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Background: Pleural effusions often cause a diagnostic dilemma. The first step in the evaluation of pleural effusions is the distinction between exudates and transudates. Aims and Objectives: The current study was designed to distinguish these two broad subgroups of pleural effusions, based on the protein carbonyl gradient between pleural fluid and serum. Materials and Methods: Forty-four exudative and thirty-six transudative pleural effusion cases based on clinical findings and evaluation by Light’s criteria were selected. Protein carbonyl was measured by established photometric method using dinitrophenylhydrazine (DNPH) method in pleural fluid and serum of all the cases. Results: Oxidative stress, estimated by the protein carbonyl gradient between pleural fluid and serum, was found to be higher in exudates when compared to transudates. No significant difference was existent between exudates of different aetiology. However, the gradient was higher in exudates from males compared to females. No such difference was found among transudates. Conclusions: Carbonyl stress being a simple easily measurable biochemical parameter, pleural fluid to serum protein carbonyl gradient may serve as a useful tool for differentiating pleural effusions based on pathogenesis.

Diabetic retinopathy (DR) is a leading cause of visual blindness. However if DR can be diagnosed and treated early, 90% of DR causing blindness can be prevented significantly. Microaneurysms (MAs) and exudates (EXs), as signs of DR, can be used for early DR diagnosis. However, MAs and EXs segmentation is a challenging task due to the low contrast of the lesions, the interference of noises, and the imbalance between the lesion areas and the background. In this paper, an enhanced residual U-Net (ERU-Net) for MAs and EXs segmentation is proposed. ERU-Net obtains three U-paths, which are composed by three upsampling paths together with one downsampling path. With such three U-paths structure, ERU-Net can enhance the corresponding features fusion and capture more details of fundus images. Also, a residual block is constructed in ERU-Net to extract more representative features. In the experiments, we evaluate the performance of ERU-Net for MAs and EXs segmentation on three public datasets, E-Ophtha, IDRiD, and DDR. The ERU-Net obtains the AUC values of 0.9956, 0.9962, 0.9801, 0.9866, 0.9679, 0.9609 for MAs and EXs segmentation on these three datasets, respectively, which are greater than that of the original U-Net. Compared with some traditional methods, convolutional neural networks and other recent U-Nets, ERU-Net also performs competitively. Besides, we have applied ERU-Net to segment optic disc (OD) on the DRISHTI-GS1 dataset, achieving the highest Jaccard index of 0.994 compared with the existing methods. The numerical results indicate that ERU-Net is a promising network for medical image segmentation.

The objective of this study was to determine the frequency of different categories of specific and general classification in canine cavitary effusions (CE), as well as their association with the underlying etiologies. The laboratorial and clinical data from 304 cases of canine CE were retrospectively assessed. In 32.9% (100 cases), at least one of the specific classification categories was established, with a subtotal predominance of neoplasia (42%), bacterial serositis (24%) and hemorrhage (16%). Neoplasia was confirmed by effusion cytology in 57.5% of the cases with histopathological confirmation. From the cases in which the specific classification was not obtained, 35.8% were classified as modified transudate, 30.4% as pure transudate, 21.1% % as exudate and 12.7% was not included in any general category. The most common causes of effusion among these cases were hypoproteinemia and/or hipoalbuminemia (HPHA) (25.8%), hepatopathy (22.5%), cardiac insufficiency (15.5%) and cytologically undetected cases of neoplasia (12.4%). In conclusion, HPHA, hepatopathy and neoplasia represents important etiologies for canine CE development. Classification of effusions, solely based on [TP] and TNCC, might be an inaccurate diagnostic tool of effusions. New laboratorial classification methods for canine CE should be researched. RESUMO O objetivo deste estudo foi determinar a frequência de diferentes categorias de classificação específica e geral em efusões cavitárias (EC) caninas, bem como sua associação com as etiologias subjacentes. Os dados laboratoriais e clínicos de 304 casos de EC canina foram avaliados retrospectivamente. Em 32,9% (100 casos), pelo menos uma das categorias específicas de classificação foi estabelecida, com predomínio subtotal de neoplasia (42%), serosite bacteriana (24%) e hemorragia (16%). A neoplasia foi confirmada pela citologia da efusão em 57,5% dos casos com confirmação histopatológica. Dos casos em que a classificação específica não foi obtida (204 casos), 35,8% foram classificados como transudato modificado, 30,4% como transudato puro, 21,1% como exsudato e 12,7% não foram incluídos em nenhuma categoria geral. As causas mais comuns de efusão nestes casos foram hipoproteinemia e/ou hipoalbuminemia (HPHA) (25,8%), hepatopatia (22,5%), insuficiência cardíaca (15,5%) e casos de neoplasia citologicamente não detectados (12,4%). Em conclusão, HPHA, hepatopatia e neoplasia representam importantes etiologias para o desenvolvimento da EC canina. A classificação geral de efusões, baseada exclusivamente em proteína e celularidade, pode ser uma ferramenta diagnóstica imprecisa. Novos métodos de classificação laboratorial para ECs caninas devem ser pesquisados.

The aim of this work was to develop a method for the automatic identification of exudates, using an unsupervised clustering approach. The ability to classify each pixel as belonging to an eventual exudate, as a warning of disease, allows for the tracking of a patient&rsquo, s status through a noninvasive approach. In the field of diabetic retinopathy detection, we considered four public domain datasets (DIARETDB0/1, IDRID, and e-optha) as benchmarks. In order to refine the final results, a specialist ophthalmologist manually segmented a random selection of DIARETDB0/1 fundus images that presented exudates. An innovative pipeline of morphological procedures and fuzzy C-means clustering was integrated in order to extract exudates with a pixel-wise approach. Our methodology was optimized, and verified and the parameters were fine-tuned in order to define both suitable values and to produce a more accurate segmentation. The method was used on 100 tested images, resulting in averages of sensitivity, specificity, and accuracy equal to 83.3%, 99.2%, and 99.1%, respectively.

Exudates are a serious complication causing blindness in diabetic retinopathy patients. The main objective of this paper is to develop a novel method to detect exudates lesions in color retinal images by using a morphology mean shift algorithm. The proposed method start with a normalization of the retinal image, contrast enhancement, noise removal, and the localization of the OD. Then, a coarse segmentation method by using mean shift provides a set of exudates and non-exudates candidates. Finally, a classification using the mathematical morphology algorithm (MMA) procedure is applied in order to keep only exudates pixels. The optimal value parameters of the MMA will facilitate an increase of the accuracy results from the solely MSA method by 13.10%. Based on a comparison between the results and ground truth images, the proposed method obtained an average sensitivity, specificity, and accuracy of detecting exudates as 98.40%, 98.13%, and 98.35%, respectively.

[EN] Estimated blind people in the world will exceed 40 million by 2025. To develop novel algorithms based on fundus image descriptors that allow the automatic classification of retinal tissue into healthy and pathological in early stages is necessary. In this paper, we focus on one of the most common pathologies in the current society: diabetic retinopathy. The proposed method avoids the necessity of lesion segmentation or candidate map generation before the classification stage. Local binary patterns and granulometric profiles are locally computed to extract texture and morphological information from retinal images. Different combinations of this information feed classification algorithms to optimally discriminate bright and dark lesions from healthy tissues. Through several experiments, the ability of the proposed system to identify diabetic retinopathy signs is validated using different public databases with a large degree of variability and without image exclusion., This work has been partially supported by the Spanish Ministry of Economy and Competitiveness through project DPI2016-77869 and GVA through project PROMETEO/2019/109

The macula is an oval-shaped area near the center of the human retina, and at its center, there is a small pit known as the fovea. The fovea contains large concentrations of cone cells and is responsible for sharp, colored vision. Macular disorders are the group of diseases that damage the macula, resulting in blurred vision or even blindness. Macular edema (ME), one of the most common types of macular disorder, is caused by fluid accumulation beneath the macula. In this paper, we present an automated system for the detection of ME from fundus images. We introduce a new automated system for the detailed grading of the severity of disease using knowledge of exudates and maculae. A new set of features is used along with a minimum distance classifier for accurate localization of the fovea, which is important for the grading of ME. The proposed system uses different hybrid features and support vector machines for segmentation of exudates. The detailed grading of ME—as both clinically significant ME and non-clinically significant ME—is done using localized foveae and segmented exudates. The proposed algorithm is validated using public and local data sets. We have achieved an average accuracy of 96.1% in the detection and grading of ME with our proposed method.

