Explainable machine learning methods and respiratory oscillometry for the diagnosis of respiratory abnormalities in sarcoidosis.

BACKGROUND: In this work, we developed many machine learning classifiers to assist in diagnosing respiratory changes associated with sarcoidosis, based on results from the Forced Oscillation Technique (FOT), a non-invasive method used to assess pulmonary mechanics. In addition to accurate results, there is a particular interest in their interpretability and explainability, so we used Genetic Programming since the classification is made with intelligible expressions and we also evaluate the feature importance in different experiments to find the more discriminative features. METHODOLOGY/PRINCIPAL FINDINGS: We used genetic programming in its traditional tree form and a grammar-based form. To check if interpretable results are competitive, we compared their performance to K-Nearest Neighbors, Support Vector Machine, AdaBoost, Random Forest, LightGBM, XGBoost, Decision Trees and Logistic Regressor. We also performed experiments with fuzzy features and tested a feature selection technique to bring even more interpretability. The data used to feed the classifiers come from the FOT exams in 72 individuals, of which 25 were healthy, and 47 were diagnosed with sarcoidosis. Among the latter, 24 showed normal conditions by spirometry, and 23 showed respiratory changes. The results achieved high accuracy (AUC > 0.90) in two analyses performed (controls vs. individuals with sarcoidosis and normal spirometry and controls vs. individuals with sarcoidosis and altered spirometry). Genetic Programming and Grammatical Evolution were particularly beneficial because they provide intelligible expressions to make the classification. The observation of which features were selected most frequently also brought explainability to the study of sarcoidosis. CONCLUSIONS: The proposed system may provide decision support for clinicians when they are struggling to give a confirmed clinical diagnosis. Clinicians may reference the prediction results and make better decisions, improving the productivity of pulmonary function services by AI-assisted workflow.

Fuzzy Pattern Tree Evolution Using Grammatical Evolution.

A novel approach to induce Fuzzy Pattern Trees using Grammatical Evolution is presented in this paper. This new method, called Fuzzy Grammatical Evolution, is applied to a set of benchmark classification problems. Experimental results show that Fuzzy Grammatical Evolution attains similar and oftentimes better results when compared with state-of-the-art Fuzzy Pattern Tree composing methods, namely Fuzzy Pattern Trees evolved using Cartesian Genetic Programming, on a set of benchmark problems. We show that, although Cartesian Genetic Programming produces smaller trees, Fuzzy Grammatical Evolution produces better performing trees. Fuzzy Grammatical Evolution also benefits from a reduction in the number of necessary user-selectable parameters, while Cartesian Genetic Programming requires the selection of three crucial graph parameters before each experiment. To address the issue of bloat, an additional version of Fuzzy Grammatical Evolution using parsimony pressure was tested. The experimental results show that Fuzzy Grammatical Evolution with this extension routinely finds smaller trees than those using Cartesian Genetic Programming without any compromise in performance. To improve the performance of Fuzzy Grammatical Evolution, various ensemble methods were investigated. Boosting was seen to find the best individuals on half the benchmarks investigated.

pyPolyBuilder: Automated Preparation of Molecular Topologies and Initial Configurations for Molecular Dynamics Simulations of Arbitrary Supramolecules.

The construction of a molecular topology file is a prerequisite for any classical molecular dynamics simulation. However, the generation of such a file may be very challenging at times, especially for large supramolecules. While many tools are available to provide topologies for large proteins and other biomolecules, the scientific community researching nonbiological systems is not equally well equipped. Here, we present a practical tool to generate topologies for arbitrary supramolecules: The pyPolyBuilder. In addition to linear polymer chains, it also provides the possibility to generate topologies of arbitrary, large, branched molecules, such as, e.g., dendrimers. Furthermore, it also generates reasonable starting structures for simulations of these molecules. pyPolyBuilder is a standalone command-line tool implemented in python. Therefore, it may be easily incorporated in persisting simulation pipelines on any operating systems and with different simulation engines. pyPolyBuilder is freely available on github: https://github.com/mssm-labmmol/pypolybuilder.

An emergency system for monitoring pulse oximetry, peak expiratory flow, and body temperature of patients with COVID-19 at home: Development and preliminary application.

