Deranged Metabolic Profile and Identification of Biomarkers in the Vitreous Humour of Patients with Proliferative Diabetic Retinopathy.

BACKGROUND: Systemic biochemical disarray has been implicated in the pathogenesis and progression of diabetes and its complications. However, the modifications in the metabolic state of the retina and its microenvironment in response to the systemic metabolic malfunction has not been documented. OBJECTIVE: The objective of the present study was to document the various biomarkers that may be involved in the pathogenesis and progression of Proliferative Diabetic Retinopathy (PDR). METHOD: The vitreous humour and plasma samples from 38 PDR, 7 Proliferative Vitreo Retinopathy (PVR) and 17 control patients undergoing pars plana vitrectomy were analysed for sixteen different biomarkers. Whole genome Single Nucleotide Polymorphism (SNP) microarray was performed on ten PDR patients' peripheral blood samples. RESULTS: The vitreous humour glucose, creatinine, micro protein, phosphorus and lactate dehydrogenase were found significantly increased in the PDR patients compared to controls. The plasma urea, creatinine and micro protein were also significantly increased. The plasma phosphorus of PDR patients on oral hypoglycemic therapy was found significantly decreased compared with PDR patients on insulin therapy and controls. SNPs previously associated with glucose (5), lactate dehydrogenase (2) and creatinine (2) levels were identified to be polymorphic homozygous (minor allele) in ≥ 60% patients in this study, suggesting enhanced susceptibility. CONCLUSION: The metabolic overactivity of the retinal microenvironment appears to play a vital role in the pathogenesis of PDR. The significantly elevated biomarkers may have diagnostic, prognostic and therapeutic significance. These findings shed light on the biochemical disarray in the vitreous humour of PDR patients that could have significant management implications.

Evaluation of the effect of pan retinal photocoagulation on optic nerve head parameters using HRT3.

PURPOSE: To evaluate the optic disc topography after pan retinal photocoagulation (PRP) in diabetic retinopathy patients using confocal scanning laser ophthalmoscopy (HRT3). METHODS: Thirty eyes of 30 diabetic patients (non-insulin-dependent diabetes mellitus) underwent PRP for proliferative diabetic retinopathy. Subjects with glaucoma or family history of glaucoma, any coexisting neuroophthalmic disease, uveitis, retinal artery or vein occlusion, corneal opacity or lasered previously were excluded from the study. Optic nerve head (ONH) of these patients were evaluated by confocal scanning laser ophthalmoscopy (HRT3) at baseline, 3, and 6 months after completion of PRP. RESULTS: There were 23 males and 7 females with a mean age of 51.8 years (36 to 67 y). The mean intraocular pressure was 13.1±2.57 mm Hg at baseline, 13.4±2.6 mm Hg at 3 months, and 13.3±2.2 mm Hg at 6 months (P=0.6). The global values of ONH parameters showed no significant change from baseline at 3 months. At 6 months, there was a significant increase in vertical cup:disc ratio (P=0.021), cup volume (P=0.036), mean cup depth (P=0.042), and maximum cup depth (P=0.05) as compared with baseline values. CONCLUSIONS: PRP induces significant changes in the ONH in patients with diabetic retinopathy as measured with HRT3. This is an important consideration in the diagnosis and evaluation of progression in glaucoma patients with diabetic retinopathy who have undergone PRP.

Nearest-neighbor nonparametric method for estimating the configurational entropy of complex molecules.

A method for estimating the configurational (i.e., non-kinetic) part of the entropy of internal motion in complex molecules is introduced that does not assume any particular parametric form for the underlying probability density function. It is based on the nearest-neighbor (NN) distances of the points of a sample of internal molecular coordinates obtained by a computer simulation of a given molecule. As the method does not make any assumptions about the underlying potential energy function, it accounts fully for any anharmonicity of internal molecular motion. It provides an asymptotically unbiased and consistent estimate of the configurational part of the entropy of the internal degrees of freedom of the molecule. The NN method is illustrated by estimating the configurational entropy of internal rotation of capsaicin and two stereoisomers of tartaric acid, and by providing a much closer upper bound on the configurational entropy of internal rotation of a pentapeptide molecule than that obtained by the standard quasi-harmonic method. As a measure of dependence between any two internal molecular coordinates, a general coefficient of association based on the information-theoretic quantity of mutual information is proposed. Using NN estimates of this measure, statistical clustering procedures can be employed to group the coordinates into clusters of manageable dimensions and characterized by minimal dependence between coordinates belonging to different clusters.

A quadricuspid aortic valve with atrial septal defect.

A quadricuspid aortic valve (QAV) is a rare congenital heart defect, previously described as an incidental finding at the time of surgery or postmortem, which is now being increasingly detected by 2D transthoracic or transesophageal echocardiogram. With advances in echocardiography, secondary cardiac anomalies are also being described in association with QAV. Herein we describe a patient with QAV with a secundum atrial septal defect.

Comparison of left atrial dimensions by transesophageal and transthoracic echocardiography.

Transesophageal echocardiography (TEE) is an established cardiovascular diagnostic technique. Left atrial (LA) size, as measured by transthoracic echocardiography (TTE), is associated with cardiovascular disease and is a risk factor for atrial fibrillation, stroke, death, and the success of cardioversion. Assessment of LA size has not been as well validated on TEE as on TTE. We determined LA size measurements in four standard views in 122 patients undergoing TEE and TTE at the same setting. In this study, we found that measurement of LA dimensions by TEE suffers from significant limitations in all views except the basal long-axis view (mid-esophageal level) with transducer plane at 120-150 degrees. This view had the best correlation with transthoracic LA measurements: r = 0.79 for TEE long axis (CI 0.71-0.85), P <.0001.