Exudates can be regarded as one of the most prevalent clinical signs of diabetic retinopathy, and the detection of exudates has important clinical significance in diabetic retinopathy diagnosis. In this paper, a novel approach named superpixel multi-feature classification for the automatic detection of exudates is developed. First, an entire image is segmented into a series of superpixels considered as candidates. Then, a total of 20 features, including 19 multi-channel intensity features and a novel contextual feature, are proposed for characterizing each candidate. A supervised multi-variable classification algorithm is also introduced to distinguish the true exudates from the spurious candidates. Finally, a novel optic disc detection technique is designed to further improve the performance of classification accuracy. Extensive experiments are carried out on two publicly available online databases, DiaretDB1, and e-ophtha EX. Compared with other state-of-the-art approaches, the experimental results show the advantages and effectiveness of the proposed approach.

Diabetes affects the capillary vessels in retina and causes vision loss. This disorder of retina due to diabetes is named as Diabetic Retinopathy (DR). Diagnosing the stages of DR is performed on a publicly available database (DiaraetDB1) via detecting the symptoms of this disease. Time-domain features are extracted and selected to classify a fundus image. Fisher’s Linear Discriminant Analysis (FLDA), Linear Bayes Normal Classifier (LDC), Decision Tree (DT) and k-Nearest Neighbor (k-NN) are used as the classification methods in the experimental benchmarking. The recognition accuracies are obtained using all features (68 features) and selected features separately. k-NN is observed as the best classification method for without feature selection case and it gives averagely 92.22% accuracy. For feature selection case, LDC gives the best average accuracy as 92.45% with maximum 7 carefully chosen features.

Background and objective Diabetic retinopathy is one of the leading disabling chronic diseases and one of the leading causes of preventable blindness in developed world. Early diagnosis of diabetic retinopathy enables timely treatment and in order to achieve it a major effort will have to be invested into automated population screening programs. Detection of exudates in color fundus photographs is very important for early diagnosis of diabetic retinopathy. Methods We use deep convolutional neural networks for exudate detection. In order to incorporate high level anatomical knowledge about potential exudate locations, output of the convolutional neural network is combined with the output of the optic disc detection and vessel detection procedures. Results In the validation step using a manually segmented image database we obtain a maximum F1 measure of 0.78. Conclusions As manually segmenting and counting exudate areas is a tedious task, having a reliable automated output, such as automated segmentation using convolutional neural networks in combination with other landmark detectors, is an important step in creating automated screening programs for early detection of diabetic retinopathy.

BACKGROUND We have studied 200 patients of pleural fluid presenting to our tertiary care centre. Presence of cases of pleural fluid is a common presentation both in pulmonary and extrapulmonary diseases. We analysed the patients having both exudates and transudates and studied the results. MATERIALS AND METHODS We selected patients above 20 years of age and classified the patients with pleural fluid as having transudates and exudates. We studied the causes of transudates and exudates. A total of 200 patients are studied in this prospective study. Diagnosis of pleural exudates is made on the basis of Light’s criteria, chest x-ray, pleural fluid analysis, CT scan in selected patients, sputum examination, bronchoscopy and bronchial washings. Moribund and non-cooperative patients and HIV positives were excluded from the study. RESULTS Among the 200 patients, 91% have exudates. 9% have transudates by Light’s criteria. Tuberculosis is the commonest cause of effusions (64.83%) followed by malignancy (13.73%) and sympneumonic or parapneumonic effusions (9.89%). Pleural effusions occurred predominantly in males. Prevalence of diabetes Mellitus among cases of tuberculous pleural effusions is 13.56%. Tuberculous effusions are predominantly right-sided. CONCLUSION Predominant cases of pleural fluid are exudates. Commonest cause of pleural effusion is Tuberculosis followed by malignancy both pulmonary and extrapulmonary and sym. and parapneumonic effusions. Prevalence of Diabetes among Tuberculous pleural effusion cases is more or less same as in general population. Cough, expectoration fever, chest pain and breathlessness are the common symptoms occurring in three fourths of the patients of tuberculous pleural effusion. Most of the cases of Tuberculous effusion are above 30 years of age. In the diagnosis of tuberculous pleural effusion, Pleural fluid ADA is very important. Pleural fluid cytology, pleural biopsy, bronchoscopy, bronchial washings and sputum examination can aid in diagnosis of aetiology. Lung cancer is the commonest cause of malignant pleural effusions. Other causes of pleural effusion though rare should be considered in selective patients.

Accurate vessel detection in retinal images is an important and difficult task. Detection is made more challenging in pathological images with the presence of exudates and other abnormalities. In this paper, we present a new unsupervised vessel segmentation approach to address this problem. A novel inpainting filter, called neighborhood estimator before filling, is proposed to inpaint exudates in a way that nearby false positives are significantly reduced during vessel enhancement. Retinal vascular enhancement is achieved with a multiple-scale Hessian approach. Experimental results show that the proposed vessel segmentation method outperforms state-of-the-art algorithms reported in the recent literature, both visually and in terms of quantitative measurements, with overall mean accuracy of 95.62% on the STARE dataset and 95.81% on the HRF dataset.

OBJECTIVE Pleural effusion refers to the excessive or abnormal accumulation of fluid in the pleural space. Pleural effusion is commonly encountered medical problem and caused by a variety of underlying pathological conditions. It is important to establish an accurate etiological diagnosis, so that the patient may be treated in the most appropriate and rational manner. METHODS This was a prospective study of 56 pleural effusion patients who are attending OPD and admitted cases in the Pulmonary Medicine department in Bhagwan Mahaveer Jain Hospital, Bangalore. The patients were subjected to through clinical history and examination. Thoracocentesis did under aseptic conditions and pleural fluid sent for investigations like protein, sugar, LDH (Lactate Dehydrogenase), ADA (Adenosine Deaminase), gram staining, AFB smear and culture by BACTEC method, cell type, cell count, and malignant cytology. Pleural biopsy was done for those who are willing for the same. Depending upon the history and clinical examinations and laboratory investigations, patients were classified as having exudates and transudates. RESULTS The total of 56 patients with pleural effusion was studied. Mean age of the study group was 43±14.6 years. 39(69.42%) patients were male and 17(30.58%) patients were female. The commonest type of effusion being tuberculosis (34) followed by malignancy (8), transudative effusion (7), synpneumonic (5) and 2 cases of empyema. The commonest presenting complaints were cough (78.32%) and breathlessness (74.76%). Polymorphs were predominant in synpneumonic effusion and empyema and lymphocytes in tubercular effusion. Pleural fluid cytology revealed elevated lymphocytes in tubercular and polymorphs in acute infections. Cytology for malignant cells was positive in 4 cases. The mean increase in ADA level in tubercular pleural effusion, malignant pleural effusion and transudative pleural effusion were 79±19.9 IU/L, 42.6±9.3 and 28.4±8.2 respectively and it was statistically significant (p

Diabetic retinopathy (DR) is characterized by the presence of red lesions (RLs), such as microaneurysms and hemorrhages, and bright lesions, such as exudates (EXs). Early DR diagnosis is paramount to prevent serious sight damage. Computer-assisted diagnostic systems are based on the detection of those lesions through the analysis of fundus images. In this paper, a novel method is proposed for the automatic detection of RLs and EXs. As the main contribution, the fundus image was decomposed into various layers, including the lesion candidates, the reflective features of the retina, and the choroidal vasculature visible in tigroid retinas. We used a proprietary database containing 564 images, randomly divided into a training set and a test set, and the public database DiaretDB1 to verify the robustness of the algorithm. Lesion detection results were computed per pixel and per image. Using the proprietary database, 88.34% per-image accuracy (ACCi), 91.07% per-pixel positive predictive value (PPVp), and 85.25% per-pixel sensitivity (SEp) were reached for the detection of RLs. Using the public database, 90.16% ACCi, 96.26% PPV_p, and 84.79% SEp were obtained. As for the detection of EXs, 95.41% ACCi, 96.01% PPV_p, and 89.42% SE_p were reached with the proprietary database. Using the public database, 91.80% ACCi, 98.59% PPVp, and 91.65% SEp were obtained. The proposed method could be useful to aid in the diagnosis of DR, reducing the workload of specialists and improving the attention to diabetic patients.