BACKGROUND: COVID-19 is characterized by a rapid change in the patient's condition, with major changes occurring over a few days. We aimed to develop and evaluate an emergency system for monitoring patients with COVID-19, which may be useful in hospitals where more severe patients stay in their homes. METHODOLOGY/PRINCIPAL FINDINGS: The system consists of the home-based patient unit, which is set up around the patient and the hospital unit, which enables the medical staff to telemonitor the patient's condition and help to send medical recommendations. The home unit allows the data transmission from the patient to the hospital, which is performed using a cell phone application. The hospital unit includes a virtual instrument developed in LabVIEW® environment that can provide a real-time monitoring of the oxygen saturation (SpO2), beats per minute (BPM), body temperature (BT), and peak expiratory flow (PEF). Abnormal events may be fast and automatically identified. After the design details are described, the system is validated by a 30-day home monitoring study in 12 controls and 12 patients with COVID-19 presenting asymptomatic to mild disease. Patients presented reduced SpO2 (p<0.0001) and increased BPM values (p<0.0001). Three patients (25%) presented PEF values between 50 and 80% of the predicted. Three of the 12 monitored patients presented events of desaturation (SpO2<92%). The experimental results were in close agreement with the involved pathophysiology, providing clear evidence that the proposed system can be a useful tool for the remote monitoring of patients with COVID-19. CONCLUSIONS: An emergency system for home monitoring of patients with COVID-19 was developed in the current study. The proposed system allowed us to quickly respond to early abnormalities in these patients. This system may contribute to conserving hospital resources for those most in need while simultaneously enabling early recognition of patients under acute deterioration, requiring urgent assessment.

Machine learning associated with respiratory oscillometry: a computer-aided diagnosis system for the detection of respiratory abnormalities in systemic sclerosis.

INTRODUCTION: The use of machine learning (ML) methods would improve the diagnosis of respiratory changes in systemic sclerosis (SSc). This paper evaluates the performance of several ML algorithms associated with the respiratory oscillometry analysis to aid in the diagnostic of respiratory changes in SSc. We also find out the best configuration for this task. METHODS: Oscillometric and spirometric exams were performed in 82 individuals, including controls (n = 30) and patients with systemic sclerosis with normal (n = 22) and abnormal (n = 30) spirometry. Multiple instance classifiers and different supervised machine learning techniques were investigated, including k-Nearest Neighbors (KNN), Random Forests (RF), AdaBoost with decision trees (ADAB), and Extreme Gradient Boosting (XGB). RESULTS AND DISCUSSION: The first experiment of this study showed that the best oscillometric parameter (BOP) was dynamic compliance, which provided moderate accuracy (AUC = 0.77) in the scenario control group versus patients with sclerosis and normal spirometry (CGvsPSNS). In the scenario control group versus patients with sclerosis and altered spirometry (CGvsPSAS), the BOP obtained high accuracy (AUC = 0.94). In the second experiment, the ML techniques were used. In CGvsPSNS, KNN achieved the best result (AUC = 0.90), significantly improving the accuracy in comparison with the BOP (p < 0.01), while in CGvsPSAS, RF obtained the best results (AUC = 0.97), also significantly improving the diagnostic accuracy (p < 0.05). In the third, fourth, fifth, and sixth experiments, different feature selection techniques allowed us to spot the best oscillometric parameters. They resulted in a small increase in diagnostic accuracy in CGvsPSNS (respectively, 0.87, 0.86, 0.82, and 0.84), while in the CGvsPSAS, the best classifier's performance remained the same (AUC = 0.97). CONCLUSIONS: Oscillometric principles combined with machine learning algorithms provide a new method for diagnosing respiratory changes in patients with systemic sclerosis. The present study's findings provide evidence that this combination may help in the early diagnosis of respiratory changes in these patients.

Can machine learning be useful as a screening tool for depression in primary care?

Depression is a widespread disease with a high economic burden and a complex pathophysiology disease that is still not wholly clarified, not to mention it usually is associated as a risk factor for absenteeism at work and suicide. Just 50% of patients with depression are diagnosed in primary care, and only 15% receive treatment. Stigmatization, the coexistence of somatic symptoms, and the need to remember signs in the past two weeks can contribute to explaining this situation. In this context, tools that can serve as diagnostic screening are of great value, as they can reduce the number of undiagnosed patients. Besides, Artificial Intelligence (AI) has enabled several fruitful applications in medicine, particularly in psychiatry. This study aims to evaluate the performance of Machine Learning (ML) algorithms in the detection of depressive patients from the clinical, laboratory, and sociodemographic data obtained from the Brazilian National Network for Research on Cardiovascular Diseases from June 2016 to July 2018. The results obtained are promising. In one of them, Random Forests, the accuracy, sensibility, and area under the receiver operating characteristic curve were, respectively, 0.89, 0.90, and 0.87.

