Predictors of low urinary quality of life in spinal cord injury patients on clean intermittent catheterization

ObjectiveClean intermittent catheterization (CIC) is a preferred method of bladder management for many patients with spinal cord injury (SCI), but long‐term adherence is low. The aim of this study is to identify factors associated with low urinary quality of life (QoL) in SCI adults performing CIC.MethodsOver 1.5 years, 1479 adults with SCI were prospectively enrolled through the Neurogenic Bladder Research Group registry, and 753 on CIC with no prior surgeries were included. Injury characteristics, complications, hand function, and Neurogenic Bladder Symptom Score (NBSS) were analyzed. The NBSS QoL question (overall satisfaction with urinary function) was dichotomized to generate comparative groups (dissatisfied vs neutral/satisfied).ResultsThe cohort was 32.9% female with a median age of 43.2 (18‐86) years, time since the injury of 9.8 (0‐48.2) years, and 69.0% had an injury at T1 or below. Overall 36.1% were dissatisfied with urinary QoL. On multivariable analysis, female gender (odds ratio [OR], 1.63; 95% confidence interval [CI], 1.15‐2.31; P = 0.016), earlier injury (OR, 0.95 per year; 95% CI, 0.93‐0.97; P < 0.001), ≥4 urinary tract infections (UTIs) per year (OR, 2.36; 95% CI, 1.47‐3.81; P = 0.001), and severe bowel dysfunction (OR, 1.42; 95% CI, 1.02‐1.98; P = 0.035) predicted dissatisfaction. Level of injury, fine motor hand function, and caregiver dependence for CIC were not associated with dissatisfaction.ConclusionsIn a mature SCI cohort, physical disability does not predict dissatisfaction with urinary QoL but severe bowel dysfunction and recurrent UTIs have a significant negative impact. With time the rates of dissatisfaction decline but women continue to be highly dissatisfied on CIC and may benefit from early intervention to minimize the burden of CIC on urinary QoL.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/149763/1/nau23983.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/149763/2/nau23983_am.pd

Reasons for cessation of clean intermittent catheterization after spinal cord injury: Results from the Neurogenic Bladder Research Group spinal cord injury registry

IntroductionClean intermittent catheterization (CIC) is recommended for bladder management after spinal cord injury (SCI) since it has the lowest complication rate. However, transitions from CIC to other less optimal strategies, such as indwelling catheters (IDCs) are common. In individuals with SCI who stopped CIC, we sought to determine how individual characteristics affect the bladder‐related quality of life (QoL) and the reasons for CIC cessation.MethodsThe Neurogenic Bladder Research Group registry is an observational study, evaluating neurogenic bladder‐related QoL after SCI. From 1479 participants, those using IDC or urinary conduit were asked if they had ever performed CIC, for how long, and why they stopped CIC. Multivariable regression, among participants discontinuing CIC, established associations between demographics, injury characteristics, and SCI complications with bladder‐related QoL.ResultsThere were 176 participants who had discontinued CIC; 66 (38%) were paraplegic and 110 (63%) were male. The most common reasons for CIC cessation among all participants were inconvenience, urinary leakage, and too many urine infections. Paraplegic participants who discontinued CIC had higher mean age, better fine motor scores, and lower educational attainment and employment. Multivariable regression revealed years since SCI was associated with worse bladder symptoms (neurogenic bladder symptom score), ≥4 urinary tract infections (UTIs) in a year was associated with worse satisfaction and feelings about bladder symptoms (SCI‐QoL difficulties), while tetraplegia was associated better satisfaction and feelings about bladder symptoms (SCI‐QoL difficulties).ConclusionsTetraplegics who have discontinued CIC have an improved QoL compared with paraplegics. SCI individuals who have discontinued CIC and have recurrent UTIs have worse QoL.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/153674/1/nau24172_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/153674/2/nau24172.pd

NEXUS/Physics: An interdisciplinary repurposing of physics for biologists

In response to increasing calls for the reform of the undergraduate science curriculum for life science majors and pre-medical students (Bio2010, Scientific Foundations for Future Physicians, Vision & Change), an interdisciplinary team has created NEXUS/Physics: a repurposing of an introductory physics curriculum for the life sciences. The curriculum interacts strongly and supportively with introductory biology and chemistry courses taken by life sciences students, with the goal of helping students build general, multi-discipline scientific competencies. In order to do this, our two-semester NEXUS/Physics course sequence is positioned as a second year course so students will have had some exposure to basic concepts in biology and chemistry. NEXUS/Physics stresses interdisciplinary examples and the content differs markedly from traditional introductory physics to facilitate this. It extends the discussion of energy to include interatomic potentials and chemical reactions, the discussion of thermodynamics to include enthalpy and Gibbs free energy, and includes a serious discussion of random vs. coherent motion including diffusion. The development of instructional materials is coordinated with careful education research. Both the new content and the results of the research are described in a series of papers for which this paper serves as an overview and context.Comment: 12 page