Introduction: We aimed to investigate the efficacy of Hounsfield unit (HU) attenuation measured on computed tomography (CT) as a non-invasive method for pleural effusion characterization. Methods: Patients with pleural effusion who underwent thoracic CT imaging and thoracentesis within a maximum of three days were included in this retrospective study (15 transudate and 36 exudate). By drawing a circular region of interest (ROI) on the section with the thickest pleural effusion in terms of anteroposterior diameter in the upper-medial-lower zone on axial images, a total of three HU values, one from each level, were averaged. An independent t-test was applied to the CT attenuation (HU) values for the transudate-exudate differentiation. A receiver operating characteristic (ROC) analysis was then made. Results: The mean attenuation±standard deviation (minimum-maximum) value for the patients with transudate was 2.17±3.76 ((-7.5)-7.5) HU, whereas the mean HU value for the patients with exudate was 8.38±6.2 ((-6)-22). The independent t test made for the transudate-exudate differentiation revealed a statistically significant difference (P=0.001). In the ROC analysis carried out to determine the cut-off value of the attenuation value of pleural effusion in the transudate-exudate differentiation, the area under the curve was found to be 82.8%. When the cut-off value was taken as 2.75HU for the area under the curve, sensitivity was found to be 84%, and specificity was 60%. Conclusion: Although CT-HU values are statistically significant in the differential diagnosis of transudate-exudate, there is still a need to establish a correlation with other tomographic findings and clinical laboratory findings.

In this paper we present a lifting wavelet based CBRIR image retrieval system that uses color and texture as visual features to describe the content of a retinal fundus images. Our contribution is of three directions. First, we use lifting wavelets 9/7 for lossy and SPL5/3 for lossless to extract texture features from arbitrary shaped retinal fundus regions separated from an image to increase the system effectiveness. This process is performed offline before query processing, therefore to answer a query our system does not need to search the entire database images; instead just a number of similar class type patient images are required to be searched for image similarity. Third, to further increase the retrieval accuracy of our system, we combine the region based features extracted from image regions, with global features extracted from the whole image, which are texture using lifting wavelet and HSV color histograms. Our proposed system has the advantage of increasing the retrieval accuracy and decreasing the retrieval time. The experimental evaluation of the system is based on a db1 online retinal fundus color image database. From the experimental results, it is evident that our system performs significantly better accuracy as compared with traditional wavelet based systems. In our simulation analysis, we provide a comparison between retrieval results based on features extracted from the whole image using lossless 5/3 lifting wavelet and features extracted using lossless 9/7 lifting wavelet and using traditional wavelet. The results demonstrate that each type of feature is effective for a particular type of disease of retinal fundus images according to its semantic contents, and using lossless 5/3 lifting wavelet of them gives better retrieval results for almost all semantic classes and outperform 4-10% more accuracy than traditional wavelet.&nbsp

Diabetic Retinopathy (DR) is a severe and widely spread eye disease. Exudates are one of the most prevalent signs during the early stage of DR and an early detection of these lesions is vital to prevent the patient’s blindness. Hence, detection of exudates is an important diagnostic task of DR, in which computer assistance may play a major role. In this paper, a system based on local feature extraction and Support Vector Machine (SVM) classification is used to develop and compare different strategies for automated detection of exudates. The main novelty of this work is allowing the detection of exudates using non-regular regions to perform the local feature extraction. To accomplish this objective, different methods for generating superpixels are applied to the fundus images of E-OPHTA database and texture and morphological features are extracted for each of the resulting regions. An exhaustive comparison among the proposed methods is also carried out., This paper was supported by the European Union’s Horizon 2020 research and innovation programme under the Project GALAHAD [H2020-ICT2016-2017, 732613]. The work of Adri´an Colomer has been supported by the Spanish Government under a FPI Grant [BES-2014-067889]. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research.

Background: In pleural effusion, differentiating exudative and transudative fluid is an important clinical evaluation. The objective of the study was to determine the efficacy of pleural fluid serum bilirubin ratio in differentiating exudative and transudative effusions. In resource-limited settings with no facilities to measure lactate dehydrogenase (LDH) levels, using pleural fluid bilirubin ratio may help in better clinical decision. Methods: It was a prospective observational study, conducted in the emergency department of Tribhuvan University Teaching Hospital. All the patients attending for emergency care with pleural effusion from 6th Jan 2015 to 5th Jan 2016 were included. The cases were divided as exudates and transudates on basis of final diagnosis. Serum and pleural fluid specimen were collected and sent for investigations. The data for various laboratory parameters especially those of lights criteria and bilirubin ratio were then analyzed and fluid nature was compared with results from parameters and final diagnoses. Results: Among 103 cases, 71.84% had exudate and 28.16% had transudate. The commonest cause of effusion was pneumonia 35.92%, second being tubercular 23.30% followed by malignant effusion 12.60%, congestive heart failure 11.65%, chronic kidney disease 10.67% and liver cirrhosis 5.82%. The mean bilirubin ratio for exudates exceeded that for transudates. Considering the cutoff point of 0.6, the sensitivity, specificity, positive predictive value and negative predictive value were respectively 88.00%, 93.00%, 97.00% & 75.00%. Conclusions: Pleural fluid serum bilirubin ratio can be utilized as a diagnostic tool for differentiating exudative and transudative effusions. Keywords: bilirubin ratio; exudates; light’s criteria; pleural effusion; transudates.

One of the major causes of eye blindness is identified to be as diabetic retinopathy, which if not detected in earlier stage would cause a serious issue. Long-term diabetes causes diabetic retinopathy. The significant key factor leading to diabetic retinopathy is exudates which affect the retina part and causes eye defects. Thus the first and foremost task in the automated detection of macular oedema is to detect the presence of these exudates. The authors use image processing techniques to detect the optic disc, exudates and the presence of macular oedema. Their method has the sensitivity 96.07%, selectivity 97.36%, and accuracy 96.62% for the exudates detection and in the case of macular oedema detection the sensitivity 97.75%, selectivity 100%, and accuracy 98.86% is achieved. The performance comparison with other methods reveals that their method can be used as a screening process for diabetic retinopathy. In addition to that, the algorithm can help to detect macular oedema.

