Specific and label-free endogenous signature of dystrophic muscle by Synchrotron deep ultraviolet radiation.

Dystrophic muscle is characterized by necrosis/regeneration cycles, inflammation, and fibro-adipogenic development. Conventional histological stainings provide essential topographical data of this remodeling but may be limited to discriminate closely related pathophysiological contexts. They fail to mention microarchitecture changes linked to the nature and spatial distribution of tissue compartment components. We investigated whether label-free tissue autofluorescence revealed by Synchrotron deep ultraviolet (DUV) radiation could serve as an additional tool for monitoring dystrophic muscle remodeling. Using widefield microscopy with specific emission fluorescence filters and microspectroscopy defined by high spectral resolution, we analyzed samples from healthy dogs and two groups of dystrophic dogs: naïve (severely affected) and MuStem cell-transplanted (clinically stabilized) animals. Multivariate statistical analysis and machine learning approaches demonstrated that autofluorescence emitted at 420-480 nm by the Biceps femoris muscle effectively discriminates between healthy, dystrophic, and transplanted dog samples. Microspectroscopy showed that dystrophic dog muscle displays higher and lower autofluorescence due to collagen cross-linking and NADH respectively than that of healthy and transplanted dogs, defining biomarkers to evaluate the impact of cell transplantation. Our findings demonstrate that DUV radiation is a sensitive, label-free method to assess the histopathological status of dystrophic muscle using small amounts of tissue, with potential applications in regenerative medicine.

Single-slice Alzheimer's disease classification and disease regional analysis with Supervised Switching Autoencoders.

BACKGROUND: Alzheimer's disease (AD) is a difficult to diagnose pathology of the brain that progressively impairs cognitive functions. Computer-assisted diagnosis of AD based on image analysis is an emerging tool to support AD diagnosis. In this article, we explore the application of Supervised Switching Autoencoders (SSAs) to perform AD classification using only one structural Magnetic Resonance Imaging (sMRI) slice. SSAs are revised supervised autoencoder architectures, combining unsupervised representation and supervised classification as one unified model. In this work, we study the capabilities of SSAs to capture complex visual neurodegeneration patterns, and fuse disease semantics simultaneously. We also examine how regions associated to disease state can be discovered by SSAs following a local patch-based approach. METHODS: Patch-based SSAs models are trained on individual patches extracted from a single 2D slice, independently for Axial, Coronal, and Sagittal anatomical planes of the brain at selected informative locations, exploring different patch sizes and network parameterizations. Then, models perform binary class prediction - healthy (CDR = 0) or AD-demented (CDR > 0) - on test data at patch level. The final subject classification is performed employing a majority rule from the ensemble of patch predictions. In addition, relevant regions are identified, by computing accuracy densities from patch-level predictions, and analyzed, supported by Atlas-based regional definitions. RESULTS: Our experiments employing a single 2D T1-w sMRI slice per subject show that SSAs perform similarly to previous proposals that rely on full volumetric information and feature-engineered representations. SSAs classification accuracy on slices extracted along the Axial, Coronal, and Sagittal anatomical planes from a balanced cohort of 40 independent test subjects was 87.5%, 90.0%, and 90.0%, respectively. A top sensitivity of 95.0% on both Coronal and Sagittal planes was also obtained. CONCLUSIONS: SSAs provided well-ranked accuracy performance among previous classification proposals, including feature-engineered and feature learning based methods, using only one scan slice per subject, instead of the whole 3D volume, as it is conventionally done. In addition, regions identified as relevant by SSAs' were, in most part, coherent or partially coherent in regard to relevant regions reported on previous works. These regions were also associated with findings from medical knowledge, which gives value to our methodology as a potential analytical aid for disease understanding.

Description of Sarcocystis scandentiborneensis sp. nov. from treeshrews (Tupaia minor, T. tana) in northern Borneo with annotations on the utility of COI and 18S rDNA sequences for species delineation.

Sarcocystis scandentiborneensis sp. nov. was discovered in histological sections of striated musculature of treeshrews (Tupaia minor, T. tana) from Northern Borneo. Sarcocysts were cigar-shaped, 102 µm-545 µm long, and on average 53 µm in diameter. The striated cyst wall varied in thickness (2-10 µm), depending on whether the finger-like, villous protrusions (VP) were bent. Ultrastructurally, sarcocysts were similar to wall type 12 but basal microtubules extended into VPs that tapered off with a unique U-shaped, electron-dense apical structure. In phylogenetic trees of the nuclear 18S rRNA gene, S. scandentiborneensis formed a distinct branch within a monophyletic subclade of Sarcocystis spp. with (colubrid) snake-rodent life cycle. We mapped all intraspecific (two haplotypes) and interspecific nucleotide substitutions to the secondary structure of the 18S rRNA gene: in both cases, the highest variability occurred within helices V2 and V4 but intraspecific variability mostly related to transitions, while transition/transversion ratios between S. scandentiborneensis, S. zuoi, and S. clethrionomyelaphis were skewed towards transversions. Lack of relevant sequences restricted phylogenetic analysis of the mitochondrial Cytochrome C oxidase subunit I (COI) gene to include only one species of Sarcocystis recovered from a snake host (S. pantherophisi) with which the new species formed a sister relationship. We confirm the presence of the functionally important elements of the COI barcode amino acid sequence of S. scandentiborneensis, whereby the frequency of functionally important amino acids (Alanine, Serine) was markedly different to other taxa of the Sarcocystidae. We regard S. scandentiborneensis a new species, highlighting that structurally or functionally important aspects of the 18S rRNA and COI could expand their utility for delineation of species. We also address the question why treeshrews, believed to be close to primates, carry a parasite that is genetically close to a Sarcocystis lineage preferably developing in the Rodentia as intermediate hosts.

