Avaliação do Risco de Parto Prematuro: Teste da Fibronectina Fetal e Medida do Colo Uterino

Objetivo: avaliar o risco de parto prematuro em gestantes com antecedentes de parto pré-termo espontâneo por meio do teste da fibronectina fetal e da medida do colo uterino pela ultra-sonografia transvaginal. Métodos: foram relacionadas 107 gestantes na 24ª, 28ª e 32ª semana de gestação para realização do teste da fibronectina fetal no conteúdo cérvico- vaginal. No mesmo período, o comprimento do colo uterino foi medido, entre o orifício interno e externo, pela ultra-sonografia transvaginal. Consideramos o colo curto quando a medida da cérvice foi menor ou igual ao ponto de corte estabelecido pela curva ROC ("receiver-operating characteristic") para predição do parto prematuro. Comparamos o resultado dos exames com a ocorrência do parto antes de 34 e 37 semanas de gestação. Resultados: a incidência do parto prematuro foi de 37,4% (40/107). O melhor ponto de corte do comprimento do colo uterino indicado pela curva ROC para maximizar sensibilidade e especificidade foi 30 mm para 24 e 28 semanas de gestação e 25 mm para 32 semanas. O teste positivo da fibronectina fetal teve um risco relativo (RR) significante apenas na 28ª semana (RR: 1,77; intervalo de confiança (IC) 95%: 1,10-2,84) para a ocorrência do parto antes de 37 semanas. O colo curto mostrou um RR significativo para ocorrência do parto antes de 37 semanas, na 24ª, 28ª e 32ª semana. O RR foi mais elevado quando o colo curto esteve presente na 24ª semana para ocorrência do parto antes de 34 semanas (RR: 4,42; IC 95%: 1,25-15,56). Conclusão: em pacientes com antecedentes de prematuridade espontânea, a medida do comprimento do colo uterino por meio da ultra-sonografia transvaginal é melhor que o teste da fibronectina fetal para avaliar o risco de parto prematuro.

Diagnosing dementia and normal aging: clinical relevance of brain ratios and cognitive performance in a Brazilian sample

The main objective of the present study was to evaluate the diagnostic value (clinical application) of brain measures and cognitive function. Alzheimer and multiinfarct patients (N = 30) and normal subjects over the age of 50 (N = 40) were submitted to a medical, neurological and cognitive investigation. The cognitive tests applied were Mini-Mental, word span, digit span, logical memory, spatial recognition span, Boston naming test, praxis, and calculation tests. The brain ratios calculated were the ventricle-brain, bifrontal, bicaudate, third ventricle, and suprasellar cistern measures. These data were obtained from a brain computer tomography scan, and the cutoff values from receiver operating characteristic curves. We analyzed the diagnostic parameters provided by these ratios and compared them to those obtained by cognitive evaluation. The sensitivity and specificity of cognitive tests were higher than brain measures, although dementia patients presented higher ratios, showing poorer cognitive performances than normal individuals. Normal controls over the age of 70 presented higher measures than younger groups, but similar cognitive performance. We found diffuse losses of tissue from the central nervous system related to distribution of cerebrospinal fluid in dementia patients. The likelihood of case identification by functional impairment was higher than when changes of the structure of the central nervous system were used. Cognitive evaluation still seems to be the best method to screen individuals from the community, especially for developing countries, where the cost of brain imaging precludes its use for screening and initial assessment of dementia.

In vitro-induced antibody production in chronic hepatitis C virus infection

The objectives of the present study were to assess the in vitro-induced anti-hepatitis C virus (HCV) antibody production (IVIAP) in relation to the clinical, biochemical, virologic and histologic variables of patients with HCV infection. The study included 57 patients (60% males) with HCV infection (anti-HCV and HCV-RNA positive). Alanine aminotransferase (ALT) was elevated in 89% of the patients. Mean viral load was 542,241 copies/ml and histology of the liver showed chronic hepatitis in 27/52 (52%) and cirrhosis in 11/52 (21%) patients. IVIAP levels were determined by immunoenzymatic assay at median absorbance of 0.781 at 450 nm. IVIAP was negative in 14% of the patients. When groups with IVIAP levels above and below the median were compared, high IVIAP levels were associated with the male sex, elevated ALT levels and more advanced disease stage. After logistic regression analysis, advanced histologic damage to the liver remained as the only independent variable associated with elevated IVIAP levels. Using a receiver operator characteristic curve, the best cut-off level for IVIAP was established (= 1.540), with 71% sensitivity and 94% specificity for the detection of more advanced disease stages (grades 3 and 4). These findings are consistent with the participation of immunological mechanisms in the genesis of the hepatic lesions induced by HCV and indicate that the IVIAP test may be useful as a noninvasive marker of liver damage either alone or in combination with other markers.

