as a topical agent for wound treatment. To mitigate toxicity issues, we have modified 600 Da BPEI with polyethylene glycol (PEG) in a straightforward one-step reaction. The PEG–BPEI molecules disable β-lactam resistance in MRSA, MRSE, and MDR-PA while also having the ability to dissolve established biofilms. PEG-BPEI accomplishes these tasks independently, resulting in a multifunction potentiation agent. We envision wound treatment with antibiotics given topically, orally, or intravenously in which external application of PEG–BPEIs disables biofilms and resistance mechanisms. In the absence of a robust pipeline of new drugs, existing drugs and regimens must be re-evaluated as combination(s) with potentiators. The PEGylation of 600 Da BPEI provides new opportunities to meet this goal with a single compound whose multifunction properties are retained while lowering acute toxicity.We want to thank Dr. Phil Bourne and acknowledge the use of the Protein Production Core (PPC) at the University of Oklahoma, Norman. PPC is supported by an Institutional Development Award (IDeA) from the National Institute of General Medical Sciences of the National Institutes of Health under grant number P20GM103640. Open Access fees paid for in whole or in part by the University of Oklahoma Libraries.Ye

Regulation of RKIP Function by Helicobacter pylori in Gastric Cancer

Helicobacter pylori (H. pylori) is a gram-negative, spiral-shaped bacterium that infects more than half of the world’s population and is a major cause of gastric adenocarcinoma. The mechanisms that link H. pylori infection to gastric carcinogenesis are not well understood. In the present study, we report that the Raf-kinase inhibitor protein (RKIP) has a role in the induction of apoptosis by H. pylori in gastric epithelial cells. Western blot and luciferase transcription reporter assays demonstrate that the pathogenicity island of H. pylori rapidly phosphorylates RKIP, which then localizes to the nucleus where it activates its own transcription and induces apoptosis. Forced overexpression of RKIP enhances apoptosis in H. pylori-infected cells, whereas RKIP RNA inhibition suppresses the induction of apoptosis by H. pylori infection. While inducing the phosphorylation of RKIP, H. pylori simultaneously targets non-phosphorylated RKIP for proteasome-mediated degradation. The increase in RKIP transcription and phosphorylation is abrogated by mutating RKIP serine 153 to valine, demonstrating that regulation of RKIP activity by H. pylori is dependent upon RKIP’s S153 residue. In addition, H. pylori infection increases the expression of Snail, a transcriptional repressor of RKIP. Our results suggest that H. pylori utilizes a tumor suppressor protein, RKIP, to promote apoptosis in gastric cancer cells

Pharmacogenomics of chemotherapeutic susceptibility and toxicity

The goal of personalized medicine is to tailor a patient's treatment strategy on the basis of his or her unique genetic make-up. The field of oncology is beginning to incorporate many of the strategies of personalized medicine, especially within the realm of pharmacogenomics, which is the study of how inter-individual genetic variation determines drug response or toxicity. A main objective of pharmacogenomics is to facilitate physician decision-making regarding optimal drug selection, dose and treatment duration on a patient-by-patient basis. Recent advances in genome-wide genotyping and sequencing technologies have supported the discoveries of a number of pharmacogenetic markers that predict response to chemotherapy. However, effectively implementing these pharmacogenetic markers in the clinic remains a major challenge. This review focuses on the contribution of germline genetic variation to chemotherapeutic toxicity and response, and discusses the utility of genome-wide association studies and use of lymphoblastoid cell lines (LCLs) in pharmacogenomic studies. Furthermore, we highlight several recent examples of genetic variants associated with chemotherapeutic toxicity or response in both patient cohorts and LCLs, and discuss the challenges and future directions of pharmacogenomic discovery for cancer treatment

Alterations of 5-Hydroxymethylcytosine in Human Cancers

Prior to 2009, 5-methylcytosine (5-mC) was thought to be the only biologically significant cytosine modification in mammalian DNA. With the discovery of the TET enzymes, which convert 5-methylcytosine (5-mC) to 5-hydroxymethylcytosine (5-hmC), however, intense interest has emerged in determining the biological function of 5-hmC. Here, we review the techniques used to study 5-hmC and evidence that alterations to 5-hmC physiology play a functional role in the molecular pathogenesis of human cancers

Analyzing Clustered Data: Why and How to Account for Multiple Observations Nested within a Study Participant?

A conventional study design among medical and biological experimentalists involves collecting multiple measurements from a study subject. For example, experiments utilizing mouse models in neuroscience often involve collecting multiple neuron measurements per mouse to increase the number of observations without requiring a large number of mice. This leads to a form of statistical dependence referred to as clustering. Inappropriate analyses of clustered data have resulted in several recent critiques of neuroscience research that suggest the bar for statistical analyses within the field is set too low. We compare naïve analytical approaches to marginal, fixed-effect, and mixed-effect models and provide guidelines for when each of these models is most appropriate based on study design. We demonstrate the influence of clustering on a between-mouse treatment effect, a within-mouse treatment effect, and an interaction effect between the two. Our analyses demonstrate that these statistical approaches can give substantially different results, primarily when the analyses include a between-mouse treatment effect. In a novel analysis from a neuroscience perspective, we also refine the mixed-effect approach through the inclusion of an aggregate mouse-level counterpart to a within-mouse (neuron level) treatment as an additional predictor by adapting an advanced modeling technique that has been used in social science research and show that this yields more informative results. Based on these findings, we emphasize the importance of appropriate analyses of clustered data, and we aim for this work to serve as a resource for when one is deciding which approach will work best for a given study