The automatic detection, segmentation, localization, and evaluation of the optic disc, macula, exudates, and hemorrhages are very important for diagnosing retinal diseases. One of the difficulties in detecting such regions of interest (RoIs) with computer vision is their symmetries, e.g., between the optic disc and exudates and also between exudates and hemorrhages. This paper proposes an original, intelligent, and high-performing image processing system for the simultaneous detection and segmentation of retinal RoIs. The basic principles of the method are image decomposition in small boxes and local texture analysis. The processing flow contains three phases: preprocessing, learning, and operating. As a first novelty, we propose proper feature selection based on statistical analysis in confusion matrices for different feature types (extracted from a co-occurrence matrix, fractal type, and local binary patterns). Mainly, the selected features are chosen to differentiate between similar RoIs. The second novelty consists of local classifier fusion. To this end, the local classifiers associated with features are grouped in global classifiers corresponding to the RoIs. The local classifiers are based on minimum distances to the representatives of classes and the global classifiers are based on confidence intervals, weights, and a voting scheme. A deep convolutional neural network, based on supervised learning, for blood vessel segmentation is proposed in order to improve the RoI detection performance. Finally, the experimental results on real images from different databases demonstrate the rightness of our methodologies and algorithms.

Extracellular Polymeric Substances (EPS),11EPS = exopolymers, extracellular polymeric substances; TEPs = transparent exopolymeric substances; DOM = dissolved organic matter; POM = particulate organic matter; DOC = dissolved organic carbon; QS = quorum sensing; TDA = tropodithietic acid; UV = ultraviolet light; RAS = recirculation aquaculture systems; MW = molecular weight. or exopolymers, play vital roles in the productivity of commercial hatcheries, yet have received little attention in aquaculture outside of their role in the biofilters of recirculating systems. Hatcheries have long dealt with EPS in microalgal cultures or larval rearing systems when quantities manifest themselves visually as biofilms or mucilaginous aggregates. The same polymers in lesser, visually undetectable quantities have the ability to attract commensal bacteria and sequester micronutrients, which can be essential in hatcheries for hygiene, stability of rearing environments, and the production of microalgae for live feeds. EPS may also serve an important role in nutrition both directly in the particle selection of filter feeders, and indirectly through their contribution to microflora in the larval gut of both fish and invertebrates. EPS have been well-studied in marine ecosystems, but their presence in hatcheries has until recently been largely overlooked, perhaps on account of their invisibility using normal microscopy and their extremely small size, both factors which may explain why they are sometimes also referred to as transparent exopolymeric substances (TEPs). This review examines recent literature for ways in which EPS affect hatchery processes, with suggestions as to how further research and development of biosensor technologies for EPS have the potential to improve production processes.

En los ultimos años el número de casos de ceguera se ha reducido significativamente. A pesar de este hecho, la Organización Mundial de la Salud estima que un 80% de los casos de pérdida de visión (285 millones en 2010) pueden ser evitados si se diagnostican en sus estadios más tempranos y son tratados de forma efectiva. Para cumplir esta propuesta se pretende que los servicios de atención primaria incluyan un seguimiento oftalmológico de sus pacientes así como fomentar campañas de cribado en centros proclives a reunir personas de alto riesgo. Sin embargo, estas soluciones exigen una alta carga de trabajo de personal experto entrenado en el análisis de los patrones anómalos propios de cada enfermedad. Por lo tanto, el desarrollo de algoritmos para la creación de sistemas de cribado automáticos juga un papel vital en este campo. La presente tesis persigue la identificacion automática del daño retiniano provocado por dos de las patologías más comunes en la sociedad actual: la retinopatía diabética (RD) y la degenaración macular asociada a la edad (DMAE). Concretamente, el objetivo final de este trabajo es el desarrollo de métodos novedosos basados en la extracción de características de la imagen de fondo de ojo y clasificación para discernir entre tejido sano y patológico. Además, en este documento se proponen algoritmos de pre-procesado con el objetivo de normalizar la alta variabilidad existente en las bases de datos publicas de imagen de fondo de ojo y eliminar la contribución de ciertas estructuras retinianas que afectan negativamente en la detección del daño retiniano. A diferencia de la mayoría de los trabajos existentes en el estado del arte sobre detección de patologías en imagen de fondo de ojo, los métodos propuestos a lo largo de este manuscrito evitan la necesidad de segmentación de las lesiones o la generación de un mapa de candidatos antes de la fase de clasificación. En este trabajo, Local binary patterns, perfiles granulométricos y la dimensión fractal se aplican de manera local para extraer información de textura, morfología y tortuosidad de la imagen de fondo de ojo. Posteriormente, esta información se combina de diversos modos formando vectores de características con los que se entrenan avanzados métodos de clasificación formulados para discriminar de manera óptima entre exudados, microaneurismas, hemorragias y tejido sano. Mediante diversos experimentos, se valida la habilidad del sistema propuesto para identificar los signos más comunes de la RD y DMAE. Para ello se emplean bases de datos públicas con un alto grado de variabilidad sin exlcuir ninguna imagen. Además, la presente tesis también cubre aspectos básicos del paradigma de deep learning. Concretamente, se presenta un novedoso método basado en redes neuronales convolucionales (CNNs). La técnica de transferencia de conocimiento se aplica mediante el fine-tuning de las arquitecturas de CNNs más importantes en el estado del arte. La detección y localización de exudados mediante redes neuronales se lleva a cabo en los dos últimos experimentos de esta tesis doctoral. Cabe destacar que los resultados obtenidos mediante la extracción de características "manual" y posterior clasificación se comparan de forma objetiva con las predicciones obtenidas por el mejor modelo basado en CNNs. Los prometedores resultados obtenidos en esta tesis y el bajo coste y portabilidad de las cámaras de adquisión de imagen de retina podrían facilitar la incorporación de los algoritmos desarrollados en este trabajo en un sistema de cribado automático que ayude a los especialistas en la detección de patrones anomálos característicos de las dos enfermedades bajo estudio: RD y DMAE., In last years, the number of blindness cases has been significantly reduced. Despite this promising news, the World Health Organisation estimates that 80% of visual impairment (285 million cases in 2010) could be avoided if diagnosed and treated early. To accomplish this purpose, eye care services need to be established in primary health and screening campaigns should be a common task in centres with people at risk. However, these solutions entail a high workload for trained experts in the analysis of the anomalous patterns of each eye disease. Therefore, the development of algorithms for automatic screening system plays a vital role in this field. This thesis focuses on the automatic identification of the retinal damage provoked by two of the most common pathologies in the current society: diabetic retinopathy (DR) and age-related macular degeneration (AMD). Specifically, the final goal of this work is to develop novel methods, based on fundus image description and classification, to characterise the healthy and abnormal tissue in the retina background. In addition, pre-processing algorithms are proposed with the aim of normalising the high variability of fundus images and removing the contribution of some retinal structures that could hinder in the retinal damage detection. In contrast to the most of the state-of-the-art works in damage detection using fundus images, the methods proposed throughout this manuscript avoid the necessity of lesion segmentation or the candidate map generation before the classification stage. Local binary patterns, granulometric profiles and fractal dimension are locally computed to extract texture, morphological and roughness information from retinal images. Different combinations of this information feed advanced classification algorithms formulated to optimally discriminate exudates, microaneurysms, haemorrhages and healthy tissues. Through several experiments, the ability of the proposed system to identify DR and AMD signs is validated using different public databases with a large degree of variability and without image exclusion. Moreover, this thesis covers the basics of the deep learning paradigm. In particular, a novel approach based on convolutional neural networks is explored. The transfer learning technique is applied to fine-tune the most important state-of-the-art CNN architectures. Exudate detection and localisation tasks using neural networks are carried out in the last two experiments of this thesis. An objective comparison between the hand-crafted feature extraction and classification process and the prediction models based on CNNs is established. The promising results of this PhD thesis and the affordable cost and portability of retinal cameras could facilitate the further incorporation of the developed algorithms in a computer-aided diagnosis (CAD) system to help specialists in the accurate detection of anomalous patterns characteristic of the two diseases under study: DR and AMD., En els últims anys el nombre de casos de ceguera s'ha reduït significativament. A pesar d'este fet, l'Organització Mundial de la Salut estima que un 80% dels casos de pèrdua de visió (285 milions en 2010) poden ser evitats si es diagnostiquen en els seus estadis més primerencs i són tractats de forma efectiva. Per a complir esta proposta es pretén que els servicis d'atenció primària incloguen un seguiment oftalmològic dels seus pacients així com fomentar campanyes de garbellament en centres regentats per persones d'alt risc. No obstant això, estes solucions exigixen una alta càrrega de treball de personal expert entrenat en l'anàlisi dels patrons anòmals propis de cada malaltia. Per tant, el desenrotllament d'algoritmes per a la creació de sistemes de garbellament automàtics juga un paper vital en este camp. La present tesi perseguix la identificació automàtica del dany retiniano provocat per dos de les patologies més comunes en la societat actual: la retinopatia diabètica (RD) i la degenaración macular associada a l'edat (DMAE) . Concretament, l'objectiu final d'este treball és el desenrotllament de mètodes novedodos basats en l'extracció de característiques de la imatge de fons d'ull i classificació per a discernir entre teixit sa i patològic. A més, en este document es proposen algoritmes de pre- processat amb l'objectiu de normalitzar l'alta variabilitat existent en les bases de dades publiques d'imatge de fons d'ull i eliminar la contribució de certes estructures retinianas que afecten negativament en la detecció del dany retiniano. A diferència de la majoria dels treballs existents en l'estat de l'art sobre detecció de patologies en imatge de fons d'ull, els mètodes proposats al llarg d'este manuscrit eviten la necessitat de segmentació de les lesions o la generació d'un mapa de candidats abans de la fase de classificació. En este treball, Local binary patterns, perfils granulometrics i la dimensió fractal s'apliquen de manera local per a extraure informació de textura, morfologia i tortuositat de la imatge de fons d'ull. Posteriorment, esta informació es combina de diversos modes formant vectors de característiques amb els que s'entrenen avançats mètodes de classificació formulats per a discriminar de manera òptima entre exsudats, microaneurismes, hemorràgies i teixit sa. Per mitjà de diversos experiments, es valida l'habilitat del sistema proposat per a identificar els signes més comuns de la RD i DMAE. Per a això s'empren bases de dades públiques amb un alt grau de variabilitat sense exlcuir cap imatge. A més, la present tesi també cobrix aspectes bàsics del paradigma de deep learning. Concretament, es presenta un nou mètode basat en xarxes neuronals convolucionales (CNNs) . La tècnica de transferencia de coneixement s'aplica per mitjà del fine-tuning de les arquitectures de CNNs més importants en l'estat de l'art. La detecció i localització d'exudats per mitjà de xarxes neuronals es du a terme en els dos últims experiments d'esta tesi doctoral. Cal destacar que els resultats obtinguts per mitjà de l'extracció de característiques "manual" i posterior classificació es comparen de forma objectiva amb les prediccions obtingudes pel millor model basat en CNNs. Els prometedors resultats obtinguts en esta tesi i el baix cost i portabilitat de les cambres d'adquisión d'imatge de retina podrien facilitar la incorporació dels algoritmes desenrotllats en este treball en un sistema de garbellament automàtic que ajude als especialistes en la detecció de patrons anomálos característics de les dos malalties baix estudi: RD i DMAE.

