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PubMed2005

The prediction of lumbar spine geometry: method development and validation.

OBJECTIVES: To develop and validate a new method of predicting the neutral lumbar spine curve from external (non-invasive electrogoniometer) measurements. BACKGROUND: Non-invasive techniques for lumbar spine geometry prediction suffer from a lack of a complete geometry description, problems with applicability to field conditions, or both. METHODS: The study consisted of three steps. First, utilizing lateral imaging (MRI and X-ray pictures) of the lumbosacral junction, the torso geometry was described using measures of lumbar lordosis via the Cobb method. Second, the relationship between imaging based measurement of lumbar spinal curvature and externally measured torso flexion angle in the sagittal plane using a goniometer was determined. Finally, method validation was performed with an independent set of nine subjects. The predicted lumbar spine curve was determined and the prediction errors were analyzed against the measured curves from digitized lateral X-ray images of the lumbosacral junction. RESULTS: The shape of the lumbar curve was described as function of three externally measured parameters. The lumbar spine Cobb angle, segmental centroid positions (S(1)-T(12)), and segmental orientations were predicted from the external lumbar motion monitor measurements, with average precisions of 5.8 degrees , 4.4 mm, and 3.9 degrees , respectively. CONCLUSIONS: The position and orientation of each segment (vertebrae and disc), along with the lumbar spine lordosis, can be predicted in the neutral posture using data from back angular measurements. RELEVANCE: The consideration of the spine as a curve is necessary to accurately quantify and describe the forces acting along the (lumbar) vertebral column for any given loading. The method could be a very useful prediction tool for industrial and laboratory experiments, as well as analytical models.

Adult↗
PubMed2006

Knowledge acquisition and development of accurate rules for predicting protein stability changes.

Knowing the mechanisms by which protein stability change is one of the most important and valuable tasks in molecular biology. The conventional methods of predicting protein stability changes mainly focus on improving prediction accuracy. However, it is desirable to extract domain knowledge from large databases that is beneficial to accurate prediction of the protein stability change. This paper presents an interpretable prediction tree method (named iPTREE) that produces explanatory rules to explore hidden knowledge accompanied with high prediction accuracy and consequently analyzes the factors influencing the protein stability changes. To evaluate iPTREE and the knowledge upon protein stability changes, a thermodynamic dataset consisting of 1615 mutants led by single point mutation from ProTherm is adopted. Being as a predictor for protein stability changes, the rule-based approach can achieve a prediction accuracy of 87%, which is better than other methods based on artificial neural networks (ANN) and support vector machines (SVM). Besides, these methods lack the ability in biological knowledge discovery. The human-interpretable rules produced by iPTREE reveal that temperature is a factor of concern in predicting protein stability changes. For example, one of interpretable rules with high support is as follows: if the introduced residue type is Alanine and temperature is between 4 degrees C and 40 degrees C, then the stability change will be negative (destabilizing). The present study demonstrates that iPTREE can easily be used in the application of protein stability changes where one requires more understandable knowledge.

Algorithms↗
PubMed2006

Prediction of pathological stage is inaccurate in men with PSA values above 20 ng/mL.

INTRODUCTION: We hypothesized that either very low (0-2.5 ng/mL) or very high (>20 ng/mL) PSA values may limit the accuracy of pathological stage predictions. To test this hypothesis, we examined 5193 consecutive patients subjected to radical prostatectomy (RP) for localized prostate cancer (PCa). MATERIAL AND METHODS: Patients were divided into three cohorts according to their pre-treatment PSA value: </=2.5 (n=331), 2.51-20 (n=4545) and >20 ng/mL (n=317). Subsequently in each cohort, the ability of PSA, clinical stage and biopsy Gleason sum was tested in multivariable logistic regression models predicting three separate endpoints: extracapsular extension (ECE), seminal vesicle invasion (SVI) and lymph node invasion (LNI). Predictive accuracy represented the performance benchmark. All models were adjusted for year of surgery and subjected to 200 bootstrap resamples to reduce overfit bias. RESULTS: For PSA </=2.5 ng/mL, predictive accuracy was 76.7%, 72.3% and 82.8% for respectively ECE, SVI and LNI. For PSA 2.51-20 ng/mL, the predictive accuracy for the same endpoints was 67.8%, 77.4% and 81.6%. Finally, for PSA >20 ng/mL, predictive accuracy was 63.6%, 63.7% and 70.6%. CONCLUSIONS: The ability to predict pathological stage in patients with PSA values in excess of 20 ng/mL significantly decreased, compared to patients with lower PSA values. Therefore, accurate staging of these patients may require alternative markers or staging schemes.

