An Sp1/Sp3 site polymorphism associated with hypermethylation of the candidate tumor suppressor gene RIL in cancer

Gene silencing due to promoter methylation is an alternative to mutations and deletions, which inactivate tumor suppressor genes (TSG) in cancer. We identified RIL by Methylated CpG Island Amplification technique as a novel aberrantly methylated gene. RIL is expressed in normal tissues and maps to the 5q31 region, frequently deleted in leukemias. We found methylation of RIL in 55/80 (69%) cancer cell lines, with highest methylation in leukemia and colon. We also observed methylation in 46/80 (58%) primary tumors, whereas normal tissues showed substantially lower degrees of methylation. RIL expression was lost in 13/16 cancer cell lines and was restored by demethylating agent. Screening of 38 cell lines and 13 primary cancers by SSCP revealed no mutations in RIL, suggesting that methylation and LOH are the primary inactivation mechanisms. Stable transfection of RIL into colorectal cancer cells resulted in reduction in cell growth, clonogenicity, and increased apoptosis upon UVC treatment, suggesting that RIL is a good candidate TSG. ^ In searching for a cause of RIL hypermethylation, we identified a 12-bp polymorphic sequence around the transcription start site of the gene that creates a long allele containing 3CTC repeat. Evolutionary studies suggested that the long allele appeared late in evolution due to insertion. Using bisulfite sequencing, in cancers heterozygous for RIL, we found that the short allele is 4.4-fold more methylated than the long allele (P = 0.003). EMSA results suggested binding of factor(s) to the inserted region of the long allele, but not to the short. EMSA mutagenesis and competition studies, as well as supershifts using nuclear extracts or recombinant Sp1 strongly indicated that those DNA binding proteins are Sp1 and Sp3. Transient transfections of RIL allele-specific expression constructs showed less than 2-fold differences in luciferase activity, suggesting no major effects of the additional Sp1 site on transcription. However, stable transfection resulted in 3-fold lower levels of transcription from the short allele 60 days post-transfection, consistent with the concept that the polymorphic Sp1 site protects against time-dependent silencing. Thus, an insertional polymorphism in the RIL promoter creates an additional Sp1/Sp3 site, which appears to protect it from silencing and methylation in cancer. ^

Sustained attention in children with two etiologies of early hydrocephalus.

Several studies have shown that children with spina bifida meningomyelocele (SBM) and hydrocephalus have attention problems on parent ratings and difficulties in stimulus orienting associated with a posterior brain attention system. Less is known about response control and inhibition associated with an anterior brain attention system. Using the Gordon Vigilance Task (Gordon, 1983), we studied error rate, reaction time, and performance over time for sustained attention, a key anterior attention function, in 101 children with SBM, 17 with aqueductal stenosis (AS; another condition involving congenital hydrocephalus), and 40 typically developing controls (NC). In SBM, we investigated the relation between cognitive attention and parent ratings of inattention and hyperactivity and explored the impact of medical variables. Children with SBM did not differ from AS or NC groups on measures of sustained attention, but they committed more errors and responded more slowly. Approximately one-third of the SBM group had attention symptoms, although parent attention ratings were not associated with task performance. Hydrocephalus does not account for the attention profile of children with SBM, which also reflects the distinctive brain dysmorphologies associated with this condition.

Assessing the improved discriminatory power of a new biomarker in prognostic models

Although the area under the receiver operating characteristic (AUC) is the most popular measure of the performance of prediction models, it has limitations, especially when it is used to evaluate the added discrimination of a new biomarker in the model. Pencina et al. (2008) proposed two indices, the net reclassification improvement (NRI) and integrated discrimination improvement (IDI), to supplement the improvement in the AUC (IAUC). Their NRI and IDI are based on binary outcomes in case-control settings, which do not involve time-to-event outcome. However, many disease outcomes are time-dependent and the onset time can be censored. Measuring discrimination potential of a prognostic marker without considering time to event can lead to biased estimates. In this dissertation, we have extended the NRI and IDI to survival analysis settings and derived the corresponding sample estimators and asymptotic tests. Simulation studies were conducted to compare the performance of the time-dependent NRI and IDI with Pencina’s NRI and IDI. For illustration, we have applied the proposed method to a breast cancer study.^ Key words: Prognostic model, Discrimination, Time-dependent NRI and IDI ^

