THE PREVALENCE AND PREDICTION OF PULMONARY FIBROSIS IN AN

This thesis describes an ancillary project to the Early Diagnosis of Mesothelioma and Lung Cancer in Prior Asbestos Workers study and was conducted to determine the effects of asbestos exposure, pulmonary function and cigarette smoking in the prediction of pulmonary fibrosis. 613 workers who were occupationally exposed to asbestos for an average of 25.9 (SD=14.69) years were sampled from Sarnia, Ontario. A structured questionnaire was administered during a face-to-face interview along with a low-dose computed tomography (LDCT) of the thorax. Of them, 65 workers (10.7%, 95%CI 8.12—12.24) had LDCT-detected pulmonary fibrosis. The model predicting fibrosis included the variables age, smoking (dichotomized), post FVC % splines and post- FEV1% splines. This model had a receiver operator characteristic area under the curve of 0.738. The calibration of the model was evaluated with R statistical program and the bootstrap optimism-corrected calibration slope was 0.692. Thus, our model demonstrated moderate predictive performance.

A kernel-based two-class classifier for imbalanced data sets

Many kernel classifier construction algorithms adopt classification accuracy as performance metrics in model evaluation. Moreover, equal weighting is often applied to each data sample in parameter estimation. These modeling practices often become problematic if the data sets are imbalanced. We present a kernel classifier construction algorithm using orthogonal forward selection (OFS) in order to optimize the model generalization for imbalanced two-class data sets. This kernel classifier identification algorithm is based on a new regularized orthogonal weighted least squares (ROWLS) estimator and the model selection criterion of maximal leave-one-out area under curve (LOO-AUC) of the receiver operating characteristics (ROCs). It is shown that, owing to the orthogonalization procedure, the LOO-AUC can be calculated via an analytic formula based on the new regularized orthogonal weighted least squares parameter estimator, without actually splitting the estimation data set. The proposed algorithm can achieve minimal computational expense via a set of forward recursive updating formula in searching model terms with maximal incremental LOO-AUC value. Numerical examples are used to demonstrate the efficacy of the algorithm.

FunFOLDQA: a quality assessment tool for protein-ligand binding site residue predictions

The estimation of prediction quality is important because without quality measures, it is difficult to determine the usefulness of a prediction. Currently, methods for ligand binding site residue predictions are assessed in the function prediction category of the biennial Critical Assessment of Techniques for Protein Structure Prediction (CASP) experiment, utilizing the Matthews Correlation Coefficient (MCC) and Binding-site Distance Test (BDT) metrics. However, the assessment of ligand binding site predictions using such metrics requires the availability of solved structures with bound ligands. Thus, we have developed a ligand binding site quality assessment tool, FunFOLDQA, which utilizes protein feature analysis to predict ligand binding site quality prior to the experimental solution of the protein structures and their ligand interactions. The FunFOLDQA feature scores were combined using: simple linear combinations, multiple linear regression and a neural network. The neural network produced significantly better results for correlations to both the MCC and BDT scores, according to Kendall’s τ, Spearman’s ρ and Pearson’s r correlation coefficients, when tested on both the CASP8 and CASP9 datasets. The neural network also produced the largest Area Under the Curve score (AUC) when Receiver Operator Characteristic (ROC) analysis was undertaken for the CASP8 dataset. Furthermore, the FunFOLDQA algorithm incorporating the neural network, is shown to add value to FunFOLD, when both methods are employed in combination. This results in a statistically significant improvement over all of the best server methods, the FunFOLD method (6.43%), and one of the top manual groups (FN293) tested on the CASP8 dataset. The FunFOLDQA method was also found to be competitive with the top server methods when tested on the CASP9 dataset. To the best of our knowledge, FunFOLDQA is the first attempt to develop a method that can be used to assess ligand binding site prediction quality, in the absence of experimental data.

