Nagaraj K. Neerchal

Evaluation of Dose-Related Effects of Aspirin on Platelet Function Results From the Aspirin-Induced Platelet Effect (ASPECT) Study

Background— The antiplatelet effect of aspirin is attributed to platelet cyclooxygenase-1 inhibition. Controversy exists on the prevalence of platelet resistance to aspirin in patients with coronary artery disease and effects of aspirin dose on inhibition. Our primary aim was to determine the degree of platelet aspirin responsiveness in patients, as measured by commonly used methods, and to study the relation of aspirin dose to platelet inhibition. Methods and Results— We prospectively studied the effect of aspirin dosing on platelet function in 125 stable outpatients with coronary artery disease randomized in a double-blind, double-crossover investigation (81, 162, and 325 mg/d for 4 weeks each over a 12-week period). At all doses of aspirin, platelet function was low as indicated by arachidonic acid (AA)-induced light transmittance aggregation, thrombelastography, and VerifyNow at any 1 dose. Resistance to aspirin was 0% to 6% in the overall group when AA was used as the agonist, whereas it was 1% to 27% by other methods [collagen and ADP-induced light transmittance aggregation, platelet function analyzer (PFA-100)]. Platelet response to aspirin as measured by collagen-induced light transmittance aggregation, ADP-induced light transmittance aggregation, PFA-100 (81 mg versus 162 mg, P≤0.05), and urinary 11-dehydrothromboxane B2 was dose-related (81 mg versus 325 mg, P=0.003). No carryover effects were observed. Conclusions— The assessment of aspirin resistance is highly assay-dependent; aspirin is an effective blocker of AA-induced platelet function at all doses, whereas higher estimates of resistance were observed with methods that do not use AA as the stimulus. The observation of dose-dependent effects despite nearly complete inhibition of AA-induced aggregation suggests that aspirin may exert antiplatelet properties through non–cyclooxygenase-1 pathways and deserves further investigation.

Spatial distribution of metals in soils in Baltimore, Maryland : Role of native parent material, proximity to major roads, housing age and screening guidelines

We investigated the spatial distribution of heavy metal above-background (anthropic) contents of Cd, Co, Cu, Cr, Fe, Mn, Ni, Pb, Ti, V, and Zn in Baltimore City surface soils and related these levels to potential contaminating sources. Composite soil samples (0-10 cm depth) were digested using a nitric and hydrochloric extraction technique. Slightly more than 10% of plots exceeded United States Environmental Protection Agency screening guidelines for Pb. In a principal component analysis, the first component corresponded to Co, Cr, and Fe, which are constituents of local mafic rocks. The second component corresponded to Cu, Pb, and Zn which were significantly higher within than beyond a 100 m buffer of the major roads within the city; furthermore, Pb and Zn were higher in older residential lots.

Environmental statistics with S-Plus

The combination of easy-to-use software with easy access to a description of the statistical methods (definitions, concepts, etc.) makes this book an excellent resource. One of the major features of this book is the inclusion of general information on environmental statistical methods and examples of how to implement these methods using the statistical software package S-Plus and the add-in modules Environmental-Stats for S-Plus, S+SpatialStats, and S-Plus for ArcView.

The Reduction in Fatigue Crack Growth Resistance of Dentin with Depth

The fatigue crack growth resistance of dentin was characterized as a function of depth from the dentino-enamel junction. Compact tension (CT) specimens were prepared from the crowns of third molars in the deep, middle, and peripheral dentin. The microstructure was quantified in terms of the average tubule dimensions and density. Fatigue cracks were grown in-plane with the tubules and characterized in terms of the initiation and growth responses. Deep dentin exhibited the lowest resistance to the initiation of fatigue crack growth, as indicated by the stress intensity threshold (ΔKth ≈ 0.8 MPa•m0.5) and the highest incremental fatigue crack growth rate (over 1000 times that in peripheral dentin). Cracks in deep dentin underwent incremental extension under cyclic stresses that were 40% lower than those required in peripheral dentin. The average fatigue crack growth rates increased significantly with tubule density, indicating the importance of microstructure on the potential for tooth fracture. Molars with deep restorations are more likely to suffer from the cracked-tooth syndrome, because of the lower fatigue crack growth resistance of deep dentin.

The development of an objective lameness scoring system for dairy herds: Pilot study

Early detection of bovine lameness offers the potential for effective treatment and effectual management of hoof and leg ailments. This technical note examines statistical relationships between visually derived lameness scores and mechanically derived limb movement variables (LMVs) for sound and lame dairy cows. The visually derived lameness scores were based on observations of arched backs while cows stood and walked. The mechanically derived LMVs were captured as cows walked freely over a patent–pending force–plate system that generates signatures of ground reaction forces. A statistical model evaluated a lameness index (LI) using peak ground reaction force (PGRF). The LI allowed the 23 cows to be classified correctly according to their visually derived lameness scores, with three exceptions. This pilot study demonstrated that the outputs of our force–plate system can be used to relate lameness scores to measurable LMVs.

Enhancing the prediction accuracy of bovine lameness models through transformations of limb movement variables.