Assessing the impact of electronic health record technology adoption on hospital labor efficiency

Information technology (IT) in the hospital organization is fast becoming a key asset, particularly in light of recent reform legislation in the United States calling for expanding the role of IT in our health care system. Future payment reductions to hospitals included in current health reform are based on expected improvements in hospital operating efficiency. Since over half of hospital expenses are for labor, improved efficiency in use of labor resources can be critical in meeting this challenge. Policy makers have touted the value of IT investments to improve efficiency in response to payment reductions. ^ This study was the first to directly examine the relationship between electronic health record (EHR) technology and staffing efficiency in hospitals. As the hospital has a myriad of outputs for inpatient and outpatient care, efficiency was measured using an industry standard performance metric – full time equivalent employees per adjusted occupied bed (FTE/AOB). Three hypotheses were tested in this study.^ To operationalize EHR technology adoption, we developed three constructs to model adoption, each of which was tested by separate hypotheses. The first hypothesis that a larger number of EHR applications used by a hospital would be associated with greater staffing efficiency (or lower values of FTE/AOB) was not accepted. Association between staffing efficiency and specific EHR applications was the second hypothesis tested and accepted with some applications showing significant impacts on observed values for FTE/AOB. Finally, the hypothesis that the longer an EHR application was used in a hospital would be associated with greater labor efficiency was not accepted as the model showed few statistically significant relationships to FTE/AOB performance. Generally, there does not appear a strong relationship between EHR usage and improved labor efficiency in hospitals.^ While returns on investment from EHR usage may not come from labor efficiencies, they may be better sought using measures of quality, contribution to an efficient and effective local health care system, and improved customer satisfaction through greater patient throughput.^

THE EFFECTS OF CURCUMIN ON BODY COMPOSITION OF PATIENTS WITH ADVANCED PANCREATIC CANCER

Cachexia is very common among patients with advanced pancreatic cancer and is a marker of poor prognosis. Weight loss in cachexia is due to both adipose and muscle compartments, and sarcopenia (severe muscle depletion) is associated with worse outcomes. Curcumin has shown a myriad of biological effects, including anti-cancer and anti-inflammatory. The ability of curcumin to attenuate cachexia and muscle loss has been tested in animal models, with conflicting results so far. The hypothesis of this study was that patients with advanced pancreatic cancer treated with curcumin for two months have less fat and muscle loss as compared to matched controls not treated with this compound. A matched 1:2 case-control retrospective study was conducted with 22 patients with pancreatic cancer who were treated with curcumin on a previous protocol and 44 untreated controls with the same diagnosis matched by age, gender, time from advanced cancer, body mass index, and number of prior therapies. Data was collected regarding oncologic treatment, medication use, weights, heights, and survival. Body composition was determined by computerized tomography analyses at two timepoints separated by 60±20 days. For treated patients, the first image was at the beginning of treatment and for controls it was determined by the matching time from advanced cancer. The evolution of body composition over time was quantitatively analyzed comparing both groups. All patients lost weight both due to fat and muscle losses, and patients treated with curcumin presented greater losses both in lean adipose body mass. Use of medications, chemotherapy, age, time from advanced cancer, baseline albumin, performance status, and number of prior therapies were not independently correlated with changes in body composition variables. Patients treated with curcumin had borderline shorter survival when compared with untreated patients. Sarcopenic treated patients had significantly shorter survival than non-sarcopenic counterparts, and sarcopenia status was not associated with survival among the controls. Treated patients with shorter survival showed a tendency to lose more lean and especially fat body mass as compared to untreated patients, maybe suggesting an effect of curcumin on shifting weight loss towards the end of life by impacting its mechanisms.

