The teacher, the physician and the person: how faculty's teaching performance influences their role modelling

Objective

Previous studies identified different typologies of role models (as teacher/supervisor, physician and person) and explored which of faculty''s characteristics could distinguish good role models. The aim of this study was to explore how and to which extent clinical faculty''s teaching performance influences residents'' evaluations of faculty''s different role modelling statuses, especially across different specialties.

Methods

In a prospective multicenter multispecialty study of faculty''s teaching performance, we used web-based questionnaires to gather empirical data from residents. The main outcome measures were the different typologies of role modelling. The predictors were faculty''s overall teaching performance and faculty''s teaching performance on specific domains of teaching. The data were analyzed using multilevel regression equations.

Results

In total 219 (69% response rate) residents filled out 2111 questionnaires about 423 (96% response rate) faculty. Faculty''s overall teaching performance influenced all role model typologies (OR: from 8.0 to 166.2). For the specific domains of teaching, overall, all three role model typologies were strongly associated with “professional attitude towards residents” (OR: 3.28 for teacher/supervisor, 2.72 for physician and 7.20 for the person role). Further, the teacher/supervisor role was strongly associated with “feedback” and “learning climate” (OR: 3.23 and 2.70). However, the associations of the specific domains of teaching with faculty''s role modelling varied widely across specialties.

Conclusion

This study suggests that faculty can substantially enhance their role modelling by improving their teaching performance. The amount of influence that the specific domains of teaching have on role modelling differs across specialties.     

Modelling seagrass growth and development to evaluate transplanting strategies for restoration

Background and Aims

Seagrasses are important marine plants that are under threat globally. Restoration by transplanting vegetative fragments or seedlings into areas where seagrasses have been lost is possible, but long-term trial data are limited. The goal of this study is to use available short-term data to predict long-term outcomes of transplanting seagrass.

Methods

A functional–structural plant model of seagrass growth that integrates data collected from short-term trials and experiments is presented. The model was parameterized for the species Posidonia australis, a limited validation of the model against independent data and a sensitivity analysis were conducted and the model was used to conduct a preliminary evaluation of different transplanting strategies.

Key Results

The limited validation was successful, and reasonable long-term outcomes could be predicted, based only on short-term data.

Conclusions

This approach for modelling seagrass growth and development enables long-term predictions of the outcomes to be made from different strategies for transplanting seagrass, even when empirical long-term data are difficult or impossible to collect. More validation is required to improve confidence in the model''s predictions, and inclusion of more mechanism will extend the model''s usefulness. Marine restoration represents a novel application of functional–structural plant modelling.     

Altitudinal changes in temperature responses of net photosynthesis and dark respiration in tropical bryophytes

Background and Aims

There is a conspicuous increase of poikilohydric organisms (mosses, liverworts and macrolichens) with altitude in the tropics. This study addresses the hypothesis that the lack of bryophytes in the lowlands is due to high-temperature effects on the carbon balance. In particular, it is tested experimentally whether temperature responses of CO₂-exchange rates would lead to higher respiratory carbon losses at night, relative to potential daily gains, in lowland compared with lower montane forests.

Methods

Gas-exchange measurements were used to determine water-, light-, CO₂- and temperature-response curves of net photosynthesis and dark respiration of 18 tropical bryophyte species from three altitudes (sea level, 500 m and 1200 m) in Panama.

Key Results

Optimum temperatures of net photosynthesis were closely related to mean temperatures in the habitats in which the species grew at the different altitudes. The ratio of dark respiration to net photosynthesis at mean ambient night and day temperatures did not, as expected, decrease with altitude. Water-, light- and CO₂-responses varied between species but not systematically with altitude.

