Models and data used to predict the abundance and distribution of Ixodes scapularis (blacklegged tick) in North America: a scoping review.

Tick-borne diseases (TBD) remain prevalent worldwide, and risk assessment of tick habitat suitability is crucial to prevent or reduce their burden. This scoping review provides a comprehensive survey of models and data used to predict I. scapularis distribution and abundance in North America. We identified 4661 relevant primary research articles published in English between January 1st, 2012, and July 18th, 2022, and selected 41 articles following full-text review. Models used data-driven and mechanistic modelling frameworks informed by diverse tick, hydroclimatic, and ecological variables. Predictions captured tick abundance (n = 14, 34.1%), distribution (n = 22, 53.6%) and both (n = 5, 12.1%). All studies used tick data, and many incorporated both hydroclimatic and ecological variables. Minimal host- and human-specific data were utilized. Biases related to data collection, protocols, and tick data quality affect completeness and representativeness of prediction models. Further research and collaboration are needed to improve prediction accuracy and develop effective strategies to reduce TBD.

Unsupervised item response theory models for assessing sample heterogeneity in patient-reported outcomes measures.

PURPOSE: Unsupervised item-response theory (IRT) models such as polytomous IRT based on recursive partitioning (IRTrees) and mixture IRT (MixIRT) models can be used to assess differential item functioning (DIF) in patient-reported outcome measures (PROMs) when the covariates associated with DIF are unknown a priori. This study examines the consistency of results for IRTrees and MixIRT models. METHODS: Data were from 4478 individuals in the Alberta Provincial Project on Outcome Assessment in Coronary Heart Disease registry who received cardiac angiography in Alberta, Canada, and completed the Hospital Anxiety and Depression Scale (HADS) depression subscale items. The partial credit model (PCM) based on recursive partitioning (PCTree) and mixture PCM (MixPCM) were used to identify covariates associated with differential response patterns to HADS depression subscale items. Model covariates included demographic and clinical characteristics. RESULTS: The median (interquartile range) age was 64.5(15.7) years, and 3522(78.5%) patients were male. The PCTree identified 4 terminal nodes (subgroups) defined by smoking status, age, and body mass index. A 3-class PCM fits the data well. The MixPCM latent classes were defined by age, disease indication, smoking status, comorbid diabetes, congestive heart failure, and chronic obstructive pulmonary disease. CONCLUSION: PCTree and MixPCM were not consistent in detecting covariates associated with differential interpretations of PROM items. Future research will use computer simulations to assess these models' Type I error and statistical power for identifying covariates associated with DIF.

A new time-varying coefficient regression approach for analyzing infectious disease data.

Since the beginning of the global pandemic of Coronavirus (SARS-COV-2), there has been many studies devoted to predicting the COVID-19 related deaths/hospitalizations. The aim of our work is to (1) explore the lagged dependence between the time series of case counts and the time series of death counts; and (2) utilize such a relationship for prediction. The proposed approach can also be applied to other infectious diseases or wherever dynamics in lagged dependence are of primary interest. Different from the previous studies, we focus on time-varying coefficient models to account for the evolution of the coronavirus. Using two different types of time-varying coefficient models, local polynomial regression models and piecewise linear regression models, we analyze the province-level data in Canada as well as country-level data using cumulative counts. We use out-of-sample prediction to evaluate the model performance. Based on our data analyses, both time-varying coefficient modeling strategies work well. Local polynomial regression models generally work better than piecewise linear regression models, especially when the pattern of the relationship between the two time series of counts gets more complicated (e.g., more segments are needed to portray the pattern). Our proposed methods can be easily and quickly implemented via existing R packages.

Accuracy of mixture item response theory models for identifying sample heterogeneity in patient-reported outcomes: a simulation study.