This is a descriptive study carried out in Khartoum state hospitals during the period from May 2012 to April 2014. The study aimed to evaluate the diagnostic role of the pleural effusion/serum (CHOL, LDH, and protein) ratios in the differentiation between exudate and transudate pleural effusion. As a part of the investigation, 135 serum and pleural effusion samples were collected from patients with accumulated plural effusion. Prior to testing, 5 mL of venous blood and 20 mL of pleural effusion samples were prepared in accordance with specific testing requirements. Exudative pleural effusions were observed in 95 (70.4%) samples, of which 64 (67.4%) belonged to male patients and 31 (32.6%) to females. The calculated means for CHOL, LDH, and protein levels in pleural effusion and serum samples between exudate and transudate effusion showed statistically significant differences with the p-value = 0.000. In distinguishing between exudative and transudate pleural effusion, a high Pearson correlation was observed between CHOL ratio and clinical diagnosis (r = 0.971), as well as between CHOL level in effusion samples and LDH level in serum samples (r = 0.867). Sensitivity, specificity, positive predictive value, and negative predictive value analysis of the parameters in the differentiation between exudate and transudate samples revealed the following values: 97.7% and 100%, 100%, and 95%, respectively, for the CHOL ratio; 86%, 97.4%, 97.4%, and 97%, respectively, for the LDH ratio; and 81.4%, 81.6%, 89.7%, and 70.4%, respectively, for the protein ratio. On the basis of the study findings, it could be concluded that estimation of CHO, LDH, and protein ratios can assist in the differentiation between exudative and transudate pleural effusion and thus patient management. Hence, this approach should be included in routine laboratory analyses of pleural effusions. Nevertheless, additional techniques should be incorporated in the diagnosis of doubtful pleural effusions, as this will improve the diagnostic sensitivity and specificity in this setting.

Diabetic Retinopathy is a severe and widely spread eye disease which can lead to blindness. One of the main symptoms for vision loss is Exudates and it could be prevented by applying an early screening process. In the Existing systems, a Fuzzy C-Means Clustering technique is used for detecting the exudates for analyzation. The main objective of this paper is, to improve the efficiency of the Exudates detection in diabetic retinopathy images. To do this, a three Stage – [TS] approach is introduced for detecting and extracting the exudates automatically from the retinal images for screening the Diabetic retinopathy. TS functions on the image in three levels such as Pre-processing the image, enhancing the image and detecting the Exudates accurately. After successful detection, the detected exudates are classified using GLCM method for finding the accuracy. The TS approach is experimented using MATLAB software and the performance evaluation can be proved by comparing the results with the existing approach’s result and with the hand-drawn ground truths images from the expert ophthalmologist.

Diabetic Retinopathy (DR) is progressive dysfunction of the retinal blood vessels caused by chronic hyperglycemia which can be a complication of diabetes type 1 or diabetes type 2. Initially, DR is asymptomatic, if not treated though it can cause low vision and blindness. Diabetic retinopathy is responsible for 1.8 million of the 37 million cases of blindness throughout the world. So the early detection of Diabetic retinopathy through proper screening is essential. The paper presents a Diabetic Retinopathy Screening System which can be used as a primary diagnosis tool by ophthalmologists in the screening process to detect symptoms of Diabetic Retinopathy. The system uses the anatomical structures such as blood vessels, exudates and microaneurysms in retinal images. The retinal images are segmented and classified as normal or DR affected images by extracting features from segmented images and the Gray Level Co-occurrence Matrix (GLCM). The classifier used is Support Vector Machine (SVM) which gives a better accuracy. The system is implemented and tested in MATLAB and LabView for the standard database and need to be optimized for real time screening of images. LabView creates distributable .EXE files and .DLL files which can be downloaded into the FPGA/DSP processor. Hardware implementation on LabView FPGA presents a small learning curve which drastically reduces development time and eliminates the need for custom hardware design.

Context: The incidence of pleural effusion is approximately one million per year. For diagnosing and treatment plan, pleural effusions have to be classified into transudate and exudate. If the diagnosis is not appropriate, it may result in severe complications. The established criterion for differentiating exudates from transudates is Light's criteria. But there were some false positive results in case of transudative effusions when Light's criteria were used. Aims: This study was done to determine the accuracy of serum effusion albumin gradient (SEAG) when compared to Light's criteria in differentiating transudates and exudates. Settings and Design: It is a prospective observational study. In the present study, the sample size is 66 patients, in whom the SEAG was used for the classification of pleural effusions with a cut-off value of 1.2 g/dl. Methods and Materials: All the blood samples were collected and biochemical parameters like total protein, albumin, and LDH were analyzed in both serum and pleural fluid using XL 640 fully automated random access analyzer. Statistical Analysis Used: Results were analyzed using SPSS software version 20. Results: 20 of 22 transudates and 41 of 44 exudates were classified correctly using SEAG. The diagnostic accuracy of SEAG (92.42%) is better than Light's criteria (87.87%) in differentiating both transudative and exudative effusions. Conclusions: The SEAG is superior to Light's criteria in identifying the transudative effusions. It is also observed that Light's criteria identified exudative effusions better than SEAG.

