Parallel hyperspectral unmixing method via split augmented lagrangian on GPU

One of the main problems of hyperspectral data analysis is the presence of mixed pixels due to the low spatial resolution of such images. Linear spectral unmixing aims at inferring pure spectral signatures and their fractions at each pixel of the scene. The huge data volumes acquired by hyperspectral sensors put stringent requirements on processing and unmixing methods. This letter proposes an efficient implementation of the method called simplex identification via split augmented Lagrangian (SISAL) which exploits the graphics processing unit (GPU) architecture at low level using Compute Unified Device Architecture. SISAL aims to identify the endmembers of a scene, i.e., is able to unmix hyperspectral data sets in which the pure pixel assumption is violated. The proposed implementation is performed in a pixel-by-pixel fashion using coalesced accesses to memory and exploiting shared memory to store temporary data. Furthermore, the kernels have been optimized to minimize the threads divergence, therefore achieving high GPU occupancy. The experimental results obtained for the simulated and real hyperspectral data sets reveal speedups up to 49 times, which demonstrates that the GPU implementation can significantly accelerate the method's execution over big data sets while maintaining the methods accuracy.

Parallel method for sparse semisupervised hyperspectral unmixing

Parallel hyperspectral unmixing problem is considered in this paper. A semisupervised approach is developed under the linear mixture model, where the abundance's physical constraints are taken into account. The proposed approach relies on the increasing availability of spectral libraries of materials measured on the ground instead of resorting to endmember extraction methods. Since Libraries are potentially very large and hyperspectral datasets are of high dimensionality a parallel implementation in a pixel-by-pixel fashion is derived to properly exploits the graphics processing units (GPU) architecture at low level, thus taking full advantage of the computational power of GPUs. Experimental results obtained for real hyperspectral datasets reveal significant speedup factors, up to 164 times, with regards to optimized serial implementation.

Advanced image processing techniques for detection and quantification of drusen

Dissertation presented to obtain the degree of Doctor of Philosophy in Electrical Engineering, speciality on Perceptional Systems, by the Universidade Nova de Lisboa, Faculty of Sciences and Technology

Parallel hyperspectral unmixing method on GPU

Many Hyperspectral imagery applications require a response in real time or near-real time. To meet this requirement this paper proposes a parallel unmixing method developed for graphics processing units (GPU). This method is based on the vertex component analysis (VCA), which is a geometrical based method highly parallelizable. VCA is a very fast and accurate method that extracts endmember signatures from large hyperspectral datasets without the use of any a priori knowledge about the constituent spectra. Experimental results obtained for simulated and real hyperspectral datasets reveal considerable acceleration factors, up to 24 times.

Unmixing hyperspectral data: independent and dependent component analysis

The development of high spatial resolution airborne and spaceborne sensors has improved the capability of ground-based data collection in the fields of agriculture, geography, geology, mineral identification, detection [2, 3], and classification [4–8]. The signal read by the sensor from a given spatial element of resolution and at a given spectral band is a mixing of components originated by the constituent substances, termed endmembers, located at that element of resolution. This chapter addresses hyperspectral unmixing, which is the decomposition of the pixel spectra into a collection of constituent spectra, or spectral signatures, and their corresponding fractional abundances indicating the proportion of each endmember present in the pixel [9, 10]. Depending on the mixing scales at each pixel, the observed mixture is either linear or nonlinear [11, 12]. The linear mixing model holds when the mixing scale is macroscopic [13]. The nonlinear model holds when the mixing scale is microscopic (i.e., intimate mixtures) [14, 15]. The linear model assumes negligible interaction among distinct endmembers [16, 17]. The nonlinear model assumes that incident solar radiation is scattered by the scene through multiple bounces involving several endmembers [18]. Under the linear mixing model and assuming that the number of endmembers and their spectral signatures are known, hyperspectral unmixing is a linear problem, which can be addressed, for example, under the maximum likelihood setup [19], the constrained least-squares approach [20], the spectral signature matching [21], the spectral angle mapper [22], and the subspace projection methods [20, 23, 24]. Orthogonal subspace projection [23] reduces the data dimensionality, suppresses undesired spectral signatures, and detects the presence of a spectral signature of interest. The basic concept is to project each pixel onto a subspace that is orthogonal to the undesired signatures. As shown in Settle [19], the orthogonal subspace projection technique is equivalent to the maximum likelihood estimator. This projection technique was extended by three unconstrained least-squares approaches [24] (signature space orthogonal projection, oblique subspace projection, target signature space orthogonal projection). Other works using maximum a posteriori probability (MAP) framework [25] and projection pursuit [26, 27] have also been applied to hyperspectral data. In most cases the number of endmembers and their signatures are not known. Independent component analysis (ICA) is an unsupervised source separation process that has been applied with success to blind source separation, to feature extraction, and to unsupervised recognition [28, 29]. ICA consists in finding a linear decomposition of observed data yielding statistically independent components. Given that hyperspectral data are, in given circumstances, linear mixtures, ICA comes to mind as a possible tool to unmix this class of data. In fact, the application of ICA to hyperspectral data has been proposed in reference 30, where endmember signatures are treated as sources and the mixing matrix is composed by the abundance fractions, and in references 9, 25, and 31–38, where sources are the abundance fractions of each endmember. In the first approach, we face two problems: (1) The number of samples are limited to the number of channels and (2) the process of pixel selection, playing the role of mixed sources, is not straightforward. In the second approach, ICA is based on the assumption of mutually independent sources, which is not the case of hyperspectral data, since the sum of the abundance fractions is constant, implying dependence among abundances. This dependence compromises ICA applicability to hyperspectral images. In addition, hyperspectral data are immersed in noise, which degrades the ICA performance. IFA [39] was introduced as a method for recovering independent hidden sources from their observed noisy mixtures. IFA implements two steps. First, source densities and noise covariance are estimated from the observed data by maximum likelihood. Second, sources are reconstructed by an optimal nonlinear estimator. Although IFA is a well-suited technique to unmix independent sources under noisy observations, the dependence among abundance fractions in hyperspectral imagery compromises, as in the ICA case, the IFA performance. Considering the linear mixing model, hyperspectral observations are in a simplex whose vertices correspond to the endmembers. Several approaches [40–43] have exploited this geometric feature of hyperspectral mixtures [42]. Minimum volume transform (MVT) algorithm [43] determines the simplex of minimum volume containing the data. The MVT-type approaches are complex from the computational point of view. Usually, these algorithms first find the convex hull defined by the observed data and then fit a minimum volume simplex to it. Aiming at a lower computational complexity, some algorithms such as the vertex component analysis (VCA) [44], the pixel purity index (PPI) [42], and the N-FINDR [45] still find the minimum volume simplex containing the data cloud, but they assume the presence in the data of at least one pure pixel of each endmember. This is a strong requisite that may not hold in some data sets. In any case, these algorithms find the set of most pure pixels in the data. Hyperspectral sensors collects spatial images over many narrow contiguous bands, yielding large amounts of data. For this reason, very often, the processing of hyperspectral data, included unmixing, is preceded by a dimensionality reduction step to reduce computational complexity and to improve the signal-to-noise ratio (SNR). Principal component analysis (PCA) [46], maximum noise fraction (MNF) [47], and singular value decomposition (SVD) [48] are three well-known projection techniques widely used in remote sensing in general and in unmixing in particular. The newly introduced method [49] exploits the structure of hyperspectral mixtures, namely the fact that spectral vectors are nonnegative. The computational complexity associated with these techniques is an obstacle to real-time implementations. To overcome this problem, band selection [50] and non-statistical [51] algorithms have been introduced. This chapter addresses hyperspectral data source dependence and its impact on ICA and IFA performances. The study consider simulated and real data and is based on mutual information minimization. Hyperspectral observations are described by a generative model. This model takes into account the degradation mechanisms normally found in hyperspectral applications—namely, signature variability [52–54], abundance constraints, topography modulation, and system noise. The computation of mutual information is based on fitting mixtures of Gaussians (MOG) to data. The MOG parameters (number of components, means, covariances, and weights) are inferred using the minimum description length (MDL) based algorithm [55]. We study the behavior of the mutual information as a function of the unmixing matrix. The conclusion is that the unmixing matrix minimizing the mutual information might be very far from the true one. Nevertheless, some abundance fractions might be well separated, mainly in the presence of strong signature variability, a large number of endmembers, and high SNR. We end this chapter by sketching a new methodology to blindly unmix hyperspectral data, where abundance fractions are modeled as a mixture of Dirichlet sources. This model enforces positivity and constant sum sources (full additivity) constraints. The mixing matrix is inferred by an expectation-maximization (EM)-type algorithm. This approach is in the vein of references 39 and 56, replacing independent sources represented by MOG with mixture of Dirichlet sources. Compared with the geometric-based approaches, the advantage of this model is that there is no need to have pure pixels in the observations. The chapter is organized as follows. Section 6.2 presents a spectral radiance model and formulates the spectral unmixing as a linear problem accounting for abundance constraints, signature variability, topography modulation, and system noise. Section 6.3 presents a brief resume of ICA and IFA algorithms. Section 6.4 illustrates the performance of IFA and of some well-known ICA algorithms with experimental data. Section 6.5 studies the ICA and IFA limitations in unmixing hyperspectral data. Section 6.6 presents results of ICA based on real data. Section 6.7 describes the new blind unmixing scheme and some illustrative examples. Section 6.8 concludes with some remarks.