PURPOSE: Mixture item response theory (MixIRT) models can be used to uncover heterogeneity in responses to items that comprise patient-reported outcome measures (PROMs). This is accomplished by identifying relatively homogenous latent subgroups in heterogeneous populations. Misspecification of the number of latent subgroups may affect model accuracy. This study evaluated the impact of specifying too many latent subgroups on the accuracy of MixIRT models. METHODS: Monte Carlo methods were used to assess MixIRT accuracy. Simulation conditions included number of items and latent classes, class size ratio, sample size, number of non-invariant items, and magnitude of between-class difference in item parameters. Bias and mean square error in item parameters and accuracy of latent class recovery were assessed. RESULTS: When the number of latent classes was correctly specified, the average bias and MSE in model parameters decreased as the number of items and latent classes increased, but specification of too many latent classes resulted in modest decrease (i.e., < 10%) in the accuracy of latent class recovery. CONCLUSION: The accuracy of MixIRT model is largely influenced by the overspecification of the number of latent classes. Appropriate choice of goodness-of-fit measures, study design considerations, and a priori contextual understanding of the degree of sample heterogeneity can guide model selection.

The Clinical, Microbiological, and Immunological Effects of Probiotic Supplementation on Prevention and Treatment of Periodontal Diseases: A Systematic Review and Meta-Analysis.

(1) Background: Periodontal diseases are a global health concern. They are multi-stage, progressive inflammatory diseases triggered by the inflammation of the gums in response to periodontopathogens and may lead to the destruction of tooth-supporting structures, tooth loss, and systemic health problems. This systematic review and meta-analysis evaluated the effects of probiotic supplementation on the prevention and treatment of periodontal disease based on the assessment of clinical, microbiological, and immunological outcomes. (2) Methods: This study was registered under PROSPERO (CRD42021249120). Six databases were searched: PubMed, MEDLINE, EMBASE, CINAHL, Web of Science, and Dentistry and Oral Science Source. The meta-analysis assessed the effects of probiotic supplementation on the prevention and treatment of periodontal diseases and reported them using Hedge's g standardized mean difference (SMD). (3) Results: Of the 1883 articles initially identified, 64 randomized clinical trials were included in this study. The results of this meta-analysis indicated statistically significant improvements after probiotic supplementation in the majority of the clinical outcomes in periodontal disease patients, including the plaque index (SMD = 0.557, 95% CI: 0.228, 0.885), gingival index, SMD = 0.920, 95% CI: 0.426, 1.414), probing pocket depth (SMD = 0.578, 95% CI: 0.365, 0.790), clinical attachment level (SMD = 0.413, 95% CI: 0.262, 0.563), bleeding on probing (SMD = 0.841, 95% CI: 0.479, 1.20), gingival crevicular fluid volume (SMD = 0.568, 95% CI: 0.235, 0.902), reduction in the subgingival periodontopathogen count of P. gingivalis (SMD = 0.402, 95% CI: 0.120, 0.685), F. nucleatum (SMD = 0.392, 95% CI: 0.127, 0.658), and T. forsythia (SMD = 0.341, 95% CI: 0.050, 0.633), and immunological markers MMP-8 (SMD = 0.819, 95% CI: 0.417, 1.221) and IL-6 (SMD = 0.361, 95% CI: 0.079, 0.644). (4) Conclusions: The results of this study suggest that probiotic supplementation improves clinical parameters, and reduces the periodontopathogen load and pro-inflammatory markers in periodontal disease patients. However, we were unable to assess the preventive role of probiotic supplementation due to the paucity of studies. Further clinical studies are needed to determine the efficacy of probiotic supplementation in the prevention of periodontal diseases.

Overgrowth of Lactobacillus in gastric cancer.

Dysbiosis of the gastric microbiome is involved in the development of gastric cancer (GC). A number of studies have demonstrated an increase in the relative abundance of Lactobacillus in GC. In this review, we present data that support the overgrowth of Lactobacillus in GC from studies on molecular and bacterial culture of the gastric microbiome, discuss the heterogenic effects of Lactobacillus on the health of human stomach, and explore the potential roles of the overgrowth of Lactobacillus in gastric carcinogenesis. Further studies are required to examine the association between Lactobacillus and GC at strain and species levels, which would facilitate to elucidate its role in the carcinogenic process.

A Randomized Controlled Trial of a Pretransplant Educational Intervention in Kidney Patients.

