Fluorescence image and microbiological analysis of biofilm retained around healthy and inflamed orthodontic miniscrews.

INTRODUCTION: Peri-miniscrew inflammation is one of the causes of orthodontic miniscrew failure. OBJECTIVE: The aim of this study was to correlate and quantify throughout autofluorescence images, PCR and microbiologic count of biofilm retained around orthodontic miniscrew and the presence of Porphyromonas gingivalis. MATERIALS AND METHODS: Forty miniscrews used for orthodontic treatment were evaluated during orthodontic treatment, collected from patients and divided into two groups: healthy and inflamed miniscrews. To be considered inflamed, the samples should present: loss of stability checked by periotest®, clinical presence of mucositis, red aspect of the gum or bleeding around the miniscrew. Immediately after removal of the miniscrews, they were photographed using a macro 100 lens and a Pentax camera coupled to a fluorescent equipment - Qscan (AioBio - Korea) with 405 nm excitation wavelength and a blue band filter. A microbiologic sample was collected with a sterile microbrush scrubbed around the miniscrew tread. Fluorescent images were analyzed with ImageJ software to quantify fluorescent intensity and fluorescent area and microbiological samples were submitted to CFU count for total contamination and q-PCR assay to quantify Porphyromonas gingivalis. RESULTS: The results showed a good correlation between CFU count and fluorescent intensity and PCR/fluorescent area. The healthy miniscrews presented less fluorescent intensity and lower CFU count when compared to inflamed miniscrews. q-PCR analysis showed a higher number of P. gingivalis contamination around inflamed miniscrews. CONCLUSION: Quantification of biofilm retained by miniscrew by images of autofluorescence is a simple and reliable method with great potential for clinical use to monitory inflammation around miniscrew and risk of loss.

Biochemical and biological evaluation of gyroxin isolated from Crotalus durissus terrificus venom

Gyroxin, a thrombin-like enzyme isolated from Crotalus durissus terrificus venom and capable of converting fibrinogen into fibrin, presents coagulant and neurotoxic activities. The aim of the present study was to evaluate such coagulant and toxic properties. Gyroxin was isolated using only two chromatographic steps - namely gel filtration (Sephadex G-75) and affinity (Benzamidine Sepharose 6B) - resulting in a sample of high purity, as evaluated by RP-HPLC C2/C18 and electrophoretic analysis that showed a molecular mass of 30 kDa. Gyroxin hydrolyzed specific chromogenic substrates, which caused it to be classified as a serine proteinase and thrombin-like enzyme. It was stable from pH 5.5 to 8.5 and inhibited by Mn²+, Cu²+, PMSF and benzamidine. Human plasma coagulation was more efficient at pH 6.0. An in vivo toxicity test showed that only behavioral alterations occurred, with no barrel rotation. Gyroxin was not able to block neuromuscular contraction in vitro, which suggests that its action, at the studied concentrations, has no effect on the peripheral nervous system.

A new fibrin sealant from Crotalus durissus terrificus venom: applications in medicine.

Fibrin sealant, a widely available tissue adhesive, has been used since 1940 in a variety of clinical applications. Commercially available fibrin sealant products are synthesized from bovine thrombin and human fibrinogen, which may transmit infectious diseases, and recipients may also develop antibodies against bovine thrombin. Bearing these disadvantages in mind, a new fibrin sealant was developed in 1989 by a group of researchers from the Center for the Study of Venoms and Venomous Animals, in Sao Paulo State, Brazil. The main purpose was to produce an adhesive fibrin without using human blood, to avoid transmitting infectious diseases. The components of this novel sealant were extracted from large animals and a serine proteinase extracted from Crotalus durissus terrificus snake venom. The applicability of this sealant was tested in animals and humans with beneficial results. The new fibrin sealant can be a useful tool clinically due to its flexibility and diversity of applications. This sealant is a biological and biodegradable product that (1) does not produce adverse reactions, (1) contains no human blood, (3) has a good adhesive capacity, (4) gives no transmission of infectious diseases, and (5) may be used as an adjuvant in conventional suture procedures. The effectiveness of this new fibrin sealant is reviewed and its development and employment are described.

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.

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.

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 percent 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.

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.