Vertex component analysis: a geometric-based approach to unmix hyperspectral data

Hyperspectral remote sensing exploits the electromagnetic scattering patterns of the different materials at specific wavelengths [2, 3]. Hyperspectral sensors have been developed to sample the scattered portion of the electromagnetic spectrum extending from the visible region through the near-infrared and mid-infrared, in hundreds of narrow contiguous bands [4, 5]. The number and variety of potential civilian and military applications of hyperspectral remote sensing is enormous [6, 7]. Very often, the resolution cell corresponding to a single pixel in an image contains several substances (endmembers) [4]. In this situation, the scattered energy is a mixing of the endmember spectra. A challenging task underlying many hyperspectral imagery applications is then decomposing a mixed pixel into a collection of reflectance spectra, called endmember signatures, and the corresponding abundance fractions [8–10]. Depending on the mixing scales at each pixel, the observed mixture is either linear or nonlinear [11, 12]. Linear mixing model holds approximately when the mixing scale is macroscopic [13] and there is negligible interaction among distinct endmembers [3, 14]. If, however, the mixing scale is microscopic (or intimate mixtures) [15, 16] and the incident solar radiation is scattered by the scene through multiple bounces involving several endmembers [17], the linear model is no longer accurate. Linear spectral unmixing has been intensively researched in the last years [9, 10, 12, 18–21]. It considers that a mixed pixel is a linear combination of endmember signatures weighted by the correspondent abundance fractions. Under this model, and assuming that the number of substances and their reflectance spectra are known, hyperspectral unmixing is a linear problem for which many solutions have been proposed (e.g., maximum likelihood estimation [8], spectral signature matching [22], spectral angle mapper [23], subspace projection methods [24,25], and constrained least squares [26]). In most cases, the number of substances and their reflectances are not known and, then, hyperspectral unmixing falls into the class of blind source separation problems [27]. Independent component analysis (ICA) has recently been proposed as a tool to blindly unmix hyperspectral data [28–31]. ICA is based on the assumption of mutually independent sources (abundance fractions), which is not the case of hyperspectral data, since the sum of abundance fractions is constant, implying statistical dependence among them. This dependence compromises ICA applicability to hyperspectral images as shown in Refs. [21, 32]. In fact, ICA finds the endmember signatures by multiplying the spectral vectors with an unmixing matrix, which minimizes the mutual information among sources. If sources are independent, ICA provides the correct unmixing, since the minimum of the mutual information is obtained only when sources are independent. This is no longer true for dependent abundance fractions. Nevertheless, some endmembers may be approximately unmixed. These aspects are addressed in Ref. [33]. Under the linear mixing model, the observations from a scene are in a simplex whose vertices correspond to the endmembers. Several approaches [34–36] have exploited this geometric feature of hyperspectral mixtures [35]. Minimum volume transform (MVT) algorithm [36] determines the simplex of minimum volume containing the data. The method presented in Ref. [37] is also of MVT type but, by introducing the notion of bundles, it takes into account the endmember variability usually present in hyperspectral mixtures. The MVT type approaches are complex from the computational point of view. Usually, these algorithms find in the first place the convex hull defined by the observed data and then fit a minimum volume simplex to it. For example, the gift wrapping algorithm [38] computes the convex hull of n data points in a d-dimensional space with a computational complexity of O(nbd=2cþ1), where bxc is the highest integer lower or equal than x and n is the number of samples. The complexity of the method presented in Ref. [37] is even higher, since the temperature of the simulated annealing algorithm used shall follow a log( ) law [39] to assure convergence (in probability) to the desired solution. Aiming at a lower computational complexity, some algorithms such as the pixel purity index (PPI) [35] and the N-FINDR [40] still find the minimum volume simplex containing the data cloud, but they assume the presence of at least one pure pixel of each endmember in the data. This is a strong requisite that may not hold in some data sets. In any case, these algorithms find the set of most pure pixels in the data. PPI algorithm uses the minimum noise fraction (MNF) [41] as a preprocessing step to reduce dimensionality and to improve the signal-to-noise ratio (SNR). The algorithm then projects every spectral vector onto skewers (large number of random vectors) [35, 42,43]. The points corresponding to extremes, for each skewer direction, are stored. A cumulative account records the number of times each pixel (i.e., a given spectral vector) is found to be an extreme. The pixels with the highest scores are the purest ones. N-FINDR algorithm [40] is based on the fact that in p spectral dimensions, the p-volume defined by a simplex formed by the purest pixels is larger than any other volume defined by any other combination of pixels. This algorithm finds the set of pixels defining the largest volume by inflating a simplex inside the data. ORA SIS [44, 45] is a hyperspectral framework developed by the U.S. Naval Research Laboratory consisting of several algorithms organized in six modules: exemplar selector, adaptative learner, demixer, knowledge base or spectral library, and spatial postrocessor. The first step consists in flat-fielding the spectra. Next, the exemplar selection module is used to select spectral vectors that best represent the smaller convex cone containing the data. The other pixels are rejected when the spectral angle distance (SAD) is less than a given thresh old. The procedure finds the basis for a subspace of a lower dimension using a modified Gram–Schmidt orthogonalizati on. The selected vectors are then projected onto this subspace and a simplex is found by an MV T pro cess. ORA SIS is oriented to real-time target detection from uncrewed air vehicles using hyperspectral data [46]. In this chapter we develop a new algorithm to unmix linear mixtures of endmember spectra. First, the algorithm determines the number of endmembers and the signal subspace using a newly developed concept [47, 48]. Second, the algorithm extracts the most pure pixels present in the data. Unlike other methods, this algorithm is completely automatic and unsupervised. To estimate the number of endmembers and the signal subspace in hyperspectral linear mixtures, the proposed scheme begins by estimating sign al and noise correlation matrices. The latter is based on multiple regression theory. The signal subspace is then identified by selectin g the set of signal eigenvalue s that best represents the data, in the least-square sense [48,49 ], we note, however, that VCA works with projected and with unprojected data. The extraction of the end members exploits two facts: (1) the endmembers are the vertices of a simplex and (2) the affine transformation of a simplex is also a simplex. As PPI and N-FIND R algorithms, VCA also assumes the presence of pure pixels in the data. The algorithm iteratively projects data on to a direction orthogonal to the subspace spanned by the endmembers already determined. The new end member signature corresponds to the extreme of the projection. The algorithm iterates until all end members are exhausted. VCA performs much better than PPI and better than or comparable to N-FI NDR; yet it has a computational complexity between on e and two orders of magnitude lower than N-FINDR. The chapter is structure d as follows. Section 19.2 describes the fundamentals of the proposed method. Section 19.3 and Section 19.4 evaluate the proposed algorithm using simulated and real data, respectively. Section 19.5 presents some concluding remarks.

