Geospatial mapping and resource utilization tool in support of a national smoke-free public housing rule.

OBJECTIVE: To advance public health support for the U.S. Department of Housing and Urban Development's smoke-free rule, the Centers for Disease Control and Prevention collaborated with the Georgia Institute of Technology to develop a geospatial mapping tool. The objective was to create a tool state and local public health agencies could use to tailor smoke-free educational materials and cessation interventions for specific public housing development resident populations. RESULTS: The resulting "Extinguish Tool" includes an interactive map of U.S. public housing developments (PHDs) and healthcare facilities that provides detailed information on individual PHDs, their proximity to existing healthcare facilities, and the demographic characteristics of residents. The tool also estimates the number of PHD residents who smoke cigarettes and calculates crude estimates of the potential economic benefits of providing cessation interventions to these residents. The geospatial mapping tool project serves as an example of a collaborative and innovative public health approach to protecting the health and well-being of the nation's two million public housing residents, including 760,000 children, from the harms of tobacco smoking and secondhand smoke exposure in the places where they live, play, and gather.

A model to estimate the probability of human immunodeficiency virus and hepatitis C infection despite negative nucleic acid testing among increased-risk organ donors.

BACKGROUND: In 2013, guidelines were released for reducing the risk of viral bloodborne pathogen transmission through organ transplantation. Eleven criteria were described that result in a donor being designated at increased infectious risk. Human immunodeficiency virus (HIV) and hepatitis C virus (HCV) transmission risk from an increased-risk donor (IRD), despite negative nucleic acid testing (NAT), likely varies based on behavior type and timing. METHODS: We developed a Monte Carlo risk model to quantify probability of HIV among IRDs. The model included NAT performance, viral load dynamics, and per-act risk of acquiring HIV by each behavior. The model also quantifies the probability of HCV among IRDs by non-medical intravenous drug use (IVDU). RESULTS: Highest risk is among donors with history of unprotected, receptive anal male-to-male intercourse with partner of unknown HIV status (MSM), followed by sex with an HIV-infected partner, IVDU, and sex with a commercial sex worker. CONCLUSION: With NAT screening, the estimated risk of undetected HIV remains small even at 1 day following a risk behavior. The estimated risk for HCV transmission through IVDU is likewise small and decreases quicker with time owing to the faster viral growth dynamics of HCV compared with HIV. These findings may allow for improved organ allocation, utilization, and recipient informed consent.

Randomization modeling to ascertain clustering patterns of human papillomavirus types detected in cervicovaginal samples in the United States.

Detection of multiple human papillomavirus (HPV) types in the genital tract is common. Associations among HPV types may impact HPV vaccination modeling and type replacement. The objectives were to determine the distribution of concurrent HPV type infections in cervicovaginal samples and examine type-specific associations. We analyzed HPV genotyping results from 32,245 cervicovaginal specimens collected from women aged 11 to 83 years in the United States from 2001 through 2011. Statistical power was enhanced by combining 6 separate studies. Expected concurrent infection frequencies from a series of permutation models, each with increasing fidelity to the real data, were compared with the observed data. Statistics were computed based on the distributional properties of the randomized data. Concurrent detection occurred more than expected with 0 or ≥3 HPV types and less than expected with 1 and 2 types. Some women bear a disproportionate burden of the HPV type prevalence. Type associations were observed that exceeded multiple hypothesis corrected significance. Multiple HPV types were detected more frequently than expected by chance and associations among particular HPV types were detected. However vaccine-targeted types were not specifically affected, supporting the expectation that current bivalent/quadrivalent HPV vaccination will not result in type replacement with other high-risk types.

Sensitive and specific peak detection for SELDI-TOF mass spectrometry using a wavelet/neural-network based approach.