Construction of neurofuzzy models for imbalanced data classification

We propose a new class of neurofuzzy construction algorithms with the aim of maximizing generalization capability specifically for imbalanced data classification problems based on leave-one-out (LOO) cross validation. The algorithms are in two stages, first an initial rule base is constructed based on estimating the Gaussian mixture model with analysis of variance decomposition from input data; the second stage carries out the joint weighted least squares parameter estimation and rule selection using orthogonal forward subspace selection (OFSS)procedure. We show how different LOO based rule selection criteria can be incorporated with OFSS, and advocate either maximizing the leave-one-out area under curve of the receiver operating characteristics, or maximizing the leave-one-out Fmeasure if the data sets exhibit imbalanced class distribution. Extensive comparative simulations illustrate the effectiveness of the proposed algorithms.

A hybrid approach to learn with imbalanced classes using evolutionary algorithms

There is an increasing interest in the application of Evolutionary Algorithms (EAs) to induce classification rules. This hybrid approach can benefit areas where classical methods for rule induction have not been very successful. One example is the induction of classification rules in imbalanced domains. Imbalanced data occur when one or more classes heavily outnumber other classes. Frequently, classical machine learning (ML) classifiers are not able to learn in the presence of imbalanced data sets, inducing classification models that always predict the most numerous classes. In this work, we propose a novel hybrid approach to deal with this problem. We create several balanced data sets with all minority class cases and a random sample of majority class cases. These balanced data sets are fed to classical ML systems that produce rule sets. The rule sets are combined creating a pool of rules and an EA is used to build a classifier from this pool of rules. This hybrid approach has some advantages over undersampling, since it reduces the amount of discarded information, and some advantages over oversampling, since it avoids overfitting. The proposed approach was experimentally analysed and the experimental results show an improvement in the classification performance measured as the area under the receiver operating characteristics (ROC) curve.

Integration of Ligand- and Target-Based Virtual Screening for the Discovery of Cruzain Inhibitors

A myriad of methods are available for virtual screening of small organic compound databases. In this study we have successfully applied a quantitative model of consensus measurements, using a combination of 3D similarity searches (ROCS and EON), Hologram Quantitative Structure Activity Relationships (HQSAR) and docking (FRED, FlexX, Glide and AutoDock Vina), to retrieve cruzain inhibitors from collected databases. All methods were assessed individually and then combined in a Ligand-Based Virtual Screening (LBVS) and Target-Based Virtual Screening (TBVS) consensus scoring, using Receiving Operating Characteristic (ROC) curves to evaluate their performance. Three consensus strategies were used: scaled-rank-by-number, rank-by-rank and rank-by-vote, with the most thriving the scaled-rank-by-number strategy, considering that the stiff ROC curve appeared to be satisfactory in every way to indicate a higher enrichment power at early retrieval of active compounds from the database. The ligand-based method provided access to a robust and predictive HQSAR model that was developed to show superior discrimination between active and inactive compounds, which was also better than ROCS and EON procedures. Overall, the integration of fast computational techniques based on ligand and target structures resulted in a more efficient retrieval of cruzain inhibitors with desired pharmacological profiles that may be useful to advance the discovery of new trypanocidal agents.

Avaliação do desempenho diagnóstico e do valor de corte para o índice de respiração rápida e superficial na predição do insucesso da extubação

OBJETIVO: Avaliar o desempenho diagnóstico do índice de respiração rápida e superficial (IRRS) na predição do insucesso da extubação de pacientes adultos em terapia intensiva e verificar a adequação do valor de corte clássico para esse índice. MÉTODOS: Estudo prospectivo realizado na unidade de terapia intensiva de adultos do Hospital das Clínicas da Faculdade de Medicina de Botucatu, através da avaliação do IRRS em 73 pacientes consecutivos considerados clinicamente prontos para extubação. RESULTADOS: O IRRS com valor de corte clássico (105 ciclos/min/L) apresentou sensibilidade de 20% e especificidade de 95% (soma = 115%). A análise da curva receiver operator characteristic (ROC) demonstrou melhor valor de corte (76,5 ciclos/min/L), o qual forneceu sensibilidade de 66% e especificidade de 74% (soma = 140%), e a área sob a curva ROC para o IRRS foi de 0,78. CONCLUSÕES: O valor de corte clássico do IRRS se mostrou inadequado nesta casuística, prevendo apenas 20% dos pacientes com falha na extubação. A obtenção do novo valor de corte permitiu um acréscimo substancial de sensibilidade, com aceitável redução da especificidade. O valor da área sob a curva ROC indicou satisfatório poder discriminativo do índice, justificando a validação de sua aplicação.