Conclusions

Drivers other than temperature-dependent metabolic rates must be more important in explaining the altitudinal gradient in bryophyte abundance. This does not discard near-zero carbon balances as a major problem for lowland species, but the main effect of temperature probably lies in increasing evaporation rates, thus restricting the time available for photosynthetic carbon gain, rather than in increasing nightly respiration rates. Since optimum temperatures for photosynthesis were so fine tuned to habitat temperatures we analysed published temperature responses of bryophyte species worldwide and found the same pattern on the large scale as we found along the tropical mountain slope we studied.     

A model of canopy photosynthesis incorporating protein distribution through the canopy and its acclimation to light, temperature and CO2

Background and Aims

The distribution of photosynthetic enzymes, or nitrogen, through the canopy affects canopy photosynthesis, as well as plant quality and nitrogen demand. Most canopy photosynthesis models assume an exponential distribution of nitrogen, or protein, through the canopy, although this is rarely consistent with experimental observation. Previous optimization schemes to derive the nitrogen distribution through the canopy generally focus on the distribution of a fixed amount of total nitrogen, which fails to account for the variation in both the actual quantity of nitrogen in response to environmental conditions and the interaction of photosynthesis and respiration at similar levels of complexity.

Model

A model of canopy photosynthesis is presented for C₃ and C₄ canopies that considers a balanced approach between photosynthesis and respiration as well as plant carbon partitioning. Protein distribution is related to irradiance in the canopy by a flexible equation for which the exponential distribution is a special case. The model is designed to be simple to parameterize for crop, pasture and ecosystem studies. The amount and distribution of protein that maximizes canopy net photosynthesis is calculated.

Key Results

The optimum protein distribution is not exponential, but is quite linear near the top of the canopy, which is consistent with experimental observations. The overall concentration within the canopy is dependent on environmental conditions, including the distribution of direct and diffuse components of irradiance.

Conclusions

The widely used exponential distribution of nitrogen or protein through the canopy is generally inappropriate. The model derives the optimum distribution with characteristics that are consistent with observation, so overcoming limitations of using the exponential distribution. Although canopies may not always operate at an optimum, optimization analysis provides valuable insight into plant acclimation to environmental conditions. Protein distribution has implications for the prediction of carbon assimilation, plant quality and nitrogen demand.     

Estimating survival in patients with operable skeletal metastases: an application of a bayesian belief network

Background

Accurate estimations of life expectancy are important in the management of patients with metastatic cancer affecting the extremities, and help set patient, family, and physician expectations. Clinically, the decision whether to operate on patients with skeletal metastases, as well as the choice of surgical procedure, are predicated on an individual patient''s estimated survival. Currently, there are no reliable methods for estimating survival in this patient population. Bayesian classification, which includes Bayesian belief network (BBN) modeling, is a statistical method that explores conditional, probabilistic relationships between variables to estimate the likelihood of an outcome using observed data. Thus, BBN models are being used with increasing frequency in a variety of diagnoses to codify complex clinical data into prognostic models. The purpose of this study was to determine the feasibility of developing Bayesian classifiers to estimate survival in patients undergoing surgery for metastases of the axial and appendicular skeleton.

Methods

We searched an institution-owned patient management database for all patients who underwent surgery for skeletal metastases between 1999 and 2003. We then developed and trained a machine-learned BBN model to estimate survival in months using candidate features based on historical data. Ten-fold cross-validation and receiver operating characteristic (ROC) curve analysis were performed to evaluate the BNN model''s accuracy and robustness.

Results

A total of 189 consecutive patients were included. First-degree predictors of survival differed between the 3-month and 12-month models. Following cross validation, the area under the ROC curve was 0.85 (95% CI: 0.80–0.93) for 3-month probability of survival and 0.83 (95% CI: 0.77–0.90) for 12-month probability of survival.

Conclusions

A robust, accurate, probabilistic naïve BBN model was successfully developed using observed clinical data to estimate individualized survival in patients with operable skeletal metastases. This method warrants further development and must be externally validated in other patient populations.     

Development and validation of a risk model for prediction of hazardous alcohol consumption in general practice attendees: the predictAL study

Background

Little is known about the risk of progression to hazardous alcohol use in people currently drinking at safe limits. We aimed to develop a prediction model (predictAL) for the development of hazardous drinking in safe drinkers.