Parallel sparse unmixing of hyperspectral data

In this paper, a new parallel method for sparse spectral unmixing of remotely sensed hyperspectral data on commodity graphics processing units (GPUs) is presented. A semi-supervised approach is adopted, which relies on the increasing availability of spectral libraries of materials measured on the ground instead of resorting to endmember extraction methods. This method is based on the spectral unmixing by splitting and augmented Lagrangian (SUNSAL) that estimates the material's abundance fractions. The parallel method is performed in a pixel-by-pixel fashion and its implementation properly exploits the GPU architecture at low level, thus taking full advantage of the computational power of GPUs. Experimental results obtained for simulated and real hyperspectral datasets reveal significant speedup factors, up to 1 64 times, with regards to optimized serial implementation.

Comparison of Disposable Pipette Extraction and Dispersive Solid-Phase Extraction in the QuEChERS Method for Analysis of Pesticides in Strawberries

In this study, we sought to assess the applicability of GC–MS/MS for the identification and quantification of 36 pesticides in strawberry from integrated pest management (IPM) and organic farming (OF). Citrate versions of QuEChERS (quick, easy, cheap, effective, rugged and safe) using dispersive solid-phase extraction (d-SPE) and disposable pipette extraction (DPX) for cleanup were compared for pesticide extraction. For cleanup, a combination of MgSO4, primary secondary amine and C18 was used for both the versions. Significant differences were observed in recovery results between the two sample preparation versions (DPX and d-SPE). Overall, 86% of the pesticides achieved recoveries (three spiking levels 10, 50 and 200 µg/kg) in the range of 70–120%, with <13% RSD. The matrix effects were also evaluated in both the versions and in strawberries from different crop types. Although not evidencing significant differences between the two methodologies were observed, however, the DPX cleanup proved to be a faster technique and easy to execute. The results indicate that QuEChERS with d-SPE and DPX and GC–MS/MS analysis achieved reliable quantification and identification of 36 pesticide residues in strawberries from OF and IPM.

Optimização e Validação da Produção de [13n]-Nh3 para Estudos Clínicos de Tomografia por Emissão de Positrões na Avaliação da Perfusão Miocárdica

A par das patologias oncológicas, as doenças do foro cardíaco, em particular a doença arterial coronária, são uma das principais causas de morte nos países industrializados, devido sobretudo, à grande incidência de enfartes do miocárdio. Uma das formas de diagnóstico e avaliação desta condição passa pela obtenção de imagens de perfusão miocárdica com radionuclídeos, realizada por Tomografia por Emissão de Positrões (PET). As soluções injectáveis de [15O]-H2O, [82Rb] e [13N]-NH3 são as mais utilizadas neste tipo de exame clínico. No Instituto de Ciências Nucleares Aplicadas à Saúde (ICNAS), a existência de um ciclotrão tem permitido a produção de uma variedade de radiofármacos, com aplicações em neurologia, oncologia e cardiologia. Recentemente, surgiu a oportunidade de iniciar exames clínicos com [13N]-NH3 para avaliação da perfusão miocárdica. É neste âmbito que surge a oportunidade do presente trabalho, pois antes da sua utilização clínica é necessário realizar a optimização da produção e a validação de todo o processo segundo as normas de Boas Práticas Radiofarmacêuticas. Após uma fase de optimização do processo, procedeu-se à avaliação dos parâmetros físico-químicos e biológicos da preparação de [13N]-NH3, de acordo com as indicações da Farmacopeia Europeia (Ph. Eur.) 8.2. De acordo com as normas farmacêuticas, foram realizados 3 lotes de produção consecutivos para validação da produção de [13N]-NH3. Os resultados mostraram um produto final límpido e ausente de cor, com valores de pH dentro do limite especificado, isto é, entre 4,5 e 8,5. A pureza química das amostras foi verificada, uma vez que relativamente ao teste colorimétrico, a tonalidade da cor da solução de [13N]-NH3 não era mais intensa que a solução de referência. As preparações foram identificadas como sendo [13N]-NH3, através dos resultados obtidos por cromatografia iónica, espectrometria de radiação gama e tempo de semi-vida. Por examinação do cromatograma obtido com a solução a ser testada, observou-se que o pico principal possuia um tempo de retenção aproximadamente igual ao pico do cromatograma obtido para a solução de referência. Além disso, o espectro de radiação gama mostrou um pico de energia 0,511 MeV e um outro adicional de 1,022 MeV para os fotões gama, característico de radionuclídeos emissores de positrões. O tempo de semi-vida manteve-se dentro do intervalo indicado, entre 9 e 11 minutos. Verificou-se, igualmente, a pureza radioquímica das amostras, correspondendo um mínimo de 99% da radioactividade total ao [13N], bem como a pureza radionuclídica, observando-se uma percentagem de impurezas inferiores a 1%, 2h após o fim da síntese. Os testes realizados para verificação da esterilidade e determinação da presença de endotoxinas bacterianas nas preparações de [13N]-NH3 apresentaram-se negativos.Os resultados obtidos contribuem, assim, para a validação do método para a produção de [13N]-NH3, uma vez que cumprem os requisitos especificados nas normas europeias, indicando a obtenção de um produto seguro e com a qualidade necessária para ser administrado em pacientes para avaliação da perfusão cardíaca por PET.

