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The Brazilian National Cancer Institute (INCA) distributes a cancer registry tool to all hospitals. The data are stored locally but then have to be physically shipped to INCA. The RHCNet is a centralized, secure, Web-based system that collects information in electronic form and consolidates it in one database using information from all Brazilian hospital-based cancer registry clients nationally. It allows the generation of a more accurate picture of the cancer occurrence in Brazil, which is important for the evaluation of treatment strategies and government decision-making. RHCNet is now entering the production phase; in its pilot phase, six hospitals and 75,000 patients were registered. By the end of 2007 it will be available at 67 centres with an estimated 350,000 patients. RHCNet is contributing to the modernization of medical practice in Brazil, providing a unified view of cancer care and combining the best telehealth practices with the latest information system technologies. [ABSTRACT FROM AUTHOR]

Abnormal tau inclusions are hallmarks of Alzheimer's disease and predictors of clinical decline. Several tau PET tracers are available for neurodegenerative disease research, opening avenues for molecular diagnosis in vivo. However, few have been approved for clinical use. Understanding the neurobiological basis of PET signal validation remains problematic because it requires a large-scale, voxel-to-voxel correlation between PET and (immuno) histological signals. Large dimensionality of whole human brains, tissue deformation impacting co-registration, and computing requirements to process terabytes of information preclude proper validation. We developed a computational pipeline to identify and segment particles of interest in billion-pixel digital pathology images to generate quantitative, 3D density maps. The proposed convolutional neural network for immunohistochemistry samples, IHCNet, is at the pipeline's core. We have successfully processed and immunostained over 500 slides from two whole human brains with three phospho-tau antibodies (AT100, AT8, and MC1), spanning several terabytes of images. Our artificial neural network estimated tau inclusion from brain images, which performs with ROC AUC of 0.87, 0.85, and 0.91 for AT100, AT8, and MC1, respectively. Introspection studies further assessed the ability of our trained model to learn tau-related features. We present an end-to-end pipeline to create terabytes-large 3D tau inclusion density maps co-registered to MRI as a means to facilitate validation of PET tracers., Competing Interests: Declaration of Competing Interest The authors declare no competing interests., (Copyright © 2021 The Author(s). Published by Elsevier Inc. All rights reserved.)

Stereotaxy is based on the precise image-guided spatial localization of targets within the human brain. Even with the recent advances in MRI technology, histological examination renders different (and complementary) information of the nervous tissue. Although several maps have been selected as a basis for correlating imaging results with the anatomical locations of sub-cortical structures, technical limitations interfere in a point-to-point correlation between imaging and anatomy due to the lack of precise correction for post-mortem tissue deformations caused by tissue fixation and processing. We present an alternative method to parcellate human brain cytoarchitectural regions, minimizing deformations caused by post-mortem and tissue-processing artifacts and enhancing segmentation by means of modified high thickness histological techniques and registration with MRI of the same specimen and into MNI space (ICBM152). A three-dimensional (3D) histological atlas of the human thalamus, basal ganglia, and basal forebrain cholinergic system is displayed. Structure's segmentations were performed in high-resolution dark-field and light-field microscopy. Bidimensional non-linear registration of the histological slices was followed by 3D registration with in situ MRI of the same subject. Manual and automated registration procedures were adopted and compared. To evaluate the quality of the registration procedures, Dice similarity coefficient and normalized weighted spectral distance were calculated and the results indicate good overlap between registered volumes and a small shape difference between them in both manual and automated registration methods. High thickness high-resolution histological slices in combination with registration to in situ MRI of the same subject provide an effective alternative method to study nuclear boundaries in the human brain, enhancing segmentation and demanding less resources and time for tissue processing than traditional methods.