SELDI-TOF mass spectrometer's compact size and automated, high throughput design have been attractive to clinical researchers, and the platform has seen steady-use in biomarker studies. Despite new algorithms and preprocessing pipelines that have been developed to address reproducibility issues, visual inspection of the results of SELDI spectra preprocessing by the best algorithms still shows miscalled peaks and systematic sources of error. This suggests that there continues to be problems with SELDI preprocessing. In this work, we study the preprocessing of SELDI in detail and introduce improvements. While many algorithms, including the vendor supplied software, can identify peak clusters of specific mass (or m/z) in groups of spectra with high specificity and low false discover rate (FDR), the algorithms tend to underperform estimating the exact prevalence and intensity of peaks in those clusters. Thus group differences that at first appear very strong are shown, after careful and laborious hand inspection of the spectra, to be less than significant. Here we introduce a wavelet/neural network based algorithm which mimics what a team of expert, human users would call for peaks in each of several hundred spectra in a typical SELDI clinical study. The wavelet denoising part of the algorithm optimally smoothes the signal in each spectrum according to an improved suite of signal processing algorithms previously reported (the LibSELDI toolbox under development). The neural network part of the algorithm combines those results with the raw signal and a training dataset of expertly called peaks, to call peaks in a test set of spectra with approximately 95% accuracy. The new method was applied to data collected from a study of cervical mucus for the early detection of cervical cancer in HPV infected women. The method shows promise in addressing the ongoing SELDI reproducibility issues.

Functional genomics of serotonin receptor 2A (HTR2A): interaction of polymorphism, methylation, expression and disease association.

Serotonergic neurotransmission plays a key role in the pathophysiology of neuropsychiatric illnesses. The functional significance of a promoter polymorphism, -1438G/A (rs6311), in one of the major genes of this system (serotonin receptor 2A, HTR2A) remains poorly understood in the context of epigenetic factors, transcription factors and endocrine influences. We used functional and structural equation modeling (SEM) approaches to assess the contributions of the polymorphism (rs6311), DNA methylation and clinical variables to HTR2A expression in chronic fatigue syndrome (CFS) subjects from a population-based study. HTR2A was up-regulated in CFS through allele-specific expression modulated by transcription factors at critical sites in its promoter: an E47 binding site at position -1,438, (created by the A-allele of rs6311 polymorphism), a glucocorticoid receptor (GR) binding site encompassing a CpG at position -1,420, and Sp1 binding at CpG methylation site -1,224. Methylation at -1,420 was strongly correlated with methylation at -1,439, a CpG site that is dependent upon the G-allele of rs6311 at position -1,438. SEM revealed a strong negative interaction between E47 and GR binding (in conjunction with cortisol level) on HTR2A expression. This study suggests that the promoter polymorphism (rs6311) can affect both transcription factor binding and promoter methylation, and this along with an individual's stress response can impact the rate of HTR2A transcription in a genotype and methylation-dependent manner. This study can serve as an example for deciphering the molecular determinants of transcriptional regulation of major genes of medical importance by integrating functional genomics and SEM approaches. Confirmation in an independent study population is required.

Benchmarking currently available SELDI-TOF MS preprocessing techniques.

SELDI protein profiling experiments can be used as a first step in studying the pathogenesis of various diseases such as cancer. There are a plethora of software packages available for doing the preprocessing of SELDI data, each with many options and written from different signal processing perspectives, offering many researchers choices they may not have the background or desire to make. Moreover, several studies have shown that mistakes in the preprocessing of the data can bias the biological interpretation of the study. For this reason, we conduct a large scale evaluation of available signal processing techniques to establish which are most effective. We use data generated from a standard, published simulation engine so that "truth" is known. We select the top algorithms by considering two logical performance metrics, and give our recommendations for research directions that are likely to be most promising. There is considerable opportunity for future contributions improving the signal processing of SELDI spectra.

Attenuated morning salivary cortisol concentrations in a population-based study of persons with chronic fatigue syndrome and well controls.

