Revised Intensity Battle Score (RIBS): Development of a Clinical Score for Predicting Poor Outcomes After Rib Fractures

BACKGROUND

Patients with rib fractures have variable clinical courses and it is difficult to predict which patients will do poorly. Ideally this prediction would happen at the time of admission to facilitate effective triage. One scoring system devised to this end, is the Battle score. This study aims to evaluate the efficacy of the Battle score as triage tool, and to re-tool it for performance in an inpatient trauma setting.

METHODS

A multivariate logistic regression model was trained on patients admitted to a level one trauma center with at least one rib fracture. A composite outcome was used to classify those who had poor outcomes. Eighteen candidate predictors were analyzed in univariate analysis, then the most promising fed into the logistic model until a triage score was built and internally validated by bootstrapping.

RESULTS

Of the 838 patients who met the inclusion criteria, 145 (17.3%) patients had a defined poor outcome. The relevant predictors included in the final scoring system were number of ribs fractured, chest tube, pulmonary contusions, chronic obstructive pulmonary disease, and Glasgow coma score. Age was not found to be predictive. This score was found to have higher fidelity in predicting poor outcomes than the original Battle score (AUROC .858 vs .649.).

DISCUSSION

An easy to calculate clinical scoring system was created to triage patients with rib fractures at the time of admission. Age may be of less importance than previously thought, while injury burden and history of lung disease may play a larger role.

Differential Co-Abundance Network Analyses for Microbiome Data Adjusted for Clinical Covariates Using Jackknife Pseudo-Values

A recent breakthrough in differential network (DN) analysis of microbiome data has been realized with the advent of next-generation sequencing technologies. The DN analysis disentangles the microbial co-abundance among taxa by comparing the network properties between two or more graphs under different biological conditions. However, the existing methods to the DN analysis for microbiome data do not adjust for other clinical differences between subjects. We propose a Statistical Approach via Pseudo-value Information and Estimation for Differential Network Analysis (SOHPIE-DNA) that incorporates additional covariates such as continuous age and categorical BMI. SOHPIE-DNA is a regression technique adopting jackknife pseudo-values that can be implemented readily for the analysis. We demonstrate through simulations that SOHPIE-DNA consistently reaches higher recall and F1-score, while maintaining similar precision and accuracy to existing methods (NetCoMi and MDiNE). Lastly, we apply SOHPIE-DNA on two real datasets from the American Gut Project and the Diet Exchange Study to showcase the utility. The analysis of the Diet Exchange Study is to showcase that SOHPIE-DNA can also be used to incorporate the temporal change of connectivity of taxa with the inclusion of additional covariates. As a result, our method has found taxa that are related to the prevention of intestinal inflammation and severity of fatigue in advanced metastatic cancer patients.

The abundance of the potential pathogen Staphylococcus hominis in the air microbiome in a dental clinic and its susceptibility to far‐UVC light

The dental clinic air microbiome incorporates microbes from the oral cavity and upper respiratory tract (URT). This study aimed to establish a reliable methodology for air sampling in a dental clinic setting and quantify the abundance of culturable mesophilic aerobic bacteria present in these samples using regression modeling. Staphylococcus hominis, a potentially pathogenic bacterium typically found in the human oropharynx and URT, was consistently isolated. S. hominis was the most abundant species of aerobic bacteria (22%–24%) and comprised 60%–80% of all Staphylococcus spp. The study also assessed the susceptibility of S. hominis to 222 nm‐far‐UVC light in laboratory experiments, which showed an exponential surface inactivation constant of k = 0.475 cm²/mJ. This constant is a critical parameter for future on‐site use of far‐UVC light as a technique for reducing pathogenic bacterial load in dental clinics.

On Approximating the pIC50 Value of COVID-19 Medicines In Silico with Artificial Neural Networks

In the case of pandemics such as COVID-19, the rapid development of medicines addressing the symptoms is necessary to alleviate the pressure on the medical system. One of the key steps in medicine evaluation is the determination of pIC50 factor, which is a negative logarithmic expression of the half maximal inhibitory concentration (IC50). Determining this value can be a lengthy and complicated process. A tool allowing for a quick approximation of pIC50 based on the molecular makeup of medicine could be valuable. In this paper, the creation of the artificial intelligence (AI)-based model is performed using a publicly available dataset of molecules and their pIC50 values. The modeling algorithms used are artificial and convolutional neural networks (ANN and CNN). Three approaches are tested-modeling using just molecular properties (MP), encoded SMILES representation of the molecule, and the combination of both input types. Models are evaluated using the coefficient of determination (R2) and mean absolute percentage error (MAPE) in a five-fold cross-validation scheme to assure the validity of the results. The obtained models show that the highest quality regression (R2¯=0.99, σR2¯=0.001; MAPE¯=0.009%, σMAPE¯=0.009), by a large margin, is obtained when using a hybrid neural network trained with both MP and SMILES.