Methods

A prospective cohort study of adult general practice attendees in six European countries and Chile followed up over 6 months. We recruited 10,045 attendees between April 2003 to February 2005. 6193 European and 2462 Chilean attendees recorded AUDIT scores below 8 in men and 5 in women at recruitment and were used in modelling risk. 38 risk factors were measured to construct a risk model for the development of hazardous drinking using stepwise logistic regression. The model was corrected for over fitting and tested in an external population. The main outcome was hazardous drinking defined by an AUDIT score ≥8 in men and ≥5 in women.

Results

69.0% of attendees were recruited, of whom 89.5% participated again after six months. The risk factors in the final predictAL model were sex, age, country, baseline AUDIT score, panic syndrome and lifetime alcohol problem. The predictAL model''s average c-index across all six European countries was 0.839 (95% CI 0.805, 0.873). The Hedge''s g effect size for the difference in log odds of predicted probability between safe drinkers in Europe who subsequently developed hazardous alcohol use and those who did not was 1.38 (95% CI 1.25, 1.51). External validation of the algorithm in Chilean safe drinkers resulted in a c-index of 0.781 (95% CI 0.717, 0.846) and Hedge''s g of 0.68 (95% CI 0.57, 0.78).

Conclusions

The predictAL risk model for development of hazardous consumption in safe drinkers compares favourably with risk algorithms for disorders in other medical settings and can be a useful first step in prevention of alcohol misuse.     

Socioeconomic status and incident type 2 diabetes mellitus: data from the Women's Health Study

Objectives

We prospectively examined whether socioeconomic status (SES) predicts incident type II diabetes (diabetes), a cardiovascular risk equivalent and burgeoning public health epidemic among women.

Methods

Participants include 23,992 women with Hb_A1c levels <6% and no CVD or diabetes at baseline followed from February 1993 to March 2007. SES was measured by education and income while diabetes was self-reported.

Results

Over 12.3 years of follow-up, 1,262 women developed diabetes. In age and race adjusted models, the relative risk of diabetes decreased with increasing education (<2 years of nursing, 2 to <4 years of nursing, bachelor''s degree, master''s degree, and doctorate: 1.0, 0.7 [95% Confidence Interval (CI), 0.6–0.8], 0.6 (95% CI, 0.5–0.7), 0.5 (95% CI, 0.4–0.6), 0.4 (95% CI, 0.3–0.5); p_trend<0.001). Adjustment for traditional and non-traditional cardiovascular risk factors attenuated this relationship (education: p_trend = 0.96). Similar associations were observed between income categories and diabetes.

Conclusion

Advanced education and increasing income were both inversely associated with incident diabetes even in this relatively well-educated cohort. This relationship was largely explained by behavioral factors, particularly body mass index.     

Visualization and curve-parameter estimation strategies for efficient exploration of phenotype microarray kinetics

Background

The Phenotype MicroArray (OmniLog® PM) system is able to simultaneously capture a large number of phenotypes by recording an organism''s respiration over time on distinct substrates. This technique targets the object of natural selection itself, the phenotype, whereas previously addressed ‘-omics’ techniques merely study components that finally contribute to it. The recording of respiration over time, however, adds a longitudinal dimension to the data. To optimally exploit this information, it must be extracted from the shapes of the recorded curves and displayed in analogy to conventional growth curves.

Methodology

The free software environment R was explored for both visualizing and fitting of PM respiration curves. Approaches using either a model fit (and commonly applied growth models) or a smoothing spline were evaluated. Their reliability in inferring curve parameters and confidence intervals was compared to the native OmniLog® PM analysis software. We consider the post-processing of the estimated parameters, the optimal classification of curve shapes and the detection of significant differences between them, as well as practically relevant questions such as detecting the impact of cultivation times and the minimum required number of experimental repeats.