Adult↗
PubMed2005

Plasmodium falciparum: evaluation of a quantitative nucleic acid sequence-based amplification assay to predict the outcome of sulfadoxine-pyrimethamine treatment of uncomplicated malaria.

A quantitative nucleic acid sequence-based amplification (QT-NASBA) assay was employed to predict retrospectively the outcome of sulfadoxine-pyrimethamine (SP) treatment of uncomplicated malaria in children aged <6 years in an endemic region. Blood samples were collected at initial diagnosis and during follow-up. Mutation-specific nested PCR methods to analyse DHFR (Arg-59) and DHPS (Glu-540) mutations that are associated with SP drug resistance were applied. Parasite genotyping was performed to distinguish between re-infection and recrudescence. Eighty-six patients were recruited of which 66 were available for follow-up. Nine children were classified as early treatment failure, 13 cases were classified as late clinical failure, 32 as late parasitological failure, and only 12 children had an adequate clinical and parasitological response. DHFR and DHPS mutations conferring SP resistance were abundant in the Plasmodium population. Blood samples obtained 7 days after treatment were used to predict retrospectively the outcome of SP treatment. QT-NASBA was able to give a correct prediction of treatment outcome in 85.7% of the cases. Positive predictive value (PPV) of QT-NASBA case was 95% (95% confidence interval = 88.3-100) and negative predictive value (NPV) was 63% (95% CI = 39.5-86.5). In contrast, microscopy correctly predicted outcome in only 37.5% of the cases. PPV of microscopy was 100% (95% CI = 73.9-100) and the NPV was 25.5% (95% CI = 13.0-38.0). The analysis of a day 7 blood sample with QT-NASBA allows for the prediction of late clinical or parasitological treatment failure in the majority of the cases analysed in the present study.

Animals↗
PubMed2005

Prediction of protein subcellular location using a combined feature of sequence.

To understand the structure and function of a protein, an important task is to know where it occurs in the cell. Thus, a computational method for properly predicting the subcellular location of proteins would be significant in interpreting the original data produced by the large-scale genome sequencing projects. The present work tries to explore an effective method for extracting features from protein primary sequence and find a novel measurement of similarity among proteins for classifying a protein to its proper subcellular location. We considered four locations in eukaryotic cells and three locations in prokaryotic cells, which have been investigated by several groups in the past. A combined feature of primary sequence defined as a 430D (dimensional) vector was utilized to represent a protein, including 20 amino acid compositions, 400 dipeptide compositions and 10 physicochemical properties. To evaluate the prediction performance of this encoding scheme, a jackknife test based on nearest neighbor algorithm was employed. The prediction accuracies for cytoplasmic, extracellular, mitochondrial, and nuclear proteins in the former dataset were 86.3%, 89.2%, 73.5% and 89.4%, respectively, and the total prediction accuracy reached 86.3%. As for the prediction accuracies of cytoplasmic, extracellular, and periplasmic proteins in the latter dataset, the prediction accuracies were 97.4%, 86.0%, and 79.7, respectively, and the total prediction accuracy of 92.5% was achieved. The results indicate that this method outperforms some existing approaches based on amino acid composition or amino acid composition and dipeptide composition.

Amino Acid Sequence↗
PubMed2006

Validation of a prediction model for the follicle-stimulating hormone response dose in women with polycystic ovary syndrome.