Poor patient knowledge about transplantation is a significant problem following kidney transplant. A video-based educational intervention was developed to supplement standard education provided by transplant teams. METHODS: A multicenter randomized controlled trial tested the intervention delivered to patients undergoing assessment or waitlisted for kidney transplant. Adult participants were randomized to the control (standard education) or the intervention group, consisting of electronic access to the videos (or digital video disks if no internet) plus standard education. Differences between groups in changes in transplant knowledge (measured by the Kidney Transplant Understanding Tool), education satisfaction, self-efficacy, and quality of life (secondary outcomes) were evaluated by a preintervention and postintervention survey. Video viewing habits were tracked and described for patients in the intervention group. RESULTS: One hundred sixty-two patients were enrolled, with 132 completing both questionnaires (n = 64 intervention and n = 68 control), with similar enrollment from 3 Canadian sites. Video viewing statistics in the complete cases indicated that 78% (50/64) watched the videos, with 70% (45/64) viewing them electronically, while 8% (5/64) received digital video disks and self-reported participation. Baseline knowledge scores in the intent-to-treat population were 55.4 ± 6.5 and 55.7 ± 7.1 in the intervention and control, respectively. The mean knowledge change in the intervention (2.1 ± 3.6) was significantly higher than in the control group (0.8 ± 3.4, P < 0.02). In the per-protocol analysis (patients with objective evidence of watching at least 80% of the videos), the knowledge improvements were 3.4 ± 3.8. Video group participants reported higher satisfaction with education (P < 0.02) and expressed positive comments in open-ended feedback. CONCLUSIONS: Electronic video education in the pretransplant setting improved knowledge and satisfaction.

A Goodness-of-fit Test for Zero-Inflated Poisson Mixed Effects Models in Tree Abundance Studies.

Field studies in ecology often make use of data collected in a hierarchical fashion, and may combine studies that vary in sampling design. For example, studies of tree recruitment after disturbance may use counts of individual seedlings from plots that vary in spatial arrangement and sampling density. To account for the multi-level design and the fact that more than a few plots usually yield no individuals, a mixed effects zero inflated Poisson model is often adopted. Although it is a convenient modeling strategy, various aspects of the model could be misspecified. A comprehensive test procedure, based on the cumulative sum of the residuals, is proposed. The test is proven to be consistent, and its convergence properties are established as well. The application of the proposed test is illustrated by a real data example and simulation studies.

Randomised controlled trial of a video intervention and behaviour contract to improve medication adherence after renal transplantation: the VECTOR study protocol.

INTRODUCTION: Non-adherence after kidney transplantation contributes to increased rejections, hospitalisations and healthcare expenditures. Although effective adherence interventions are sorely needed, increasing education and support to transplant recipients demands greater use of care providers' time and resources in a healthcare system that is stretched. The objective of this clinical trial is to determine the effectiveness of an electronically delivered video series and adherence behaviour contract on improving medication adherence to immunosuppressant medications. METHODS AND ANALYSIS: A multicentre, parallel arm, randomised controlled trial will be conducted with four sites across North America (Saskatoon, Calgary, Halifax, Chicago). Adult patients will be randomised (1:1) to either the intervention (ie, home-based video education +behaviour contract plus usual care) or usual care alone. De novo transplant recipients will be enrolled prior to their hospital discharge and will be provided with electronic access to the video intervention (immediately) and adherence contract (1 month post-transplant). Follow-up electronic surveys will be provided at 3 and 12 months postenrolment. The primary outcome will be adherence at 12 months post-transplant, as measured by self-report Basel Assessment of Adherence to Immunosuppressive medications and immunosuppressant levels. Secondary outcomes include the difference in knowledge score between the intervention and control in groups (measured by the Kidney Transplant Understanding Tool); differences in self-efficacy (Generalised Self-efficacy Scale), Beliefs of Medicine Questionnaire (BMQ), quality of life (Short Form-12), patient satisfaction and cost utilisation. The study aims to recruit at least 200 participants across participating sites. ETHICS AND DISSEMINATION: Ethical approval was obtained from the University of Saskatchewan Behavioural Ethics Committee (Beh 18-63), and all patients provide informed consent prior to participating. This educational intervention aims to improve information retention and self-efficacy, leading to improved medication adherence after kidney transplantation, at low cost, with little impact to existing healthcare personnel. If proven beneficial, delivery can be easily implemented into standard of care. TRIAL REGISTRATION NUMBER: NCT03540121; Pre-results.

Effects of Multiple Forms of Information Bias on Estimated Prevalence of Suicide Attempts According to Sexual Orientation: An Application of a Bayesian Misclassification Correction Method to Data From a Systematic Review.