Differential diagnosis of asthma and restrictive respiratory diseases by combining forced oscillation measurements, machine learning and neuro-fuzzy classifiers.

To design machine learning classifiers to facilitate the clinical use and increase the accuracy of the forced oscillation technique (FOT) in the differential diagnosis of patients with asthma and restrictive respiratory diseases. FOT and spirometric exams were performed in 97 individuals, including controls (n = 20), asthmatic patients (n = 38), and restrictive (n = 39) patients. The first experiment of this study showed that the best FOT parameter was the resonance frequency, providing moderate accuracy (AUC = 0.87). In the second experiment, a neuro-fuzzy classifier and different supervised machine learning techniques were investigated, including k-nearest neighbors, random forests, AdaBoost with decision trees, and support vector machines with a radial basis kernel. All classifiers achieved high accuracy (AUC ≥ 0.9) in the differentiation between patient groups. In the third and fourth experiments, the use of different feature selection techniques allowed us to achieve high accuracy with only three FOT parameters. In addition, the neuro-fuzzy classifier also provided rules to explain the classification. Neuro-fuzzy and machine learning classifiers can aid in the differential diagnosis of patients with asthma and restrictive respiratory diseases. They can assist clinicians as a support system providing accurate diagnostic options.

Trichomonas Infection in a Community of Free-Ranging Domestic and Wild Columbiformes and Bonelli's Eagle (Aquila fasciata).

Trichomonas gallinae is a pathogen of conservation relevance, whose main maintenance hosts are Columbiformes, but spillover to avian predators has been described. The goal of this study was to characterize the epidemiology of Trichomonas spp. in a community of free-ranging domestic and wild Columbiformes and an endangered predator, Bonelli's eagle Aquila fasciata. We surveyed 253 live-captured Rock doves, 16 nestling Bonelli's eagles and 41 hunted Columbiformes. Oro-esophageal swabs were incubated in culture media and Trichomonas spp. isolated from Bonelli's eagle (6.3%, CI95 1.1-28.3), Turtle dove Streptopelia turtur (56.3%, CI95 39.3-71.8), Wood pigeon Columba palumbus (83.3%, CI95 43.7-97.0) and Rock dove Columba livia (68.4%, CI95 62.4-73.8). Infected Rock doves showed significantly poorer body condition than uninfected ones (p = 0.022). From a subset of 32 isolates, 18S and ITS1/5.8S/ITS2 rRNA genes were sequenced and Maximum-Likelihood trees inferred. Four ribotypes of Trichomonas spp. were identified. In this study area Trichomonas spp. seem to persist in a multi-host system involving several species of Columbiformes. Conservation actions aimed at increasing the availability of trophic resources for Bonelli's eagles through Rock dove restocking should consider the risk of pathogen transmission and of introduction of alien strains.

Respiratory resistance and reactance in adults with sickle cell anemia: Part 2-Fractional-order modeling and a clinical decision support system for the diagnosis of respiratory disorders.