Intestinal parasites in school food handlers in the city of Uberlândia, Minas Gerais, Brazil

In order to verify the presence of intestinal parasites in food handlers, stool samples were collected from 104 cooks and their helpers that were working in food preparation in 20 public elementary schools, in various areas of the city of Uberlândia, Minas Gerais, Brazil. The samples were collected during the months of November and December, 1988, in plastic flasks containing a 10% formaldehyde solution and processed by the Hoffmann, Pons & Janer method. The sediment was examined using triplicate slides. All individuals were females aged between 24 to 69 years. Intestinal parasites were found in 85.0% of the studied schools and 47.1% of the studied food handlers (cooks and helpers) were found to be positive. Among the 49 infected food handlers, 32 (65.3%) carried a single parasite and 17 (34.7%) carried two parasites. The following intestinal parasites were found: Giardia lamblia (21.1%), Entamoeba coli (21.1%), hookworms (9.6%), Ascaris lumbricoides (5.8%), Entamoeba histolytica (2.9%), Hymenolepis nana (1.9%), Strongyloides stercoralis (1.0%). These data emphasize the need for a rigid semi-annual control in all school food handlers, including diagnosis, specific treatment and orientation about the mechanisms of transmission of the intestinal parasites.

Enzyme linked immunosorbent assay for rubella antibodies: a simple method of antigen production. A preliminary report

A simple method of rubella antigen production by treatment with sodium desoxycholate for use in enzyme immunoassay (IMT-ELISA) is presented. When this assay was compared with a commercial test (Enzygnost-Rubella, Behring), in the study of 108 sera and 118 filter paper blood samples, 96.9% (219/226) overall agreement and correlation coefficient of 0.90 between absorbances were observed. Seven samples showed discordant results, negative by the commercial kit and positive by our test. Four of those 7 samples were available, being 3 positive by HI.