Accuracy of sagittal abdominal diameter as predictor of abdominal fat among Brazilian adults: a comparation with waist circumference

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

In vitro comparison of laser fluorescence performance with visual examination for detection of occlusal caries in permanent and primary molars

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Classification of masses in mammographic image using wavelet domain features and polynomial classifier

Breast cancer is the most common cancer among women. In CAD systems, several studies have investigated the use of wavelet transform as a multiresolution analysis tool for texture analysis and could be interpreted as inputs to a classifier. In classification, polynomial classifier has been used due to the advantages of providing only one model for optimal separation of classes and to consider this as the solution of the problem. In this paper, a system is proposed for texture analysis and classification of lesions in mammographic images. Multiresolution analysis features were extracted from the region of interest of a given image. These features were computed based on three different wavelet functions, Daubechies 8, Symlet 8 and bi-orthogonal 3.7. For classification, we used the polynomial classification algorithm to define the mammogram images as normal or abnormal. We also made a comparison with other artificial intelligence algorithms (Decision Tree, SVM, K-NN). A Receiver Operating Characteristics (ROC) curve is used to evaluate the performance of the proposed system. Our system is evaluated using 360 digitized mammograms from DDSM database and the result shows that the algorithm has an area under the ROC curve Az of 0.98 ± 0.03. The performance of the polynomial classifier has proved to be better in comparison to other classification algorithms. © 2013 Elsevier Ltd. All rights reserved.

Avaliação da acurácia do teste imunoenzimático e de sua contribuição no seguimento de pacientes com paracoccidioidomicose em tratamento e no diagnóstico de doença reativada

Pós-graduação em Doenças Tropicais - FMB

Prediction and probability of neonatal outcome in isolated congenital diaphragmatic hernia using multiple ultrasound parameters

Objectives To evaluate the accuracy and probabilities of different fetal ultrasound parameters to predict neonatal outcome in isolated congenital diaphragmatic hernia (CDH). Methods Between January 2004 and December 2010, we evaluated prospectively 108 fetuses with isolated CDH (82 left-sided and 26 right-sided). The following parameters were evaluated: gestational age at diagnosis, side of the diaphragmatic defect, presence of polyhydramnios, presence of liver herniated into the fetal thorax (liver-up), lung-to-head ratio (LHR) and observed/expected LHR (o/e-LHR), observed/expected contralateral and total fetal lung volume (o/e-ContFLV and o/e-TotFLV) ratios, ultrasonographic fetal lung volume/fetal weight ratio (US-FLW), observed/expected contralateral and main pulmonary artery diameter (o/e-ContPA and o/eMPA) ratios and the contralateral vascularization index (Cont-VI). The outcomes were neonatal death and severe postnatal pulmonary arterial hypertension (PAH). Results Neonatal mortality was 64.8% (70/108). Severe PAH was diagnosed in 68 (63.0%) cases, of which 63 died neonatally (92.6%) (P < 0.001). Gestational age at diagnosis, side of the defect and polyhydramnios were not associated with poor outcome (P > 0.05). LHR, o/eLHR, liver-up, o/e-ContFLV, o/e-TotFLV, US-FLW, o/eContPA, o/e-MPA and Cont-VI were associated with both neonatal death and severe postnatal PAH (P < 0.001). Receiver-operating characteristics curves indicated that measuring total lung volumes (o/e-TotFLV and US-FLW) was more accurate than was considering only the contralateral lung sizes (LHR, o/e-LHR and o/e-ContFLV; P < 0.05), and Cont-VI was the most accurate ultrasound parameter to predict neonatal death and severe PAH (P < 0.001). Conclusions Evaluating total lung volumes is more accurate than is measuring only the contralateral lung size. Evaluating pulmonary vascularization (Cont-VI) is the most accurate predictor of neonatal outcome. Estimating the probability of survival and severe PAH allows classification of cases according to prognosis. Copyright (C) 2011 ISUOG. Published by John Wiley & Sons, Ltd.