BACKGROUND: A better understanding of sickle cell anemia (SCA) and improvements in drug therapy and health policy have contributed to the emergence of a large population of adults living with this disease. The mechanisms by which SCA produces adverse effects on the respiratory system of these patients are largely unknown. Fractional-order (FrOr) models have a high potential to improve pulmonary clinical science and could be useful for diagnostic purposes, offering accurate models with an improved ability to mimic nature. Part 2 of this two-part study examines the changes in respiratory mechanics in patients with SCA using the new perspective of the FrOr models. These results are compared with those obtained in traditional forced oscillation (FOT) parameters, investigated in Part 1 of the present study, complementing this first analysis. METHODOLOGY/PRINCIPAL FINDINGS: The data consisted of three categories of subjects: controls (n = 23), patients with a normal spirometric exam (n = 21) and those presenting restriction (n = 24). The diagnostic accuracy was evaluated by investigating the area under the receiver operating characteristic curve (AUC). Initially, it was observed that biomechanical changes in SCA included increased values of fractional inertance, as well as damping and hysteresivity (p<0.001). The correlation analysis showed that FrOr parameters are associated with functional exercise capacity (R = -0.57), pulmonary diffusion (R = -0.71), respiratory muscle performance (R = 0.50), pulmonary flows (R = -0.62) and airway obstruction (R = 0.60). Fractional-order modeling showed high diagnostic accuracy in the detection of early respiratory abnormalities (AUC = 0.93), outperforming spirometry (p<0.03) and standard FOT analysis (p<0.01) used in Part 1 of this study. A combination of machine learning methods with fractional-order modeling further improved diagnostic accuracy (AUC = 0.97). CONCLUSIONS: FrOr modeling improved our knowledge about the biomechanical abnormalities in adults with SCA. Changes in FrOr parameters are associated with functional exercise capacity decline, abnormal pulmonary mechanics and diffusion. FrOr modeling outperformed spirometric and traditional forced oscillation analyses, showing a high diagnostic accuracy in the diagnosis of early respiratory abnormalities that was further improved by an automatic clinical decision support system. This finding suggested the potential utility of this combination to help identify early respiratory changes in patients with SCA.

Respiratory resistance and reactance in adults with sickle cell anemia: Correlation with functional exercise capacity and diagnostic use.

BACKGROUND: The improvement in sickle cell anemia (SCA) care resulted in the emergence of a large population of adults living with this disease. The mechanisms of lung injury in this new population are largely unknown. The forced oscillation technique (FOT) represents the current state-of-the-art in the assessment of lung function. The present work uses the FOT to improve our knowledge about the respiratory abnormalities in SCA, evaluates the associations of FOT with the functional exercise capacity and investigates the early detection of respiratory abnormalities. METHODOLOGY/PRINCIPAL FINDINGS: Spirometric classification of restrictive abnormalities resulted in three categories: controls (n = 23), patients with a normal exam (n = 21) and presenting pulmonary restriction (n = 24). FOT analysis showed that, besides restrictive changes (reduced compliance; p<0.001), there is also an increase in respiratory resistance (p<0.001) and ventilation heterogeneity (p<0.01). FOT parameters are associated with functional exercise capacity (R = -0.38), pulmonary diffusion (R = 0.66), respiratory muscle performance (R = 0.41), pulmonary volumes (R = 0.56) and airway obstruction (R = 0.54). The diagnostic accuracy was evaluated by investigating the area under the receiver operating characteristic curve (AUC). A combination of FOT and machine learning (ML) classifiers showed adequate diagnostic accuracy in the detection of early respiratory abnormalities (AUC = 0.82). CONCLUSIONS: In this study, the use of FOT showed that adults with SCA develop a mixed pattern of respiratory disease. Changes in FOT parameters are associated with functional exercise capacity decline, abnormal pulmonary mechanics and diffusion. FOT associated with ML methods accurately diagnosed early respiratory abnormalities. This suggested the potential utility of the FOT and ML clinical decision support systems in the identification of respiratory abnormalities in patients with SCA.

High-accuracy detection of airway obstruction in asthma using machine learning algorithms and forced oscillation measurements.

BACKGROUND AND OBJECTIVES: The main pathologic feature of asthma is episodic airway obstruction. This is usually detected by spirometry and body plethysmography. These tests, however, require a high degree of collaboration and maximal effort on the part of the patient. There is agreement in the literature that there is a demand of research into new technologies to improve non-invasive testing of lung function. The purpose of this study was to develop automatic classifiers to simplify the clinical use and to increase the accuracy of the forced oscillation technique (FOT) in the diagnosis of airway obstruction in patients with asthma. METHODS: The data consisted of FOT parameters obtained from 75 volunteers (39 with obstruction and 36 without). Different supervised machine learning (ML) techniques were investigated, including k-nearest neighbors (KNN), random forest (RF), AdaBoost with decision trees (ADAB) and feature-based dissimilarity space classifier (FDSC). RESULTS: The first part of this study showed that the best FOT parameter was the resonance frequency (AUC = 0.81), which indicates moderate accuracy (0.70-0.90). In the second part of this study, the use of the cited ML techniques was investigated. All the classifiers improved the diagnostic accuracy. Notably, ADAB and KNN were very close to achieving high accuracy (AUC = 0.88 and 0.89, respectively). Experiments including the cross products of the FOT parameters showed that all the classifiers improved the diagnosis accuracy and KNN was able to reach a higher accuracy range (AUC = 0.91). CONCLUSIONS: Machine learning classifiers can help in the diagnosis of airway obstruction in asthma patients, and they can assist clinicians in airway obstruction identification.