OBJECTIVE: To validate a published model for the prediction of the individual FSH response dose for gonadotropin induction of ovulation in polycystic ovary syndrome (PCOS). DESIGN: Structured, complete, and carefully monitored patient-based data collection to test the external validity of the prediction model. SETTING: Twenty-nine hospitals in The Netherlands. PATIENT(S): Eighty-five clomiphene citrate (CC)-resistant women with PCOS. INTERVENTION(S): Ovulation induction in a chronic low-dose step-up FSH regimen. MAIN OUTCOME MEASURE(S): Predicted individual FSH response dose, defined as follicle growth >10 mm in diameter on ultrasound. RESULT(S): The model, using the women's body mass index, CC response, initial serum FSH level, and initial serum insulin-to-glucose ratio was studied in the validation sample. Overall, the FSH response dose predicted by the model was higher than the observed response dose. The predictive performance of the model was poor, with an R(2) of 0.11, and the average prediction error was 35 IU. CONCLUSION(S): The external validity of the model predicting the individual FSH response dose was inadequate in women with CC-resistant PCOS undergoing ovulation induction with recombinant FSH in a low-dose step-up regimen.

Adult↗
PubMed2026

Gastrointestinal digestion governs insect protein hydrolysis and predicted bioactive peptide release: Species-dependent implications for functional food applications.

This study investigates the digestion of insect proteins and the release of predicted bioactive peptides during human gastrointestinal digestion. Using the Infogest in vitro model, mealworm, cricket, and black soldier fly larvae (BSFL) proteins were digested and analyzed through discovery proteomics and bioinformatics to identify predicted bioactive peptides. Sequential windowed acquisition of all theoretical fragment ion mass spectra (SWATH-MS) quantified insect proteins including predicted bioactive peptide precursor proteins, the precursors of predicted bioactive peptides. Results indicated that gastrointestinal digestion strongly influences peptide release, with the gastric phase exhibiting a richer predicted bioactive peptide profile than the small intestinal phase. Many predicted bioactive peptides were rapidly hydrolysed under small intestine conditions, which may lead to reduced stability or diminished activity in vivo, potentially explaining why certain peptides show strong bioactivity in vitro but limited effects in vivo. Additionally, predicted bioactive peptide release varied by insect species, influenced by genetic factors and peptide abundance. These findings highlight the importance of species selection and consideration of proteolytic digestion patterns in optimizing insect-derived bioactive peptides for functional foods and nutraceutical applications.

Animals↗
PubMed2006

QSPR models for the prediction of apparent volume of distribution.

An estimate of volume of distribution (V(d)) is of paramount importance both in drug choice as well as maintenance and loading dose calculations in therapeutics. It can also be used in the prediction of drug biological half life. This study employs quantitative structure-pharmacokinetic relationship (QSPR) techniques for the prediction of volume of distribution. Values of V(d) for 129 drugs were collated from the literature. Structural descriptors consisted of partitioning, quantum mechanical, molecular mechanical, and connectivity parameters calculated by specialized software and pK(a) values obtained from ACD labs/log D database. Genetic algorithm and stepwise regression analyses were used for variable selection and model development. Models were validated using a leave-many-out procedure. QSPR analyses resulted in a number of significant models for acidic and basic drugs separately, and for all the drugs. Validation studies showed that mean fold error of predictions for the selected models were between 1.79 and 2.17. Although separate QSPR models for acids and bases resulted in lower prediction errors than models for all the drugs, the external validation study showed a limited applicability for the equation obtained for acids. Therefore, the universal model that requires only calculated structural descriptors was recommended. The QSPR model is able to predict the volume of distribution of drugs belonging to different chemical classes with a prediction error similar to that of the other more complicated prediction methods including the commonly practiced interspecies scaling. The structural descriptors in the model can be interpreted based on the known mechanisms of distribution and the molecular structures of the drugs.

Algorithms↗
PubMed2005

Prediction of outcomes in trauma: anatomic or physiologic parameters?