Prolonged diabetes ultimately leads to Diabetic Retinopathy (DR) which is one of the leading causes of preventable blindness in the world. Through advanced image analysis techniques are used for abnormalities detection in retina that define and correlate the severity of DR. A thorough study is done in this area in recent past years and on the basis of these studies we have developed a computer based prediction model that is used to determine the severity of DR. To identify severity DR, we have analyzed the human eye image. We have extracted some important features from human eye image i.e. Blood Artery, Optical disc, Exudates. Based on these image and data we have designed an automated system for the determination of DR severity. This automated DR severity assessment methods can be used to predict the clinical case and conditions when young clinicians would agree or disagree with their more experienced fellow members. The algorithms described in this study may be used in clinical practice to validate or invalidate the diagnoses. Algorithms or method developed here may also be used for pooling diagnostic knowledge for serving mankind. Here we have described a computational based low cost retinal diagnostic approach which can aid an ophthalmologist to quickly diagnose the various stages of DR. This system can accept retinal images and can successfully detect any pathological condition associated with DR.

Aim: There is a controversy in the usefulness of computed tomography (CT) in differentiating transudate or exudates. The aim of this study is to evaluate the utility of CT in characterizing pleural effusions. Material and Method: We evaluated 100 patients with pleural effusions who underwent chest CT and diagnostic thoracentesis. Effusions were classified as exudates or transudative on the basis of Light’s criteria. CTs were reviewed by two radiologists to determine the mean Hounsfield unit value of an effusion and other CT features including loculation and pleural thickening. Results: Pleural fluid was exudative in 58 patients and transudative in 42 patients. Exudative effusion significantly had higher mean attenuation values (8.1±5.69 vs. 3.53±4.23 HU; p4.5 HU, 66.25% and 75.00% for loculation, and 78.38% and 54.10% for pleural thickening. Discussion: Attenuation value and loculation had relatively good sensitivity and specificity in comparison to other features and could better differentiate between effusions. However, in comparison to thoracentesis, CT scan is not a more reliable method to evaluate the nature of the effusion and would be useful only in cases with contraindications of thoracentesis.

Microaneurysm (MA), small dark round dots on retinal fundus image is the earliest clinical sign of Diabetic Retinopathy (DR) disease. MA detection at early stage can help to reduce the blindness. In such cases the retinal fundus images produced by fluorescent oscilloscope are often noisy and low in contrast. Detecting the Microaneurysms using those fundus images is very difficult for ophthalmologist. In the present paper, we propose a method using location based contrast enhancement process, popularly known as Contrast Limited Adaptive Histogram Equalization (CLAHE) for the detection of retinal changes in DR images. CLAHE is an adaptive extension of Histogram Equalization which helps in dynamic preservation of the local contrast characteristics of an image. The proposed algorithm divides the retinal fundus image into a number of small, non-overlapping contextual tiles. Following CLAHE at each tile separately, median filtering of DR images is carried out in order to smooth the background noise. Results of the proposed algorithm show a considerable improvement in the enhancement of DR image quality. The average sensitivity and specificity of this Diabetes Screening System revealed as 82.64% and 99.98% respectively.

Objective: To assess the efficacy of pleural fluid cholesterol in differentiating transudates and exudates as compared with Light’s criteria. Methods: Patients with pleural effusion during a 6-month period were enrolled in the study and underwent thoracentesis. Pleural fluid was analyzed for the levels of protein, lactate dehydrogenase (LDH), and cholesterol. Etiological diagnosis, which was established after considering clinical and biochemical factors, was the gold standard for comparison. Cut-off values for pleural fluid cholesterol were taken as 60 mg/dL and 45 mg/dL. Results: A total of 53 patients were included for final analysis. Of them, 19 were with transudates and 34 with exudates in their pleural fluids. The sensitivity, specificity, positive predictive value, and negative predictive value of the pleural fluid cholesterol (cut-off >45 mg/dL) were 97.06%, 94.74%, 97.06%, and 94.74%, respectively, for identifying exudates. These values were differentiating better than those obtained by Light’s criteria for pleural fluid cholesterol (cut-off >60 mg/dL) (p45 mg/dL) gave a higher specificity (100%) and positive predictive value (100%) but a lower sensitivity (82.93%) and negative predictive value (63.16%). Conclusion: Pleural fluid cholesterol is better than Light’s criteria for the differentiation of transudates and exudates and is less cumbersome as it does not require a simultaneous blood sampling. Cut-off value of pleural fluid cholesterol for differentiating transudates and exudates should be 45 mg/dL. Further studies are warranted to assess the efficacy of the combination of pleural fluid protein and cholesterol as criteria for classifying effusions.

Currently, medical image processing draws intense interests of scien- tists and physicians to aid in clinical diagnosis. The retinal Fundus image is widely used in the diagnosis and treatment of various eye diseases such as Diabetic Retinopathy, glaucoma etc. If these diseases are detected and treated early, many of the visual losses can be pre- vented. This paper presents the methods to detect main features of Fundus images such as optic disk, fovea, exudates and blood vessels. To determine the optic Disk and its centre we find the brightest part of the Fundus. The candidate region of fovea is defined an area circle. The detection of fovea is done by using its spatial relationship with optic disk. Exudates are found using their high grey level variation and their contours are determined by means of morphological recon- struction techniques. The blood vessels are highlighted using bottom hat transform and morphological dilation after edge detection. All the enhanced features are then combined in the Fundus image for the detection of abnormalities in eye.

International audience; Diabetic retinopathy (DR) and age related macular degeneration (ARMD) are among the major causes of visual impairment all over the world. DR is mainly characterized by small red spots, namely microaneurysms and bright lesions, specifically exudates. However, ARMD is mainly identified by tiny yellow or white deposits called drusen. Since exudates might be the only visible signs of the early diabetic retinopathy, there is an increase demand for automatic diagnosis of retinopathy. Exudates and drusen may share similar appearances; as a result discriminating between them plays a key role in improving screening performance. In this research, we investigative the role of bag of words approach in the automatic diagnosis of retinopathy diabetes. Initially, the color retinal images are preprocessed in order to reduce the intra and inter patient variability. Subsequently, SURF (Speeded up Robust Features), HOG (Histogram of Oriented Gradients), and LBP (Local Binary Patterns) descriptors are extracted. We proposed to use single-based and multiple-based methods to construct the visual dictionary by combining the histogram of word occurrences from each dictionary and building a single histogram. Finally, this histogram representation is fed into a support vector machine with linear kernel for classification. The introduced approach is evaluated for automatic diagnosis of normal and abnormal color retinal images with bright lesions such as drusen and exudates. This approach has been implemented on 430 color retinal images, including six publicly available datasets, in addition to one local dataset. The mean accuracies achieved are 97.2% and 99.77% for single-based and multiple-based dictionaries respectively.

WOS: 000234554200012, PubMed ID: 16228298, The first step in the diagnosis of pleural effusions is the distinction between exudates and transudates. The aim of this study was to evaluate the usefulness of various parameters for the differentiation of pleural exudates and transudates. We recorded clinical characteristics, final diagnoses, and measured pleural fluid and serum levels of albumin, protein, LDH, cholesterol, and bilirubin of 381 consecutive patients with pleural effusion. Seventy-one (23%) pleural effusions were transudates and 236 were exudates. As a single criterion, the pleural fluid to serum albumin ratio > 0.5 was the most accurate parameter (88.4%). An albumin gradient of 12 g/l had an accuracy of 75% in the whole population but it detected 96% of transudative effusions in patients treated with diuretics. Light's criteria and abbreviated Light's criteria had similar accuracies, 87.8% vs. 88.2%, respectively. In conclusions, different alternatives can be used instead of Light's criteria.