Conclusions

We provide a comprehensive framework for data visualization and parameter estimation according to user choices. A flexible graphical representation strategy for displaying the results is proposed, including 95% confidence intervals for the estimated parameters. The spline approach is less prone to irregular curve shapes than fitting any of the considered models or using the native PM software for calculating both point estimates and confidence intervals. These can serve as a starting point for the automated post-processing of PM data, providing much more information than the strict dichotomization into positive and negative reactions. Our results form the basis for a freely available R package for the analysis of PM data.     

A unifying conceptual model for the environmental responses of isoprene emissions from plants

Background and Aims

Isoprene is the most important volatile organic compound emitted by land plants in terms of abundance and environmental effects. Controls on isoprene emission rates include light, temperature, water supply and CO₂ concentration. A need to quantify these controls has long been recognized. There are already models that give realistic results, but they are complex, highly empirical and require separate responses to different drivers. This study sets out to find a simpler, unifying principle.

Methods

A simple model is presented based on the idea of balancing demands for reducing power (derived from photosynthetic electron transport) in primary metabolism versus the secondary pathway that leads to the synthesis of isoprene. This model''s ability to account for key features in a variety of experimental data sets is assessed.

Key results

The model simultaneously predicts the fundamental responses observed in short-term experiments, namely: (1) the decoupling between carbon assimilation and isoprene emission; (2) a continued increase in isoprene emission with photosynthetically active radiation (PAR) at high PAR, after carbon assimilation has saturated; (3) a maximum of isoprene emission at low internal CO₂ concentration (c_i) and an asymptotic decline thereafter with increasing c_i; (4) maintenance of high isoprene emissions when carbon assimilation is restricted by drought; and (5) a temperature optimum higher than that of photosynthesis, but lower than that of isoprene synthase activity.

Conclusions

A simple model was used to test the hypothesis that reducing power available to the synthesis pathway for isoprene varies according to the extent to which the needs of carbon assimilation are satisfied. Despite its simplicity the model explains much in terms of the observed response of isoprene to external drivers as well as the observed decoupling between carbon assimilation and isoprene emission. The concept has the potential to improve global-scale modelling of vegetation isoprene emission.     

Local spatial and temporal processes of influenza in Pennsylvania, USA: 2003-2009

Background

Influenza is a contagious respiratory disease responsible for annual seasonal epidemics in temperate climates. An understanding of how influenza spreads geographically and temporally within regions could result in improved public health prevention programs. The purpose of this study was to summarize the spatial and temporal spread of influenza using data obtained from the Pennsylvania Department of Health''s influenza surveillance system.

Methodology and Findings

We evaluated the spatial and temporal patterns of laboratory-confirmed influenza cases in Pennsylvania, United States from six influenza seasons (2003–2009). Using a test of spatial autocorrelation, local clusters of elevated risk were identified in the South Central region of the state. Multivariable logistic regression indicated that lower monthly precipitation levels during the influenza season (OR = 0.52, 95% CI: 0.28, 0.94), fewer residents over age 64 (OR = 0.27, 95% CI: 0.10, 0.73) and fewer residents with more than a high school education (OR = 0.76, 95% CI: 0.61, 0.95) were significantly associated with membership in this cluster. In addition, time series analysis revealed a temporal lag in the peak timing of the influenza B epidemic compared to the influenza A epidemic.

Conclusions

These findings illustrate a distinct spatial cluster of cases in the South Central region of Pennsylvania. Further examination of the regional transmission dynamics within these clusters may be useful in planning public health influenza prevention programs.     

Attitudinal and demographic predictors of measles-mumps-rubella vaccine (MMR) uptake during the UK catch-up campaign 2008-09: cross-sectional survey

Background and Objective

Continued suboptimal measles-mumps-rubella (MMR) vaccine uptake has re-established measles epidemic risk, prompting a UK catch-up campaign in 2008–09 for children who missed MMR doses at scheduled age. Predictors of vaccine uptake during catch-ups are poorly understood, however evidence from routine schedule uptake suggests demographics and attitudes may be central. This work explored this hypothesis using a robust evidence-based measure.