Evaluation of the Perceived Pedestrian Level of Service in the post COVID-19 era: The case of Thessaloniki, Greece

The promotion of active transportation modes in urban areas is a key challenge towards the minimization of motor traffic externalities. After the COVID-19 outbreak, cities around the world started investing heavily in infrastructure for pedestrians and cyclists towards the enhancement of social distancing. However, infrastructure adequacy in the post COVID-19 era needs to be evaluated. Level of Service (LOS) as it is proposed by the Highway Capacity Manual (HCM) is the most common methodological tool to assess pedestrian facilities. Nonetheless, pedestrians’ perceptions are more than needed especially in the post COVID-19 era. In this paper an online survey is conducted for the assessment of the main pedestrian facilities in the city of Thessaloniki, Greece. Respondents’ sociodemographic characteristics, their social distancing perception as well as their travel habits after the COVID-19 outbreak are concerned. The main research goal is to reveal the most significant factors that affect pedestrians’ perceived level of service (PLOS) using regression modeling. The results can shed light on respondents’ perceptions about PLOS in the post COVID-19 era. Last, results can assist in policy making for the promotion of active transport modes in urban areas with respect to the current health recommendations for public spaces.

Distinct components of alert fatigue in physicians' responses to a noninterruptive clinical decision support alert

OBJECTIVE

Clinical decision support (CDS) alerts may improve health care quality but "alert fatigue" can reduce provider responsiveness. We analyzed how the introduction of competing alerts affected provider adherence to a single depression screening alert.

MATERIALS AND METHODS

We analyzed the audit data from all occurrences of a CDS alert at a large academic health system. For patients who screen positive for depression during ambulatory visits, a noninterruptive alert was presented, offering a number of relevant documentation actions. Alert adherence was defined as the selection of any option offered within the alert. We assessed the effect of competing clinical guidance alerts presented during the same encounter and the total of all CDS alerts that the same provider had seen in the prior 90 days, on the probability of depression screen alert adherence, adjusting for physician and patient characteristics.

RESULTS

The depression alert fired during 55 649 office visits involving 418 physicians and 40 474 patients over 41 months. After adjustment, physicians who had seen the most alerts in the prior 90 days were much less likely to respond (adjusted OR highest-lowest quartile, 0.38; 95% CI 0.35-0.42; P < .001). Competing alerts in the same visit further reduced the likelihood of adherence only among physicians in the middle two quartiles of alert exposure in the prior 90 days.

CONCLUSIONS

Adherence to a noninterruptive depression alert was strongly associated with the provider's cumulative alert exposure over the past quarter. Health systems should monitor providers' recent alert exposure as a measure of alert fatigue.

On-line weight estimation of broiler carcass and cuts by a computer vision system

In a broiler carcass production conveyor system, inspection, monitoring, and grading carcass and cuts based on computer vision techniques are challenging due to cuts segmentation and ambient light conditions issues. This study presents a depth image-based broiler carcass weight prediction system. An Active Shape Model was developed to segment the carcass into 4 cuts (drumsticks, breasts, wings, and head and neck). Five regression models were developed based on the image features for each weight estimation (carcass and its cuts). The Bayesian-ANN model outperformed all other regression models at 0.9981 R² and 0.9847 R² in the whole carcass and head and neck weight estimation. The RBF-SVR model surpassed all the other drumstick, breast, and wings weight prediction models at 0.9129 R², 0.9352 R², and 0.9896 R², respectively. This proposed technique can be applied as a nondestructive, nonintrusive, and accurate on-line broiler carcass production system in the automation of chicken carcass and cuts weight estimation.

Regression Models, Fantastic Beasts, and Where to Find Them: A Simple Tutorial for Ecologists Using R

Regression modeling is a workhorse of statistical ecology that allows to find relationships between a response variable and a set of explanatory variables. Despite being one of the fundamental statistical ideas in ecological curricula, regression modeling can be complex and subtle. This paper is intended as an applied protocol to help students understand the data, select the most appropriate models, verify assumptions, and interpret the output. Basic ecological questions are tackled using data from a fictional series, “Fantastic beasts and where to find them,” with the aim to show how statistical thinking can foster curiosity, creativity and imagination in ecology, from the formulation of hypotheses to the interpretation of results.

A 5-Year Follow-up of Patients Treated for Full-Thickness Rotator Cuff Tears: A Prospective Cohort Study

Background:

The evidence in support of operative versus nonoperative management of rotator cuff tears (RCTs) is limited, based primarily on observational studies of lower scientific merit.

Purpose:

To (1) compare the efficacy of operative versus nonoperative management of full-thickness RCTs across time and (2) detect variables that predict success within each group.

Study Design:

Cohort study; Level of evidence, 2.

Methods:

We included patients with symptomatic full-thickness RCTs who were enrolled in an institutional shoulder registry. Patient enrollment began in 2009 and continued until early 2018. The following outcome measures were collected at baseline, then 6 months, 1 year, and annually up to 5 years postoperatively: Western Ontario Rotator Cuff Index (WORC), American Shoulder and Elbow Surgeons (ASES) score, Veterans RAND 12-Item Health Survey (VR-12) mental and physical component subscales (MCS and PCS, respectively), 100-point Single Assessment Numeric Evaluation (SANE) rating, and 100-point visual analog scale (VAS) for pain and for patient satisfaction. We performed regression models for all outcome variables across all 5 years of follow-up and included the following predictor variables: treatment type (operative vs nonoperative), sex, age, symptom duration, smoking status, diabetes status, injury side, and obesity status.