CONTEXT: A substantial body of research on the pathophysiology of chronic fatigue syndrome (CFS) has focused on hypothalamic-pituitary-adrenal axis dysregulation. The cortisol awakening response has received particular attention as a marker of hypothalamic-pituitary-adrenal axis dysregulation. OBJECTIVE: The objective of the current study was to evaluate morning salivary cortisol profiles in persons with CFS and well controls identified from the general population. DESIGN AND SETTING: We conducted a case-control study at an outpatient research clinic. CASES AND OTHER PARTICIPANTS: We screened a sample of 19,381 residents of Georgia and identified those with CFS and a matched sample of well controls. Seventy-five medication-free CFS cases and 110 medication-free well controls provided complete sets of saliva samples. MAIN OUTCOME MEASURES: We assessed free cortisol concentrations in saliva collected on a regular workday immediately upon awakening and 30 and 60 min after awakening. RESULTS: There was a significant interaction effect, indicating different profiles of cortisol concentrations over time between groups, with the CFS group showing an attenuated morning cortisol profile. Notably, we observed a sex difference in this effect. Women with CFS exhibited significantly attenuated morning cortisol profiles compared with well women. In contrast, cortisol profiles were similar in men with CFS and male controls. CONCLUSIONS: CFS was associated with an attenuated morning cortisol response, but the effect was limited to women. Our results suggest that a sex difference in hypocortisolism may contribute to increased risk of CFS in women.

Perception versus polysomnographic assessment of sleep in CFS and non-fatigued control subjects: results from a population-based study.

BACKGROUND: Complaints of unrefreshing sleep are a prominent component of chronic fatigue syndrome (CFS); yet, polysomnographic studies have not consistently documented sleep abnormalities in CFS patients. We conducted this study to determine whether alterations in objective sleep characteristics are associated with subjective measures of poor sleep quality in persons with CFS. METHODS: We examined the relationship between perceived sleep quality and polysomnographic measures of nighttime and daytime sleep in 35 people with CFS and 40 non-fatigued control subjects, identified from the general population of Wichita, Kansas and defined by empiric criteria. Perceived sleep quality and daytime sleepiness were assessed using clinical sleep questionnaires. Objective sleep characteristics were assessed by nocturnal polysomnography and daytime multiple sleep latency testing. RESULTS: Participants with CFS reported unrefreshing sleep and problems sleeping during the preceding month significantly more often than did non-fatigued controls. Participants with CFS also rated their quality of sleep during the overnight sleep study as significantly worse than did control subjects. Control subjects reported significantly longer sleep onset latency than latency to fall asleep as measured by PSG and MSLT. There were no significant differences in sleep pathology or architecture between subjects with CFS and control subjects. CONCLUSION: People with CFS reported sleep problems significantly more often than control subjects. Yet, when measured these parameters and sleep architecture did not differ between the two subject groups. A unique finding requiring further study is that control, but not CFS subjects, significantly over reported sleep latency suggesting CFS subjects may have an increased appreciation of sleep behaviour that may contribute to their perception of sleep problems.

Incomplete assembly of IgA2m(2) in Chinese hamster ovary cells.

Myeloma and Chinese hamster ovary (CHO) cells are frequently used for the production of recombinant antibodies. With increasing interest in producing recombinant IgA for protection against infectious agents, it is essential to characterize the IgA produced in these cells. Here we show that while myeloma cells secrete IgA2m(2) predominantly as H(2)L(2), CHO cells secrete H(2)L and H(2) in addition to fully assembled H(2)L(2). When the CHO cells also synthesize J chain and secretory component (SC), polymeric IgA and secretory IgA in which SC is disulfide bonded to the polymeric IgA are produced. Blocking cysteines on purified IgA2m(2) protein by alkylating with iodoacetamide stabilizes the disulfide bonds between the H and L chains suggesting that the disulfide bonds between H and L chains are unstable. Taken together our results suggest that the covalent assembly of IgA2m(2) is different in myeloma and CHO cells.

Linear data mining the Wichita clinical matrix suggests sleep and allostatic load involvement in chronic fatigue syndrome.