Background: Immunofluorescence (IF) plays a major role in quantifying protein expression in situ and understanding cell function. It is widely applied in assessing disease mechanisms and in drug discovery research. Automation of IF analysis can transform studies using experimental cell models. However, IF analysis of postmortem human tissue relies mostly on manual interaction, often subjected to low-throughput and prone to error, leading to low inter and intra-observer reproducibility. Human postmortem brain samples challenges neuroscientists because of the high level of autofluorescence caused by accumulation of lipofuscin pigment during aging, hindering systematic analyses. We propose a method for automating cell counting and classification in IF microscopy of human postmortem brains. Our algorithm speeds up the quantification task while improving reproducibility., New Method: Dictionary learning and sparse coding allow for constructing improved cell representations using IF images. These models are input for detection and segmentation methods. Classification occurs by means of color distances between cells and a learned set., Results: Our method successfully detected and classified cells in 49 human brain images. We evaluated our results regarding true positive, false positive, false negative, precision, recall, false positive rate and F1 score metrics. We also measured user-experience and time saved compared to manual countings., Comparison With Existing Methods: We compared our results to four open-access IF-based cell-counting tools available in the literature. Our method showed improved accuracy for all data samples., Conclusion: The proposed method satisfactorily detects and classifies cells from human postmortem brain IF images, with potential to be generalized for applications in other counting tasks., (Copyright © 2017 Elsevier B.V. All rights reserved.)

A computational pipeline combining texture analysis and pattern classification algorithms was developed for investigating associations between high-resolution MRI features and histological data. This methodology was tested in the study of dentate gyrus images of sclerotic hippocampi resected from refractory epilepsy patients. Images were acquired using a simple surface coil in a 3.0T MRI scanner. All specimens were subsequently submitted to histological semiquantitative evaluation. The computational pipeline was applied for classifying pixels according to: a) dentate gyrus histological parameters and b) patients' febrile or afebrile initial precipitating insult history. The pipeline results for febrile and afebrile patients achieved 70% classification accuracy, with 78% sensitivity and 80% specificity [area under the reader observer characteristics (ROC) curve: 0.89]. The analysis of the histological data alone was not sufficient to achieve significant power to separate febrile and afebrile groups. Interesting enough, the results from our approach did not show significant correlation with histological parameters (which per se were not enough to classify patient groups). These results showed the potential of adding computational texture analysis together with classification methods for detecting subtle MRI signal differences, a method sufficient to provide good clinical classification. A wide range of applications of this pipeline can also be used in other areas of medical imaging., (Copyright © 2012 Wiley Periodicals, Inc.)

The authors report 11 cases of congenital disproportion of fibers, confirmed through clinical and complementary examinations. In these 11 cases early fibrotendinous retractions were frequent and CK proved to be high. At muscle biopsy histochemistry revealed a selective atrophy of type I fibers. This is a rarely frequent congenital dystrophy, of slow progression and benign evolution.

The case of an 11-year-old boy with external ophthalmoparesia, tetraparesia and bilateral eyelid ptosis is reported. He was 7-years-old when first symptoms appeared. Anticholinesterasic drugs were used. He was submitted to muscle biopsy. The results of histochemistry analysis showed storage of granulous material at the subsarcolemmal region of muscle fibers by SDH. Increase in the number of mitochondria with electron dense bodies was found at electron microscopy. Anticholinesterasic drugs administration was interrupted and consequently he got worse, and bouts of dyspnea occurred. Due to this worsening anticholinesterasic agents were reintroduced together with prednisone, and he improved. Due to clinical and histological expressions we think it is possible that morphological mitochondrial alterations may occur also in myasthenia gravis.

The authors report the first Fukuyama type congenital progressive muscular dystrophy case described in Brazil, and confirmed through clinical findings and complementary tests. Emphasis is given to the presence of early fibrotendinous retractions and impairment of the central nervous system, which constitute the fundamental characteristics of this affection. This disease is very common in Japan but very seldom described in other countries. Its etiopathogeny has not yet been defined.

Two brothers presented to us with a progressive myoclonic syndrome with slight cerebellar symptoms. Neurological examination disclosed moderate cerebellar signs and pale optic discs; asymmetric, asynchronous and arrhythmic myoclonus, an arrthesthesic deficit and no muscular weakness. EEG background activity was moderately slow with no irritative discharges. CT was normal in both cases. Intermittent photic stimulation increased the frequency of the myoclonic jerks, which became bilateral and synchronous, progressing to a generalized tonic-clonic seizure. EPs and MRI in one case were normal. Anticonvulsant drugs were ineffective. The diagnosis of mitochondrial encephalomyopathy was based on the finding, in muscle specimens, of thickened basement membranes with myofibrillary degeneration and increased number of mitochondria peripherally distributed and with a dense granular matrix and some vacuoles. The clinical and EEG data suggest a subcortical origin for this type of myoclonic syndrome.