Results:

A total of 595 patients were included. Longitudinal mixed-effects regression revealed that patients who received operative treatment did better across time on all outcomes. Women (n = 242; 40.7%) did not fare as well as did men on the ASES, WORC, or VR-12 PCS. Older patients tended to improve less on the VR-12 PCS and more on the VR12-MCS. Patients with longer symptom duration at baseline had better scores across time on the ASES, WORC, VAS for pain, and SANE. Current or recent smokers and patients with diabetes tended to have lower scores on all measures across time. For changes in scores from baseline, patients in the operative group improved to a larger degree out to 3 years compared with those in the nonoperative group.

Conclusion:

Patients with RCTs tended to improve regardless of whether they received operative or nonoperative treatment, but patients who underwent operative treatment improved faster. There appear to be several predictors of improved and worsened outcomes for patients with RCTs undergoing operative or nonoperative treatment.

Predicting Re-Exacerbation Timing and Understanding Prolonged Exacerbations: An Analysis of Patients with COPD in the ECLIPSE Cohort

Purpose

Understanding risk factors for an acute exacerbation of chronic obstructive pulmonary disease (AECOPD) is important for optimizing patient care. We re-analyzed data from the Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints (ECLIPSE) study (NCT00292552) to identify factors predictive of re-exacerbations and associated with prolonged AECOPDs.

Methods

Patients with COPD from ECLIPSE with moderate/severe AECOPDs were included. The end of the first exacerbation was the index date. Timing of re-exacerbation risk was assessed in patients with 180 days’ post-index-date follow-up data. Factors predictive of early (1–90 days) vs late (91–180 days) vs no re-exacerbation were identified using a multivariable partial-proportional-odds-predictive model. Explanatory logistic-regression modeling identified factors associated with prolonged AECOPDs.

Results

Of the 1,554 eligible patients from ECLIPSE, 1,420 had 180 days’ follow-up data: more patients experienced early (30.9%) than late (18.7%) re-exacerbations; 50.4% had no re-exacerbation within 180 days. Lower post-bronchodilator FEV₁ (P=0.0019), a higher number of moderate/severe exacerbations on/before index date (P<0.0001), higher St. George’s Respiratory Questionnaire total score (P=0.0036), and season of index exacerbation (autumn vs winter, P=0.00164) were identified as predictors of early (vs late/none) re-exacerbation risk within 180 days. Similarly, these were all predictors of any (vs none) re-exacerbation risk within 180 days. Median moderate/severe AECOPD duration was 12 days; 22.7% of patients experienced a prolonged AECOPD. The odds of experiencing a prolonged AECOPD were greater for severe vs moderate AECOPDs (adjusted odds ratio=1.917, P=0.002) and lower for spring vs winter AECOPDs (adjusted odds ratio=0.578, P=0.017).

Conclusion

Prior exacerbation history, reduced lung function, poorer respiratory-related quality-of-life (greater disease burden), and season may help identify patients who will re-exacerbate within 90 days of an AECOPD. Severe AECOPDs and winter AECOPDs are likely to be prolonged and may require close monitoring.

Doug Altman: Driving critical appraisal and improvements in the quality of methodological and medical research

Doug Altman was a visionary leader and one of the most influential medical statisticians of the last 40 years. Based on a presentation in the "Invited session in memory of Doug Altman" at the 40th Annual Conference of the International Society for Clinical Biostatistics (ISCB) in Leuven, Belgium and our long-standing collaborations with Doug, we discuss his contributions to regression modeling, reporting, prognosis research, as well as some more general issues while acknowledging that we cannot cover the whole spectrum of Doug's considerable methodological output. His statement "To maximize the benefit to society, you need to not just do research but do it well" should be a driver for all researchers. To improve current and future research, we aim to summarize Doug's messages for these three topics.

Estimation of COVID-19 Epidemiology Curve of the United States Using Genetic Programming Algorithm

Estimation of the epidemiology curve for the COVID-19 pandemic can be a very computationally challenging task. Thus far, there have been some implementations of artificial intelligence (AI) methods applied to develop epidemiology curve for a specific country. However, most applied AI methods generated models that are almost impossible to translate into a mathematical equation. In this paper, the AI method called genetic programming (GP) algorithm is utilized to develop a symbolic expression (mathematical equation) which can be used for the estimation of the epidemiology curve for the entire U.S. with high accuracy. The GP algorithm is utilized on the publicly available dataset that contains the number of confirmed, deceased and recovered patients for each U.S. state to obtain the symbolic expression for the estimation of the number of the aforementioned patient groups. The dataset consists of the latitude and longitude of the central location for each state and the number of patients in each of the goal groups for each day in the period of 22nd January 2020-3rd December 2020. The obtained symbolic expressions for each state are summed up to obtain symbolic expressions for estimation of each of the patient groups (confirmed, deceased and recovered). These symbolic expressions are combined to obtain the symbolic expression for the estimation of the epidemiology curve for the entire U.S. The obtained symbolic expressions for the estimation of the number of confirmed, deceased and recovered patients for each state achieved R2 score in the ranges 0.9406-0.9992, 0.9404-0.9998 and 0.9797-0.99955, respectively. These equations are summed up to formulate symbolic expressions for the estimation of the number of confirmed, deceased and recovered patients for the entire U.S. with achieved R2 score of 0.9992, 0.9997 and 0.9996, respectively. Using these symbolic expressions, the equation for the estimation of the epidemiology curve for the entire U.S. is formulated which achieved R2 score of 0.9933. Investigation showed that GP algorithm can produce symbolic expressions for the estimation of the number of confirmed, recovered and deceased patients as well as the epidemiology curve not only for the states but for the entire U.S. with very high accuracy.