An analysis of the electrocardiogram QT interval

No reports testing the efficacy of the use of the QT/RR ratio <1/2 for detecting a normal QTc interval were found in the literature. The objective of the present study was to determine if a QT/RR ratio <=1/2 can be considered to be equal to the normal QTc and to compare the QT and QTc measured and calculated clinically and by a computerized electrocardiograph. Ratios (140 QT/RR) of 28 successive electrocardiograms obtained from 28 consecutive patients in a tertiary level teaching hospital were analyzed clinically by 5 independent observers and by a computerized electrocardiograph. The QT/RR ratio provided 56% sensitivity and 78% specificity, with an area under the receiver operator characteristic curve of 75.8% (95%CI: 0.68 to 0.84). The divergence in QT and QTc interval measurements between clinical and computerized evaluation were 0.01 ± 0.03 s (95%CI: 0.04-0.02) and 0.01 ± 0.04 s (95%CI: -0.05-0.03), respectively. The QT and QTc values measured clinically and by a computerized electrocardiograph were similar. The QT/RR ratio <=1/2 was not a satisfactory index for QTc evaluation because it could not predict a normal QTc value.

Selection of patients for myocardial perfusion scintigraphy based on fuzzy sets theory applied to clinical-epidemiological data and treadmill test results

Coronary artery disease (CAD) is a worldwide leading cause of death. The standard method for evaluating critical partial occlusions is coronary arteriography, a catheterization technique which is invasive, time consuming, and costly. There are noninvasive approaches for the early detection of CAD. The basis for the noninvasive diagnosis of CAD has been laid in a sequential analysis of the risk factors, and the results of the treadmill test and myocardial perfusion scintigraphy (MPS). Many investigators have demonstrated that the diagnostic applications of MPS are appropriate for patients who have an intermediate likelihood of disease. Although this information is useful, it is only partially utilized in clinical practice due to the difficulty to properly classify the patients. Since the seminal work of Lotfi Zadeh, fuzzy logic has been applied in numerous areas. In the present study, we proposed and tested a model to select patients for MPS based on fuzzy sets theory. A group of 1053 patients was used to develop the model and another group of 1045 patients was used to test it. Receiver operating characteristic curves were used to compare the performance of the fuzzy model against expert physician opinions, and showed that the performance of the fuzzy model was equal or superior to that of the physicians. Therefore, we conclude that the fuzzy model could be a useful tool to assist the general practitioner in the selection of patients for MPS.

Evaluation of a 1-h 75-g oral glucose tolerance test in the diagnosis of gestational diabetes

In order to evaluate the performance of a 1-h 75-g oral glucose tolerance test (OGTT) for the diagnosis of gestational diabetes mellitus (GDM), a cohort of 4998 women, 20 years or older, without previous diabetes being treated in prenatal care clinics in Brazil answered a questionnaire and performed a 75-g OGTT including fasting, 1-h and 2-h glucose measurements between their 24th and 28th gestational weeks. Pregnancy outcomes were transcribed from medical registries. GDM was defined according to WHO criteria (fasting: ≥126 mg/dL; 2-h value: ≥140 mg/dL) and macrosomia as a birth weight equal to or higher than 4000 g. Areas under the receiver operator characteristic curve (AUC) were compared and diagnostic properties of various cut-off points were evaluated. The AUCs for the prediction of macrosomia were 0.606 (0.572-0.637) for the 1-h and 0.589 (0.557-0.622) for the 2-h plasma glucose test. Similar predictability was demonstrable regarding combined adverse outcomes: 0.582 (0.559-0.604) for the 1-h test and 0.572 (0.549-0.595) for the 2-h test. When the 1-h glucose test was evaluated against a diagnosis of GDM defined by the 2-h glucose test, the AUC was 0.903 (0.886-0.919). The cut-off point that maximized sensitivity (83%) and specificity (83%) was 141 mg/dL, identifying 21% of the women as positive. A cut-off point of 160 mg/dL, with lower sensitivity (62%), had higher specificity (94%), labeling 8.6% as positive. Detection of GDM can be done with a 1-h 75-g OGTT: the value of 160 mg/dL has the same diagnostic performance as the conventional 2-h value (140 mg/dL). The simplification of the test may improve coverage and timing of the diagnosis of GDM.