BACKGROUND: Prediction of outcomes after injury has traditionally incorporated measures of injury severity, but recent studies suggest that including physiologic and shock measures can improve accuracy of anatomic-based models. A recent single-institution study described a mortality predictive equation [f(x) = 3.48 - .22 (GCS) - .08 (BE) + .08 (Tx) + .05 (ISS) + .04 (Age)], where GSC is Glasgow Coma Score, BE is base excess, Tx is transfusion requirement, and ISS is Injury Severity Score, which had 63% sensitivity, 94% specificity, (receiver operating characteristic [ROC] 0.96), but did not provide comparative data for other models. We have previously documented that the Physiologic Trauma Score, including only physiologic variables (systemic inflammatory response syndrome, Glasgow Coma Score, age) also accurately predicts mortality in trauma. The objective of this study was to compare the predictive abilities of these statistical models in trauma outcomes. METHODS: Area under the ROC curve of sensitivity versus 1-specificity was used to assess predictive ability and measured discrimination of the models. RESULTS: The study cohort consisted of 15,534 trauma patients (80% blunt mechanism) admitted to a Level I trauma center over a 3-year period (mean age 37 +/- 18 years; mean Injury Severity Score 10 +/- 10; mortality 4%). Sensitivity of the new predictive model was 45%, specificity was 96%, ROC was 0.91, validating this new trauma outcomes model in our institution. This was comparable with area under the ROC for Revised Trauma Score (ROC 0.88), Trauma and Injury Severity Score (ROC 0.97), and Physiologic Trauma Score (ROC 0.95), but superior compared with admission Glasgow Coma Score (ROC 0.79), Injury Severity Score (ROC 0.79), and age (ROC 0.60). CONCLUSIONS: The predictive ability of this new model is superior to anatomic-based models such as Injury Severity Score, but comparable with other physiologic-based models such as Revised Trauma Score, Physiologic Trauma Score and Trauma, and Injury Severity Score.

Adult↗
PubMed2006

Applied tracer tests in fractured rock: Can we predict natural gradient solute transport more accurately than fracture and matrix parameters?

Applied tracer tests provide a means to estimate aquifer parameters in fractured rock. The traditional approach to analysing these tests has been using a single fracture model to find the parameter values that generate the best fit to the measured breakthrough curve. In many cases, the ultimate aim is to predict solute transport under the natural gradient. Usually, no confidence limits are placed on parameter values and the impact of parameter errors on predictions of solute transport is not discussed. The assumption inherent in this approach is that the parameters determined under forced conditions will enable prediction of solute transport under the natural gradient. This paper considers the parameter and prediction uncertainty that might arise from analysis of breakthrough curves obtained from forced gradient applied tracer tests. By adding noise to an exact solution for transport in a single fracture in a porous matrix we create multiple realisations of an initial breakthrough curve. A least squares fitting routine is used to obtain a fit to each realisation, yielding a range of parameter values rather than a single set of absolute values. The suite of parameters is then used to make predictions of solute transport under lower hydraulic gradients and the uncertainty of estimated parameters and subsequent predictions of solute transport is compared. The results of this study show that predictions of breakthrough curve characteristics (first inflection point time, peak arrival time and peak concentration) for groundwater flow speeds with orders of magnitude smaller than that at which a test is conducted can sometimes be determined even more accurately than the fracture and matrix parameters.

Geological Phenomena↗
PubMed2004

Predicting postoperative intraocular lens position and refraction.

PURPOSE: To predict the postoperative IOL position and refraction as accurately as possible independent of individualization of the parameters. SETTING: Universitats-Augenklinik, Mainz, Germany, and Vienna, Austria. METHODS: One patient cohort (189 eyes, Vienna) was used to calibrate the prediction method, which was then applied to a second cohort (65 eyes, Mainz). All calculations were based on consistent numerical ray tracing of the pseudophakic eye using the original manufacturer's intraocular lens (IOL) data (radii, thickness, refractive index). A new algorithm to predict IOL position was developed. Ultrasound (US) axial lengths were calibrated relative to partial coherence interferometry (PCI). Corneal radii extracted from topography were checked against radii measured with the IOLMaster (Zeiss) and by Littmann keratometry. RESULTS: Zero mean prediction errors for IOL position and refraction were obtained without adjusting the parameters and with PCI lengths or US lengths calibrated relative to the PCI values. There was no significant loss of accuracy of US data compared to PCI data. Corneal radii extracted from topography were slightly but statistically significantly different from the Littmann values, and they were more accurate than the latter with respect to prediction error. The measured mean central IOL position (distance from posterior corneal surface) for all IOL types was 4.580 mm, a value very close to the mean recalculated from A-constants (4.587 mm). The difference in the individual central IOL position relative to the mean value depended only linearly (ie, no higher orders such as square or cubic are needed) on axial length, with the mean central IOL position as a free parameter. This parameter should be 4.6 +/- 0.2 mm (the same value as independently measured or recalculated) to obtain zero steepness of the prediction error as a function of axial length, producing zero bias for long and short eyes. CONCLUSIONS: Calculation errors from formulas and confusing adjusting parameters can be avoided if calculations and measurements are performed on a clear and simple physical basis. Nevertheless, an individual prediction error, typically 0.5 to 1.0 diopter, seems to be unavoidable.