Application of the random forest method in studies of local lymph node assay based skin sensitization data.

The random forest and classification tree modeling methods are used to build predictive models of the skin sensitization activity of a chemical. A new two-stage backward elimination algorithm for descriptor selection in the random forest method is introduced. The predictive performance of the random forest model was maximized by tuning voting thresholds to reflect the unbalanced size of classification groups in available data. Our results show that random forest with a proposed backward elimination procedure outperforms a single classification tree and the standard random forest method in predicting Local Lymph Node Assay based skin sensitization activity. The proximity measure obtained from the random forest is a natural similarity measure that can be used for clustering of chemicals. Based on this measure, the clustering analysis partitioned the chemicals into several groups sharing similar molecular patterns. The improved random forest method demonstrates the potential for future QSAR studies based on a large number of descriptors or when the number of available data points is limited.

Structure-activity models for contact sensitization.

Allergic contact dermatitis (ACD) is a widespread cause of workers' disabilities. Although some substances found in the workplace are rigorously tested, the potential of the vast majority of chemicals to cause skin sensitization remains unknown. At the same time, exhaustive testing of all chemicals in workplaces is costly and raises ethical concerns. New approaches to developing information for risk assessment based on computational (quantitative) structure-activity relationship [(Q)SAR] methods may be complementary to and reduce the need for animal testing. Virtually any number of existing, de novo, and even preconceived compounds can be screened in silico at a fraction of the cost of animal testing. This work investigates the utility of ACD (Q)SAR modeling from the occupational health perspective using two leading software products, DEREK for Windows and TOPKAT, and an original method based on logistic regression methodology. It is found that the correct classification of (Q)SAR predictions for guinea pig data achieves values of 73.3, 82.9, and 87.6% for TOPKAT, DEREK for Windows, and the logistic regression model, respectively. The correct classification using LLNA data equals 73.0 and 83.2% for DEREK for Windows and the logistic regression model, respectively.

Estimation of the absolute internal-rotation entropy of molecules with two torsional degrees of freedom from stochastic simulations.

A method of statistical estimation is applied to the problem of evaluating the absolute entropy of internal rotation in a molecule with two torsional degrees of freedom. The configurational part of the entropy is obtained as that of the joint probability density of an arbitrary form represented by a two-dimensional Fourier series, the coefficients of which are statistically estimated using a sample of the torsional angles of the molecule obtained by a stochastic simulation. The internal rotors in the molecule are assumed to be attached to a common frame, and their reduced moments of inertia are initially calculated as functions of the two torsional angles, but averaged over all the remaining internal degrees of freedom using the stochastic-simulation sample of the atomic configurations of the molecule. The torsional-angle dependence of the reduced moments of inertia can be also averaged out, and the absolute internal-rotation entropy of the molecule is obtained in a good approximation as the sum of the configurational entropy and a kinetic contribution fully determined by the averaged reduced moments of inertia. The method is illustrated using Monte Carlo simulations of isomers of stilbene and halogenated derivatives of propane. The two torsional angles in cis-stilbene are found to be much more strongly correlated than those in trans-stilbene, while the degree of the angular correlation in propane increases strongly on substitution of hydrogen atoms with chlorine.

Statistical thermodynamics of internal rotation in a hindering potential of mean force obtained from computer simulations.

A method of statistical estimation is applied to the problem of one-dimensional internal rotation in a hindering potential of mean force. The hindering potential, which may have a completely general shape, is expanded in a Fourier series, the coefficients of which are estimated by fitting an appropriate statistical-mechanical distribution to the random variable of internal rotation angle. The function of reduced moment of inertia of an internal rotation is averaged over the thermodynamic ensemble of atomic configurations of the molecule obtained in stochastic simulations. When quantum effects are not important, an accurate estimate of the absolute internal rotation entropy of a molecule with a single rotatable bond is obtained. When there is more than one rotatable bond, the "marginal" statistical-mechanical properties corresponding to a given internal rotational degree of freedom are reduced. The method is illustrated using Monte Carlo simulations of two public health relevant halocarbon molecules, each having a single internal-rotation degree of freedom, and a molecular dynamics simulation of an immunologically relevant polypeptide, in which several dihedral angles are analyzed.

Predicting Fault Prone Modules by the Dempster-Shafer Belief Networks.

This paper describes a novel methodology for predicting fault prone modules. The methodology is based on Dempster-Shafer (D-S) belief networks. Our approach consists of three steps: First, building the Dempster-Shafer network by the induction algorithm; Second, selecting the predictors (attributes) by the logistic procedure; Third, feeding the predictors describing the modules of the current project into the inducted Dempster-Shafer network and identifying fault prone modules. We applied this methodology to a NASA dataset. The prediction accuracy of our methodology is higher than that achieved by logistic regression or discriminant analysis on the same dataset.

Estimating distributions with increasing failure rate in an imperfect repair model.

A failed system is repaired minimally if after failure, it is restored to the working condition of an identical system of the same age. We extend the nonparametric maximum likelihood estimator (MLE) of a system's lifetime distribution function to test units that are known to have an increasing failure rate. Such items comprise a significant portion of working components in industry. The order-restricted MLE is shown to be consistent. Similar results hold for the Brown-Proschan imperfect repair model, which dictates that a failed component is repaired perfectly with some unknown probability, and is otherwise repaired minimally. The estimators derived are motivated and illustrated by failure data in the nuclear industry. Failure times for groups of emergency diesel generators and motor-driven pumps are analyzed using the order-restricted methods. The order-restricted estimators are consistent and show distinct differences from the ordinary MLEs. Simulation results suggest significant improvement in reliability estimation is available in many cases when component failure data exhibit the IFR property.