Exposição a fungos dos trabalhadores dos ginásios com piscina

RESUMO - Os trabalhadores dos ginásios com piscinas apresentam maior prevalência de lesões fúngicas, como a Tinea pedis e a onicomicose, devido às características intrínsecas da sua actividade profissional, pois apresentam mais horas por dia de exposição à contaminação fúngica das superfícies. Esta situação verifica-se não só por serem os que mais frequentam os locais possíveis de estarem contaminados, como é o caso de balneários, vestiários e zona envolvente às piscinas, mas também porque algumas das actividades desenvolvidas são realizadas com os pés descalços. Além disso, a utilização de roupa sintética e de calçado ocluso, que retêm a sudação excessiva, favorece o desenvolvimento fúngico. Constituiu objectivo deste trabalho conhecer o risco de infecção e/ou lesão (Tinea pedis e onicomicose) nos trabalhadores dos ginásios com piscina e a sua eventual relação com a exposição à contaminação fúngica (ar e superfícies) dos locais de trabalho. Foram descritas as variáveis ambientais e biológicas que influenciam a infecção e/ou lesão fúngica em ambiente profissional e exploradas eventuais associações entre essas mesmas variáveis. Foram também conhecidas as diferenças da contaminação fúngica das superfícies entre as duas principais estações do ano (Verão e Inverno) e entre antes e depois da lavagem e desinfecção. O estudo realizado possui uma componente transversal, em que se pretendeu descrever os fenómenos ambientais e biológicos da contaminação fúngica em ambiente profissional e explorar eventuais associações entre variáveis; uma componente longitudinal, em que foram conhecidas as diferenças sazonais da contaminação fúngica das superfícies; e, ainda, uma componente quase experimental, em que foi analisada a distribuição fúngica nas superfícies antes e depois da lavagem e desinfecção. Na vertente transversal foi considerada uma amostra de 10 ginásios com piscina e outra amostra de, pelo menos, 10 profissionais de cada estabelecimento, perfazendo um total de 124 trabalhadores (75 Homens - 60,48% e 49 Mulheres - 39,52%). Foram realizadas 258 colheitas biológicas aos pés dos trabalhadores, efectuada a avaliação ambiental da contaminação fúngica dos estabelecimentos através de 50 colheitas de amostras de ar e 120 colheitas de amostras de superfícies (60 antes e 60 depois da lavagem e desinfecção) e efectuados os respectivos processamento laboratorial e identificação fúngica. Foram também avaliadas as variáveis ambientais temperatura, humidade relativa e velocidade do ar, preenchidas 10 grelhas de observação, com o objectivo de efectuar o registo de informação sobre as variáveis que xx influenciam a exposição ocupacional às espécies fúngicas e, ainda, completadas 124 grelhas de observação inerentes à colheita de material biológico, de modo a realizar o registo dos profissionais com lesão e outras informações pertinentes para a análise laboratorial. Todos os 124 trabalhadores responderam a um questionário, em simultâneo à realização das colheitas biológicas, de modo a conhecer algumas das variáveis individuais e profissionais com pertinência para o presente estudo. Num dos estabelecimentos, foram também estudadas as diferenças da contaminação fúngica das superfícies entre antes e depois da lavagem e desinfecção e, ainda, entre as duas estações do ano (Verão e Inverno). Nesse estabelecimento, foram realizadas 36 colheitas de superfícies antes e 36 colheitas depois da lavagem e desinfecção, em 6 dias diferentes da semana, durante 6 semanas sequenciais em cada estação do ano, completando um total de 72 colheitas de superfícies. Foi ainda criado e aplicado um método para estabelecer um padrão de exposição profissional a fungos nas superfícies, de modo a permitir definir níveis semi-quantitativos de estimação do risco de infecção fúngica dos trabalhadores dos ginásios com piscinas. Para o critério da Gravidade, considerou-se que a gravidade da contaminação e, consequentemente, da possível lesão, está intimamente relacionada com a espécie fúngica envolvida. Foram calculadas as médias da contaminação fúngica por cada estabelecimento antes da lavagem e desinfecção, de modo a estabelecer os níveis de Frequência e, em relação à Exposição, foram estabelecidos intervalos para agrupar as horas semanais de trabalho. Dos 124 trabalhadores que participaram no estudo, 58 (46,8%) possuíam lesões visíveis. Nesses 58, as Leveduras foram as mais isoladas (41,4%), seguidas dos Dermatófitos (24,1%) e de Fungos Filamentosos Não Dermatófitos (6,9%). Candida parapsilosis e Rhodotorula sp. foram as Leveduras mais frequentemente isoladas (20,2%); no caso dos Dermatófitos, Trichophyton rubrum foi a espécie mais frequente (55,5%) e, relativamente aos Fungos Filamentosos Não Dermatófitos, Penicillium sp. foi o mais isolado (15,6%), seguido do género Fusarium (12,5%). No que concerne à contaminação fúngica das superfícies, 37 fungos filamentosos foram isolados. Fusarium foi o género mais frequente, antes e depois da lavagem e desinfecção (19,1% - 17,2%). Em relação aos fungos leveduriformes, 12 leveduras diferentes foram identificadas, tendo sido os géneros Cryptococcus (40,6%) e Candida (49,3%) os mais frequentes antes e depois da lavagem e desinfecção, respectivamente. Em relação à contaminação fúngica do ar, foram identificados 25 fungos filamentosos diferentes, em que os 3 géneros mais frequentemente isolados foram Cladosporium (36,6%), Penicillium (19,0%) e Aspergillus (10,2%). Relativamente às leveduras, foi identificado o género xxi Rhodotorula (87,5%) e as espécies Trichosporon mucoides e Cryptococcus unigutulattus (12,5%). Verificou-se associação, ao nível de significância de 5%, entre lesão visível e horas semanais e entre lesão visível e tempo de profissão, comprovando a influência da duração da exposição ao factor de risco (contaminação fúngica do ambiente profissional), para a presença de lesão visível nos trabalhadores expostos (Tinea pedis e onicomicose), ficando demonstrada a relação entre a exposição ao factor de risco em estudo – exposição profissional a fungos – com os efeitos para a saúde. As variáveis ambientais avaliadas (temperatura, humidade relativa e velocidade do ar) não influenciaram a contaminação fúngica do ar e das superfícies, não tendo sido evidenciada nenhuma relação estatisticamente significativa (p>0,05). Contudo, verificou-se influência do número de ocupantes que frequentaram cada um dos estabelecimentos nas médias das unidades formadoras de colónias por metro quadrado nas superfícies antes da lavagem e desinfecção. Não se verificou correlação entre os resultados quantitativos da contaminação fúngica do ar e a das superfícies dos 10 estabelecimentos monitorizados. No entanto, verificaram-se diferenças significativas, ao nível de significância de 10%, entre a contaminação fúngica das superfícies e a contaminação fúngica do ar (p<0,1), tendo-se constatado que apesar de 50% dos valores mais baixos terem sido superiores na contaminação fúngica do ar, a contaminação fúngica das superfícies apresentou-se com maior variabilidade quantitativa. Em relação às diferenças significativas na contaminação fúngica das superfícies nos 10 estabelecimentos entre antes e depois da lavagem e desinfecção, apenas se verificou redução significativa (p<0,05) da contaminação fúngica depois da lavagem e desinfecção nos balneários e vestiários masculinos em relação aos fungos leveduriformes. No estabelecimento seleccionado, verificou-se que a relação entre a contaminação fúngica e a temperatura e humidade relativa não foi significativa (p>0,05) em ambas as estações do ano e também não se constatou influência dos ocupantes nos valores médios das unidades formadoras de colónias por metro quadrado das superfícies antes da lavagem e desinfecção em ambas as estações de ano. Em quase todas as situações em que se verificaram diferenças significativas entre as duas estações do ano, verificou-se um aumento das unidades formadoras de colónias por metro quadrado no Inverno, com excepção do total das unidades formadoras de colónias por metro quadrado antes da lavagem e desinfecção nos balneários e vestiários masculinos em que se verificou aumento no Verão. Constatou-se também que apenas ocorreu redução da xxii contaminação fúngica depois da lavagem e desinfecção nas escadas de acesso no Inverno e nos balneários e vestiários masculinos no Verão. Com a aplicação do método para estabelecer um padrão de exposição profissional a fungos nas superfícies obteve-se, nos 10 estabelecimentos, com Nível de Risco Mínimo 65 locais (54,2%), com Nível de Risco Médio 23 locais (19,2%) e com Nível de Risco Elevado 32 locais (26,6%). Próximo do jacuzzi e junto ao tanque foram os locais com mais classificações de Nível de Risco Elevado. No estabelecimento seleccionado verificou-se que, no Verão, depois da lavagem e desinfecção, ocorreu um maior número de locais classificados no Nível de Risco Elevado e, no Inverno, constatou-se a situação inversa, tendo sido observado maior número de locais com Nível de Risco Elevado antes da lavagem e desinfecção. Junto ao tanque e nas escadas de acesso à zona envolvente ao jacuzzi e tanque foram os locais com mais classificações de Nível de Risco Elevado, no Verão e no Inverno. Foram isolados nas superfícies fungos comuns aos isolados nos trabalhadores. Antes da lavagem e desinfecção, 30,3% dos fungos foram isolados nas superfícies e nos trabalhadores e depois desses procedimentos 45,5% dos fungos foram também isolados comummente. As Leveduras foram as mais isoladas comummente e as que se verificaram mais frequentes antes e depois da lavagem e desinfecção da superfícies e, também, nos resultados das colheitas biológicas realizadas aos trabalhadores, foram o género Rhodotorula e a espécie Candida parapsilosis, permitindo confirmar que a infecção fúngica dos trabalhadores está relacionada com a contaminação fúngica das superfícies. Concluiu-se que é necessária a intervenção em Saúde Ocupacional no âmbito da vigilância ambiental e da vigilância da saúde, com o intuito de diminuir a prevalência das infecções fúngicas. Para a prossecução desse objectivo, sugere-se a implementação de medidas preventivas, nomeadamente: o controlo da contaminação fúngica das superfícies mediante procedimentos de lavagem e desinfecção eficazes, de modo a minimizar a contaminação fúngica das superfícies; a identificação precoce da infecção através da realização de colheitas biológicas periódicas aos trabalhadores, inseridas num protocolo de vigilância da saúde; e, ainda, a sensibilização para a aplicação de medidas de higiene pessoal e o tratamento das patologias. A aplicação do método criado para estabelecer um padrão de exposição profissional a fungos nas superfícies