Protein expression based multimarker analysis of breast cancer samples

<p>Abstract</p> <p>Background</p> <p>Tissue microarray (TMA) data are commonly used to validate the prognostic accuracy of tumor markers. For example, breast cancer TMA data have led to the identification of several promising prognostic markers of survival time. Several studies have shown that TMA data can also be used to cluster patients into clinically distinct groups. Here we use breast cancer TMA data to cluster patients into distinct prognostic groups.</p> <p>Methods</p> <p>We apply weighted correlation network analysis (WGCNA) to TMA data consisting of 26 putative tumor biomarkers measured on 82 breast cancer patients. Based on this analysis we identify three groups of patients with low (5.4%), moderate (22%) and high (50%) mortality rates, respectively. We then develop a simple threshold rule using a subset of three markers (p53, Na-KATPase-β1, and TGF β receptor II) that can approximately define these mortality groups. We compare the results of this correlation network analysis with results from a standard Cox regression analysis.</p> <p>Results</p> <p>We find that the rule-based grouping variable (referred to as WGCNA*) is an independent predictor of survival time. While WGCNA* is based on protein measurements (TMA data), it validated in two independent Affymetrix microarray gene expression data (which measure mRNA abundance). We find that the WGCNA patient groups differed by 35% from mortality groups defined by a more conventional stepwise Cox regression analysis approach.</p> <p>Conclusions</p> <p>We show that correlation network methods, which are primarily used to analyze the relationships between gene products, are also useful for analyzing the relationships between patients and for defining distinct patient groups based on TMA data. We identify a rule based on three tumor markers for predicting breast cancer survival outcomes.</p

Methodology and software to detect viral integration site hot-spots

<p>Abstract</p> <p>Background</p> <p>Modern gene therapy methods have limited control over where a therapeutic viral vector inserts into the host genome. Vector integration can activate local gene expression, which can cause cancer if the vector inserts near an oncogene. Viral integration hot-spots or 'common insertion sites' (CIS) are scrutinized to evaluate and predict patient safety. CIS are typically defined by a minimum density of insertions (such as 2-4 within a 30-100 kb region), which unfortunately depends on the total number of observed VIS. This is problematic for comparing hot-spot distributions across data sets and patients, where the VIS numbers may vary.</p> <p>Results</p> <p>We develop two new methods for defining hot-spots that are relatively independent of data set size. Both methods operate on distributions of VIS across consecutive 1 Mb 'bins' of the genome. The first method 'z-threshold' tallies the number of VIS per bin, converts these counts to z-scores, and applies a threshold to define high density bins. The second method 'BCP' applies a Bayesian change-point model to the z-scores to define hot-spots. The novel hot-spot methods are compared with a conventional CIS method using simulated data sets and data sets from five published human studies, including the X-linked ALD (adrenoleukodystrophy), CGD (chronic granulomatous disease) and SCID-X1 (X-linked severe combined immunodeficiency) trials. The BCP analysis of the human X-linked ALD data for two patients separately (774 and 1627 VIS) and combined (2401 VIS) resulted in 5-6 hot-spots covering 0.17-0.251% of the genome and containing 5.56-7.74% of the total VIS. In comparison, the CIS analysis resulted in 12-110 hot-spots covering 0.018-0.246% of the genome and containing 5.81-22.7% of the VIS, corresponding to a greater number of hot-spots as the data set size increased. Our hot-spot methods enable one to evaluate the extent of VIS clustering, and formally compare data sets in terms of hot-spot overlap. Finally, we show that the BCP hot-spots from the repopulating samples coincide with greater gene and CpG island density than the median genome density.</p> <p>Conclusions</p> <p>The z-threshold and BCP methods are useful for comparing hot-spot patterns across data sets of disparate sizes. The methodology and software provided here should enable one to study hot-spot conservation across a variety of VIS data sets and evaluate vector safety for gene therapy trials.</p

Representing 3D Space in Working Memory: Spatial Images from Vision, Hearing, Touch, and Language

The chapter deals with a form of transient spatial representation referred to as a spatial image. Like a percept, it is externalized, scaled to the environment, and can appear in any direction about the observer. It transcends the concept of modality, as it can be based on inputs from the three spatial senses, from language, and from long-term memory. Evidence is presented that supports each of the claimed properties of the spatial image, showing that it is quite different from a visual image. Much of the evidence presented is based on spatial updating. A major concern is whether spatial images from different input modalities are functionally equivalent— that once instantiated in working memory, the spatial images from different modalities have the same functional characteristics with respect to subsequent processing, such as that involved in spatial updating. Going further, the research provides some evidence that spatial images are amodal (i.e., do not retain modality-specific features)

Inferring the Transcriptional Landscape of Bovine Skeletal Muscle by Integrating Co-Expression Networks

Background: Despite modern technologies and novel computational approaches, decoding causal transcriptional regulation remains challenging. This is particularly true for less well studied organisms and when only gene expression data is available. In muscle a small number of well characterised transcription factors are proposed to regulate development. Therefore, muscle appears to be a tractable system for proposing new computational approaches. Methodology/Principal Findings: Here we report a simple algorithm that asks "which transcriptional regulator has the highest average absolute co-expression correlation to the genes in a co-expression module?" It correctly infers a number of known causal regulators of fundamental biological processes, including cell cycle activity (E2F1), glycolysis (HLF), mitochondrial transcription (TFB2M), adipogenesis (PIAS1), neuronal development (TLX3), immune function (IRF1) and vasculogenesis (SOX17), within a skeletal muscle context. However, none of the canonical pro-myogenic transcription factors (MYOD1, MYOG, MYF5, MYF6 and MEF2C) were linked to muscle structural gene expression modules. Co-expression values were computed using developing bovine muscle from 60 days post conception (early foetal) to 30 months post natal (adulthood) for two breeds of cattle, in addition to a nutritional comparison with a third breed. A number of transcriptional landscapes were constructed and integrated into an always correlated landscape. One notable feature was a 'metabolic axis' formed from glycolysis genes at one end, nuclear-encoded mitochondrial protein genes at the other, and centrally tethered by mitochondrially-encoded mitochondrial protein genes. Conclusions/Significance: The new module-to-regulator algorithm complements our recently described Regulatory Impact Factor analysis. Together with a simple examination of a co-expression module's contents, these three gene expression approaches are starting to illuminate the in vivo transcriptional regulation of skeletal muscle development