Design

Cross-sectional self-administered questionnaire with objective behavioural outcome.

Setting and Participants

365 UK parents, whose children were aged 5–18 years and had received <2 MMR doses before the 2008–09 UK catch-up started.

Main Outcome Measures

Parents'' attitudes and demographics, parent-reported receipt of invitation to receive catch-up MMR dose(s), and catch-up MMR uptake according to child''s medical record (receipt of MMR doses during year 1 of the catch-up).

Results

Perceived social desirability/benefit of MMR uptake (OR = 1.76, 95% CI = 1.09–2.87) and younger child age (OR = 0.78, 95% CI = 0.68–0.89) were the only independent predictors of catch-up MMR uptake in the sample overall. Uptake predictors differed by whether the child had received 0 MMR doses or 1 MMR dose before the catch-up. Receipt of catch-up invitation predicted uptake only in the 0 dose group (OR = 3.45, 95% CI = 1.18–10.05), whilst perceived social desirability/benefit of MMR uptake predicted uptake only in the 1 dose group (OR = 9.61, 95% CI = 2.57–35.97). Attitudes and demographics explained only 28% of MMR uptake in the 0 dose group compared with 61% in the 1 dose group.

Conclusions

Catch-up MMR invitations may effectively move children from 0 to 1 MMR doses (unimmunised to partially immunised), whilst attitudinal interventions highlighting social benefits of MMR may effectively move children from 1 to 2 MMR doses (partially to fully immunised). Older children may be best targeted through school-based programmes. A formal evaluation element should be incorporated into future catch-up campaigns to inform their continuing improvement.     

Age at menarche, the leg length to sitting height ratio, and risk of diabetes in middle-aged and elderly Chinese men and women

Aims

To evaluate the associations of age at menarche and the leg length-to-sitting-height ratio, markers of adolescent growth, with risk of diabetes in later life.

Materials and Methods

Information from 69,385 women and 55,311 men, aged 40–74 years from the Shanghai Women''s Health Study and Shanghai Men''s Health Study, were included in the current analyses. Diabetes status was ascertained through biennial in person follow-up. Cox models, with age as the time scale, were used.

Results

There were 2369 cases of diabetes (1831 women; 538 men) during an average of 7.3 and 3.6 years of follow-up of the women and men, respectively. In females, menarche age was inversely associated with diabetes risk after adjustment for birth cohort, education, and income (HR = 0.95, 0.92–0.98). In both genders, leg length-to-sitting-height ratio was inversely related to diabetes (HR = 0.88, 0.80–0.97 for men; HR = 0.91, 0.86–0.96 for women) after adjustment for birth cohort, education, and income. Further adjustment for adult BMI at study enrollment completely eliminated the associations of age at menarche (HR = 0.99, 0.96–1.02) and the leg length-to-sitting-height ratio (HR = 1.00, 0.91–1.10 for men; HR = 1.01, 0.96–1.07 for women) with diabetes risk.

Conclusions

Our study suggests that markers of an early age at peak height velocity, i.e. early menarche age and low leg-length-to-sitting height ratio, may be associated with diabetes risk later in life and this association is likely to be mediated through obesity.     

A functional�Cstructural kiwifruit vine model integrating architecture, carbon dynamics and effects of the environment

Background and Aims

Functional–structural modelling can be used to increase our understanding of how different aspects of plant structure and function interact, identify knowledge gaps and guide priorities for future experimentation. By integrating existing knowledge of the different aspects of the kiwifruit (Actinidia deliciosa) vine''s architecture and physiology, our aim is to develop conceptual and mathematical hypotheses on several of the vine''s features: (a) plasticity of the vine''s architecture; (b) effects of organ position within the canopy on its size; (c) effects of environment and horticultural management on shoot growth, light distribution and organ size; and (d) role of carbon reserves in early shoot growth.