Integrated multimedia electronic patient record and graph-based image information for cerebral tumors.

Current electronic patient record (EPR) implementations do not incorporate medical images, nor structural information extracted from them, despite images increasing role for diagnosis. This paper presents an integration framework into EPRs of anatomical and pathological knowledge extracted from segmented magnetic resonance imaging (MRI), applying a graph of representation for anatomical and functional information for individual patients. Focusing on cerebral tumors examination and patient follow-up, multimedia EPRs were created and evaluated through a 3D navigation application, developed with open-source libraries and standards. Results suggest that the enhanced clinical information scheme could lead to original changes in the way medical experts utilize image-based information.

Dataset of anomalies and malicious acts in a cyber-physical subsystem.

This article presents a dataset produced to investigate how data and information quality estimations enable to detect aNomalies and malicious acts in cyber-physical systems. Data were acquired making use of a cyber-physical subsystem consisting of liquid containers for fuel or water, along with its automated control and data acquisition infrastructure. Described data consist of temporal series representing five operational scenarios - Normal, aNomalies, breakdown, sabotages, and cyber-attacks - corresponding to 15 different real situations. The dataset is publicly available in the .zip file published with the article, to investigate and compare faulty operation detection and characterization methods for cyber-physical systems.

Information quality measurement of medical encoding support based on usability.

Medical encoding support systems for diagnoses and medical procedures are an emerging technology that begins to play a key role in billing, reimbursement, and health policies decisions. A significant problem to exploit these systems is how to measure the appropriateness of any automatically generated list of codes, in terms of fitness for use, i.e. their quality. Until now, only information retrieval performance measurements have been applied to estimate the accuracy of codes lists as quality indicator. Such measurements do not give the value of codes lists for practical medical encoding, and cannot be used to globally compare the quality of multiple codes lists. This paper defines and validates a new encoding information quality measure that addresses the problem of measuring medical codes lists quality. It is based on a usability study of how expert coders and physicians apply computer-assisted medical encoding. The proposed measure, named ADN, evaluates codes Accuracy, Dispersion and Noise, and is adapted to the variable length and content of generated codes lists, coping with limitations of previous measures. According to the ADN measure, the information quality of a codes list is fully represented by a single point, within a suitably constrained feature space. Using one scheme, our approach is reliable to measure and compare the information quality of hundreds of codes lists, showing their practical value for medical encoding. Its pertinence is demonstrated by simulation and application to real data corresponding to 502 inpatient stays in four clinic departments. Results are compared to the consensus of three expert coders who also coded this anonymized database of discharge summaries, and to five information retrieval measures. Information quality assessment applying the ADN measure showed the degree of encoding-support system variability from one clinic department to another, providing a global evaluation of quality measurement trends.

Development framework for a patient-centered record.

Patient records have been developed to support the physician-oriented medical activity scheme. One recommended yet rarely studied alternative, expected to improve healthcare, is the patient-centered record. We propose a development framework for such record, which includes domain-specific database models at the conceptual level, analyzing the fundamental role of complementary information destined to ensure proper patient understanding of related clinical situations. A patient-centered awareness field study of user requirements and medical workflow was carried out in three medical services and two technical units to identify the most relevant elements of the framework, and compared to the definitions of a theoretical approach. Three core data models - centered on the patient, medical personnel, and complementary patient information, corresponding to the determined set of entities, information exchanges and actors roles, constitute the technical recommendations of the development framework. An open source proof of concept prototype was developed to show the model feasibility. The resulting patient-centered record development framework implies particular medical personnel contributions to supply complementary information.

Computer-assisted venous thrombosis volume quantification.

Venous thrombosis (VT) volume assessment, by verifying its risk of progression when anticoagulant or thrombolytic therapies are prescribed, is often necessary to screen life-threatening complications. Commonly, VT volume estimation is done by manual delineation of few contours in the ultrasound (US) image sequence, assuming that the VT has a regular shape and constant radius, thus producing significant errors. This paper presents and evaluates a comprehensive functional approach based on the combination of robust anisotropic diffusion and deformable contours to calculate VT volume in a more accurate manner when applied to freehand 2-D US image sequences. Robust anisotropic filtering reduces image speckle noise without generating incoherent edge discontinuities. Prior knowledge of the VT shape allows initializing the deformable contour, which is then guided by the noise-filtering outcome. Segmented contours are subsequently used to calculate VT volume. The proposed approach is integrated into a system prototype compatible with existing clinical US machines that additionally tracks the acquired images 3-D position and provides a dense Delaunay triangulation required for volume calculation. A predefined robust anisotropic diffusion and deformable contour parameter set enhances the system usability. Experimental results pertinence is assessed by comparison with manual and tetrahedron-based volume computations, using images acquired by two medical experts of eight plastic phantoms and eight in vitro VTs, whose independently measured volume is the reference ground truth. Results show a mean difference between 16 and 35 mm(3) for volumes that vary from 655 to 2826 mm(3). Two in vivo VT volumes are also calculated to illustrate how this approach could be applied in clinical conditions when the real value is unknown. Comparative results for the two experts differ from 1.2% to 10.08% of the smallest estimated value when the image acquisition cadences are similar.