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BACKGROUND AND OBJECTIVE: Current studies based on digital biopsy images have achieved satisfactory results in detecting colon cancer despite their limited visual spectral range. Such methods may be less accurate when applied to samples taken from the tumor margin region or to samples containing multiple diagnoses. In contrast with the traditional computer vision approach, micro-FTIR hyperspectral images quantify the tissue-light interaction on a histochemical level and characterize different tissue pathologies, as they present a unique spectral signature. Therefore, this paper investigates the possibility of using hyperspectral images acquired over micro-FTIR absorbance spectroscopy to characterize healthy, inflammatory, and tumor colon tissues. METHODS: The proposed method consists of modeling hyperspectral data into a voxel format to detect the patterns of each voxel using fully connected deep neural network. A web-based computer-aided diagnosis tool for inference is also provided. RESULTS: Our experiments were performed using the K-fold cross-validation protocol in an intrapatient approach and achieved an overall accuracy of 99% using a deep neural network and 96% using a linear support vector machine. Through the experiments, we noticed the high performance of the method in characterizing such tissues using deep learning and hyperspectral images, indicating that the infrared spectrum contains relevant information and can be used to assist pathologists during the diagnostic process.

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Verruciform xanthoma (VX) is a rare benign lesion of unknown etiology, with a rough or papillary aspect, painless, sessile, well-defined, most lesions do not exceed 2 cm in their largest diameter, the degree of keratinization of the surface influences color, varying white to red, affecting mainly the gingiva and alveolar mucosa, and can also be seen in skin and genital. Herein, we present a report a clinical case of oral verruciform xanthoma in the buccal mucosa associated with the lichen planus lesion, as well as the morphological and immunohistochemical characteristics of the lesion. The clinical diagnostic hypothesis of oral lichen planus of the white reticular lesions on the buccal mucosa and on the tongue was confirmed by histopathology before a subepithelial connective tissue exhibiting intense inflammatory infiltrate in a predominantly lymphocytic band. In contrast, the hypothesis of the verrucous lesion in the left buccal mucosa was leukoplakia, with histopathological evidence showing exophytic and digitiform proliferations with parakeratin plugs between the papillary projections. Subepithelial connective tissue was characterized by macrophages with foamy cytoplasm (xanthoma cells). An immunohistochemical examination was performed, showing positivity for CD68, a macrophage marker, in addition to testing by Schiff's periodic acid (PAS) with diastasis, which was detected the presence of lipids inside these macrophages. The patient is free of recurrences of verruciform xanthoma and is being monitored due to the presence of lesions of oral lichen planus.

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The prediction and detection of radiation-related caries (RRC) are crucial to manage the side effects of the head and the neck cancer (HNC) radiotherapy (RT). Despite the demands for the prediction of RRC, no study proposes and evaluates a prediction method. This study introduces a method based on artificial intelligence neural network to predict and detect either regular caries or RRC in HNC patients under RT using features extracted from panoramic radiograph. We selected fifteen HNC patients (13 men and 2 women) to analyze, retrospectively, their panoramic dental images, including 420 teeth. Two dentists manually labeled the teeth to separate healthy and teeth with either type caries. They also labeled the teeth by resistant and vulnerable, as predictive labels telling about RT aftermath caries. We extracted 105 statistical/morphological image features of the teeth using PyRadiomics. Then, we used an artificial neural network classifier (ANN), firstly, to select the best features (using maximum weights) and then label the teeth: in caries and non-caries while detecting RRC, and resistant and vulnerable while predicting RRC. To evaluate the method, we calculated the confusion matrix, receiver operating characteristic (ROC), and area under curve (AUC), as well as a comparison with recent methods. The proposed method showed a sensibility to detect RRC of 98.8% (AUC = 0.9869) and to predict RRC achieved 99.2% (AUC = 0.9886). The proposed method to predict and detect RRC using neural network and PyRadiomics features showed a reliable accuracy able to perform before starting RT to decrease the side effects on susceptible teeth.

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Nerve morphometry is known to produce relevant information for the evaluation of several phenomena, such as nerve repair, regeneration, implant, transplant, aging, and different human neuropathies. Manual morphometry is laborious, tedious, time consuming, and subject to many sources of error. Therefore, in this paper, we propose a new method for the automated morphometry of myelinated fibers in cross-section light microscopy images. Images from the recurrent laryngeal nerve of adult rats and the vestibulocochlear nerve of adult guinea pigs were used herein. The proposed pipeline for fiber segmentation is based on the techniques of competitive clustering and concavity analysis. The evaluation of the proposed method for segmentation of images was done by comparing the automatic segmentation with the manual segmentation. To further evaluate the proposed method considering morphometric features extracted from the segmented images, the distributions of these features were tested for statistical significant difference. The method achieved a high overall sensitivity and very low false-positive rates per image. We detect no statistical difference between the distribution of the features extracted from the manual and the pipeline segmentations. The method presented a good overall performance, showing widespread potential in experimental and clinical settings allowing large-scale image analysis and, thus, leading to more reliable results.