Multiple epidemiologic studies demonstrate a disparity in suicide risk between sexual minority (lesbian, gay, bisexual) and heterosexual populations; however, both "exposure" (sexual minority status) and outcome (suicide attempts) may be affected by information bias related to errors in self-reporting. We therefore applied a Bayesian misclassification correction method to account for possible information biases. A systematic literature search identified studies of lifetime suicide attempts in sexual minority and heterosexual adults, and frequentist meta-analysis was used to generate unadjusted estimates of relative risk. A Bayesian model accounting for prior information about sensitivity and specificity of exposure and outcome measures was used to adjust for misclassification biases. In unadjusted frequentist analysis, the relative risk of lifetime suicide attempt comparing sexual minority with heterosexual groups was 3.38 (95% confidence interval: 2.65, 4.32). In Bayesian reanalysis, the estimated prevalence was slightly reduced in heterosexual adults and increased in sexual minority adults, yielding a relative risk of 4.67 (95% credible interval: 3.94, 5.73). The disparity in lifetime suicide attempts between sexual minority and heterosexual adults is greater than previously estimated, when accounting for multiple forms of information bias. Additional research on the impact of information bias in studies of sexual minority health should be pursued.

Predictive accuracy of particle filtering in dynamic models supporting outbreak projections.

BACKGROUND: While a new generation of computational statistics algorithms and availability of data streams raises the potential for recurrently regrounding dynamic models with incoming observations, the effectiveness of such arrangements can be highly subject to specifics of the configuration (e.g., frequency of sampling and representation of behaviour change), and there has been little attempt to identify effective configurations. METHODS: Combining dynamic models with particle filtering, we explored a solution focusing on creating quickly formulated models regrounded automatically and recurrently as new data becomes available. Given a latent underlying case count, we assumed that observed incident case counts followed a negative binomial distribution. In accordance with the condensation algorithm, each such observation led to updating of particle weights. We evaluated the effectiveness of various particle filtering configurations against each other and against an approach without particle filtering according to the accuracy of the model in predicting future prevalence, given data to a certain point and a norm-based discrepancy metric. We examined the effectiveness of particle filtering under varying times between observations, negative binomial dispersion parameters, and rates with which the contact rate could evolve. RESULTS: We observed that more frequent observations of empirical data yielded super-linearly improved accuracy in model predictions. We further found that for the data studied here, the most favourable assumptions to make regarding the parameters associated with the negative binomial distribution and changes in contact rate were robust across observation frequency and the observation point in the outbreak. CONCLUSION: Combining dynamic models with particle filtering can perform well in projecting future evolution of an outbreak. Most importantly, the remarkable improvements in predictive accuracy resulting from more frequent sampling suggest that investments to achieve efficient reporting mechanisms may be more than paid back by improved planning capacity. The robustness of the results on particle filter configuration in this case study suggests that it may be possible to formulate effective standard guidelines and regularized approaches for such techniques in particular epidemiological contexts. Most importantly, the work tentatively suggests potential for health decision makers to secure strong guidance when anticipating outbreak evolution for emerging infectious diseases by combining even very rough models with particle filtering method.

Latent variable mixture models to test for differential item functioning: a population-based analysis.

BACKGROUND: Comparisons of population health status using self-report measures such as the SF-36 rest on the assumption that the measured items have a common interpretation across sub-groups. However, self-report measures may be sensitive to differential item functioning (DIF), which occurs when sub-groups with the same underlying health status have a different probability of item response. This study tested for DIF on the SF-36 physical functioning (PF) and mental health (MH) sub-scales in population-based data using latent variable mixture models (LVMMs). METHODS: Data were from the Canadian Multicentre Osteoporosis Study (CaMos), a prospective national cohort study. LVMMs were applied to the ten PF and five MH SF-36 items. A standard two-parameter graded response model with one latent class was compared to multi-class LVMMs. Multivariable logistic regression models with pseudo-class random draws characterized the latent classes on demographic and health variables. RESULTS: The CaMos cohort consisted of 9423 respondents. A three-class LVMM fit the PF sub-scale, with class proportions of 0.59, 0.24, and 0.17. For the MH sub-scale, a two-class model fit the data, with class proportions of 0.69 and 0.31. For PF items, the probabilities of reporting greater limitations were consistently higher in classes 2 and 3 than class 1. For MH items, respondents in class 2 reported more health problems than in class 1. Differences in item thresholds and factor loadings between one-class and multi-class models were observed for both sub-scales. Demographic and health variables were associated with class membership. CONCLUSIONS: This study revealed DIF in population-based SF-36 data; the results suggest that PF and MH sub-scale scores may not be comparable across sub-groups defined by demographic and health status variables, although effects were frequently small to moderate in size. Evaluation of DIF should be a routine step when analysing population-based self-report data to ensure valid comparisons amongst sub-groups.