Machine learning algorithms and forced oscillation measurements to categorise the airway obstruction severity in chronic obstructive pulmonary disease.

The purpose of this study was to develop automatic classifiers to simplify the clinical use and increase the accuracy of the forced oscillation technique (FOT) in the categorisation of airway obstruction level in patients with chronic obstructive pulmonary disease (COPD). The data consisted of FOT parameters obtained from 168 volunteers (42 healthy and 126 COPD subjects with four different levels of obstruction). The first part of this study showed that FOT parameters do not provide adequate accuracy in identifying COPD subjects in the first levels of obstruction, as well as in discriminating between close levels of obstruction. In the second part of this study, different supervised machine learning (ML) techniques were investigated, including k-nearest neighbour (KNN), random forest (RF) and support vector machines with linear (SVML) and radial basis function kernels (SVMR). These algorithms were applied only in situations where high categorisation accuracy [area under the Receiver Operating Characteristic curve (AUC)≥0.9] was not achieved with the FOT parameter alone. It was observed that KNN and RF classifiers improved categorisation accuracy. Notably, in four of the six cases studied, an AUC≥0.9 was achieved. Even in situations where an AUC≥0.9 was not achieved, there was a significant improvement in categorisation performance (AUC≥0.83). In conclusion, machine learning classifiers can help in the categorisation of COPD airway obstruction. They can assist clinicians in tracking disease progression, evaluating the risk of future disease exacerbations and guiding therapy.

An improved method of early diagnosis of smoking-induced respiratory changes using machine learning algorithms.

The purpose of this study was to develop an automatic classifier to increase the accuracy of the forced oscillation technique (FOT) for diagnosing early respiratory abnormalities in smoking patients. The data consisted of FOT parameters obtained from 56 volunteers, 28 healthy and 28 smokers with low tobacco consumption. Many supervised learning techniques were investigated, including logistic linear classifiers, k nearest neighbor (KNN), neural networks and support vector machines (SVM). To evaluate performance, the ROC curve of the most accurate parameter was established as baseline. To determine the best input features and classifier parameters, we used genetic algorithms and a 10-fold cross-validation using the average area under the ROC curve (AUC). In the first experiment, the original FOT parameters were used as input. We observed a significant improvement in accuracy (KNN=0.89 and SVM=0.87) compared with the baseline (0.77). The second experiment performed a feature selection on the original FOT parameters. This selection did not cause any significant improvement in accuracy, but it was useful in identifying more adequate FOT parameters. In the third experiment, we performed a feature selection on the cross products of the FOT parameters. This selection resulted in a further increase in AUC (KNN=SVM=0.91), which allows for high diagnostic accuracy. In conclusion, machine learning classifiers can help identify early smoking-induced respiratory alterations. The use of FOT cross products and the search for the best features and classifier parameters can markedly improve the performance of machine learning classifiers.

Machine learning algorithms and forced oscillation measurements applied to the automatic identification of chronic obstructive pulmonary disease.

The purpose of this study is to develop a clinical decision support system based on machine learning (ML) algorithms to help the diagnostic of chronic obstructive pulmonary disease (COPD) using forced oscillation (FO) measurements. To this end, the performances of classification algorithms based on Linear Bayes Normal Classifier, K nearest neighbor (KNN), decision trees, artificial neural networks (ANN) and support vector machines (SVM) were compared in order to the search for the best classifier. Four feature selection methods were also used in order to identify a reduced set of the most relevant parameters. The available dataset consists of 7 possible input features (FO parameters) of 150 measurements made in 50 volunteers (COPD, n = 25; healthy, n = 25). The performance of the classifiers and reduced data sets were evaluated by the determination of sensitivity (Se), specificity (Sp) and area under the ROC curve (AUC). Among the studied classifiers, KNN, SVM and ANN classifiers were the most adequate, reaching values that allow a very accurate clinical diagnosis (Se > 87%, Sp > 94%, and AUC > 0.95). The use of the analysis of correlation as a ranking index of the FOT parameters, allowed us to simplify the analysis of the FOT parameters, while still maintaining a high degree of accuracy. In conclusion, the results of this study indicate that the proposed classifiers may contribute to easy the diagnostic of COPD by using forced oscillation measurements.