servirá não só para a estimação do risco de infecção fúngica dos trabalhadores de ginásios com piscinas, mas também para facilitar o estabelecimento de valores fúngicos de referência, a implementação de medidas correctivas adequadas e imediatas e, ainda, a prevenção de infecções fúngicas, não só nos ginásios com piscina, mas também noutros contextos profissionais. ------------ SUMMARY - Gyms with swimming pools workers have higher prevalence of fungal injuries, such as Tinea pedis and onychomycosis. This is due to their work intrinsic characteristics, since they have more hours per day of exposure to surfaces fungal contamination. This occurs not only because they attend sites most likely to be contaminated, such as showers, changing rooms and pool surrounding area, but also because some of the activities are done barefoot. Furthermore, synthetic clothing and occluded footwear use, which retain the excessive sweating, promotes fungal development. The aim of this study was to know gymnasiums with swimming pool workers infection and/or injury (Tinea pedis and onychomycosis) risk, and its possible relationship with exposure to workplace fungal contamination (air and surfaces). This study describes environmental and biological variables that influence infection and/or fungal injury in a professional setting and explored possible associations between these variables. Differences in surfaces fungal contamination between the two main seasons (summer and winter), as well between before and after cleaning and disinfection were known. It was developed a study with an cross-sectional perspective, that aimed to describe the biological and environmental phenomena of fungal contamination in a professional environment and explore possible associations between variables; an longitudinal perspective in which were known surfaces fungal contamination seasonal differences; and also with an almost experimental perspective that analyzed surfaces fungal distribution before and after cleaning and disinfection. The cross-sectional perspective comprised 10 gyms with swimming pool sample, and another sample of, at least, 10 professionals in each establishment totalling 124 workers (75 men – 60,48%, and 49 women – 39,52%). Were performed 258 biological samples at workers feet, environmental fungal contamination evaluation from the establishments through 50 air samples and 120 surfaces samples (60 before and 60 after cleaning and disinfection) and conducted their laboratory processing and fungal identification. Were also evaluated environmental variables, such as temperature, relative humidity and air velocity completed 10 observation grids, in order to obtain data about variables that affect occupational exposure to fungal species, and also completed 124 observation grids inherent to biological material collection, in order to know the professionals with injury and other relevant information for laboratory analysis. All 124 workers answered to a questionnaire at the same time that occur biological samples collection, in order to xxv obtain information about some of the individual and professional variables with relevance to this study. In one of the establishments were also studied differences concerning surfaces fungal contamination between before and after cleaning and disinfection, and also between two main seasons (summer and winter). In this setting, there were performed 36 surfaces samples before and 36 surfaces samples after cleaning and disinfection on 6 different week days for 6 sequential weeks in each season, totalling 72 surfaces samples. It was also created and implemented a method to establish a pattern for surfaces fungal occupational exposure, in order to help define semi-quantitative levels estimation to fungal infection risk in gyms with swimming pools workers. For Gravity criterion it was considered that contamination severity and, thus, the possible injury are closely related to implicate fungal species. Was calculated fungal contamination average by each establishment prior cleaning and disinfection, in order to establish Frequency levels. Regarding Exposure, were established weekly hours group intervals spent in professional activity. From the 124 professionals tested, 58 (46,8%) had visible injuries. In the 58 workers, Yeasts were the most isolated (41,4%), followed by Dermatophytes (24,1%) and Other Filamentous Fungi Besides Dermatophytes (6,9%). Candida parapsilosis and Rhodotorula sp. were the most frequently isolated Yeasts (20,2% for each), from Dermatophytes, Trichophyton rubrum was the most frequently isolated species (55,5%) and from Other Filamentous Fungi Besides Dermatophytes, Penicillium sp. was the most frequent (15,6%), followed by Fusarium genera (12,5%). Regarding surfaces fungal contamination, 37 filamentous fungi were isolated. Fusarium genera was the most frequent, before and after cleaning and disinfection (19,1% - 17,2%). Considering yeasts, 12 different yeasts were identified, being Cryptococcus (40,6%) and Candida (49,3%) genera the more frequent before and after cleaning and disinfection, respectively. In relation to air fungal contamination, 25 different filamentous fungi were identified and the 3 most frequently isolated genera were Cladosporium (36,6%), Penicillium (19,0%) and Aspergillus (10,2%). For yeasts, were identified Rhodotorula genera (87,5%), and also the species Trichosporon mucoides and Cryptococcus unigutulattus (12,5%). Was found association with 5% significance level, between visible injury and weekly hours and between visible injury and occupation time, confirming exposure duration influence to risk factor (work environment fungal contamination) for the visible injury presence in exposed workers (Tinea pedis and onychomycosis), being confirmed the relation between the study exposure risk - occupational exposure to fungi - with health effects. xxvi Environmental variables evaluated (temperature, relative humidity and air velocity) did not affect air and surfaces fungal contamination and wasn’t found no statistically significant relation (p>0,05). However, there was evidence that occupant’s number influence surfaces colony forming units mean per square meter before cleaning and disinfection. There was no correlation between quantitative data from air fungal contamination and surfaces fungal contamination from the 10 establishments monitored. However, there were significant differences with 10% significance level, between surfaces and air fungal contamination (p<0,1), and despite 50% of the lowest rates were higher in air fungal contamination, it was found that surfaces fungal contamination had more quantitative variability. Regarding differences from the 10 establishments surfaces fungal contamination, between before and after cleaning and disinfection, there was only a significant reduction (p<0,05) in fungal contamination after cleaning and disinfection in male changing rooms for yeasts. In the selected establishment, it was found that relation between fungal contamination and temperature and relative humidity was not significant (p>0,05) in both seasons, and also there wasn’t no influence observed from occupants in surfaces colony forming units mean per square meters before cleaning and disinfection in both seasons. In almost all situations where significant differences between the two seasons were shown, there was a colony-forming units per square meter increase in winter. There was an exception in total colony forming units per square meter before cleaning and disinfection in male changing room’s exception, where there was an increase in summer. Furthermore, was found that only occur a reduction in fungal contamination after cleaning and disinfection, on access stairs in winter, as well as in male changing rooms in summer. With application from the method to establish pattern for surfaces fungal occupational exposure, it was obtained, in the 10 establishments, 65 sites with Low Risk Level (54,2%), 23 sites with Average Risk Level (19,2%) and 32 sites with High Risk Level (26,6%). Near swimming pool and jacuzzi were the places with more High Risk Level classifications. In the selected establishment, was found that in the summer, after cleaning and disinfection, there were a greater number of sites classified as High Risk Level, and in winter it was found the opposite situation, being noted more places with High Risk Level before cleaning and disinfection. Next to swimming pool and access stairs to swimming pool and jacuzzi were the places with more High Risk Level classifications in Summer and Winter. Were isolated common fungi in surfaces and in workers. Prior to cleaning and disinfection 30,3% of fungi were isolated on surfaces and workers, and after 45,5% of fungi were also xxvii commonly isolated. The Yeasts were the most commonly isolated and the most frequent before and after surfaces cleaning and disinfection, and also in workers biological samples, were Rhodotorula genera and Candida parapsilosis, allowing confirming that workers fungal infection is related with surfaces fungal contamination. It was concluded that Occupational Health intervention it is necessary, in environmental monitoring and health surveillance perspective, in order to reduce fungal infections prevalence. To achieve this objective, preventive measures implementation it’s recommended, including: surfaces fungal contamination control, through effective cleaning and disinfecting in order to minimize surfaces fungal contamination; early infection identification by performing periodic biological sampling from workers, included in a health surveillance protocol; and also personal hygiene and diseases treatment awareness. Application of the created method to establish pattern for surfaces fungal occupational exposure, will be useful not only for estimating workers from gymnasiums with swimming pools fungal infection risk, but also to facilitate fungal reference values stipulation, effective and corrective measures implementation, and also, fungal infections prevention, not only in gymnasiums with swimming pool, but also in other professional settings.