Artificial neural network models for predicting 1-year mortality in elderly patients with intertrochanteric fractures in China

The mortality rate of older patients with intertrochanteric fractures has been increasing with the aging of populations in China. The purpose of this study was: 1) to develop an artificial neural network (ANN) using clinical information to predict the 1-year mortality of elderly patients with intertrochanteric fractures, and 2) to compare the ANN's predictive ability with that of logistic regression models. The ANN model was tested against actual outcomes of an intertrochanteric femoral fracture database in China. The ANN model was generated with eight clinical inputs and a single output. ANN's performance was compared with a logistic regression model created with the same inputs in terms of accuracy, sensitivity, specificity, and discriminability. The study population was composed of 2150 patients (679 males and 1471 females): 1432 in the training group and 718 new patients in the testing group. The ANN model that had eight neurons in the hidden layer had the highest accuracies among the four ANN models: 92.46 and 85.79% in both training and testing datasets, respectively. The areas under the receiver operating characteristic curves of the automatically selected ANN model for both datasets were 0.901 (95%CI=0.814-0.988) and 0.869 (95%CI=0.748-0.990), higher than the 0.745 (95%CI=0.612-0.879) and 0.728 (95%CI=0.595-0.862) of the logistic regression model. The ANN model can be used for predicting 1-year mortality in elderly patients with intertrochanteric fractures. It outperformed a logistic regression on multiple performance measures when given the same variables.

Comparison of non-HDL cholesterol and waist circumference vs. triglyceride levels and waist circumference in predicting coronary heart disease risk

BACKGROUND: Dyslipidemia is recognized as a major cause of coronary heart disease (CHD). Emerged evidence suggests that the combination of triglycerides (TG) and waist circumference can be used to predict the risk of CHD. However, considering the known limitations of TG, non-high-density lipoprotein (non-HDL = Total cholesterol - HDL cholesterol) cholesterol and waist circumference model may be a better predictor of CHD. PURPOSE: The Framingham Offspring Study data were used to determine if combined non-HDL cholesterol and waist circumference is equivalent to or better than TG and waist circumference (hypertriglyceridemic waist phenotype) in predicting risk of CHD. METHODS: A total of3,196 individuals from Framingham Offspring Study, aged ~ 40 years old, who fasted overnight for ~ 9 hours, and had no missing information on nonHDL cholesterol, TG levels, and waist circumference measurements, were included in the analysis. Receiver Operator Characteristic Curve (ROC) Area Under the Curve (AUC) was used to compare the predictive ability of non-HDL cholesterol and waist circumference and TG and waist circumference. Cox proportional-hazards models were used to examine the association between the joint distributions of non-HDL cholesterol, waist circumference, and non-fatal CHD; TG, waist circumference, and non-fatal CHD; and the joint distribution of non-HDL cholesterol and TG by waist circumference strata, after adjusting for age, gender, smoking, alcohol consumption, diabetes, and hypertension status. RESULTS: The ROC AUC associated with non-HDL cholesterol and waist circumference and TG and waist circumference are 0.6428 (CI: 0.6183, 0.6673) and 0.6299 (CI: 0.6049, 0.6548) respectively. The difference in the ROC AVC is 1.29%. The p-value testing if the difference in the ROC AVCs between the two models is zero is 0.10. There was a strong positive association between non-HDL cholesterol and the risk for non-fatal CHD within each TO levels than that for TO levels within each level of nonHDL cholesterol, especially in individuals with high waist circumference status. CONCLUSION: The results suggest that the model including non-HDL cholesterol and waist circumference may be superior at predicting CHD compared to the model including TO and waist circumference.

