Determinação de carbaril utilizando testes ELISA (Enzyme-linked immunosorbent assay) e CLAE com detecção por arranjo de diodos

ELISAs have been applied to pesticide residue analysis due to their high sensitivity and selectivity. However, some ELISAs performance may be affected by matrix components. In this work, ELISA for carbaryl in water samples was checked for interference by naturally occurring fulvic acids. The results suggested that the high fulvic acid concentration (ž³30 mg L-1) and acidic pH conditions (pH 4.0) interfere with the signal detection decreasing the method sensitivity. A dilution of the samples and adjust to pH 8.0 are appropriate to minimize the matrix interferences in the ELISA method. Good correlation between ELISA and HPLC-DAD results was observed.

Detection of incipient left ventricular hypertrophy in mild to moderate arterial hypertension with normal electrocardiogram and echocardiogram: a new use for signal-averaged electrocardiography

OBJECTIVE: To assess signal-averaged electrocardiogram (SAECG) for diagnosing incipient left ventricular hypertrophy (LVH). METHODS: A study with 115 individuals was carried out. The individuals were divided as follows: GI - 38 healthy individuals; GII - 47 individuals with mild to moderate hypertension and normal findings on echocardiogram and ECG; and GIII - 30 individuals with hypertension and documented LVH. The magnitude vector of the SAECG was analyzed with the high-pass cutoff frequency of 40 Hz through the bidirectional four-pole Butterworth high-pass digital filter. The mean quadratic root of the total QRS voltage (RMST) and the two-dimensional integral of the QRS area of the spectro-temporal map were analyzed between 0 and 30 Hz for the frequency domain (Int FD), and between 40 and 250 Hz for the time domain (Int TD). The electrocardiographic criterion for LVH was based on the Cornell Product. Left ventricular mass was calculated with the Devereux formula. RESULTS: All parameters analyzed increased from GI to GIII, except for Int FD (GII vs GIII) and RMST log (GII vs GIII). Int TD showed greater accuracy for detecting LVH with an appropriate cutoff > 8 (sensitivity of 55%, specificity of 81%). Positive values (> 8) were found in 56.5% of the G II patients and in 18.4% of the GI patients (p< 0.0005). CONCLUSION: SAECG can be used in the early diagnosis of LVH in hypertensive patients with normal ECG and echocardiogram.

RT-PCR-ELISA for detection of Apple stem grooving virus in apple trees

A method to detect Apple stem grooving virus (ASGV) based on reverse transcription polymerase chain reaction (RT-PCR) was developed using primers ASGV4F-ASGV4R targeting the viral replicase gene, followed by a sandwich hybridisation, in microtiter plates, for colorimetric detection of the PCR products. The RT-PCR was performed with the Titan™ RT-PCR system, using AMV and diluted crude extracts of apple (Malus domestica) leaf or bark for the first strand synthesis and a mixture of Taq and PWO DNA polymerase for the PCR step. The RT-PCR products is hybridised with both a biotin-labelled capture probe linked to a streptavidin-coated microtiter plate and a digoxigenin (DIG)-labelled detection probe. The complex was detected with an anti-DIG conjugate labelled with alkaline phosphatase. When purified ASGV was added to extracts of plant tissue, as little as 400 fg of the virus was detected with this method. The assay with ASGV4F-ASGV4R primers specifically detected the virus in ASGV-infected apple trees from different origins, whereas no signal was observed with amplification products obtained with primers targeting the coat protein region of the ASGV genome or with primers specific for Apple chlorotic leaf spot virus (ACLSV) and Apple stem pitting virus (ASPV). The technique combines the power of PCR to increase the number of copies of the targeted gene, the specificity of DNA hybridization, and the ease of colorimetric detection and sample handling in microplates.

Detection of Horizontal Two-Phase Flow Patterns Through a Neural Network Model

One of the main problems related to the transport and manipulation of multiphase fluids concerns the existence of characteristic flow patterns and its strong influence on important operation parameters. A good example of this occurs in gas-liquid chemical reactors in which maximum efficiencies can be achieved by maintaining a finely dispersed bubbly flow to maximize the total interfacial area. Thus, the ability to automatically detect flow patterns is of crucial importance, especially for the adequate operation of multiphase systems. This work describes the application of a neural model to process the signals delivered by a direct imaging probe to produce a diagnostic of the corresponding flow pattern. The neural model is constituted of six independent neural modules, each of which trained to detect one of the main horizontal flow patterns, and a last winner-take-all layer responsible for resolving when two or more patterns are simultaneously detected. Experimental signals representing different bubbly, intermittent, annular and stratified flow patterns were used to validate the neural model.