The influence of contact pressure, moisture, and compression time on the performance of cotton anti-contamination clothing

The objectives of this research were (1) to study the effect of contact pressure, compression time, and liquid (moisture content of the fabric) on the transfer by sliding contact of non-fixed surface contamination to protective clothing constructed from uncoated, woven fabrics, (2) to study the effect of contact pressure, compression time, and liquid content on the subsequent penetration through the fabric, and (3) to determine if varying the type of contaminant changes the effect of contact pressure, compression time, and liquid content on the transfer by sliding contact and penetration of non-fixed surface contamination. ^ It was found that the combined influence of the liquid (moisture content of the fabric), load (contact pressure), compression time, and their interactions significantly influenced the penetration of all three test agents, sucrose- 14C, triolein-3H, and starch-14C through 100% cotton fabric. The combined influence of the statistically significant main effects and their interactions increased the penetration of triolein- 3H by 32,548%, sucrose-14C by 7,006%, and starch- 14C by 1,900%. ^

An evaluation of sleep hypnotic and stimulant effect on human performance using a computerized psychological test battery

This study evaluated the administration-time-dependent effects of a stimulant (Dexedrine 5-mg), a sleep-inducer (Halcion 0.25-mg) and placebo (control) on human performance. The investigation was conducted on 12 diurnally active (0700-2300) male adults (23-38 yrs) using a double-blind, randomized sixway-crossover three-treatment, two-timepoint (0830 vs 2030) design. Performance tests were conducted hourly during sleepless 13-hour studies using a computer generated, controlled and scored multi-task cognitive performance assessment battery (PAB) developed at the Walter Reed Army Institute of Research. Specific tests were Simple and Choice Reaction Time, Serial Addition/Subtraction, Spatial Orientation, Logical Reasoning, Time Estimation, Response Timing and the Stanford Sleepiness Scale. The major index of performance was "Throughput", a combined measure of speed and accuracy.^ For the Placebo condition, Single and Group Cosinor Analysis documented circadian rhythms in cognitive performance for the majority of tests, both for individuals and for the group. Performance was best around 1830-2030 and most variable around 0530-0700 when sleepiness was greatest (0300).^ Morning Dexedrine dosing marginally enhanced performance an average of 3% with reference to the corresponding in time control level. Dexedrine AM also increased alertness by 10% over the AM control. Dexedrine PM failed to improve performance with reference to the corresponding PM control baseline. With regard to AM and PM Dexedrine administrations, AM performance was 6% better with subjects 25% more alert.^ Morning Halcion administration caused a 7% performance decrement and 16% increase in sleepiness and a 13% decrement and 10% increase in sleepiness when administered in the evening compared to corresponding in time control data. Performance was 9% worse and sleepiness 24% greater after evening versus morning Halcion administration.^ These results suggest that for evening Halcion dosing, the overnight sleep deprivation occurring in coincidence with the nadir in performance due to circadian rhythmicity together with the CNS depressant effects combine to produce performance degradation. For Dexedrine, morning administration resulted in only marginal performance enhancement; Dexedrine in the evening was less effective, suggesting the 5-mg dose level may be too low to counteract the partial sleep deprivation and nocturnal nadir in performance. ^