Prediction of hypertensive disorders in pregnancy by combined uterine artery Doppler, serum biomarkers and maternal characteristics

Objectif: Évaluer l'efficacité du dépistage de l’hypertension gestationnelle par les caractéristiques démographiques maternelles, les biomarqueurs sériques et le Doppler de l'artère utérine au premier et au deuxième trimestre de grossesse. Élaborer des modèles prédictifs de l’hypertension gestationnelle fondées sur ces paramètres. Methods: Il s'agit d'une étude prospective de cohorte incluant 598 femmes nullipares. Le Doppler utérin a été étudié par échographie transabdominale entre 11 +0 à 13 +6 semaines (1er trimestre) et entre 17 +0 à 21 +6 semaines (2e trimestre). Tous les échantillons de sérum pour la mesure de plusieurs biomarqueurs placentaires ont été recueillis au 1er trimestre. Les caractéristiques démographiques maternelles ont été enregistrées en même temps. Des courbes ROC et les valeurs prédictives ont été utilisés pour analyser la puissance prédictive des paramètres ci-dessus. Différentes combinaisons et leurs modèles de régression logistique ont été également analysés. Résultats: Parmi 598 femmes, on a observé 20 pré-éclampsies (3,3%), 7 pré-éclampsies précoces (1,2%), 52 cas d’hypertension gestationnelle (8,7%) , 10 cas d’hypertension gestationnelle avant 37 semaines (1,7%). L’index de pulsatilité des artères utérines au 2e trimestre est le meilleur prédicteur. En analyse de régression logistique multivariée, la meilleure valeur prédictive au 1er et au 2e trimestre a été obtenue pour la prévision de la pré-éclampsie précoce. Le dépistage combiné a montré des résultats nettement meilleurs comparés avec les paramètres maternels ou Doppler seuls. Conclusion: Comme seul marqueur, le Doppler utérin du deuxième trimestre a la meilleure prédictive pour l'hypertension, la naissance prématurée et la restriction de croissance. La combinaison des caractéristiques démographiques maternelles, des biomarqueurs sériques maternels et du Doppler utérin améliore l'efficacité du dépistage, en particulier pour la pré-éclampsie nécessitant un accouchement prématuré.

Évaluation d’un prototype de détecteur de glucose dans le tissu interstitiel sans aiguille, le PGS (Photonic Glucose Sensor)

Objectif : Déterminer la fiabilité et la précision d’un prototype d’appareil non invasif de mesure de glucose dans le tissu interstitiel, le PGS (Photonic Glucose Sensor), en utilisant des clamps glycémiques multi-étagés. Méthodes : Le PGS a été évalué chez 13 sujets avec diabète de type 1. Deux PGS étaient testés par sujet, un sur chacun des triceps, pour évaluer la sensibilité, la spécificité, la reproductibilité et la précision comparativement à la technique de référence (le Beckman®). Chaque sujet était soumis à un clamp de glucose multi-étagé de 8 heures aux concentrations de 3, 5, 8 et 12 mmol/L, de 2 heures chacun. Résultats : La corrélation entre le PGS et le Beckman® était de 0,70. Pour la détection des hypoglycémies, la sensibilité était de 63,4%, la spécificité de 91,6%, la valeur prédictive positive (VPP) 71,8% et la valeur prédictive négative (VPN) 88,2%. Pour la détection de l’hyperglycémie, la sensibilité était de 64,7% et la spécificité de 92%, la VPP 70,8% et la VPN : 89,7%. La courbe ROC (Receiver Operating Characteristics) démontrait une précision de 0,86 pour l’hypoglycémie et de 0,87 pour l’hyperglycémie. La reproductibilité selon la « Clark Error Grid » était de 88% (A+B). Conclusion : La performance du PGS était comparable, sinon meilleure que les autres appareils sur le marché(Freestyle® Navigator, Medtronic Guardian® RT, Dexcom® STS-7) avec l’avantage qu’il n’y a pas d’aiguille. Il s’agit donc d’un appareil avec beaucoup de potentiel comme outil pour faciliter le monitoring au cours du traitement intensif du diabète. Mot clés : Diabète, diabète de type 1, PGS (Photonic Glucose Sensor), mesure continue de glucose, courbe ROC, « Clark Error Grid».