Adult↗
PubMed2006

Prediction of the effective postoperative (intraocular lens) anterior chamber depth.

PURPOSE: To investigate methods to predict the effective postoperative anterior chamber depth (ACD) based on a large patient sample. SETTING: University Eye Clinic, Aarhus Kommunehospital, Aarhus, Denmark. METHODS: Based on 6698 consecutive cataract operations with recorded postoperative refractive results, the postoperative effective ACD was calculated in each case and studied by multiple linear regression for covariance with a number of preoperatively defined variables including the axial length by ultrasonography, preoperative ACD, lens thickness, corneal radius by keratometry, subjective refraction, patient age, and corneal white-to-white diameter, the latter of which was available in a subgroup of 900 cases. RESULTS: The postoperative effective ACD was significantly correlated with 6 preoperative variables (in decreasing order): axial length, preoperative ACD, keratometry reading, lens thickness, refraction, and patient age (R = 0.49, P < .000001). Age showed the weakest correlation (P = .02) and could be omitted with no significant decrease in the total correlation coefficient. Using the 5 most significant variables, the ACD could be predicted according to a regression formula with an accuracy of 82.1% of the predictions within 0.5 mm. When this ACD algorithm was used in retrospect in the intraocular lens (IOL) power calculation, the refractive prediction error decreased by 10% from the error associated with a previously published 4-variable algorithm and decreased 28% from the error using no individual ACD method other than the average ACD (P < .00001). CONCLUSIONS: The postoperative ACD was significantly correlated with and hence predictable by a 5-variable regression method incorporating the preoperative axial length, ACD, keratometry reading, lens thickness, and refraction as the most significant variables. The statistical relationship can be used to create a new ACD prediction algorithm to incorporate in a modern "thick lens" IOL power calculation formula with significant improvement in the accuracy of the refractive predictions as a result.

Adult↗
PubMed2004

Predicting the distribution of ground beetle species (Coleoptera, Carabidae) in Britain using land cover variables.

Predictions of plant and animal species distributions are important for conservation and for the assessment of large-scale ecosystem change. Land cover data are becoming more widely available for use in land management and conservation. We use a logistic regression modelling approach to investigate the utility of these data for modelling. The relationship between the distribution of 137 British ground beetles species and land cover was investigated using data from 1,687 10 km national grid squares. Land cover data were simplified using ordination and the axes used as predictors in logistic regression with presence absence data for individual beetle species as response variables. Significant regression models were generated for all species with first and second axis scores. The amounts of variation explained by models were generally low, but predictions derived from models generally matched the known distributions of the species in Britain. Species with coastal preferences were poorly modelled and predicted to occur throughout lowland Britain whilst a number of species occurring in southern Britain were predicted to occur into Scotland. A validation exercise comparing model predictions with new data from a survey of 59 10 km(2) produced mixed results with the distribution of grassland species being better predicted than riverine species. Jack-knifing was used to assess the robustness of models for four species which differed in their apparent responses to the land cover variables. Methods for improving the predictive power of these models and their potential for use in assessing the impact of global climate change are discussed.

Animals↗
PubMed2005

The Bournemouth Questionnaire: can it be used to monitor and predict treatment outcome in chiropractic patients with persistent low back pain?