Application of colorimetric indicators to predict the fermentation stage of kimchi

Cabbage kimchi, a popular side dish in Korean cuisine, produces several fermentation by-products (FBPs). Kimchi is praised for its flavor, taste, and texture when suitably fermented at 0.7% to 0.9% total acidity, or a pH of approximately 4.1. Beyond this acidity level, the quality of the product decreases, negatively impacting consumers' purchase preferences. Therefore, the current study seeks to develop an optimally fermented (OptF) kimchi indicator that can be inserted into product packaging to evaluate its utility at 4 and 10 °C. A gradual change in the total color difference (ΔE) was observed during the kimchi fermentation stage, and the highest ΔE values were observed at 4 (34.87) and 10 °C (37.99), after 9 weeks. Moreover, the color-change response function value F(Xc) was more linear at 4 and 10 °C (0.981 and 0.984, respectively) compared to the ΔE over time, during kimchi fermentation. Coefficients of determination for F(Xc)-carbon dioxide (0.983), F(Xc)-pH (0.979), and F(Xc)-titratable acidity (0.974) were sufficient to meet the optimal polynomial regression model, while that for F(Xc)-lactic acid bacteria (0.881) was not. Standardized residuals of predicted data indicated that 95% of the residuals were in the range of -2.0 to 2.0. The regression analysis further suggested that the OptF kimchi indicator could be used as a kimchi fermentation indicator. PRACTICAL APPLICATION: Cabbage kimchi, a traditional Korean fermented food, produces several fermentation by-products. After the optimal fermenting stage, the sensory evaluation of cabbage kimchi and consumers' purchase preference decreases. This study describes an optimally fermented kimchi indicator and its utility at 4 and 10 °C. Our results demonstrate the ability of this indicator to predict the freshness and fermentation stage of kimchi without the need for sensory evaluation. This method could help increase the purchase preference for commercial kimchi.

Modeling and Optimization in Investigating Thermally Sprayed Ni-Based Self-Fluxing Alloy Coatings: A Review

In investigating thermally sprayed Ni-based self-fluxing alloy coatings, widely applied under conditions of wear, corrosion, and high temperatures, designed experiments and statistical methods as a basis for modeling and optimization have become an important tool in making valid and comparable conclusions. Therefore, this paper gives an overview of investigating Ni-based self-fluxing alloy coatings deposited by thermal spraying by the use of designed experiments and statistical methods. The investigation includes the period of the last two decades and covers the treatments of flame spraying, high-velocity oxy/air fuel spraying, plasma spraying, plasma-transferred arc welding, and laser cladding. The main aim was to separate input variables, as well as measured responses, and to point out the importance of correct application of statistical design of experiment. After the review of the papers, it was concluded that investigators have used the process knowledge to analyze and interpret the results of the statistical analysis of experimental data, which is in fact the best way of using the design of experiment in every research. Nevertheless, more attention should be given to correct planning and conducting the experiments to derive the models suitable for the prediction of measured response and which could be an appropriate input for single- or multi-objective optimization.

[Inversion of aboveground biomass of Pinus tabuliformis plantations based on GF-2 data]

Pinus tabuliformis is an important afforestation species in the Loess Plateau. Quick and accurate estimation of aboveground biomass (AGB) of P. tabuliformis plantations plays an important role in monitoring regional forest resources. Here, we used multi-spectral remote sensing data of domestic satellite GF-2 and the field data to estimate the aboveground biomass of P. tabuliformis plantations in Shibao forest farm of Huanglong Mountain in Shaanxi Province. We calculated eight texture features and five vegetation indices, and then built models based four texture windows (3×3, 5×5, 7×7, 9×9) by using five regression methods including normal regression, stepwise regression, ridge regression, Lasso regression and principal component regression. We used the leave-one-out cross validation (LOOCV) to test the estimation accuracy of each model. We found serious multi-collinearity relationships between the extracted remote sensing factors. Most of the remote sensing factors had significant correlations with aboveground biomass of P. tabuliformis plantations. GF-2 data could achieve higher accuracy in the inversion of aboveground biomass of P. tabuliformis plantations in the Shibao forest farm. The best estimation result was the principal component regression model using 9×9 texture window, and the worst one was the normal regression model using 3×3 texture window. Inversion of aboveground biomass of P. tabuliformis plantation using domestic high-resolution satellite imagery could provide a scientific basis for forestry biomass monitoring, resource management, and sustainable management in the forestry departments of northwest China.