----------------- RÉSUMÉ - Les travailleurs des gymnases avec des piscines présentent souvent des infections fongiques, telles que Tinea pedis et aussi des onychomycoses, dues à leur activité professionnel, parce qu’ils restent plus longtemps tout prés des surfaces avec une certaine contamination fongique. Toute cette situation est due non seulement parce qu’ils sont ceux qui fréquentent plus souvent les places plus contaminées: des balnéaires, des vestiaires et des zones autour des piscines, mais aussi ils réalisent des activités aux pieds nus ou avec des chaussures très fermés et encore quelques fois avec des vêtements synthétiques. Tout cela emmène à une grande sudation ce qui aidera au développement fongique. Un objective de ce travaille a été connaître le risque d’infection et/ou présence de lésion (Tinea pedis et des onychomycoses) dans les travailleurs des gymnases avec des piscines et leur éventuel rapport avec l’exposition à la contamination fongique (de l’air et des surfaces) dans leurs locaux de travaille. On a décrit aussi des variables d’environnement et biologiques qui ont une certaine influence dans les infections fongiques dans tout l’environnement professionnel et aussi approfondir des éventuels associations entre ces même variables. On a encore reconnu des différences de la contamination fongique avant et après des lavages et désinfection de ces surfaces. Aussi on a trouvé des différences de contamination en Été et en Hiver. Cet étude a un composante transversale, en visant la description des phénomènes de contamination fongique biologique et de l'environnement dans un environnement professionnel et l’étude des associations possibles entre les variables; une composante longitudinale dans laquelle ils étaient connus comme des variations saisonnières de la contamination fongique des surfaces, et même; un quasi-composante expérimentale, où elle a examiné la répartition des champignons surfaces avant et après le lavage et la désinfection. Dans la composante transversale on été considérés 1 échantillons de 10 gymnases avec des piscines et un autre échantillon de au moins 10 professionnels de chaque établissement dans un total 124 travailleurs (75 hommes - 60,48% et 49 femmes - 39,52%). On a réalisé 258 prélèvements aux pieds des travailleurs et on a effectué en simultané la validation par contamination fongique de l’environnement par 50 prélèvements de l’air et par 120 prélèvements de surfaces (60 avant et 60 après des lavages et des désinfections) et on a effectué leur traitement en laboratoire et l’identification fongique. On a fait aussi l’évaluation des variables de l’environnement, la température, l’humidité relative et la vitesse de l’air. On a remplie 10 tableaux xxix d’observation, avec l’objective d’obtenir des informations sur les variables qu’influenceront l’exposition occupationnel aux souches fongiques, et encore 124 tableaux d’observation liée au prélèvement du matériel biologique, pour réaliser le registre des professionnels avec des lésions et des autres informations pertinentes pour une analyse laboratoire. Tous ces 124 travailleurs ont rempli un questionnaire au même temps que les prélèvements biologiques, afin de connaître quelques variables individuels et professionnels importants pour cet étude. Dans un des établissements on a aussi étudié les différences fongiques des surfaces parmi avant et après les lavages et de la désinfection et encore parmi l’Été et l’Hiver. Dans ce même établissement on a réalisé 36 prélèvements des surfaces avant et 36 après des lavages et de la désinfection, pendant 6 jours différents de la semaine, pendant 6 semaines en chaque saison de l’année, dans un total de 72 prélèvements des surfaces. On a encore crié et appliqué une méthode pour établir un standard d’exposition professionnelle au fungi sur les surfaces, afin de permettre la définition des niveaux semi quantitative d’estimation des risques d’infection fongique des travailleurs des gymnases avec des piscines. Pour le critère de Gravité, il a été considéré que la gravité de la contamination, et donc les possibles dommages, est étroitement liée aux espèces fongiques impliquées. Nous avons calculé la moyenne de la contamination fongique par chaque établissement avant le lavage et la désinfection afin d'établir les niveaux de Fréquence et, par rapport à l'Exposition, ont été crées pour regrouper les intervalles d'heures hebdomadaires consacrées à l'activité professionnelle en question. Sur les 124 travailleurs qui ont participé à l'étude, 58 (46,8%) avaient des lésions visibles. Parmi ces 58, les Levures ont été les plus isolées (41,4%), suivis par des Dermatophytes (24,1%) et des Filamenteux Non Dermatophytes (6,9%). Candida parapsilosis and Rhodotorula sp. ont été les Levures les plus fréquemment isolées (20,2%); dans le cas des Dermatophytes, Trichophyton rubrum est le plus fréquent (55,5%) et pour les Filamenteux Non Dermatophytes, Penicillium sp. a été le plus isolé (15,6%), suivi par Fusarium sp. (12,5%). En ce qui concerne la contamination fongique des surfaces, 37 champignons filamenteux ont été isolés. Le genre Fusarium est le plus fréquent avant et après le lavage et la désinfection (19,1% - 17,2%). Pour la levure, 12 levures différentes ont été identifiées, ayant été Cryptococcus sp. (40,6%) et Candida sp. (49,3%) les plus fréquents avant et après le lavage et la désinfection, respectivement. En ce qui concerne la contamination fongique de l'air, on a identifié 25 différents champignons filamenteux, où les 3 genres les plus fréquemment isolés étaient Cladosporium (36,6%), Penicillium (19,0%) et Aspergillus (10,2%). Pour les levures, il a été identifié le genre xxx Rhodotorula (87,5%) et les espèces Trichosporon mucoides et Cryptococcus unigutulattus (12,5%). On a vérifié une association, au niveau de signification de 5%, entre les lésions visibles et les heures hebdomadaires et entre les lésions visibles et la durée d’occupation, ce qui confirme l'influence de la durée de l'exposition aux facteurs de risque (contamination fongique dans le milieu de travail) pour la présence des lésions visibles chez les travailleurs exposés (Tinea pedis et onychomycose), en démontrant une relation entre l'exposition au facteur de risque dans ces études - l'exposition professionnelle aux champignons - avec les effets sur la santé. Les variables environnementales évalué (température, humidité relative et la vitesse de l'air) ne modifient pas la contamination fongique de l'air et des surfaces; donc, n'a pas été démontré aucune relation statistiquement significative (p>0,05). Cependant, il y a une influence du nombre d'occupants qui ont participé à chacun des établissements en moyenne des unités formant colonie par mètre carré sur la surface avant le lavage et la désinfection. Il n'y avait pas de corrélation entre les résultats quantitatifs de la contamination fongique de l'air et des surfaces des 10 établissements surveillés, cependant il existe des différences importantes, au niveau de signification de 10% entre la contamination fongique des surfaces et de la contamination fongique de l'air (p <0,1), on a constaté que malgré 50% des niveaux les plus bas étaient plus élevés dans la contamination fongique de l'air, la contamination fongique des surfaces présentée une plus grande variabilité quantitativement. En ce qui concerne les différences de la contamination fongique des surfaces dans les 10 établissements entre avant et après le lavage et la désinfection, il y avait seulement une réduction significative (p<0,05) de la contamination fongique après le lavage et la désinfection dans les balnéaires et vestiaires pour les hommes par rapport aux levures. Lors de l'établissement choisi, on a constaté que le rapport entre la contamination fongique et la température et l'humidité relative n'était pas significatif (p>0,05) dans les deux saisons et aussi on n’a pas observé l'influence des occupants en moyenne des unités formant colonie par mètres carrés de surfaces avant le lavage et la désinfection dans les deux saisons de l'année. Dans presque toutes les situations ou on a vérifié des différences significatives entre les deux saisons, il ya eu une augmentation des unités formant des colonies par mètre carré en Hiver, à l'exception du total des unités formant des colonies par mètre carré avant le lavage et désinfection dans les balnéaires et vestiaires des hommes où il y a eu une augmentation en Été. On a également été constaté que seulement a eu une réduction de la contamination des xxxi champignons après la désinfection de l'escalier d'accès en Hiver et dans les balnéaires et vestiaires des hommes en Été. Avec la méthode pour établir standard d’exposition professionnelle au fungi sur les surfaces on a obtenu dans les 10 établissements, avec le Niveau de Risque Faible de 65 places (54,2%), avec le Niveau de Risque Moyen 23 places (19,2%) et 32 places avec le Niveau de Risque Élevé (26,6%). Près du jacuzzi et près de la piscine sont les lieux avec des plus évaluations de Niveau de Risque Élevé. Lors de l'établissement choisi, il a été constaté que, dans l'Été, après le lavage et la désinfection, un plus grand nombre de places évaluées comme présentant un Niveau de Risque Élevé et en Hiver on a constaté la situation inverse avec de nombreux points de Niveau de Risque Élevé avant le lavage et la désinfection. A côté de la piscine et les escaliers ont été les lieux avec plus grands classifications de Niveau de Risque Élevé en Été et en Hiver. On a isolé, chez les travailleurs, des champignons communs aux isolés sur les surfaces. Avant le lavage et la désinfection, 30,3% des champignons ont été isolés sur les travailleurs et sur les surfaces et, après ces procédures, 45,5% des champignons ont été isolés fréquemment. Les levures les plus souvent isolées et les plus fréquentes avant et après le lavage et la désinfection des surfaces, et aussi dans les résultats d'échantillons biologiques prélevés sur les travailleurs, étaient du genre Rhodotorula et les espèces de Candida parapsilosis, ce qui permet confirmer que l'infection fongique des travailleurs est liée à la contamination fongique des surfaces. On a conclu qu’il est nécessaire l'intervention en Santé Occupationnelle sous la surveillance de l'environnement et sous la surveillance de la santé, afin de réduire la prévalence des infections fongiques. Pour atteindre cet objectif, nous suggérons la mise en oeuvre de mesures préventives, y compris: le contrôle de la contamination fongique des surfaces par des méthodes de lavage et de désinfection afin de minimiser la contamination fongique des surfaces, l'identification précoce de l'infection avec des prélèvements biologiques périodiques, notamment un protocole pour la surveillance de la santé, et aussi la conscience du sens de l'hygiène personnelle et le traitement des pathologies. La méthode mise en place pour l’établissement d’un standard d’exposition professionnelle au fungi sur les surfaces, servira à estimer non seulement le risque d'infection fongique des travailleurs dans les gymnases avec des piscines, mais aussi pour faciliter l'établissement de valeurs de référence de champignons, l'application des mesures correctives immédiates et appropriées, et aussi la prévention des infections fongiques, non seulement dans les gymnases avec piscine, mais aussi dans d'autres contextes professionnels.