Usefulness of clinical findings, nerve conduction studies and ultrasonography to predict response to surgical release in idiopathic carpal tunnel syndrome.

[EN] OBJECTIVES: To assess the usefulness of clinical findings, nerve conduction studies and ultrasonography performed by a rheumatologist to predict success in patients with idiopathic carpal tunnel syndrome (CTS) undergoing median nerve release. METHODS: Ninety consecutive patients with CTS (112 wrists) completed a specific CTS questionnaire and underwent physical examination and nerve conduction studies. Ultrasound examination was performed by a rheumatologist who was blind to any patient's data. Outcome variables were improvement >25% in symptoms of the CTS questionnaire and patient's overall satisfaction (5-point Likert scale) at 3 months postoperatively. Success was defined as improvement in both outcome variables. Receiver operating characteristics (ROC) curves and logistic regression analyses were used to assess the best predictive combination of preoperative findings. RESULTS: Success was achieved in 63% of the operated wrists. Utility parameters and area under the ROC curve (AUC) for individual findings was poor, ranging from 0.481 of the nerve conduction study to 0.634 of the cross-sectional area at tunnel outlet. Logistic regression identified the preoperative US parameters as the best predictive variables for success after 3 months. The best predictive combination (AUC=0.708) included a negative Phalen maneuver, plus absence of thenar atrophy, plus less than moderately abnormalities on nerve conduction studies plus a large maximal cross-sectional area along the tunnel by ultrasonography. CONCLUSION: Although cross-sectional area of the median nerve was the only predictor of success after three months of surgical release, isolated preoperative findings are not reliable predictors of success in patients with idiopathic CTS. A combination of findings that include ultrasound improves prediction.

Etablierung und Optimierung von Therapeutischem Drug Monitoring für die Psychopharmakotherapie von Patienten mit Substanzabhängigkeit