Soft Computing Approach for Optimization of siRNA Efficiency Prediction for Post-transcriptional Gene Silencing

Post-transcriptional gene silencing by RNA interference is mediated by small interfering RNA called siRNA. This gene silencing mechanism can be exploited therapeutically to a wide variety of disease-associated targets, especially in AIDS, neurodegenerative diseases, cholesterol and cancer on mice with the hope of extending these approaches to treat humans. Over the recent past, a significant amount of work has been undertaken to understand the gene silencing mediated by exogenous siRNA. The design of efficient exogenous siRNA sequences is challenging because of many issues related to siRNA. While designing efficient siRNA, target mRNAs must be selected such that their corresponding siRNAs are likely to be efficient against that target and unlikely to accidentally silence other transcripts due to sequence similarity. So before doing gene silencing by siRNAs, it is essential to analyze their off-target effects in addition to their inhibition efficiency against a particular target. Hence designing exogenous siRNA with good knock-down efficiency and target specificity is an area of concern to be addressed. Some methods have been developed already by considering both inhibition efficiency and off-target possibility of siRNA against agene. Out of these methods, only a few have achieved good inhibition efficiency, specificity and sensitivity. The main focus of this thesis is to develop computational methods to optimize the efficiency of siRNA in terms of “inhibition capacity and off-target possibility” against target mRNAs with improved efficacy, which may be useful in the area of gene silencing and drug design for tumor development. This study aims to investigate the currently available siRNA prediction approaches and to devise a better computational approach to tackle the problem of siRNA efficacy by inhibition capacity and off-target possibility. The strength and limitations of the available approaches are investigated and taken into consideration for making improved solution. Thus the approaches proposed in this study extend some of the good scoring previous state of the art techniques by incorporating machine learning and statistical approaches and thermodynamic features like whole stacking energy to improve the prediction accuracy, inhibition efficiency, sensitivity and specificity. Here, we propose one Support Vector Machine (SVM) model, and two Artificial Neural Network (ANN) models for siRNA efficiency prediction. In SVM model, the classification property is used to classify whether the siRNA is efficient or inefficient in silencing a target gene. The first ANNmodel, named siRNA Designer, is used for optimizing the inhibition efficiency of siRNA against target genes. The second ANN model, named Optimized siRNA Designer, OpsiD, produces efficient siRNAs with high inhibition efficiency to degrade target genes with improved sensitivity-specificity, and identifies the off-target knockdown possibility of siRNA against non-target genes. The models are trained and tested against a large data set of siRNA sequences. The validations are conducted using Pearson Correlation Coefficient, Mathews Correlation Coefficient, Receiver Operating Characteristic analysis, Accuracy of prediction, Sensitivity and Specificity. It is found that the approach, OpsiD, is capable of predicting the inhibition capacity of siRNA against a target mRNA with improved results over the state of the art techniques. Also we are able to understand the influence of whole stacking energy on efficiency of siRNA. The model is further improved by including the ability to identify the “off-target possibility” of predicted siRNA on non-target genes. Thus the proposed model, OpsiD, can predict optimized siRNA by considering both “inhibition efficiency on target genes and off-target possibility on non-target genes”, with improved inhibition efficiency, specificity and sensitivity. Since we have taken efforts to optimize the siRNA efficacy in terms of “inhibition efficiency and offtarget possibility”, we hope that the risk of “off-target effect” while doing gene silencing in various bioinformatics fields can be overcome to a great extent. These findings may provide new insights into cancer diagnosis, prognosis and therapy by gene silencing. The approach may be found useful for designing exogenous siRNA for therapeutic applications and gene silencing techniques in different areas of bioinformatics.

Estudio del rendimiento académico universitario basado en curvas ROC.