TIME SERIES ANALYSIS AS INPUT FOR PREDICTIVE MODELING: PREDICTING CARDIAC ARREST IN A PEDIATRIC INTENSIVE CARE UNIT

The first manuscript, entitled "Time-Series Analysis as Input for Clinical Predictive Modeling: Modeling Cardiac Arrest in a Pediatric ICU" lays out the theoretical background for the project. There are several core concepts presented in this paper. First, traditional multivariate models (where each variable is represented by only one value) provide single point-in-time snapshots of patient status: they are incapable of characterizing deterioration. Since deterioration is consistently identified as a precursor to cardiac arrests, we maintain that the traditional multivariate paradigm is insufficient for predicting arrests. We identify time series analysis as a method capable of characterizing deterioration in an objective, mathematical fashion, and describe how to build a general foundation for predictive modeling using time series analysis results as latent variables. Building a solid foundation for any given modeling task involves addressing a number of issues during the design phase. These include selecting the proper candidate features on which to base the model, and selecting the most appropriate tool to measure them. We also identified several unique design issues that are introduced when time series data elements are added to the set of candidate features. One such issue is in defining the duration and resolution of time series elements required to sufficiently characterize the time series phenomena being considered as candidate features for the predictive model. Once the duration and resolution are established, there must also be explicit mathematical or statistical operations that produce the time series analysis result to be used as a latent candidate feature. In synthesizing the comprehensive framework for building a predictive model based on time series data elements, we identified at least four classes of data that can be used in the model design. The first two classes are shared with traditional multivariate models: multivariate data and clinical latent features. Multivariate data is represented by the standard one value per variable paradigm and is widely employed in a host of clinical models and tools. These are often represented by a number present in a given cell of a table. Clinical latent features derived, rather than directly measured, data elements that more accurately represent a particular clinical phenomenon than any of the directly measured data elements in isolation. The second two classes are unique to the time series data elements. The first of these is the raw data elements. These are represented by multiple values per variable, and constitute the measured observations that are typically available to end users when they review time series data. These are often represented as dots on a graph. The final class of data results from performing time series analysis. This class of data represents the fundamental concept on which our hypothesis is based. The specific statistical or mathematical operations are up to the modeler to determine, but we generally recommend that a variety of analyses be performed in order to maximize the likelihood that a representation of the time series data elements is produced that is able to distinguish between two or more classes of outcomes. The second manuscript, entitled "Building Clinical Prediction Models Using Time Series Data: Modeling Cardiac Arrest in a Pediatric ICU" provides a detailed description, start to finish, of the methods required to prepare the data, build, and validate a predictive model that uses the time series data elements determined in the first paper. One of the fundamental tenets of the second paper is that manual implementations of time series based models are unfeasible due to the relatively large number of data elements and the complexity of preprocessing that must occur before data can be presented to the model. Each of the seventeen steps is analyzed from the perspective of how it may be automated, when necessary. We identify the general objectives and available strategies of each of the steps, and we present our rationale for choosing a specific strategy for each step in the case of predicting cardiac arrest in a pediatric intensive care unit. Another issue brought to light by the second paper is that the individual steps required to use time series data for predictive modeling are more numerous and more complex than those used for modeling with traditional multivariate data. Even after complexities attributable to the design phase (addressed in our first paper) have been accounted for, the management and manipulation of the time series elements (the preprocessing steps in particular) are issues that are not present in a traditional multivariate modeling paradigm. In our methods, we present the issues that arise from the time series data elements: defining a reference time; imputing and reducing time series data in order to conform to a predefined structure that was specified during the design phase; and normalizing variable families rather than individual variable instances. The final manuscript, entitled: "Using Time-Series Analysis to Predict Cardiac Arrest in a Pediatric Intensive Care Unit" presents the results that were obtained by applying the theoretical construct and its associated methods (detailed in the first two papers) to the case of cardiac arrest prediction in a pediatric intensive care unit. Our results showed that utilizing the trend analysis from the time series data elements reduced the number of classification errors by 73%. The area under the Receiver Operating Characteristic curve increased from a baseline of 87% to 98% by including the trend analysis. In addition to the performance measures, we were also able to demonstrate that adding raw time series data elements without their associated trend analyses improved classification accuracy as compared to the baseline multivariate model, but diminished classification accuracy as compared to when just the trend analysis features were added (ie, without adding the raw time series data elements). We believe this phenomenon was largely attributable to overfitting, which is known to increase as the ratio of candidate features to class examples rises. Furthermore, although we employed several feature reduction strategies to counteract the overfitting problem, they failed to improve the performance beyond that which was achieved by exclusion of the raw time series elements. Finally, our data demonstrated that pulse oximetry and systolic blood pressure readings tend to start diminishing about 10-20 minutes before an arrest, whereas heart rates tend to diminish rapidly less than 5 minutes before an arrest.