Bayesian adjustment for the misclassification in both dependent and independent variables with application to a breast cancer study.

In this paper, we propose a Bayesian method to address misclassification errors in both independent and dependent variables. Our work is motivated by a study of women who have experienced new breast cancers on two separate occasions. We call both cancers primary, because the second is usually not considered as the result of a metastasis spreading from the first. Hormone receptors (HRs) are important in breast cancer biology, and it is well recognized that the measurement of HR status is subject to errors. This discordance in HR status for two primary breast cancers is of concern and might be an important reason for treatment failure. To sort out the information on true concordance rate from the observed concordance rate, we consider a logistic regression model for the association between the HR status of the two cancers and introduce the misclassification parameters (i.e., sensitivity and specificity) accounting for the misclassification in HR status. The prior distribution for sensitivity and specificity is based on how HR status is actually assessed in laboratory procedures. To account for the nonlinear effect of one error-free covariate, we introduce the B-spline terms in the logistic regression model. Our findings indicate that the true concordance rate of HR status between two primary cancers is greater than the observed value. Copyright © 2016 John Wiley & Sons, Ltd.

Differential Item Functioning in the SF-36 Physical Functioning and Mental Health Sub-Scales: A Population-Based Investigation in the Canadian Multicentre Osteoporosis Study.

BACKGROUND: Self-reported health status measures, like the Short Form 36-item Health Survey (SF-36), can provide rich information about the overall health of a population and its components, such as physical, mental, and social health. However, differential item functioning (DIF), which arises when population sub-groups with the same underlying (i.e., latent) level of health have different measured item response probabilities, may compromise the comparability of these measures. The purpose of this study was to test for DIF on the SF-36 physical functioning (PF) and mental health (MH) sub-scale items in a Canadian population-based sample. METHODS: Study data were from the prospective Canadian Multicentre Osteoporosis Study (CaMos), which collected baseline data in 1996-1997. DIF was tested using a multiple indicators multiple causes (MIMIC) method. Confirmatory factor analysis defined the latent variable measurement model for the item responses and latent variable regression with demographic and health status covariates (i.e., sex, age group, body weight, self-perceived general health) produced estimates of the magnitude of DIF effects. RESULTS: The CaMos cohort consisted of 9423 respondents; 69.4% were female and 51.7% were less than 65 years. Eight of 10 items on the PF sub-scale and four of five items on the MH sub-scale exhibited DIF. Large DIF effects were observed on PF sub-scale items about vigorous and moderate activities, lifting and carrying groceries, walking one block, and bathing or dressing. On the MH sub-scale items, all DIF effects were small or moderate in size. CONCLUSIONS: SF-36 PF and MH sub-scale scores were not comparable across population sub-groups defined by demographic and health status variables due to the effects of DIF, although the magnitude of this bias was not large for most items. We recommend testing and adjusting for DIF to ensure comparability of the SF-36 in population-based investigations.

Response shift and disease activity in inflammatory bowel disease.

PURPOSE: Response shift (RS) may mask true change in health-related quality of life in longitudinal studies. People with chronic conditions may experience RS as they adapt to their disease, but it is unknown whether fluctuations in disease activity will influence the presence of RS. The study purpose was to test for RS in individuals with inflammatory bowel disease (IBD), a condition characterized by periods of symptom flares and remission. METHODS: Data were from the Manitoba IBD Cohort Study (N = 388). Multi-group confirmatory factor analysis (MG-CFA) and a RS detection method based on structural equation modeling were used to test for reconceptualization, reprioritization, and recalibration RS in participants with consistent active, consistent inactive, and inconsistent disease activity over a 6-month period on the SF-36. RESULTS: The MG-CFA revealed that a weak invariance model with equal factor loadings across groups was the best fit to the baseline SF-36 data. Reconceptualization, uniform recalibration, and non-uniform recalibration RS was detected in the consistent active group, but effect sizes were small. For the consistent inactive group, recalibration RS was observed and effect sizes were small to moderate. For the inconsistent disease activity group, small-to-moderate recalibration RS effects were observed. There was no evidence of reprioritization. CONCLUSIONS: Individuals with a chronic disease may exhibit RS even if they are not actively experiencing symptoms on a consistent basis. Heterogeneity in the type and magnitude of RS effects may be observed in chronic disease patients who experience changes in disease symptoms.