Automatic identification of Chronic Obstructive Pulmonary Disease Based on forced oscillation measurements and artificial neural networks.

The purpose of this study is to develop an automatic classifier based on Artificial Neural Networks (ANNs) to help the diagnostic of Chronic Obstructive Pulmonary Disease (COPD) using forced oscillation measurements (FOT). The classifier inputs are the parameters provided by the FOT and the output is the indication if the parameters indicate COPD or not. The available dataset consists of 7 possible input features (FOT parameters) of 90 measurements made in 30 volunteers. Two feature selection methods (the analysis of the linear correlation and forward search) were used in order to identify a reduced set of the most relevant parameters. Two different training strategies for the ANNs were used and the performance of resulting networks were evaluated by the determination of accuracy, sensitivity (Se), specificity (Sp) and AUC. The ANN classifiers presented high accuracy (Se > 0.9, Se > 0.9 and AUC > 0.9) both in the complete and the reduce sets of FOT parameters. This indicates that ANNs classifiers may contribute to easy the diagnostic of COPD using forced oscillation measurements.

Diagnóstico da Síndrome de Turner: a experiência do Instituto Estadual de Diabetes e Endocrinologia - Rio de Janeiro, de 1970 a 2008 / Diagnosis of Turner's Syndrome: the experience of the Rio de Janeiro State Institute of Diabetes and Endocrinology between 1970 and 2008

OBJETIVOS: descrever a experiência no diagnóstico da Síndrome de Turner (ST), focalizando a distribuição dos cromossomos, a idade, os sinais e sintomas característicos, conforme as fases da vida (lactância, infância, adolescência e adulta). MÉTODOS: estudo descritivo com 178 pacientes, atendidos de 1970 até 2008. Para análise estatística das diferenças percentuais usou-se o Epi-Info-2000 e para as diferenças entre as médias de idades o teste t de Student e o ANOVA. RESULTADOS: os cariótipos encontrados foram: 79 com 45,X (35,4 por cento), 36 com isocromossomo Xq (20,2 por cento) e 63 com outros mosaicos (35,4 por cento). A média de idade do diagnóstico foi de 12,6 anos, sendo menor naquelas com 45,X. Tiveram o diagnóstico feito na lactância 11,3 por cento das pacientes, 25,3 por cento na infância, 51,1 por cento na adolescência e 12,4 por cento na fase adulta. Daquelas diagnosticadas antes dos cinco anos de idade, 70,6 por cento apresentaram 45,X. Os sinais que levaram à suspeita diagnóstica na lactância foram o pescoço alado e o linfedema congênito de pés/mãos associados às dismorfias típicas; na infância e adolescência foi a baixa estatura. Cubitus valgus foi encontrado em 72,5 por cento das pacientes e orelhas anômalas em 65 por cento das pacientes diagnosticadas com menos de um ano de idade. CONCLUSÃO: o diagnóstico da ST é desnecessariamente atrasado, levando-se em consideração que algumas características típicas podem já estar presentes desde o nascimento.

OBJECTIVES: to describe the Rio de Janeiro State Institute of Diabetes and Endocrinology's experience in diagnosing Turner Syndrome (TS), focusing on the distribution of chromosomes, age, and typical signs and symptoms, according to life stage (breast feeding, childhood, adolescence and adulthood). METHODS: a descriptive study was conducted of 178 patients, attending the Institute between 1970 and 2008 for the purposes of statistical analysis of the percentage differences using Epi-Info-2000 and of the differences between the mean ages using Student's t test and ANOVA Results: the caryotypes found were: 79 with 45,X (35.4 percent), 36 with isochromosome Xq (20.2 percent) and 63 with other mosaics (35.4 percent). The mean age on diagnosis was 12.6 years, this figure being lower in patients with 45,X. The syndrome was diagnosed during breast feeding in 11.3 percent of patients, during childhood in 25.3 percent, during adolescence in 51.1 percent, and in 12.4 percent in adulthood. In those diagnosed before the age of five years, 70,6 percent had 45,X, signs that led to a suspected diagnosis during breast feeding were a webbed neck and congenital lymphedema in the hands and feet associated with typical dysmorphias. In childhood and adolescence the sign was short stature. Cubitus valgus was found in 72.5 percent of patients and abnormal ears in 65 percent of those diagnosed at an age of less than one year. CONCLUSION: diagnosis of TS does not necessarily have to be late, as some typical characteristics may already be present at birth.