A simple and inexprensive method to generate a microaerophilic atmosphere for the isolation of Campylobacter sp

Faeces of 138 chickens were inoculated on Blaser agar plates. One set of plates was incubated in jars with CampyPak envelopes. The others were incubated in "Zip-lock" plastic bags (7 x X in.) and a microaerophilic atmosphere was generated exhaling into the "Zip-lock" plastic bag, after holding the breath for 20 sec. Then, the bag was pressed to evacuate its atmosphere, inflated again, and pressed (4 times), and finally sealed. Campylobacter was isolated from 127 (96.2%) of samples incubated in jars with gas generator envelopes and from 129 (98%) of the specimens incubated into the bags. The proposed methodology offers good savings for cost-conscious laboratories.

Studies on prevalence of Strongyloides infection in Holambra and Maceió, Brazil, by the agar plate faecal culture method

Prevalence of Strongyloides stercoralis infection in three areas of Brazil was surveyed by a recently developed faecal culture method (an agar plate culture). The Strongyloides infection was confirmed in 11.3% of 432 subjects examined. The diagnostic efficacy of the agar plate culture was as high as 93.9% compared to only 28.5% and 26.5% by the Harada-Mori filter paper culture and faecal concentration methods, when faecal samples were examined simultaneously by these three methods. Among the 49 positive samples, about 60% were confirmed to be positive only by the agar plate culture. These results indicate that the agar plate culture is a sensitive new tool for the correct diagnosis of chronic Strongyloides infection.