Methods

Using the L-system modelling platform, a functional–structural plant model of a kiwifruit vine was created that integrates architectural development, mechanistic modelling of carbon transport and allocation, and environmental and management effects on vine and fruit growth. The branching pattern was captured at the individual shoot level by modelling axillary shoot development using a discrete-time Markov chain. An existing carbon transport resistance model was extended to account for several source/sink components of individual plant elements. A quasi-Monte Carlo path-tracing algorithm was used to estimate the absorbed irradiance of each leaf.

Key Results

Several simulations were performed to illustrate the model''s potential to reproduce the major features of the vine''s behaviour. The model simulated vine growth responses that were qualitatively similar to those observed in experiments, including the plastic response of shoot growth to local carbon supply, the branching patterns of two Actinidia species, the effect of carbon limitation and topological distance on fruit size and the complex behaviour of sink competition for carbon.

Conclusions

The model is able to reproduce differences in vine and fruit growth arising from various experimental treatments. This implies it will be a valuable tool for refining our understanding of kiwifruit growth and for identifying strategies to improve production.     

Effects of precipitation increase on soil respiration: a three-year field experiment in subtropical forests in china

Background

The aim of this study was to determine response patterns and mechanisms of soil respiration to precipitation increases in subtropical regions.

Methodology/Principal Findings

Field plots in three typical forests [i.e. pine forest (PF), broadleaf forest (BF), and pine and broadleaf mixed forest (MF)] in subtropical China were exposed under either Double Precipitation (DP) treatment or Ambient Precipitation (AP). Soil respiration, soil temperature, soil moisture, soil microbial biomass and fine root biomass were measured over three years. We tested whether precipitation treatments influenced the relationship of soil respiration rate (R) with soil temperature (T) and soil moisture (M) using R = (a+cM)exp(bT), where a is a parameter related to basal soil respiration; b and c are parameters related to the soil temperature and moisture sensitivities of soil respiration, respectively. We found that the DP treatment only slightly increased mean annual soil respiration in the PF (15.4%) and did not significantly change soil respiration in the MF and the BF. In the BF, the increase in soil respiration was related to the enhancements of both soil fine root biomass and microbial biomass. The DP treatment did not change model parameters, but increased soil moisture, resulting in a slight increase in soil respiration. In the MF and the BF, the DP treatment decreased soil temperature sensitivity b but increased basal soil respiration a, resulting in no significant change in soil respiration.

Conclusion/Significance

Our results indicate that precipitation increasing in subtropical regions in China may have limited effects on soil respiration.     

Reliability and validity of the Ethiopian version of the hospital anxiety and depression scale (HADS) in HIV infected patients

Background

The hospital anxiety and depression scale (HADS) is a widely used instrument for evaluating psychological distress from anxiety and depression. HADS has not yet been validated in Ethiopia. The aim of this study was to evaluate the reliability and validity of the Amharic (Ethiopian language) version of HADs among HIV infected patients.

Methods

The translated scale was administered to 302 HIV/AIDS patients on follow up for and taking anti-retroviral treatment. Consistency assessment was conducted using Cronbach''s alpha, test-retest reliability using intra-class correlation coefficients (ICC). Construct validity was examined using principal components analysis (PCA). Parallel analysis, Kaiser''s criterion and the scree test were used for factor extraction.

Results

The internal consistency was 0.78 for the anxiety, 0.76 for depression subscales and 0.87 for the full scale of HADS. The intra-class correlation coefficient (ICC) was 80%, 86%, and 84% for the anxiety and depression subscales, and total score respectively. PCA revealed a one dimensional scale.

Conclusion

This preliminary validation study of the Ethiopian version of the HADs indicates that it has promising acceptability, reliability and validity. The adopted scale has a single underlying dimension as indicated by Razavi''s model. The HADS can be used to examine psychological distress in HIV infected patients. Findings are discussed and recommendations made.     