Leber′s disease is a form of primary retinal telangiectasia characterized by the presence of abnormalities in the retinal vasculature. It is an idiopathic, unilateral condition with male predilection. It is not associated with any other systemic or ocular disease. The disease has a very slow progression and can be complicated by vascular dilatations, neovascularizations, thromboses, retrovitreal hemorrhages, and macular changes. We present a case of Leber′s miliary aneurysms in an asymptomatic 22-year-old male patient. His fundus examination showed aneurysmal dilatations with lipid exudation. Intravenous fluorescein angiography showed early and late leakage as well as capillary dropout with late hypofluorescence. In this case, the patient was treated with laser photocoagulation of the retina.

In this study, Brucella abortus RB51 could be detected in the vaginal exudates of 5 out of 26 aborted cows following vaccination of three dairy cattle herds of 3810 animals including heifers and pregnant and non-pregnant cows by RB51 vaccine. Abortion associated with isolation of RB51 vaccinal strain from recently vaccinated cows in this study served as evidence that the vaccinal RB51 strain was the cause of abortion due to extensive bacterial multiplication and colonization in the uterus of vaccinated pregnant cows

Background The care of individuals with a fungating malignant wound represents challenging cancer management not only for patients and their families but also for health care professionals. Understanding the difficulties faced by nurses when they care for patients with such a wound can help guide practice and service development. The aim of this study was to investigate how many patients with a malignant fungating wounds did nurses see and what kind of difficulties in caring for patients with a malignant fungating wound did nurses experience. Method A survey was conducted in three different geographical regions of Switzerland over a 6 month period. Results A total of 269 nurses participated in this survey. The nurses reported the prevalence rate of fungating wounds as 6.6%. There was a difference in perceived prevalence between the regions. The most frequent location for such wounds was the breast in women with breast cancer. The appearance was mainly ulcerating. The main difficulties nurses experienced in the management of patients related to malodour, pain and difficulties in applying the dressings to the wound. The most frequently used dressing was an absorbent dressing. The difficulties nurses had and what kind of dressings they used were closely connected. Conclusions and implications for practice Strategies need to be explored to help understand the diverse range of wound problems including physical and psychological components. Furthermore, there is a need to raise the awareness of existing wound management specialists in the support of cancer patients.

This paper presents a novel algorithm based on Fuzzy Morphology for the computer-assisted enhancement of exudates in fundus images of human retina for the diagnosis of diabetic retinopathy. Diabetic retinopathy is a common disease in diabetic persons. The disease is diagnosed by the presence of exudates in the macular region. Here, we use Fuzzy Morphology for the enhancement of exudates. The fundus image is first converted to gray scale followed by a series of fuzzy erosion and fuzzy dilation (morphological closing operation) with a diamond shaped structuring element. Finally, the resulting image is added to the original image to transform into enhanced one. Experiments were performed on a database of variety of fundus images with and without the disease. The experiments led to convincing results and the images were enhanced for easier clinical examination. Refereed/Peer-reviewed

The aim of this work was to quantify low molecular weight organic acids in the rhizosphere of plants grown in a sewage sludge-treated media, and to assess the correlation between the release of the acids and the concentrations of trace-elements in the shoots of the plants. The species utilized in the experiment were cultivated in sand and sewage sludge-treated sand. The acetic, citric, lactic, and oxalic acids, were identified and quantified by high performance liquid chromatography in samples collected from a hydroponics system. Averages obtained from each treatment, concentration of trace elements in shoots and concentration of organic acids in the rhizosphere, were compared by Tukey test, at 5% of probability. Linear correlation analysis was applied to verify an association between the concentrations of organic acids and of trace elements. The average composition of organic acids for all plants was: 43.2, 31.1, 20.4 and 5.3% for acetic, citric, lactic, and oxalic acids, respectively. All organic acids evaluated, except for the citric acid, showed a close statistical agreement with the concentrations of Cd, Cu, Ni, and Zn found in the shoots. There is a positive relationship between organic acids present in the rhizosphere and trace element phytoavailability. O objetivo deste estudo foi quantificar ácidos orgânicos de baixo peso molecular presentes na rizosfera de plantas cultivadas na presença de lodo de esgoto e avaliar a correlação entre a liberação dos ácidos e a concentração de elementos-traço nas plantas. As espécies utilizadas no experimento foram cultivadas em areia e areia com lodo de esgoto. Os ácidos acético, cítrico, lático e oxálico foram identificados e quantificados por cromatografia líquida de alta eficiência em amostras coletadas de um sistema hidropônico. As médias obtidas em cada tratamento, para concentração de elementos-traço na parte aérea e para concentração de ácidos orgânicos rizosféricos, foram comparadas pelo teste de Tukey, a 5% de probabilidade. A análise de correlação linear foi utilizada para verificar a associação entre as concentrações de ácidos orgânicos e elementos-traço. A média de composição dos ácidos orgânicos para todas as plantas foi 43,2, 31,1, 20,4 e 5,3%, para os ácidos acético, cítrico, lático e oxálico, respectivamente. Todos os ácidos orgânicos, com exceção do cítrico, correlacionaram-se com as concentrações de Cd, Cr, Cu, Ni e Zn na parte aérea das plantas. Existe associação entre ácidos orgânicos rizosféricos e fitodisponibilidade de elementos-traço.

SummaryBackgroundThe role of angiogenesis in the pathogenesis of pleural effusion (PE) has not been determined. The expression of angiogenic factors may represent useful markers for the diagnosis and prediction of disease outcome. To measure the pleural fluid (PF) and serum levels of vascular endothelial growth factor (VEGF), basic fibroblast growth factor (bFGF) and Tie receptor tyrosine kinase (Tie-2) in order to investigate their role in the pathogenesis of PEs.MethodsSixty-seven, 17 with transudative PEs due to heart failure and 50 with exudative PEs (malignant, 22; inflammatory, 15; undiagnosed, 13) were included in the study. PF and serum levels of the growth factors (VEGF, bFGF and Tie-2) were measured using enzyme-linked immunosorbent assays.ResultsPF and serum VEGF levels but not bFGF and Tie-2 levels were higher (p