The issue of modeling bovine lameness was explored by testing the hypothesis that B-spline transformation of limb movement variables (LMV) employed in predictive models improved model accuracy. The objectives were to determine the effect of number of B-spline knots and the degree of the underlying polynomial approximation (degree of freedom) on model accuracy. Knot number used in B-spline transformation improved model accuracy by improving model specificity and to a lesser extent model sensitivity. Degree of polynomial approximation had no effect on model predictive accuracy from the data set of 261 cows. Model stability, defined as changes in predictive accuracy associated with the superimposition of perturbations (0.5 and 1.0%) in LMV on the measured data, was explored. Model specificity and to a lesser degree, sensitivity, increased with increased knot number across data set perturbations. Specificity and sensitivity increased by 43 and 11%, respectively, when knot number increased from 0 to 7 for a perturbation level of 0.5%. When the perturbation level was 1%, the corresponding increases in specificity and sensitivity were 32 and 4%, respectively. Nevertheless, different levels of LMV perturbation varied the optimal knot number associated with highest model accuracy. The optimal knot number for 0.5% perturbation was 8, whereas for 1% perturbation the optimal knot number was 7. The B-spline transformation improved specificity and sensitivity of predictive models for lameness, provided the appropriate number of knots was selected.

Large Cluster Results for Two Parametric Multinomial Extra Variation Models

Abstract Two parametric extra variation models are considered. Approximate closed-form expressions are given for the Fisher information matrices. The expressions are useful in computing maximum likelihood estimates and obtaining large cluster efficiencies. A simulation study shows that the approximations perform very well even in clusters of moderate size. The models are applied in illustrative examples. A goodness-of-fit test is developed that is applicable even when the cluster sizes are unequal. The null distribution of the test statistic is shown to be well approximated by a chi-squared distribution. For the cluster size configurations in the examples, the test also has high power in distinguishing between the two models considered. The goodness-of-fit test shows that the new model provides adequate description of the data from the three experiments designed to study induced mutagenic effect.

Diversity in the magnitude of hind limb unloading occurs with similar forms of lameness in dairy cows

The objective of the study was to evaluate the relationship of veterinary clinical assessments of lameness to probability estimates of lameness predicted from vertical kinetic measures. We hypothesized that algorithm-derived probability estimates of lameness would accurately reflect vertical measures in lame limbs even though vertical changes may not inevitably occur in all lameness. Kinetic data were collected from sound (n=179) and unilaterally lame (n=167) dairy cattle with a 1-dimensional, parallel force plate system that registered vertical ground reaction force signatures of all four limbs as cows freely exited the milking parlour. Locomotion was scored for each hind limb using a 1-5 locomotion score system (1=sound, 5=severely lame). Pain response in the interdigital space was quantified with an algometer and pain response in the claw was quantified with a hoof tester fitted with a pressure gage. Lesions were assigned severity scores (1=minimal pathology to 5=severe pathology). Lameness diminished the magnitude of peak ground reaction forces, average ground reaction forces, Fourier transformed ground reaction forces, stance times and vertical impulses in the lame limbs of unilaterally lame cows. The only effect of lameness on the opposite sound limb was increased magnitude of stance times and vertical impulses in unilaterally lame cows. Symmetry measures of the peak ground reaction forces, average ground reaction forces, Fourier transformed ground reaction forces, stance times and vertical impulses between the left and right hind limbs were also affected in unilateral lameness. Paradoxically, limbs with clinically similar lesion and locomotion scores and pain responses were associated with a broad range of load-transfer off the limb. Substantial unloading and changes in the vertical limb variables occurred in some lameness while minimal unloading and changes in vertical limb variables occurred in other lameness. Corresponding probability estimates of lameness accurately reflected changes in the vertical parameters of limbs and generated low probability estimates of lameness when minimal unloading occurred. Failure to transfer load off limbs with pain reactions, locomotion abnormalities and lesions explained much of the limited sensitivity in lameness detection with vertical limb variables.

An improved method for the computation of maximum likeliood estimates for multinomial overdispersion models

In this article, we consider the maximum likelihood estimation of two commonly used overdispersion models, namely, the Dirichlet-multinomial distribution (DM), due to Mosimann (Biometrika 49 (1962) 65), and a finite mixture distribution (FM) proposed by Morel and Nagaraj (Biometrika 80 (1993) 363), and Neerchal and Morel (J. Amer. Statist. Assoc. 93 (1998) 1078). These models have been successfully used in the literature for modeling overdispersion in multinomial data. Maximum likelihood estimation of the parameters of these models using the classical Fisher scoring method poses certain computational challenges. In the case of DM, the challenges are overcome by noting that the Fisher information matrix can be computed using the beta-binomial distribution (BB), which is the univariate version of DM. On the other hand, in the case of FM, an approximation theorem can be used to obtain a two-stage procedure for computing the maximum likelihood estimates. Simulation results show that the two-stage procedure is faster without loosing any accuracy.

Measuring early pre-symptomatic changes in locomotion of SOD1-G93A rats—A rodent model of amyotrophic lateral sclerosis

A locomotion analysis system for laboratory rats is presented. The system produces locomotion parameters (LPs) in 4 different domains: force, space, time and frequency. Video images of the walking rats are used to associate the system signals with individual limbs. Numerous LPs can be derived for every test run when the rat walks through the system on the way to sweets and a personal toy placed at the exit. This manuscript demonstrates that in order to differentiate SOD1-G93A mutant rat, a model of amyotrophic lateral sclerosis (ALS), from a Sprague Dawley (SD) control rat at a pre-symptomatic stage, one has only to use 8 key parameters. These 8 parameters are the bio-markers of ALS. The spline-based transformed values of these parameters are used as explanatory variables of a logistic regression model. This model predicts the probability that the examined rat belongs to the SOD1-G93A group. The model differentiates faultlessly between the SOD1 and control groups from the very first time the rats walked through the system at 51 days old. This system provides a new paradigm for ALS diagnosis, and it can have a significant impact on the development of new therapeutic procedures for ALS. The methodology presented in this manuscript can further address the development and validation of therapeutic procedures for other neurological diseases that affect locomotion.