Disentangling legacy effects from environmental filters of postfire assembly of boreal tree assemblages.

Disturbance plays a key role in driving ecological responses by creating opportunities for new ecological communities to assemble and by directly influencing the outcomes of assembly. Legacy effects (such as seed banks) and environmental filters can both influence community assembly, but their effects are impossible to separate with observational data. Here, we used seeding experiments in sites covering a broad range of postdisturbance conditions to tease apart the effects of seed availability, environmental factors, and disturbance characteristics on early community assembly after fire. We added seed of four common boreal trees to experimental plots in 55 replicate sites in recently burned areas of black spruce forest in northwestern North America. Seed addition treatments increased the probability of occurrence for all species, indicating a widespread potential for seed limitation to affect patterns of recruitment after fire. Small-seeded. species (aspen and birch) were most sensitive to environmental factors such as soil moisture and organic layer depth, suggesting a role for niche-based environmental filtering in community assembly. Fire characteristics related to severity and frequency were also important drivers of seedling regeneration, indicating the potential for disturbance to mediate environmental filters and legacy effects on seed availability. Because effects of seed availability are typically impossible to disentangle from environmental constraints on recruitment in observational studies, legacy effects contingent on vegetation history may be misinterpreted as being driven by strong environmental filters. Results from the seeding experiments suggest that vegetation legacies affecting seed availability play a pivotal role in shaping patterns of community assembly after fire in these low-diversity boreal forests.

Discordance in hormone receptor status among primary, metastatic, and second primary breast cancers: biological difference or misclassification?

INTRODUCTION: Discordance in hormone receptor status has been observed between two breast tumors of the same patients; however, the degree of heterogeneity is debatable with regard to whether it reflects true biological difference or the limited accuracy of receptor assays. METHODS: A Bayesian misclassification correction method was applied to data on hormone receptor status of two primary breast cancers from the Surveillance, Epidemiology, and End Results database between 1990 and 2010 and to data on primary breast cancer and paired recurrent/metastatic disease assembled from a meta-analysis of the literature published between 1979 and 2014. RESULTS: The sensitivity and specificity of the estrogen receptor (ER) assay were estimated to be 0.971 and 0.920, respectively. After correcting for misclassification, the discordance in ER between two primary breast cancers was estimated to be 1.2% for synchronous ipsilateral pairs, 5.0% for synchronous contralateral pairs, 14.6% for metachronous ipsilateral pairs, and 25.0% for metachronous contralateral pairs. Technical misclassification accounted for 53%-83% of the ER discordance between synchronous primary cancers and 11%-25% of the ER discordance between metachronous cancers. The corrected discordance in ER between primary tumors and recurrent or metastatic lesions was 12.4%, and there were more positive-to-negative changes (10.1%) than negative-to-positive changes (2.3%). Similar patterns were observed for progesterone receptor (PR), although the overall discordance in PR was higher. CONCLUSION: A considerable proportion of discordance in hormone receptor status can be attributed to misclassification in receptor assessment, although the accuracy of receptor assays was excellent. Biopsy of recurrent tumors for receptor retesting should be conducted after considering feasibility, cost, and previous ER/PR status.

Molecular and antimicrobial susceptibility analyses distinguish clinical from bovine Escherichia coli O157 strains.