Development,Calibration, and Validation of a U.S. White Male Population-Based Simulation Model of Esophageal Adenocarcinoma

Background

The incidence of esophageal adenocarcinoma (EAC) has risen rapidly in the U.S. and western world. The aim of the study was to begin the investigation of this rapid rise by developing, calibrating, and validating a mathematical disease simulation model of EAC using available epidemiologic data.

Methods

The model represents the natural history of EAC, including the essential biologic health states from normal mucosa to detected cancer. Progression rates between health states were estimated via calibration, which identified distinct parameter sets producing model outputs that fit epidemiologic data; specifically, the prevalence of pre-cancerous lesions and EAC cancer incidence from the published literature and Surveillance, Epidemiology, and End Results (SEER) data. As an illustrative example of a clinical and policy application, the calibrated and validated model retrospectively analyzed the potential benefit of an aspirin chemoprevention program.

Results

Model outcomes approximated calibration targets; results of the model''s fit and validation are presented. Approximately 7,000 cases of EAC could have been prevented over a 30-year period if all white males started aspirin chemoprevention at age 40 in 1965.

Conclusions

The model serves as the foundation for future analyses to determine a cost-effective screening and management strategy to prevent EAC morbidity and mortality.     

A blood-based screening tool for Alzheimer's disease that spans serum and plasma: findings from TARC and ADNI

Context

There is no rapid and cost effective tool that can be implemented as a front-line screening tool for Alzheimer''s disease (AD) at the population level.

Objective

To generate and cross-validate a blood-based screener for AD that yields acceptable accuracy across both serum and plasma.

Design, Setting, Participants

Analysis of serum biomarker proteins were conducted on 197 Alzheimer''s disease (AD) participants and 199 control participants from the Texas Alzheimer''s Research Consortium (TARC) with further analysis conducted on plasma proteins from 112 AD and 52 control participants from the Alzheimer''s Disease Neuroimaging Initiative (ADNI). The full algorithm was derived from a biomarker risk score, clinical lab (glucose, triglycerides, total cholesterol, homocysteine), and demographic (age, gender, education, APOE*E4 status) data.

Major Outcome Measures

Alzheimer''s disease.

Results

11 proteins met our criteria and were utilized for the biomarker risk score. The random forest (RF) biomarker risk score from the TARC serum samples (training set) yielded adequate accuracy in the ADNI plasma sample (training set) (AUC = 0.70, sensitivity (SN) = 0.54 and specificity (SP) = 0.78), which was below that obtained from ADNI cerebral spinal fluid (CSF) analyses (t-tau/Aβ ratio AUC = 0.92). However, the full algorithm yielded excellent accuracy (AUC = 0.88, SN = 0.75, and SP = 0.91). The likelihood ratio of having AD based on a positive test finding (LR+) = 7.03 (SE = 1.17; 95% CI = 4.49–14.47), the likelihood ratio of not having AD based on the algorithm (LR−) = 3.55 (SE = 1.15; 2.22–5.71), and the odds ratio of AD were calculated in the ADNI cohort (OR) = 28.70 (1.55; 95% CI = 11.86–69.47).

Conclusions

It is possible to create a blood-based screening algorithm that works across both serum and plasma that provides a comparable screening accuracy to that obtained from CSF analyses.     

Amantadine for dyskinesias in Parkinson's disease: a randomized controlled trial

Background

Dyskinesias are some of the major motor complications that impair quality of life for patients with Parkinson''s disease. The purpose of the present study was to investigate the efficacy of amantadine in Parkinson''s disease patients suffering from dyskinesias.

Methods

In this multi-center, double-blind, randomized, placebo-controlled, cross-over trial, 36 patients with Parkinson''s disease and dyskinesias were randomized, and 62 interventions, which included amantadine (300 mg /day) or placebo treatment for 27 days, were analyzed. At 15 days after washout, the treatments were crossed over. The primary outcome measure was the changes in the Rush Dyskinesia Rating Scale (RDRS) during each treatment period. The secondary outcome measures were changes in the Unified Parkinson''s Disease Rating Scale part IVa (UPDRS-IVa, dyskinesias), part IVb (motor fluctuations), and part III (motor function).