Perda de seguimento e transmissão vertical do HIV na cidade de Joinville, SC - Brasil

Objetivos: Avaliar a prevalência do vírus HIV nas gestantes, a taxa de transmissão vertical (TV) e a perda de seguimento dos recém-nascidos (RNs). Sujeitos e métodos: Estudo coorte retrospectivo. Foram avaliadas as notificações das gestantes HIV+ na cidade de Joinville, no período de janeiro de 2000 à julho de 2006, as gestantes foram rastreadas para o HIVdurante o pré-natal ou no momento do parto. Foi recomendado o AZT, conforme o protocolo ACTG 076, indica-se cesariana eletiva nos casos selecionados e contraindicada a amamentação. Os RNs foram seguidosaté 18 meses pós-parto e considerou-se perda de seguimento a interrupção do acompanhamento. Resultados: No período, foram realizados 53.936 partos, 305 gestantes confirmaram o diagnóstico de HIV,resultando em uma prevalência de 0,56%. Dessas, 283 tiveram RN vivos. A perda do seguimento dos RNs foi de 64 (22.6%) casos. Nos RNs acompanhados até 18 meses, encontrou-se uma taxa de TV de 5.47%. Naanálise anual das taxas de TV, nota-se que em 2006 houve maior perda de seguimento (p<0.001), enquanto que os demais anos não apresentam diferença entre si (p=0,365). Em relação à positividade para HIV no RN aos 18 meses, ressalta-se que não houve diferença (p=0,265)entre os anos estudados. O ano (2000) com maior taxa de TV (15%) foi o de menor perda de seguimento (5%), e o ano de 2006, de menor taxa de TV (0%) foi o de maior perda de seguimento (62,5%). Conclusão: Encontrou-se uma prevalência de 0,56%, uma taxa de TV de 5,47% e uma perda de seguimento de 22,6%.

Objectives: To assess the prevalence of HIV in pregnant women, the rate of vertical transmission (VT) and loss to follow-up of newborns (NB).Participants and Methods: It is a retrospective cohort study. Reports of HIV positive pregnant women in the city of Joinville, from January 2000 to July 2006 were assessed. Pregnant women were screened for HIV during prenatal care or during delivery. AZT was recommended in compliance with ACTG 076 protocol. Elective cesarean section in the selected cases is indicated. Breastfeeding is contraindicated. Newborn follow up took place until 18 months after delivery. Loss to follow up was defined as the interruption of monitoring. Results: In the study period, a total of 53.936 births took place, out of which 305 (0.56%) pregnant women were HIV positive, totaling a prevalence of 0.56%. Two hundred eighty three of them delivered NB. Loss to follow-up took place in 64 cases (22.6%). In those NB monitored up to 18 months a VT rate of 5.47% was found. Analysis of yearly VT rates shows that higher loss to follow up took place in 2006 (p<0.001), whereas the other years show no difference among them(p=0.365). With regards to HIV positive NB at 18 months, no difference was found (p=0.265) among the years analyzed. The year (2000) with the highest VT rate (15%) was the one with the lowest loss to follow up (5%). The year (2006) with the lowest VT rate (0%) was the onewith the highest loss to follow up (62.5%). Conclusion: A prevalence of 0.56%, a VT rate of 5.47% and a loss to follow-up of 22.6% were found.