OBJECTIVES: To provide a mathematical introduction to the Wichita (KS, USA) clinical dataset, which is all of the nongenetic data (no microarray or single nucleotide polymorphism data) from the 2-day clinical evaluation, and show the preliminary findings and limitations, of popular, matrix algebra-based data mining techniques. METHODS: An initial matrix of 440 variables by 227 human subjects was reduced to 183 variables by 164 subjects. Variables were excluded that strongly correlated with chronic fatigue syndrome (CFS) case classification by design (for example, the multidimensional fatigue inventory [MFI] data), that were otherwise self reporting in nature and also tended to correlate strongly with CFS classification, or were sparse or nonvarying between case and control. Subjects were excluded if they did not clearly fall into well-defined CFS classifications, had comorbid depression with melancholic features, or other medical or psychiatric exclusions. The popular data mining techniques, principle components analysis (PCA) and linear discriminant analysis (LDA), were used to determine how well the data separated into groups. Two different feature selection methods helped identify the most discriminating parameters. RESULTS: Although purely biological features (variables) were found to separate CFS cases from controls, including many allostatic load and sleep-related variables, most parameters were not statistically significant individually. However, biological correlates of CFS, such as heart rate and heart rate variability, require further investigation. CONCLUSIONS: Feature selection of a limited number of variables from the purely biological dataset produced better separation between groups than a PCA of the entire dataset. Feature selection highlighted the importance of many of the allostatic load variables studied in more detail by Maloney and colleagues in this issue [1] , as well as some sleep-related variables. Nonetheless, matrix linear algebra-based data mining approaches appeared to be of limited utility when compared with more sophisticated nonlinear analyses on richer data types, such as those found in Maloney and colleagues [1] and Goertzel and colleagues [2] in this issue.

Chronic fatigue syndrome and high allostatic load.

STUDY POPULATION: We examined the relationship between chronic fatigue syndrome (CFS) and allostatic load in a population-based, case-control study of 43 CFS patients and 60 nonfatigued, healthy controls from Wichita, KS, USA. METHODS: An allostatic load index was computed for all study participants using available laboratory and clinical data, according to a standard algorithm for allostatic load. Logistic regression analysis was used to compute odds ratios (ORs) as estimates of relative risk in models that included adjustment for matching factors and education; 95% confidence intervals (CIs) were computed to estimate the precision of the ORs. RESULTS: CFS patients were 1.9-times more likely to have a high allostatic load index than controls (95% CI = 0.75, 4.75) after adjusting for education level, in addition to matching factors. The strength of this association increased in a linear trend across categories of low, medium and high levels of allostatic load (p = 0.06). CONCLUSION: CFS was associated with a high level of allostatic load. The three allostatic load components that best discriminated cases from controls were waist:hip ratio, aldosterone and urinary cortisol.

Combinations of single nucleotide polymorphisms in neuroendocrine effector and receptor genes predict chronic fatigue syndrome.

OBJECTIVE: This paper asks whether the presence of chronic fatigue syndrome (CFS) can be more accurately predicted from single nucleotide polymorphism (SNP) profiles than would occur by chance. METHODS: Specifically, given SNP profiles for 43 CFS patients, together with 58 controls, we used an enumerative search to identify an ensemble of conjunctive rules that predict whether a patient has CFS. RESULTS: The accuracy of the rules reached 76.3%, with the highest accuracy rules yielding 49 true negatives, 15 false negatives, 28 true positives and nine false positives (odds ratio [OR] 8.94, p < 0.0001). Analysis of the SNPs used most frequently in the overall ensemble of rules gave rise to a list of 'most important SNPs', which was not identical to the list of 'most differentiating SNPs' that one would calculate via studying each SNP independently. The top three genes containing the SNPs accounting for the highest accumulated importances were neuronal tryptophan hydroxylase (TPH2), catechol-O-methyltransferase (COMT) and nuclear receptor subfamily 3, group C, member 1 glucocorticoid receptor (NR3C1). CONCLUSION: The fact that only 28 out of several million possible SNPs predict whether a person has CFS with 76% accuracy indicates that CFS has a genetic component that may help to explain some aspects of the illness.

Allostatic load is associated with symptoms in chronic fatigue syndrome patients.