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BACKGROUND: The use of the knowledge produced by sciences to promote human health is the main goal of translational medicine. To make it feasible we need computational methods to handle the large amount of information that arises from bench to bedside and to deal with its heterogeneity. A computational challenge that must be faced is to promote the integration of clinical, socio-demographic and biological data. In this effort, ontologies play an essential role as a powerful artifact for knowledge representation. Chado is a modular ontology-oriented database model that gained popularity due to its robustness and flexibility as a generic platform to store biological data; however it lacks supporting representation of clinical and socio-demographic information. RESULTS: We have implemented an extension of Chado - the Clinical Module - to allow the representation of this kind of information. Our approach consists of a framework for data integration through the use of a common reference ontology. The design of this framework has four levels: data level, to store the data; semantic level, to integrate and standardize the data by the use of ontologies; application level, to manage clinical databases, ontologies and data integration process; and web interface level, to allow interaction between the user and the system. The clinical module was built based on the Entity-Attribute-Value (EAV) model. We also proposed a methodology to migrate data from legacy clinical databases to the integrative framework. A Chado instance was initialized using a relational database management system. The Clinical Module was implemented and the framework was loaded using data from a factual clinical research database. Clinical and demographic data as well as biomaterial data were obtained from patients with tumors of head and neck. We implemented the IPTrans tool that is a complete environment for data migration, which comprises: the construction of a model to describe the legacy clinical data, based on an ontology; the Extraction, Transformation and Load (ETL) process to extract the data from the source clinical database and load it in the Clinical Module of Chado; the development of a web tool and a Bridge Layer to adapt the web tool to Chado, as well as other applications. CONCLUSIONS: Open-source computational solutions currently available for translational science does not have a model to represent biomolecular information and also are not integrated with the existing bioinformatics tools. On the other hand, existing genomic data models do not represent clinical patient data. A framework was developed to support translational research by integrating biomolecular information coming from different "omics" technologies with patient's clinical and socio-demographic data. This framework should present some features: flexibility, compression and robustness. The experiments accomplished from a use case demonstrated that the proposed system meets requirements of flexibility and robustness, leading to the desired integration. The Clinical Module can be accessed in http://dcm.ffclrp.usp.br/caib/pg=iptrans.

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A long-standing challenge of content-based image retrieval (CBIR) systems is the definition of a suitable distance function to measure the similarity between images in an application context which complies with the human perception of similarity. In this paper, we present a new family of distance functions, called attribute concurrence influence distances (AID), which serve to retrieve images by similarity. These distances address an important aspect of the psychophysical notion of similarity in comparisons of images: the effect of concurrent variations in the values of different image attributes. The AID functions allow for comparisons of feature vectors by choosing one of two parameterized expressions: one targeting weak attribute concurrence influence and the other for strong concurrence influence. This paper presents the mathematical definition and implementation of the AID family for a two-dimensional feature space and its extension to any dimension. The composition of the AID family with L (p) distance family is considered to propose a procedure to determine the best distance for a specific application. Experimental results involving several sets of medical images demonstrate that, taking as reference the perception of the specialist in the field (radiologist), the AID functions perform better than the general distance functions commonly used in CBIR.

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Desenvolvimento de um software para cadastro e recuperação de informações em Antropologia Forense baseado no protocolo desenvolvido durante o projeto “UK –Brazil Scientific Cooperation – Forensic Anthropology and Identification of Human Remains”. Metodologia: Por se tratar de um aplicativo acessado via Browser (software que permite o acesso à Internet, como o Microsoft Internet Explorer®) foi necessária a escolha de uma linguagem de programação que se enquadrasse nesse requisito juntamente com uma aplicação servidora. A linguagem escolhida foi PHP® e a aplicação servidora foi o Apache®. Para o armazenamento dos dados foi escolhido o Sistema Gerenciador de Banco de Dados MySQL®...

A Software development for registration and recovery of information on Objective: A Software development for registration and recovery of information on Forensic Anthropology, based on the protocol developed during the project “UK – Brazil Scientific Cooperation – Forensic Anthropology and Identification of Human Remains”. Methods: Considering it is a Browser accessed application (software that allows Internet access, as Microsoft Internet Explorer®), it was necessary to choose an adequate programming language to this requirement as the server application. The chosen language was PHP® and the server application was Apache®. For data storage it was chosen the Data Bank Managing System MySQL®. Forensic Anthropology, based on the protocol developed during the project “UK – Brazil ScientificCooperation – Forensic Anthropology and Identification of Human Remains”. Methods: Considering it is a Browser accessed application (software that allows Internet access, as Microsoft InternetExplorer®), it was necessary to choose an adequate programming language to this requirement as the server application. The chosen language was PHP® and the server application was Apache®. For data storage it was chosen the Data Bank Managing System MySQL®...

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