PURPOSE: To evaluate the clinical and laboratory characteristics of pleural effusions secondary to tuberculosis (TB) or cancer (CA). METHODS: A total of 326 patients with pleural effusion due to TB (n=182) or CA (n=144) were studied. The following parameters were analyzed: patient gender, age and pleural effusion characteristics (size, location, macroscopic fluid aspect, protein concentration, lactate dehydrogenase (DHL) and adenosine deaminase activity (ADA) and nucleated cell counts). RESULTS: Young male patients predominated in the tuberculosis group. The effusions were generally moderate in size and unilateral in both groups. Yellow-citrine fluid with higher protein (p < 0.001) levels predominated in effusions from the tuberculosis group (5.3 + 0.8 g/dL) when compared to the CA group (4.2 ± 1.0 g/dL), whereas DHL levels were more elevated in CA (1,177 ± 675 x 1,030 ± 788 IU; p = 0.003) than in TB. As expected, ADA activity was higher in the TB group (107.6 ± 44.2 x 30.6 ± 57.5 U/L; p < 0.001). Both types of effusions presented with high nucleated cell counts, which were more pronounced in the malignant group (p < 0.001). TB effusion was characterized by a larger percentage of leukocytes and lymphocytes (p < 0.001) and a smaller number of mesothelial cells (p = 0.005). Lymphocytes and macrophages were the predominant nucleated cell in neoplastic effusions. CONCLUSION: Our results demonstrate that in lymphocytic pleural exudate obtained from patients with clinical and radiological evidence of tuberculosis, protein and ADA were the parameters that better characterize these effusions. In the same way, when the clinical suspicion is malignancy, serous-hemorrhagic lymphocytic fluid should be submitted to oncotic cytology once this easy and inexpensive exam reaches a high diagnostic performance (;@; 80%). In this context, we suggest thoracocentesis with fluid biochemical and cytological examination as the first diagnostic approach for these patients. OBJETIVO: Avaliar as características clínicas e laboratoriais de derrames pleurais secundários à tuberculose ou câncer. MÉTODOS: Um total de 326 pacientes com derrame pleural por tuberculose (n=182) ou câncer (n=144) foi avaliado. Os seguintes parâmetros foram analisados: sexo e idade dos pacientes e características do líquido pleural (tamanho, localização, aspecto macroscópico, concentração de proteínas, atividade da desidrogenase lática (DHL) e da adenosina deaminase (ADA) e contagem de células nucleadas). RESULTADOS: A tuberculose pleural predominou nos pacientes mais jovens e do sexo masculino. Em ambos os grupos, os derrames pleurais foram de tamanho moderado e unilaterais. Derrames com aspecto amarelo-citrino com níveis mais elevados de proteínas predominaram na tuberculose (5,3 ± 0,8 g/dL), quando comparados aos neoplásicos (4,2 ± 1,0 g/dL), enquanto que níveis mais elevados de DHL foram observados nos derrames neoplásicos (1.177 ± 675 x 1.030 ± 788 UI; p = 0,003). Conforme esperado, a atividade da ADA foi maior na tuberculose que no câncer (107,6 ± 44,2 x 30,6 ± 57,5 U/L; p < 0,001). Ambos os derrames apresentaram alta celularidade, embora mais pronunciada no grupo neoplásico (p < 0,001). Os derrames de etiologia tuberculosa se caracterizaram por apresentar uma maior percentagem de leucócitos e de linfócitos (p < 0,001) e um pequeno número de células mesoteliais (p = 0,005). Linfócitos e macrófagos foram as células nucleadas que predominaram nos derrames pleurais malignos. CONCLUSÃO: Nossos resultados demonstram que em exsudatos pleurais linfocíticos de pacientes com evidências clínicas e radiológicas de tuberculose, os níveis de proteína e de ADA foram os parâmetros que melhor caracterizaram esses derrames. Da mesma maneira, quando a suspeita clínica é câncer, um líquido serohemorrágico e linfocítico deve ser submetido à citologia oncótica, uma vez que este exame laboratorial de fácil realização e baixo custo apresenta alto desempenho diagnóstico (;@; 80%). Neste contexto, sugerimos que a toracocentese, com exames bioquímicos e citológicos do líquido pleural, seja a primeira abordagem diagnóstica do paciente.

Purpose: To describe whether quantitative assessment of early changes in the morphology of retinopathy lesions can predict development of vision-threatening diabetic maculopathy.Methods: We used a nested case-control study, and we studied 11 type 2 diabetes patients who had developed visual loss secondary to diabetic maculopathy. For each diabetes patient, we also studied three matched control patients who had been followed for a comparable period of time without developing visual loss. Fundus photographs describing the early development of retinopathy were digitized and subjected to a full manual quantitative grading on a computer monitor. Differences in the early development of retinal morphology were compared between the two groups. The outcome parameters were changes in the number and area of haemorrhages and exudates in different regions of the fundus, and the weighted distance of these lesions from the fovea and the optic disc.Results: In patients who developed visual loss secondary to diabetic maculopathy there was significant early progression in the total area and number of haemorrhages and exudates. The haemorrhages had progressed in all retinal areas except the area around the optic disc and the temporal vascular arcades. The exudates had progressed temporally from the fovea and in the retinal periphery.Conclusions: The results suggest that a quantitative description of the regional development of early diabetic retinopathy may help in identifying patients who will later develop vision-threatening maculopathy.

Background: Differentiating into transudate or exudate is the first step in the evaluation of effusions. Light's criteria is the standard but a significant number of transudates may not be differentiated based on these criteria. Acute phase proteins (APP) are present in plasma, which increase or decrease by about 25% during an acute inflammatory response. Ceruloplasmin (CP) is a positive APP. Hence, this study was done to know the diagnostic value of pleural fluid (pf) CP and pf to serum ceruloplasmin ratio (CPr) to differentiate the pleural effusion (PE) into exudate and transudate as compared to Light's criteria. Materials and Methods: Setting: Medical wards of St John's Medical College Hospital, Bangalore. Design: Cross-sectional descriptive study. Patients with PE were divided into exudate and transudate by definitive diagnosis. pfCP, CPr and Light's criteria were compared with definitive diagnosis for the differentiation of pf into exudate and transudate. Results: The mean value of the pfCP and CPr was found to be significantly different between exudates and transudates. Sensitivity and specificity of pfCP at ≥ 13.34 mg/dl is 89.7% and 83.3%, CPr at ≥ 0.37 is 91.4% and 83.3%, Light's criteria 94.82% and 83.3%, respectively. Light's criteria, pfCP and CPr have similar PPV (98%) with Light's criteria having higher NPV (62.5%) than pfCP (45%) and CPr (50%), respectively. CPr has higher NPV than pfCP. Conclusions: pfCP and CPr can differentiate pf into exudate and transudate with comparable PPV to Light's criteria.

SummaryBackgroundTriggering receptor expressed on myeloid cells (TREM)-1 is a recently described molecule that plays an important role in myeloid cell-activated inflammatory responses. The aim of this study was to investigate the expression of TREM-1 in pleural effusions of various causes.MethodsFor this cross-sectional, observational study conducted between February and August 2005 in Taiwan, 74 patients with pleural effusions of varying etiology were investigated. Soluble TREM-1 (sTREM-1) was measured in pleural fluid samples, and cells in the fluid were assessed for surface expression of TREM-1.ResultsConcentrations of sTREM-1 were significantly higher in infectious and neoplastic pleural effusions (189.1±36.7 and 69.9±22.8ng/ml, mean±sem) than in transudates (10.1±5.3ng/ml; P

Diabetic retinopathy (DR) is a leading cause of vision loss, caused by damage to the retina from complications of diabetes. Analysis of the retinal photographs for key characteristics of DR can result in early diagnosis and better management of DR. This paper presents a method for automated analysis and classification of the retina as DR or non-DR using two-field mydriatic fundus photography. The optic disc region is located by multi-level wavelet decomposition and recursive region growing from an automatically identified seed point. Blood vessels are extracted by applying histogram analysis on the two median filtered images. Red lesions are detected using three stage intensity transformation and white lesions from multi-level histogram analysis. The final classification of the retina as DR or non-DR is based on an aggregate of the lesions extracted from each image. The proposed method has been validated against diagnosis by a panel of expert ophthalmologists on images from 368 patients. The observed sensitivity and specificity were 80% and 50% respectively. The results show that automated screening based on two-field photography can be applied in routine screening.

Regular screening for the development of diabetic retinopathy is imperative for an early diagnosis and a timely treatment, thus preventing further progression of the disease. The conventional screening techniques based on manual observation by qualified physicians can be very time consuming and prone to error. In this paper, a novel automated screening model based on deep learning for the semantic segmentation of exudates in color fundus images is proposed with the implementation of an end-to-end convolutional neural network built upon UNet architecture. This encoder-decoder network is characterized by the combination of a contracting path and a symmetrical expansive path to obtain precise localization with the use of context information. The proposed method was validated on E-OPHTHA and DIARETDB1 public databases achieving promising results compared to current state-of-theart methods., This paper was supported by the European Union’s Horizon 2020 research and innovation programme under the Project GALAHAD [H2020-ICT2016-2017, 732613]. The work of Adri´an Colomer has been supported by the Spanish Government under a FPI Grant [BES-2014-067889]. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research.