OBJECTIVES: To further explore the relationship between chronic fatigue syndrome (CFS) and allostatic load (AL), we conducted a computational analysis involving 43 patients with CFS and 60 nonfatigued, healthy controls (NF) enrolled in a population-based case-control study in Wichita (KS, USA). We used traditional biostatistical methods to measure the association of high AL to standardized measures of physical and mental functioning, disability, fatigue and general symptom severity. We also used nonlinear regression technology embedded in machine learning algorithms to learn equations predicting various CFS symptoms based on the individual components of the allostatic load index (ALI). METHODS: An ALI was computed for all study participants using available laboratory and clinical data on metabolic, cardiovascular and hypothalamic-pituitary-adrenal (HPA) axis factors. Physical and mental functioning/impairment was measured using the Medical Outcomes Study 36-item Short Form Health Survey (SF-36); current fatigue was measured using the 20-item multidimensional fatigue inventory (MFI); frequency and intensity of symptoms was measured using the 19-item symptom inventory (SI). Genetic programming, a nonlinear regression technique, was used to learn an ensemble of different predictive equations rather just than a single one. Statistical analysis was based on the calculation of the percentage of equations in the ensemble that utilized each input variable, producing a measure of the 'utility' of the variable for the predictive problem at hand. Traditional biostatistics methods include the median and Wilcoxon tests for comparing the median levels of subscale scores obtained on the SF-36, the MFI and the SI summary score. RESULTS: Among CFS patients, but not controls, a high level of AL was significantly associated with lower median values (indicating worse health) of bodily pain, physical functioning and general symptom frequency/intensity. Using genetic programming, the ALI was determined to be a better predictor of these three health measures than any subcombination of ALI components among cases, but not controls.

Chronic fatigue syndrome--a clinically empirical approach to its definition and study.

BACKGROUND: The lack of standardized criteria for defining chronic fatigue syndrome (CFS) has constrained research. The objective of this study was to apply the 1994 CFS criteria by standardized reproducible criteria. METHODS: This population-based case control study enrolled 227 adults identified from the population of Wichita with: (1) CFS (n = 58); (2) non-fatigued controls matched to CFS on sex, race, age and body mass index (n = 55); (3) persons with medically unexplained fatigue not CFS, which we term ISF (n = 59); (4) CFS accompanied by melancholic depression (n = 27); and (5) ISF plus melancholic depression (n = 28). Participants were admitted to a hospital for two days and underwent medical history and physical examination, the Diagnostic Interview Schedule, and laboratory testing to identify medical and psychiatric conditions exclusionary for CFS. Illness classification at the time of the clinical study utilized two algorithms: (1) the same criteria as in the surveillance study; (2) a standardized clinically empirical algorithm based on quantitative assessment of the major domains of CFS (impairment, fatigue, and accompanying symptoms). RESULTS: One hundred and sixty-four participants had no exclusionary conditions at the time of this study. Clinically empirical classification identified 43 subjects as CFS, 57 as ISF, and 64 as not ill. There was minimal association between the empirical classification and classification by the surveillance criteria. Subjects empirically classified as CFS had significantly worse impairment (evaluated by the SF-36), more severe fatigue (documented by the multidimensional fatigue inventory), more frequent and severe accompanying symptoms than those with ISF, who in turn had significantly worse scores than the not ill; this was not true for classification by the surveillance algorithm. CONCLUSION: The empirical definition includes all aspects of CFS specified in the 1994 case definition and identifies persons with CFS in a precise manner that can be readily reproduced by both investigators and clinicians.

Optimization of codon pair use within the (GGGGS)3 linker sequence results in enhanced protein expression.

Here, we report that a significant increase in recombinant fusion antibody expression can be accomplished by adjusting the nucleotide sequence to conform to certain codon pairing rules. We investigated the expression of a protein in which a single chain Fv specific for HER2/neu with VH and VL joined by a flexible (GGGGS)3 linker was linked to the CH3 of a human anti-rat transferrin receptor IgG3 heavy chain with the same flexible (GGGGS)3 linker. In initial experiments we failed to achieve significant expression of this protein. However, when we made a single nucleotide change in each (GGGGS)3 linker we were able to achieve expression The change of one nucleotide within each linker did not alter either the amino acid sequence or the frequency score of these codon triplets' usage in mammalian cells. Instead they removed two codon pairs predicted to be detrimental to expression. In a transient transfection assay we find that this change results in an over 30-fold increase in expression that is not the result of an increase in the level of accumulated mRNA. In addition, the changes made it possible to isolate stably transfected mammalian cell clones producing high levels of fusion protein, which had not been possible using the original gene.