Quantification of Visual Field Variability in Glaucoma: Implications for Visual Field Prediction and Modeling

Purpose

To quantify visual field (VF) variability as a function of threshold sensitivity and location, and to compare weighted pointwise linear regression (PLR) with unweighted PLR and pointwise exponential regression (PER) for data fit and prediction ability.

Methods

Two datasets were used for this retrospective study. The first was used to characterize and estimate VF variability, and included a total of 4,747 eyes of 3,095 glaucoma patients with six or more VFs and 3 years or more of follow-up. After performing PER for each series, standard deviation of residuals was quantified for each decibel of sensitivity as a measure of variability. A separate dataset was used to test and compare unweighted PLR, weighted PLR, and PER for data fit and prediction, and included 261 eyes of 176 primary open-angle glaucoma patients with 10 or more VFs and 6 years or more of follow-up.

Results

The degree of variability changed as a function of threshold sensitivity with a zenith and a nadir at 33 and 11 dB, respectively. Variability decreased with eccentricity and was higher in the central 10° (P < 0.001). Differences among the methods for data fit were negligible. PER was the best model to predict future sensitivity values in the mid term and long term.

Conclusions

VF variability increases with the severity of glaucoma damage and decreases with eccentricity. Weighted linear regression neither improves model fit nor prediction. PER exhibited the best prediction ability, which is likely related to the nonlinear nature of long-term glaucomatous perimetric decay.

Translational Relevance

This study suggests that taking into account heteroscedasticity has no advantage in VF modeling.

Assessment of heterogeneity in an individual participant data meta-analysis of prediction models: An overview and illustration

Clinical prediction models aim to provide estimates of absolute risk for a diagnostic or prognostic endpoint. Such models may be derived from data from various studies in the context of a meta‐analysis. We describe and propose approaches for assessing heterogeneity in predictor effects and predictions arising from models based on data from different sources. These methods are illustrated in a case study with patients suffering from traumatic brain injury, where we aim to predict 6‐month mortality based on individual patient data using meta‐analytic techniques (15 studies, n = 11 022 patients). The insights into various aspects of heterogeneity are important to develop better models and understand problems with the transportability of absolute risk predictions.

Monitoring Antibiotic Usage in German Dairy and Beef Cattle Farms-A Longitudinal Analysis

It is well-established that antimicrobial use is a major factor for the development of antimicrobial resistance. To analyze the associations between antimicrobial resistance and usage of antimicrobial agents, data from monitoring and surveillance systems are crucial. Within the project VetCAb (Veterinary Consumption of Antibiotics), antibiotic usage data in German livestock is regularly collected and evaluated. Based on a cross-sectional study in 2011, the project was continued as the longitudinal study VetCAb-Sentinel with ongoing participant recruitment and data collection from 2013. The data collection is based on official German application and delivery forms (ADF), voluntarily provided by veterinarians and farmers. In this study the results of antibiotic usage data of dairy cows, dairy calves and beef cattle were described, using a semi-annual treatment frequency, and 95,944 ADF issued between 2011 and 2015 were analyzed. Results show that the median of the treatment frequency in dairy calf and beef cattle holdings slightly decreased from 0.4 to 0.3 and from 0.2 to 0 days, respectively, whereas the median in dairy cow holdings ranged between 1.9 and 2.3 during the observed period. Temporal changes and the effect of the factors "farm size" and "region" on the treatment frequency were investigated, using multiple linear mixed and logistic regression models. Generally, the factor "time" has a statistically significant impact on the treatment frequency in all production types. In addition, a temporal trend test over the first six half-years shows that an increasing linear trend can be stated in dairy cows and dairy calves (p = 0.017; p = 0.004, respectively). If the time-period is extended to all eight half-years under study, this turns into a quadratic effect (dairy cows: p = 0.006; dairy calves: p < 0.001). In dairy calves and beef cattle the factor "farm size" also has a statistically significant impact. The factor "region," in contrast, shows no statistically significant impact at all. Compared to other livestock populations in Germany, the use of antimicrobials in dairy cows, dairy calves, and beef cattle appears to be low, but varies across several associated factors. Considering these effects, it is recommended that the size of dairy calf and beef cattle holdings is regularly considered in the evaluation of antimicrobial usage data over time.

Ecological modeling over seven years to describe the number of host-seeking Amblyomma americanum in each life stage in northeast Missouri

Amblyomma americanum (L.), the lone star tick, is a vector of pathogens in humans and other animals throughout the United States. Our objective was to characterize how environmental factors influence patterns of A. americanum activity throughout its life cycle by creating statistical models that describe the number of active off-host larvae, nymphs, and adults in northeast Missouri from 2007 to 2013. Ticks were collected every other week from a permanent sampling grid in a second-growth forest and in an old field habitat. Each of the three life stage models considered six meteorological variables and one biotic variable. Regression modeling was used to make candidate models which were evaluated with eight selection criteria. Best-selected models were useful in describing seasonality and magnitude of A. americanum activity for larvae, nymphs, and adults. While distinct subsets of environmental variables were optimal in each life stage, all three models incorporated cumulative degree days, habitat, and number of ticks in the previous life stage. These models further elucidate how environmental and demographic factors influence patterns of host-seeking activity throughout the A. americanum life cycle, providing insight into how changing climate may impact risk of tick-borne pathogen transmission.