OBJECTIVE: To investigate the Bournemouth Questionnaire (BQ) as a baseline, monitoring of progress, and prognostic instrument in chiropractic patients with persistent low back pain (LBP). STUDY DESIGN: Predictive and concurrent validation study. STUDY PARTICIPANTS AND SETTING: One hundred fifteen Norwegian chiropractors collected prospective data on 875 patients with persistent LBP, defined as LBP for at least 2 weeks at baseline and a minimum of 30 days totaling within the preceding year. METHODS: Data collection took place at first consultation, fourth visit, and 3 months using the BQ, the revised Oswestry questionnaire, and a 10-point pain box scale. Follow-up at 12 months included the BQ, Oswestry questionnaire, and additional questions on the number of days with LBP and the number of days off work in the past year. DATA ANALYSIS: Frequency of reporting of each 7 items in the BQ at baseline was identified as median value with 10th and 90th percentiles. Concurrent analyses of the 2 questionnaires were made at the 4 points in time with calculation of mean differences with limits of agreement together with Bland-Altman plots. Logistic regression was used to identify and compare the predictive values of the questionnaires and to test the relevance of each individual item in the BQ. RESULTS: The median baseline values of the 7 items in the BQ ranged from 2 to 5. The 2 questionnaires did not agree on patients' status, and mean differences between the Oswestry questionnaire and the BQ were largest when patients reported higher scores. The predictive values for the 2 questionnaires were low, with no significant difference between the 2. The predictive value of the BQ could be improved by removing most of the 7 items. Certain items can predict specific outcomes. CONCLUSIONS: The BQ is not a useful instrument to identify baseline status, monitor progress, or predict the 1-year progress in chiropractic patients having persistent LBP. However, certain individual items are useful to predict specific outcomes.

Absenteeism↗
PubMed2005

Heart rate variability predicts trauma patient outcome as early as 12 h: implications for military and civilian triage.

BACKGROUND: Our previous work demonstrated dense physiological data capture in the intensive care unit (ICU), defined a new vital sign Cardiac Volatility Related Dysfunction (CVRD) reflecting reduced heart rate variability, and demonstrated CVRD predicts death during the hospital stay adjusting for age and injury severity score (ISS). We hypothesized a more precise definition of variability in integer heart rate improves predictive power earlier in ICU stay, without adjusting for covariates. METHODS: Approximately 120 million integer heart rate (HR) data points were prospectively collected and archived from 1316 trauma ICU patients, linked to outcome data, and de-identified. HR standard deviation was computed in each 5-min interval (HR(SD5)). HR(SD5) logistic regression identified ranges predictive of death. The study group was randomly divided. Integer heart rate variability (% time HR(SD5) in predictive distribution ranges) models were developed on the first set (N = 658) at 1, 2, 4, 6, 8, 12, and 24 h after ICU admission, and validated on the second set (N = 658). RESULTS: HR(SD5) is bimodal, predicts death at low (0.1-0.9 bpm) and survival at high (1.8-2.6 bpm) ranges. HRV predicts death as early as 12 h (ROC = 0.67). HRV in a moving 1-h window is a simple graphic display technique. CONCLUSIONS: Dense physiological data capture allows calculation of HRV, which: 1) Independently predicts hospital death in trauma patients at 12 h; 2) Shows early differences by mortality in groups of patients when viewed in a moving window; and 3) May have implications for military and civilian triage.

Adult↗
PubMed2006

A stochastic mathematical model of methicillin resistant Staphylococcus aureus transmission in an intensive care unit: predicting the impact of interventions.

OBJECTIVES: To estimate the transmission rate of MRSA in an intensive care unit (ICU) in an 800 bed Australian teaching hospital and predict the impact of infection control interventions. METHODS: A mathematical model was developed which consisted of four compartments: colonised and uncolonised patients and contaminated and uncontaminated health-care workers (HCWs). Patient movements, MRSA acquisition and daily prevalence data were collected from an ICU over 939 days. Hand hygiene compliance and the probability of MRSA transmission from patient to HCW per discordant contact were measured during the study. Attack rate and reproduction ratio were estimated using Bayesian methods. The impact of a number of interventions on attack rate was estimated using both stochastic and deterministic versions of the model. RESULTS: The mean number of secondary cases arising from the ICU admission of colonised patients, also called the ward reproduction ratio, R(w), was estimated to be 0.50 (95% CI 0.39-0.62). The attack rate was one MRSA transmission per 160 (95% CI 130-210) uncolonised-patient days. Results were not sensitive to uncertainty in measured model parameters (hand hygiene rate and transmission probability per contact). Hand hygiene was predicted to be the most effective intervention. Decolonisation was predicted to be relatively ineffective. Increasing HCW numbers was predicted to increase MRSA transmission, in the absence of patient cohorting. The predictions of the stochastic model differed from those of the deterministic model, with lower levels of colonisation predicted by the stochastic model. CONCLUSIONS: The number of secondary cases of MRSA colonisation within the ICU in this study was below unity. Transmission of MRSA was sustained through admission of colonised patients. Stochastic model simulations give more realistic predictions in hospital ward settings than deterministic models. Increasing staff does not necessarily lead to reduced transmission of nosocomial pathogens.