Therapeutisches Drug Monitoring (TDM) findet Anwendung in der Therapie mit Immunosuppressiva, Antibiotika, antiretroviraler Medikation, Antikonvulsiva, Antidepressiva und auch Antipsychotika, um die Effizienz zu steigern und das Risiko von Intoxikationen zu reduzieren. Jedoch ist die Anwendung von TDM für Substanzen, die Einsatz finden in der Rückfallprophylaxe, der Substitution oder dem Entzug von Abhängigkeitserkrankungen nicht etabliert. Für diese Arbeit wurde im ersten Schritt eine sensitive Rating-Skala mit 22 Items entwickelt, mit Hilfe derer der theoretische Nutzen von TDM in der Pharmakotherapie von substanzbezogenen Abhängigkeitserkrankungen auf der Basis von pharmakologischen Eigenschaften der Medikamente und von Patientencharakteristika evaluiert wurde. Die vorgenommene Einschätzung zeigte für Bupropion, Buprenorphin, Disulfiram (oder einen Metaboliten), Methadon (chirale Bestimmung wenn möglich) und Naltrexon einen potentiellen Nutzen von TDM.rnFür die meisten Medikamente, die zur Behandlung von Abhängigkeitserkrankungen zugelassen sind, fehlen valide Messverfahren für TDM. Im Alltag werden überwiegend Drogen Screening-Tests in Form immunologischer Schnelltests angewendet. Für die Anwendung von TDM wurden in dieser Arbeit chromatographische Verfahren für die Bestimmung von Naltrexon und 6β-Naltrexol, Bupropion und Hydroxybupropion sowie R,S-Methadon und R,S-2-Ethyliden-1,5-dimethyl-3,3-diphenylpyrrolidin entwickelt, optimiert und validiert. Es handelt sich dabei HPLC-UV-Methoden mit Säulenschaltung sowie zur Bestimmung von Naltrexon und 6β-Naltrexol zusätzlich eine LC-MS/MS-Methode. Voraussetzung für die Interpretation der Plasmaspiegel ist im Wesentlichen die Kenntnis eines therapeutischen Bereichs. Für Naltrexon und seinen aktiven Metaboliten 6β-Naltrexol konnte eine signifikante Korrelation zwischen dem auftretenden Craving und der Summenkonzentration gefunden werden. Mittels Receiver-Operation-Characteristics-Kurven-Analyse wurde ein Schwellenwert von 16,6 ng/ml ermittelt, oberhalb dessen mit einem erhöhten Ansprechen gerechnet werden kann. Für Levomethadon wurde bezüglich der Detoxifikationsbehandlung ein Zusammenhang in der prozentualen Reduktion des Plasmaspiegels und den objektiven und subjektiven Entzugssymptomen gefunden. rnDoch nicht nur die Wirkstoffe, sondern auch das Patientenmerkmal substanzbezogene Abhängigkeit wurde charakterisiert, zum einen bezüglich pharmakokinetischer Besonderheiten, zum anderen in Hinsicht auf die Therapietreue (Adhärenz). Für Patienten mit komorbider Substanzabhängigkeit konnte eine verminderte Adhärenz unabhängig von der Hauptdiagnose gezeigt werden. Die Betrachtung des Einflusses von veränderten Leberwerten zeigt für komorbide Patienten eine hohe Korrelation mit dem Metabolisiererstatus, nicht aber für Patienten ohne Substanzabhängigkeit.rnÜbergeordnetes Ziel von TDM ist die Erhöhung der Therapiesicherheit und die Steigerung der Therapieeffizienz. Dies ist jedoch nur möglich, wenn TDM im klinischen Alltag integriert ist und korrekt eingesetzt wird. Obwohl es klare Evidenz für TDM von psychiatrischer Medikation gibt, ist die Diskrepanz zwischen Laborempfehlung und der klinischen Umsetzung hoch. Durch Intensivierung der interdisziplinären Zusammenarbeit zwischen Ärzten und Labor, der Entwicklung von interaktivem TDM (iTDM), konnte die Qualität der Anwendung von TDM verbessert und das Risiko von unerwünschten Arzneimittelwirkungen vermindert werden. rnInsgesamt konnte durch die eigenen Untersuchungen gezeigt werden, dass TDM für die medikamentöse Einstellung von Patienten mit Abhängigkeitserkrankung sinnvoll ist und dass optimales TDM eine interdisziplinäre Zusammenarbeit erfordert.rn

Mannitol dry powder challenge in comparison with exercise testing in children

Rationale Mannitol dry powder (MDP) challenge is an indirect bronchial provocation test, which is well studied in adults but not established for children. Objective We compared feasibility, validity, and clinical significance of MDP challenge with exercise testing in children in a clinical setting. Methods Children aged 6–16 years, referred to two respiratory outpatient clinics for possible asthma diagnosis, underwent standardized exercise testing followed within a week by an MDP challenge (Aridol™, Pharmaxis, Australia). Agreement between the two challenge tests using Cohen's kappa and receiving operating characteristic (ROC) curves was compared. Results One hundred eleven children performed both challenge tests. Twelve children were excluded due to exhaustion or insufficient cooperation (11 at the exercise test, 1 at the MDP challenge), leaving 99 children (mean ± SD age 11.5 ± 2.7 years) for analysis. MDP tests were well accepted, with minor side effects and a shorter duration than exercise tests. The MDP challenge was positive in 29 children (29%), the exercise test in 21 (21%). Both tests were concordant in 83 children (84%), with moderate agreement (κ = 0.58, 95% CI 0.39–0.76). Positive and negative predictive values of the MDP challenge for exercise-induced bronchoconstriction were 68% and 89%. The overall ability of MDP challenge to separate children with or without positive exercise tests was good (area under the ROC curve 0.83). Conclusions MDP challenge test is feasible in children and is a suitable alternative for bronchial challenge testing in childhood. Pediatr. Pulmonol. 2011; 46:842–848. © 2011 Wiley-Liss, Inc.