Use of a Regression Model to Study Host-Genomic Determinants of Phage Susceptibility in MRSA

Staphylococcus aureus is a major agent of nosocomial infections. Especially in methicillin-resistant strains, conventional treatment options are limited and expensive, which has fueled a growing interest in phage therapy approaches. We have tested the susceptibility of 207 clinical S. aureus strains to 12 (nine monovalent) different therapeutic phage preparations and subsequently employed linear regression models to estimate the influence of individual host gene families on resistance to phages. Specifically, we used a two-step regression model setup with a preselection step based on gene family enrichment. We show that our models are robust and capture the data’s underlying signal by comparing their performance to that of models build on randomized data. In doing so, we have identified 167 gene families that govern phage resistance in our strain set and performed functional analysis on them. This revealed genes of possible prophage or mobile genetic element origin, along with genes involved in restriction-modification and transcription regulators, though the majority were genes of unknown function. This study is a step in the direction of understanding the intricate host-phage relationship in this important pathogen with the outlook to targeted phage therapy applications.

Trial-Level Regressor Modulation for Functional Magnetic Resonance Imaging Designs Requiring Strict Periodicity of Stimulus Presentations: Illustrated Using a Go/No-Go Task

Computer-based assessment of many cognitive processes (eg, anticipatory and response readiness processes) requires the use of invariant stimulus display times (SDT) and intertrial intervals (ITI). Although designs with invariant SDTs and ITIs have been used in functional magnetic resonance imaging (fMRI) research, such designs are problematic for fMRI studies because of collinearity issues. This study examined regressor modulation with trial-level reaction times (RT) as a method for improving signal detection in a go/no-go task with invariant SDTs and ITIs. The effects of modulating the go regressor were evaluated with respect to the detection of BOLD signal-change for the no-go condition. BOLD signal-change to no-go stimuli was examined when the go regressor was based on a (a) canonical hemodynamic response function (HRF), (b) RT-based amplitude-modulated (AM) HRF, and (c) RT-based amplitude and duration modulated (A&DM) HRF. Reaction time–based modulation reduced the collinearity between the go and no-go regressors, with A&DM producing the greatest reductions in correlations between the regressors, and greater reductions in the correlations between regressors were associated with longer mean RTs and greater RT variability. Reaction time–based modulation increased statistical power for detecting group-level no-go BOLD signal-change across a broad set of brain regions. The findings show the efficacy of using regressor modulation to increase power in detecting BOLD signal-change in fMRI studies in which circumstances dictate the use of temporally invariant stimulus presentations.

A novel transdermal nanoethosomal gel of betahistine dihydrochloride for weight gain control: in-vitro and in-vivo characterization

Background

Betahistine dihydrochloride (BDH) is a histamine analog used to control weight gain, with short elimination half-life and gastric irritation as side effects.

Objective

The aim of the current investigation is to formulate and optimize a topical BDH ethosomal gel for weight gain control.

Materials and methods

Box–Behnken design was applied to study the effect of independent variables: phosphatidylcholine (PC), propylene glycol (PG), and ethanol on vesicle size; entrapment efficiency; % drug release; and flux. The morphology and zeta potential of the optimized formulation were evaluated. The % drug release, flux, and pharmacodynamics of the optimized formulation gel were studied.

Results

The size and entrapment efficiency percent had a direct positive relationship with the concentration of PC and negative relationship with ethanol and PG. The % drug release and flux decreased with increasing PC and PG, while ethanol enhanced both responses. Regression modeling indicated a good correlation between dependent and independent variables, where F16 was chosen as the optimized formulation. F16 showed well-defined spherical vesicles and zeta potential of −24 mV, and % release from the gel exceeded 99.5% over 16 h with the flux of 0.28 mg/cm²/h. Food intake and weight gain of rats were significantly decreased after transdermal application of the BDH ethosomal gel when compared with control, placebo, and BDH gel. The histopathological findings proved the absence of inflammation and decrease in adipose tissue.

Conclusion

Results obtained showed a significant, sustained transdermal absorption of BDH ethosomal gel and, consequently, a decrease in food intake and weight gain.

Modeling Variables With a Spike at Zero: Examples and Practical Recommendations

In most epidemiologic studies and in clinical research generally, there are variables with a spike at zero, namely variables for which a proportion of individuals have zero exposure (e.g., never smokers) and among those exposed the variable has a continuous distribution. Different options exist for modeling such variables, such as categorization where the nonexposed form the reference group, or ignoring the spike by including the variable in the regression model with or without some transformation or modeling procedures. It has been shown that such situations can be analyzed by adding a binary indicator (exposed/nonexposed) to the regression model, and a method based on fractional polynomials with which to estimate a suitable functional form for the positive portion of the spike-at-zero variable distribution has been developed. In this paper, we compare different approaches using data from 3 case-control studies carried out in Germany: the Mammary Carcinoma Risk Factor Investigation (MARIE), a breast cancer study conducted in 2002-2005 (Flesch-Janys et al., Int J Cancer. 2008;123(4):933-941); the Rhein-Neckar Larynx Study, a study of laryngeal cancer conducted in 1998-2000 (Dietz et al., Int J Cancer. 2004;108(6):907-911); and a lung cancer study conducted in 1988-1993 (Jöckel et al., Int J Epidemiol. 1998;27(4):549-560). Strengths and limitations of different procedures are demonstrated, and some recommendations for practical use are given.