Results

RDRS improved in 64% and 16% of patients treated with amantadine or placebo, respectively, with significant differences between treatments. The adjusted odds-ratio for improvement by amantadine was 6.7 (95% confidence interval, 1.4 to 31.5). UPDRS-IVa was improved to a significantly greater degree in amantadine-treated patients [mean (SD) of 1.83 (1.56)] compared with placebo-treated patients [0.03 (1.51)]. However, there were no significant effects on UPDRS-IVb or III scores.

Conclusions

Results from the present study demonstrated that amantadine exhibited efficacious effects against dyskinesias in 60–70% of patients.

Trial Registration

UMIN Clinical Trial Registry UMIN000000780     

Funding source and research report quality in nutrition practice-related research

Background

The source of funding is one of many possible causes of bias in scientific research. One method of detecting potential for bias is to evaluate the quality of research reports. Research exploring the relationship between funding source and nutrition-related research report quality is limited and in other disciplines the findings are mixed.

Objective

The purpose of this study is to determine whether types of funding sources of nutrition research are associated with differences in research report quality.

Design

A retrospective study of research reporting quality, research design and funding source was conducted on 2539 peer reviewed research articles from the American Dietetic Association''s Evidence Analysis Library® database.

Results

Quality rating frequency distributions indicate 43.3% of research reports were rated as positive, 50.1% neutral, and 6.6% as negative. Multinomial logistic regression results showed that while both funding source and type of research design are significant predictors of quality ratings (χ2 = 118.99, p<0.001), the model''s usefulness in predicting overall research report quality is little better than chance. Compared to research reports with government funding, those not acknowledging any funding sources, followed by studies with University/hospital funding were more likely to receive neutral vs positive quality ratings, OR = 1.85, P <0.001 and OR = 1.54, P<0.001, respectively and those that did not report funding were more likely to receive negative quality ratings (OR = 4.97, P<0.001). After controlling for research design, industry funded research reports were no more likely to receive a neutral or negative quality rating than those funded by government sources.

Conclusion

Research report quality cannot be accurately predicted from the funding source after controlling for research design. Continued vigilance to evaluate the quality of all research regardless of the funding source and to further understand other factors that affect quality ratings are warranted.     

Impact of natalizumab on cognitive performances and fatigue in relapsing multiple sclerosis: a prospective, open-label, two years observational study

Background and Objectives

Natalizumab reduces the relapse rate and magnetic resonance imaging activity in patients with Relapsing-Remitting Multiple Sclerosis (RRMS). So far the influence of natalizumab on cognitive functions and fatigue in MS remains uncertain. The aim of this prospective, open-label, observational study was to evaluate the possible effects of natalizumab on cognition and fatigue measures in RRMS patients treated for up to two years.

Methods

Cognitive performances were examined by the Rao''s Brief Repeatable Battery (BRB), the Stroop test (ST) and the Cognitive Impairment Index (CII), every 12 months. Patients who failed in at least 3 tests of the BRB and the ST were classified as cognitively impaired (CI). Fatigue Severity Scale (FSS) was administered every 12 months to assess patient''s self-reported fatigue. One hundred and 53 patients completed 1 and 2 year-natalizumab treatment, respectively.

Results

After 1 year of treatment the percentage of CI patients decreased from 29% (29/100) at baseline to 19% (19/100) (p = 0.031) and the mean baseline values of CII (13.52±6.85) and FSS (4.01±1.63) scores were significantly reduced (10.48±7.12, p<0.0001 and 3.61±1.56, p = 0.008). These significant effects were confirmed in the subgroup of patients treated up to two years.

Conclusions

These results demonstrate that a short-term NTZ treatment may significantly improve cognitive performances and fatigue in RRMS patients.