Se estudia el rendimiento académico universitario de los alumnos a través de las calificaciones de entrada a la Universidad. En particular, centrado en las licenciaturas de Economía (LE) y Administración y Dirección de Empresas (LADE) de la Universidad de Murcia, utilizando como medida del rendimiento académico de los alumnos asignaturas de matemáticas de dichas titulaciones. Para ello, se realiza el análisis de los datos mediante la técnica estadística curva ROC (Receiver Operating Characteristic); este método proporciona una medida que permite discriminar entre alumnos que obtienen un buen rendimiento académico y uno malo, así como comparar distintos parámetros de clasificación de dicho rendimiento .

Resolution and discrimination - two sides of the same coin

The notions of resolution and discrimination of probability forecasts are revisited. It is argued that the common concept underlying both resolution and discrimination is the dependence (in the sense of probability theory) of forecasts and observations. More specifically, a forecast has no resolution if and only if it has no discrimination if and only if forecast and observation are stochastically independent. A statistical tests for independence is thus also a test for no resolution and, at the same time, for no discrimination. The resolution term in the decomposition of the logarithmic scoring rule, and the area under the Receiver Operating Characteristic will be investigated in this light.

Exploring the association of diary product intake with the fatty acids C15:0 and C17:0 measured from dried blood spots in a multi-population cohort: findings from the Food4Me study

Scope: The use of biomarkers in the objective assessment of dietary intake is a high priority in nutrition research. The aim of this study was to examine pentadecanoic acid (C15:0) and heptadecanoic acid (C17:0) as biomarkers of dairy foods intake. Methods and results: The data used in the present study were obtained as part of the Food4me Study. Estimates of C15:0 and C17:0 from dried blood spots and intakes of dairy from an FFQ were obtained from participants (n=1,180) across 7 countries. Regression analyses were used to explore associations of biomarkers with dairy intake levels and receiver operating characteristic (ROC) analyses were used to evaluate the fatty acids. Significant positive associations were found between C15:0 and total intakes of high-fat dairy products. C15:0 showed good ability to distinguish between low and high consumers of high-fat dairy products. Conclusion: C15:0 can be used as a biomarker of high-fat dairy intake and of specific high-fat dairy products. Both C15:0 and C17:0 performed poorly for total dairy intake highlighting the need for caution when using these in epidemiological studies.

A Computer Vision Framework For Finger-Tapping Evaluation In Parkinson's Disease

Objective: To define and evaluate a Computer-Vision (CV) method for scoring Paced Finger-Tapping (PFT) in Parkinson's disease (PD) using quantitative motion analysis of index-fingers and to compare the obtained scores to the UPDRS (Unified Parkinson's Disease Rating Scale) finger-taps (FT). Background: The naked-eye evaluation of PFT in clinical practice results in coarse resolution to determine PD status. Besides, sensor mechanisms for PFT evaluation may cause patients discomfort. In order to avoid cost and effort of applying wearable sensors, a CV system for non-invasive PFT evaluation is introduced. Methods: A database of 221 PFT videos from 6 PD patients was processed. The subjects were instructed to position their hands above their shoulders besides the face and tap the index-finger against the thumb consistently with speed. They were facing towards a pivoted camera during recording. The videos were rated by two clinicians between symptom levels 0-to-3 using UPDRS-FT. The CV method incorporates a motion analyzer and a face detector. The method detects the face of testee in each video-frame. The frame is split into two images from face-rectangle center. Two regions of interest are located in each image to detect index-finger motion of left and right hands respectively. The tracking of opening and closing phases of dominant hand index-finger produces a tapping time-series. This time-series is normalized by the face height. The normalization calibrates the amplitude in tapping signal which is affected by the varying distance between camera and subject (farther the camera, lesser the amplitude). A total of 15 features were classified using K-nearest neighbor (KNN) classifier to characterize the symptoms levels in UPDRS-FT. The target ratings provided by the raters were averaged. Results: A 10-fold cross validation in KNN classified 221 videos between 3 symptom levels with 75% accuracy. An area under the receiver operating characteristic curves of 82.6% supports feasibility of the obtained features to replicate clinical assessments. Conclusions: The system is able to track index-finger motion to estimate tapping symptoms in PD. It has certain advantages compared to other technologies (e.g. magnetic sensors, accelerometers etc.) for PFT evaluation to improve and automate the ratings