Clinical assessment of the effect of digital filtering on the detection of ventricular late potentials

Ventricular late potentials are low-amplitude signals originating from damaged myocardium and detected on the body surface by ECG filtering and averaging. Digital filters present in commercial equipment may interfere with the ability of arrhythmia stratification. We compared 40-Hz BiSpec (BI) and classical 40- to 250-Hz band-pass Butterworth bidirectional (BD) filters in terms of impact on time domain variables and diagnostic properties. In a transverse retrospective age-adjusted case-control study, 221 subjects with sinus rhythm without bundle branch block were divided into three groups after signal-averaged ECG acquisition: GI (N = 40), clinically normal controls, GII (N = 158), subjects with coronary heart disease without sustained monomorphic ventricular tachycardia (SMVT), and GIII (N = 23), subjects with heart disease and documented SMVT. Conventional variables analyzed from vector magnitude data after averaging to 0.3 µV final noise were obtained by application of each filter to the averaged signal, and evaluated in pairs by numerical comparison and by diagnostic agreement assessment, using conventional and optimized thresholds of normality. Significant differences were found between BI and BD variables in all groups, with diagnostic results showing significant disagreement between both filters [kappa value of 0.61 (P<0.05) for GII and 0.31 for GIII (P = NS)]. Sensitivity for SMVT was lower with BI than with BD (65.2 vs 91.3%, respectively, P<0.05). Filters provided significantly different numerical and diagnostic results and the BI filter showed only limited clinical application to risk stratification of ventricular arrhythmia.

Clinical signs of pneumonia in children: association with and prediction of diagnosis by fuzzy sets theory

The present study compares the performance of stochastic and fuzzy models for the analysis of the relationship between clinical signs and diagnosis. Data obtained for 153 children concerning diagnosis (pneumonia, other non-pneumonia diseases, absence of disease) and seven clinical signs were divided into two samples, one for analysis and other for validation. The former was used to derive relations by multi-discriminant analysis (MDA) and by fuzzy max-min compositions (fuzzy), and the latter was used to assess the predictions drawn from each type of relation. MDA and fuzzy were closely similar in terms of prediction, with correct allocation of 75.7 to 78.3% of patients in the validation sample, and displaying only a single instance of disagreement: a patient with low level of toxemia was mistaken as not diseased by MDA and correctly taken as somehow ill by fuzzy. Concerning relations, each method provided different information, each revealing different aspects of the relations between clinical signs and diagnoses. Both methods agreed on pointing X-ray, dyspnea, and auscultation as better related with pneumonia, but only fuzzy was able to detect relations of heart rate, body temperature, toxemia and respiratory rate with pneumonia. Moreover, only fuzzy was able to detect a relationship between heart rate and absence of disease, which allowed the detection of six malnourished children whose diagnoses as healthy are, indeed, disputable. The conclusion is that even though fuzzy sets theory might not improve prediction, it certainly does enhance clinical knowledge since it detects relationships not visible to stochastic models.

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.

Detection of patients with functional dyspepsia using wavelet transform applied to their electrogastrogram

The aim of the present study was to develop a classifier able to discriminate between healthy controls and dyspeptic patients by analysis of their electrogastrograms. Fifty-six electrogastrograms were analyzed, corresponding to 42 dyspeptic patients and 14 healthy controls. The original signals were subsampled, filtered and divided into the pre-, post-, and prandial stages. A time-frequency transformation based on wavelets was used to extract the signal characteristics, and a special selection procedure based on correlation was used to reduce their number. The analysis was carried out by evaluating different neural network structures to classify the wavelet coefficients into two groups (healthy subjects and dyspeptic patients). The optimization process of the classifier led to a linear model. A dimension reduction that resulted in only 25% of uncorrelated electrogastrogram characteristics gave 24 inputs for the classifier. The prandial stage gave the most significant results. Under these conditions, the classifier achieved 78.6% sensitivity, 92.9% specificity, and an error of 17.9 ± 6% (with a 95% confidence level). These data show that it is possible to establish significant differences between patients and normal controls when time-frequency characteristics are extracted from an electrogastrogram, with an adequate component reduction, outperforming the results obtained with classical Fourier analysis. These findings can contribute to increasing our understanding of the pathophysiological mechanisms involved in functional dyspepsia and perhaps to improving the pharmacological treatment of functional dyspeptic patients.

Evaluation of 99mTc-HYNIC-βAla-Bombesin(7-14) as an agent for pancreas tumor detection in mice

Pancreatic adenocarcinoma is important in oncology because of its high mortality rate. Deaths may be avoided if an early diagnosis could be achieved. Several types of tumors overexpress gastrin-releasing peptide receptors (GRPr), including pancreatic cancer cells. Thus, a radiolabeled peptide derivative of gastrin-releasing peptide (GRP) may be useful as a specific imaging probe. The purpose of the present study was to evaluate the feasibility of using99mTc-HYNIC-βAla-Bombesin(7-14)as an imaging probe for Capan-1 pancreatic adenocarcinoma. Xenographic pancreatic tumor was developed in nude mice and characterized by histopathological analysis. Biodistribution studies and scintigraphic images were carried out in tumor-bearing nude mice. The two methods showed higher uptake by pancreatic tumor when compared to muscle (used as control), and the tumor-to-muscle ratio indicated that99mTc-HYNIC-βAla-Bombesin(7-14)uptake was four-fold higher in tumor cells than in other tissues. Scintigraphic images also showed a clear signal at the tumor site. The present data indicate that99mTc-HYNIC-βAla-Bombesin(7-14)may be useful for the detection of pancreatic adenocarcinoma.