Estimation of pyrethroid pesticide intake using regression modeling of food groups based on composite dietary samples

Population-based estimates of pesticide intake are needed to characterize exposure for particular demographic groups based on their dietary behaviors. Regression modeling performed on measurements of selected pesticides in composited duplicate diet samples allowed (1) estimation of pesticide intakes for a defined demographic community, and (2) comparison of dietary pesticide intakes between the composite and individual samples. Extant databases were useful for assigning individual samples to composites, but they could not provide the breadth of information needed to facilitate measurable levels in every composite. Composite sample measurements were found to be good predictors of pyrethroid pesticide levels in their individual sample constituents where sufficient measurements are available above the method detection limit. Statistical inference shows little evidence of differences between individual and composite measurements and suggests that regression modeling of food groups based on composite dietary samples may provide an effective tool for estimating dietary pesticide intake for a defined population.

Significance of mast cell distribution in placental tissue and membranes in spontaneous preterm birth

Background

Preterm birth is a common cause of adverse neonatal and childhood outcomes, in both the short and long term. Preterm labor is commonly associated with inflammation at the maternal–fetal interface. There is some indirect evidence that mast cells (MCs) might represent a link between hormonal influences and local reactions leading to the onset of labor.

Patients and methods

The placentas and membranes of 51 uncomplicated spontaneous term births were compared to those from 50 spontaneous preterm births. Immunohistochemical staining for MC tryptase was undertaken allowing MC concentration, location, and degranulation status to be determined. Regression modeling was used to compare results.

Results

There were no significant differences in the demographic characteristics of the two cohorts. There were significantly more MCs in the decidua for term births than preterm births (P=0.03). The presence of histological chorioamnionitis did not affect MC concentrations.

Conclusion

Despite evidence suggesting a possible role for MCs in spontaneous preterm birth, this study found that the concentration of decidual MCs was in fact significantly lower in preterm compared to term birth.

Age, weight and decompression sickness in rats

OBJECTIVE

The aim of this study was to determine if, after controlling for weight, age is associated with decompression sickness (DCS) in rats.

METHODS

Following compression-decompression, male rats aged 11 weeks were observed for DCS. After two weeks recovery, surviving rats were re-dived using the same compression-decompression profile.

RESULTS

In this experiment, there was a clear difference between DCS outcome at ages 11 or 13 weeks in matched rats (p = 0.002).

DISCUSSION

Even with weight included in the model, age was significantly associated with DCS (p = 0.01), yet after removal of weight the association was much stronger (p = 0.002).

CONCLUSION

We believe that age is likely to be found associated with the probability of DCS in a larger dataset with a wider range of parameters, after accounting for the effect of weight.

Modeling the biotic and abiotic factors that describe the number of active off-host Amblyomma americanum larvae

Amblyomma americanum (L.) is a three-host tick that spends most of its life off-host and is an important vector of pathogens in the eastern United States. Our objectives were to develop a predictive statistical model that describes the number of active, off-host larvae from 2007 to 2011 and to determine the environmental variables associated with this pattern. Data used in this study came from monitoring conducted in northeast Missouri in which off-host ticks were collected from a permanent plot in a forest and an old field habitat every other week from approximately February to December. Variables examined were day length, degree days, total precipitation prior to sampling, wind speed, saturation deficit, number of adults prior to sampling, and collection site. Of the four regression models tested, the negative binomial model was selected. Fitted candidate models were compared relative to one another using values of eight model selection criteria and model averaging was used to develop a predictive model. The residual plots indicated that the weighted average model performs well in describing the number of larvae. Of the variables considered, the number of larvae was most strongly associated with increasing degree days, the number of active adults prior to sampling, and the forested site.

Effect of treatment and mammography detection on breast cancer survival over time: 1990-2007

BACKGROUND

The extent to which improvements over time in breast cancer survival are related to earlier detection by mammography or to more effective treatments is not known.

METHODS

At a comprehensive cancer care center, the authors conducted a retrospective cohort study of women ages 50 to 69 years who were diagnosed with invasive breast cancer (stages I through III) and were followed over 3 periods (1990-1994, 1995-1999, and 2000-2007). Data were abstracted from patient charts and included detection method, diagnosis, treatment, and follow-up for vital status in the institutional breast cancer registry (n = 2998). The method of detection was categorized as patient or physician detected or mammography detected. Cox proportional hazards models were used to estimate adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) for 5-year disease-specific survival in relation to detection method and treatment factors, and differences in survival were analyzed using the Kaplan-Meier method.

RESULTS

Fifty-eight percent of breast cancers were mammography detected, and 42% were patient or physician detected; 56% of tumors were stage I, 31% were stage II, and 13% were stage III. The average length of follow-up was 10.71 years. The combined 5-year disease-specific survival rate was 89% from 1990 to 1994, 94% from 1995 to 1999, and 96% from 2000 to 2007 (P < .001). In an adjusted model, mammography detection (HR, 0.43; 95% CI, 0.27-0.70), hormone therapy (HR, 0.47; 95% CI, 0.30-0.75), and taxane-containing chemotherapy (HR, 0.61; 95% CI, 0.37-0.99) were significantly associated with a decreased risk of disease-specific mortality.