Malófagos de galinhas-dangola (Numida meleagris, L. 1758) em criações extensivas no estado do Rio de Janeiro

Um estudo sobre os principais malófagos de 35 galinhas-dangola provenientes de cinco municípios do estado do Rio deJaneiro (Barra Mansa, Maricá, Itaboraí, Cachoeira de Macacu e Cambuci) foi realizado a partir da pesquisa de penas, coleta depiolhos, contagem de espécimens, acondicionamento em álcool 70%GL. Das 35 aves estudadas, 100% apresentaram-separasitadas. Oito espécies foram e suas respectivas prevalências foram: Menopon galinae (100%); Menacanthus stramineus(2,8%); Menacanthus pallidulus (2,8%); Colpocephalum turbinatum (2,8%); Lipeurus caponis (31,4%); Lipeurus tropicalis(2,8%); Goniodes gigas (25,7%) e Goniocotes gallinae (40%). O poliparasitismo foi observado, sendo a associação, Menopongallinae x Goniocotes gallinae, a mais prevalente. O contato das galinhas-dangola com diferentes espécies de aves domésticaspode ter propiciado o parasitismo de malófagos comuns às outras espécies de aves. Pela primeira vez no Brasil são registradosem galinhas-dangola espécies de piolhos Menacanthus stramineus, Menacanthus pallidulus, Colpocephalum turbinatum,Lipeurus caponis, Lipeurus tropicalis e Goniodes gigas. As espécies Menacanthus pallidulus, Colpocephalum turbinatum eLipeurus tropicalis foram registradas pela primeira vez neste hospedeiro, na literatura mundial.

Malófagos de galinhas-d’angola (Numida meleagris, L. 1758) em criações extensivas no estado do Rio de Janeiro / Chewing lice of free reared guinea-fowls (Numida meleagris, L. 1758) in the state of Rio de Janeiro

Um estudo sobre os principais malófagos de 35 galinhas-d’angola provenientes de cinco municípios do estado do Rio deJaneiro (Barra Mansa, Maricá, Itaboraí, Cachoeira de Macacu e Cambuci) foi realizado a partir da pesquisa de penas, coleta depiolhos, contagem de espécimens, acondicionamento em álcool 70 por centoGL. Das 35 aves estudadas, 100 por cento apresentaram-separasitadas. Oito espécies foram e suas respectivas prevalências foram: Menopon galinae (100 por cento); Menacanthus stramineus(2,8 por cento); Menacanthus pallidulus (2,8 por cento); Colpocephalum turbinatum (2,8 por cento); Lipeurus caponis (31,4 por cento); Lipeurus tropicalis(2,8 por cento); Goniodes gigas (25,7 por cento) e Goniocotes gallinae (40 por cento). O poliparasitismo foi observado, sendo a associação, Menopongallinae x Goniocotes gallinae, a mais prevalente. O contato das galinhas-d’angola com diferentes espécies de aves domésticaspode ter propiciado o parasitismo de malófagos comuns às outras espécies de aves. Pela primeira vez no Brasil são registradosem galinhas-d’angola espécies de piolhos Menacanthus stramineus, Menacanthus pallidulus, Colpocephalum turbinatum,Lipeurus caponis, Lipeurus tropicalis e Goniodes gigas. As espécies Menacanthus pallidulus, Colpocephalum turbinatum eLipeurus tropicalis foram registradas pela primeira vez neste hospedeiro, na literatura mundial.

A total of 35 guinea-fowls, Numida meleagris, were examined for lice at five municipalities in the Rio de Janeiro State, Brazil(Barra Mansa, Maricá, Itaboraí, Cachoeiras de Macacú e Cambuci). All birds were infested by lice. Eigth chewing lice specieswere encountered and their respective prevalence were found: Menopon galinae (100 percent); Menacanthus stramineus (2,8 percent)Menacanthus pallidulus (2,8 percent); Colpocephalum turbinatum (2,8 percent) Lipeurus caponis (31,4 percent); Lipeurus tropicalis (2,8 percent)Goniodes gigas (25,7 percent) and Goniocotes gallinae (40 percent) . The polyparasitism was observed being the association betweenMenopon gallinae and Goniocotes gallinae, the most prevalent one. The contact among guinea-fowls and other domestic birdsmight have been responsible for the parasitism with lice common to other bird species. For the first time it was registered inBrazil the occurrence of the following chewing lice in guinea-fowls: Menacanthus stramineus, Menacanthus pallidulusColpocephalum turbinatum, Lipeurus caponis, Lipeurus tropicalis and Goniodes gigas. The chewing lice species: Menacanthuspallidulus, Colpocephalum turbinatum and Lipeurus tropicalis were registered for the first time in guinea-fowls.