Bayes Theorem↗
PubMed2005

Validation of in silico prediction by in vitro immunoserological results of fine epitope mapping on citrate synthase specific autoantibodies.

In silico antibody-antigen binding predictions are generally employed in research to rationalize epitope development. These techniques are widely spread despite their technical limitations. To validate the results of these bioinformatic calculations evidence based comparative in vitro studies are necessary. We have used a well-conserved mitochondrial inner membrane antigen-citrate synthase to develop a model for comparative analysis of the predicted and the immunoserologically verified epitopes of circulating autoantibodies. Epitopes were predicted using accepted tools: the GCG Wisconsin package and TEPITOPE 2000. An overlapping multipin ELISA assay--covering 49% of the citrate synthase molecule--was developed to map autoantibody epitopes of individuals (healthy, systemic autoimmune, and heart transplanted) in different immunopathological conditions. From the 40 synthesized decapeptides 34 were predicted in silico and 27 were validated in vitro. Thirty-two percent of epitopes were recognized by majority of sera 47% by at least one sera. False positive predictions were 21%. There was major difference in the recognized epitope pattern under different immunopathological conditions. Our results suggest that special databases are needed for training and weighing prediction methods by clinically well-characterized samples, due to the differences in the immune response under different health status. The development of these special algorithms needs a new approach. A high number of samples under these special immunological conditions are to be mapped and then used for the "fine tuning" of different prediction algorithms.

Amino Acid Sequence↗
PubMed2006

A multifocal electroretinogram model predicting the development of diabetic retinopathy.

The prevalence of diabetes has been accelerating at an alarming rate in the last decade; some describe it as an epidemic. Diabetic eye complications are the leading cause of blindness in adults aged 25-74 in the United States. Early diagnosis and development of effective preventatives and treatments of diabetic retinopathy are essential to save sight. We describe efforts to establish functional indicators of retinal health and predictors of diabetic retinopathy. These indicators and predictors will be needed as markers of the efficacy of new therapies. Clinical trials aimed at either prevention or early treatments will rely heavily on the discovery of sensitive methods to identify patients and retinal locations at risk, as well as to evaluate treatment effects. We report on recent success in revealing local functional changes of the retina with the multifocal electroretinogram (mfERG). This objective measure allows the simultaneous recording of responses from over 100 small retinal patches across the central 45 degrees field. We describe the sensitivity of mfERG implicit time measurement for revealing functional alterations of the retina in diabetes, the local correspondence between functional (mfERG) and structural (vascular) abnormalities in eyes with early nonproliferative retinopathy, and longitudinal studies to formulate models to predict the retinal sites of future retinopathic signs. A multivariate model including mfERG implicit time delays and 'person' risk factors achieved 86% sensitivity and 84% specificity for prediction of new retinopathy development over one year at specific locations in eyes with some retinopathy at baseline. A preliminary test of the model yielded very positive results. This model appears to be the first to predict, quantitatively, the retinal locations of new nonproliferative diabetic retinopathy development over a one-year period. In a separate study, the predictive power of a model was assessed over one- and two-year follow-ups. This permitted successful prediction of new retinopathy development in eyes with and without retinopathy at baseline. Finally, we briefly describe our current research efforts to (a) locally predict future sight-threatening diabetic macular edema, (b) investigate local retinal function change in adolescent patients with diabetes, and (c) better understand the physiological bases of the mfERG delays. The ability to predict the retinal locations of future retinopathy based on mfERG implicit time provides clinicians a powerful tool to screen, follow-up, and even consider early prophylactic treatment of the retinal tissue in diabetic patients. It also aids identification of 'at risk' populations for clinical trials of candidate therapies, which may greatly reduce their cost by decreasing the size of the needed sample and the duration of the trial.

Diabetic Retinopathy↗