CONCLUSIONS

Better breast cancer survival over time was related to mammography detection, hormone therapy, and taxane-containing chemotherapy. Treatment improvements alone are not sufficient to explain the observed survival improvements over time.

Models of glaucomatous visual field loss

PURPOSE

To evaluate and compare the ability of pointwise linear, exponential, and logistic functions, and combinations of functions, to model the longitudinal behavior of visual field (VF) series and predict future VF loss in patients with glaucoma.

METHODS

Visual field series from 782 eyes (572 patients) with open-angle glaucoma had greater than 6 years of follow-up and 12 VFs performed. Threshold sensitivities from the first 5 years at each location were regressed with linear, exponential, and logistic functions to estimate model parameters. A multiple-model approach applied the model with the lowest root mean square error (RMSE) at each location as the preferred model for future predictions. Predictions for each model were compared at 1, 2, 3, and 5 years after the last VF used to determine model parameters.

RESULTS

There were no clinically important differences between any of the models tested for fit; however, the logistic function had the lowest average RMSE (P < 0.001). For predictions, the exponential model consistently had the lowest average prediction RMSE for all time intervals (P < 0.001); the multiple-model approach did not perform better than the exponential model (P < 0.001).

CONCLUSIONS

While the logistic model best fit glaucomatous VF behavior over a long time period, the exponential model provided the best average predictions. A multiple-model approach for VF predictions was associated with a greater prediction error than with the best-performing single-model approach. A model's goodness of fit is not indicative of its predictive ability for measurements of glaucomatous VFs.

Transcription factor co-localization patterns affect human cell type-specific gene expression

BACKGROUND

Cellular development requires the precise control of gene expression states. Transcription factors are involved in this regulatory process through their combinatorial binding with DNA. Information about transcription factor binding sites can help determine which combinations of factors work together to regulate a gene, but it is unclear how far the binding data from one cell type can inform about regulation in other cell types.

RESULTS

By integrating data on co-localized transcription factor binding sites in the K562 cell line with expression data across 38 distinct hematopoietic cell types, we developed regression models to describe the relationship between the expression of target genes and the transcription factors that co-localize nearby. With K562 binding sites identifying the predictors, the proportion of expression explained by the models is statistically significant only for monocytic cells (p-value< 0.001), which are closely related to K562. That is, cell type specific binding patterns are crucial for choosing the correct transcription factors for the model. Comparison of predictors obtained from binding sites in the GM12878 cell line with those from K562 shows that the amount of difference between binding patterns is directly related to the quality of the prediction. By identifying individual genes whose expression is predicted accurately by the binding sites, we are able to link transcription factors FOS, TAF1 and YY1 to a sparsely studied gene LRIG2. We also find that the activity of a transcription factor may be different depending on the cell type and the identity of other co-localized factors.

CONCLUSION

Our approach shows that gene expression can be explained by a modest number of co-localized transcription factors, however, information on cell-type specific binding is crucial for understanding combinatorial gene regulation.

Activity of xenoestrogens at nanomolar concentrations in the E-Screen assay

BACKGROUND

Certain effects induced by endocrine-disrupting chemicals (EDCs) may occur at dose levels lower than those normally tested in toxicology, but few systematic dose-response studies have been carried out in the low-dose range.

OBJECTIVES

The high statistical power afforded by a high-throughput in vitro assay such as the E-Screen assay was exploited with the aim of producing low-dose estimates for 24 estrogenic chemicals, including endogenous hormones and xenoestrogens.

RESULTS

Unusual dose-response curves with inverted U-shapes were not observed in the low-dose range. Instead, many chemicals exhibited curves with very small gradients at low doses, and this complicated the reliable estimation of low effects. Systematic comparisons between the outcomes of hypothesis-testing procedures (lowest observed effect concentrations--LOECs, no observed effect concentrations--NOECs) and regression modeling approaches (EC(01)--effective concentration causing a 1% effect, EC(05)--effective concentration causing a 5% effect) produced estimates that agreed reasonably well. In many cases, NOECs were shown to be associated with proliferative responses of 1-2%. This is in contrast with the widespread perception of NOECs as values that signal complete absence of effects. For many of the tested xenoestrogens, the NOECs, EC(01), and EC(05) were in the nanomolar range, and comparisons with measured serum and adipose tissue levels in Europe revealed considerable overlaps in some cases.

CONCLUSIONS

Our studies illustrate the difficulties that may be encountered during the estimation of low doses in vivo. High statistical power is required when the underlying dose-response curves are shallow. Through the use of large sample sizes and numerous repeats, the experimental power of the E-Screen assay was sufficiently high to measure effect magnitudes of around 1-2% with reliability. However, such resources are usually not available for in vivo testing, with the consequence that the statistical detection limits are considerably higher. If this coincides with shallow dose-response curves in the low-effect range (which is normally not measurable in vivo), the limited resolving power of in vivo assays may seriously constrain low-dose testing.