A population-based study combining (i) antimicrobial, (ii) genetic, and (iii) virulence analyses with molecular evolutionary analyses revealed segregative characteristics distinguishing human clinical and bovine Escherichia coli O157 strains from western Canada. Human (n = 50) and bovine (n = 50) strains of E. coli O157 were collected from Saskatchewan and Manitoba in 2006 and were analyzed by using the six-marker lineage-specific polymorphism assay (LSPA6), antimicrobial susceptibility analysis, the colicin assay, plasmid and virulence profiling including the eae, ehxA, espA, iha, stx1, stx2, stx2c, stx2d, stx2d-activatable, stx2e, and stx2f virulence-associated genes, and structure analyses. Multivariate logistic regression and Fisher's exact test strongly suggested that antimicrobial susceptibility was the most distinctive characteristic (P = 0.00487) associated with human strains. Among all genetic, virulence, and antimicrobial determinants, resistance to tetracycline (P < 0.000) and to sulfisoxazole (P < 0.009) were the most strongly associated segregative characteristics of bovine E. coli O157 strains. Among 11 virulence-associated genes, stx2c showed the strongest association with E. coli O157 strains of bovine origin. LSPA6 genotyping showed the dominance of the lineage I genotype among clinical (90%) and bovine (70%) strains, indicating the importance of lineage I in O157 epidemiology and ecology. Population structure analysis revealed that the more-diverse bovine strains came from a unique group of strains characterized by a high degree of antimicrobial resistance and high frequencies of lineage II genotypes and stx2c variants. These findings imply that antimicrobial resistance generated among bovine strains of E. coli O157 has a large impact on the population of this human pathogen.

Relative importance measures for reprioritization response shift.

PURPOSE: Response shift (RS), a change in the meaning of an individual's self-evaluation of a target construct, such as health-related quality of life (HRQOL), can affect the interpretation of change in measures of the construct collected over time. This study proposes new statistical methods to test for reprioritization RS, in which the relative importance of HRQOL domains changes over time. METHODS: The methods use descriptive discriminant analysis or logistic regression models and bootstrap inference to test for change in relative importance weights (method 1) or ranks (method 2) for discriminating between patient groups at two occasions. The methods are demonstrated using data from the Manitoba Inflammatory Bowel Disease (IBD) Cohort Study (n = 388). Reprioritization of domains from the IBD Questionnaire (IBDQ) and SF-36 was investigated for groups with active and inactive disease symptoms. RESULTS: The IBDQ bowel symptoms and SF-36 bodily pain domains had the highest ranks for group discrimination. Using Method 1, there was evidence of reprioritization RS in the IBDQ social functioning domain and the SF-36 bodily pain and social functioning domains. Method 2 did not detect change for any of the domains. CONCLUSIONS: Compared to IBD patients without active disease symptoms, those with active symptoms were likely to change the meaning of their self-evaluations of pain and social interactions. Further research is needed to compare these new RS detection methods under a variety of data analytic conditions before recommendations about the optimal method can be made.

Aspergillus nidulans galactofuranose biosynthesis affects antifungal drug sensitivity.

The cell wall is essential for fungal survival in natural environments. Many fungal wall carbohydrates are absent from humans, so they are a promising source of antifungal drug targets. Galactofuranose (Galf) is a sugar that decorates certain carbohydrates and lipids. It comprises about 5% of the Aspergillus fumigatus cell wall, and may play a role in systemic aspergillosis. We are studying Aspergillus wall formation in the tractable model system, A. nidulans. Previously we showed single-gene deletions of three sequential A. nidulans Galf biosynthesis proteins each caused similar hyphal morphogenesis defects and 500-fold reduced colony growth and sporulation. Here, we generated ugeA, ugmA and ugtA strains controlled by the alcA(p) or niiA(p) regulatable promoters. For repression and expression, alcA(p)-regulated strains were grown on complete medium with glucose or threonine, whereas niiA(p)-regulated strains were grown on minimal medium with ammonium or nitrate. Expression was assessed by qPCR and colony phenotype. The alcA(p) and niiA(p) strains produced similar effects: colonies resembling wild type for gene expression, and resembling deletion strains for gene repression. Galf immunolocalization using the L10 monoclonal antibody showed that ugmA deletion and repression phenotypes correlated with loss of hyphal wall Galf. None of the gene manipulations affected itraconazole sensitivity, as expected. Deletion of any of ugmA, ugeA, ugtA, their repression by alcA(p) or niiA(p), OR, ugmA overexpression by alcA(p), increased sensitivity to Caspofungin. Strains with alcA(p)-mediated overexpression of ugeA and ugtA had lower caspofungin sensitivity. Galf appears to play an important role in A. nidulans growth and vigor.