Adipose-derived mesenchymal stromal (stem) cells differentiate to osteoblast and chondroblast lineages upon incubation with conditioned media from dental pulp stem cell-derived osteoblasts and auricle cartilage chondrocytes.

The potential of adipose-derived mesenchymal stromal (stem) cells (ADSCs) to differentiate into either osteoblasts or chondrocytes is controversial. In this study we investigated the multicapacity potential of ADSCs to differentiate towards adipocyte, osteoblast, and chondrocyte lineages when cells are seeded onto plastic in comparison with incubation with conditioned media (CM) obtained from differentiated cell types.ADSCs, obtained from liposuctions, were characterized for mesenchymal and hematopoietic markers by cytofluorimetry. Their differentiation capacity towards adipocytes, osteoblasts, and chondrocytes was investigated by histochemistry methods (Oil-Red-O staining, Safranin O and Alizarin Red staining, respectively). Dental pulp stem cells (DPSCs) and dedifferentiated auricle derived-chondrocytes were differentiated towards osteoblastic and chondrocytic lineages respectively, and the CM obtained from these cultures was used to induce differentiation of ADSCs. ADSCs were positive for mesenchymal markers (CD29, CD105, CD73, CD44), but not for hematopoietic lineage markers (CD14, CD34, CD45) and this behavior was conserved from the isolation up to the fifth passage. While ADSCs were readily differentiated in adipocytes, they were not towards chondrocytes and osteoblastic lineages, a behavior different from that of bone marrow-derived MSCs that differentiated into the three lineages at two weeks post-induction. Only ADSCs treated with CM from cultured chondrocytes and DPSCs, produced glycosaminoglycans and mineralized matrix. These results indicate that ADSCs need growth/morphogenic factor supplementation from the tissue environment to be appropriately differentiated to mesodermic lineages.

Modeling LGN responses during free-viewing: a possible role of microscopic eye movements in the refinement of cortical orientation selectivity.

Neural activity appears to be essential for the normal development of the orientation-selective responses of cortical cells. It has been proposed that the correlated activity of LGN cells is a crucial component for shaping the receptive fields of cortical simple cells into adjacent, oriented subregions alternately receiving ON- and OFF-center excitatory geniculate inputs. After eye opening, the spatiotemporal structure of neural activity in the early stages of the visual pathway depends not only on the characteristics of the environment, but also on the way the environment is scanned. In this study, we use computational modeling to investigate how eye movements might affect the refinement of orientation tuning in the presence of a Hebbian scheme of synaptic plasticity. Visual input consisting of natural scenes scanned by varying types of eye movements was used to activate a spatiotemporal model of LGN cells. In the presence of different types of movement, significantly different patterns of activity were found in the LGN. Specific patterns of correlation required for the development of segregated cortical receptive field subregions were observed in the case of micromovements, but were not seen in the case of saccades or static presentation of natural visual input. These results suggest an important role for the eye movements occurring during fixation in the refinement of orientation selectivity.

Registration of neural maps through value-dependent learning: modeling the alignment of auditory and visual maps in the barn owl's optic tectum.

In the optic tectum (OT) of the barn owl, visual and auditory maps of space are found in close alignment with each other. Experiments in which such alignment has been disrupted have shown a considerable degree of plasticity in the auditory map. The external nucleus of the inferior colliculus (ICx), an auditory center that projects massively to the tectum, is the main site of plasticity; however, it is unclear by what mechanisms the alignment between the auditory map in the ICx and the visual map in the tectum is established and maintained. In this paper, we propose that such map alignment occurs through a process of value-dependent learning. According to this paradigm, value systems, identifiable with neuromodulatory systems having diffuse projections, respond to innate or acquired salient cues and modulate changes in synaptic efficacy in many brain regions. To test the self-consistency of this proposal, we have developed a computer model of the principal neural structures involved in the process of auditory localization in the barn owl. This is complemented by simulations of aspects of the barn owl phenotype and of the experimental environment. In the model, a value system is activated whenever the owl carries out a foveation toward an auditory stimulus. A term representing the diffuse release of a neuromodulator interacts with local pre- and postsynaptic events to determine synaptic changes in the ICx. Through large-scale simulations, we have replicated a number of experimental observations on the development of spatial alignment between the auditory and visual maps during normal visual experience, after the retinal image is shifted through prismatic goggles, and after the reestablishment of normal visual input. The results suggest that value-dependent learning is sufficient to account for the registration of auditory and visual maps of space in the OT of the barn owl, and they lead to a number of experimental predictions.

Sealing properties of a new epoxy resin-based root-canal sealer.

In this study a new epoxy-resin-based endodontic sealer, AH Plus, was tested in vitro for apical leakage. The conventional sealer AH26, for which information is already available, was used as the control. The root canals of 72 single-rooted teeth were prepared biomechanically using a stepback technique before lateral condensation of gutta-percha with one of the two sealers. Teeth were immersed in 5% methylene blue dye for 2, 4 or 10 days. The roots were split longitudinally so that the extent of dye penetration could be measured with a stereomicroscope. The mean extent at 2 days was 0.4 mm for AH26 and 1.4 mm for AH Plus. Neither material produced a complete apical seal and leakage increased with the duration of immersion in dye. The differences between observation periods as well as between materials with respect to dye penetration were statistically significant ANOVA, (P < 0.005).

Automatic analysis of hand radiographs for the assessment of skeletal age: a subsymbolic approach.

The assessment of skeletal maturity is crucial for the analysis of growth disorders and plays an important role in paediatrics. For this reason, several methods have been developed for estimating skeletal maturity. Among them, the Tanner and Whitehouse method (TW2), which is based on the analysis of hand radiographs, is usually considered the most accurate and reliable. Nevertheless, TW2 is applied only in a small fraction of cases, due to its complexity and long examination times. Thus, the development of automated systems which reliably implement this method is highly desirable. However, major difficulties have been found in the development of computer-based systems for the assessment of skeletal maturity. In particular the extraction of the bones of interest has proved to be extremely challenging. In this paper, we propose a system architecture for the implementation of the TW2 method, which is based on artificial neural networks. For each bone considered, the maturation stage is determined by means of a two-step process which first locates the position of the bone in the radiograph and then analyzes the bone shape. Experimental results obtained with our implementation of the carpal version of TW2 are in good agreement with those provided by trained observers.

Neural network segmentation of magnetic resonance spin echo images of the brain.

This paper describes a neural network system to segment magnetic resonance (MR) spin echo images of the brain. Our approach relies on the analysis of MR signal decay and on anatomical knowledge; the system processes two early echoes of a standard multislice sequence. Three main subsystems can be distinguished. The first implements a model of MR signal decay; it synthesizes a four-echo multiecho sequence, in order to add images characterized by long echo-times to the input sequence. The second subsystem exploits a priori anatomical knowledge by producing an image, in which pixels belonging to brain parenchyma are highlighted. Such anatomical information allows the following submodule to distinguish biologically different tissues with similar water content, and hence similar appearance, which might produce misclassifications. The grey levels of the reconstructed sequence and the output of the second module are processed by the third subsystem, which performs the segmentation of the sequence. Each pixel is assigned to one of five different tissue classes that can be revealed with brain MR spin echo imaging. With a suitable encoding, a five-level segmented image can then be produced. The system is based on feed-forward networks trained with the back-propagation algorithm; experiments to assess its performance have been carried out on both simulated and clinical images.

A neural network architecture for understanding discrete three-dimensional scenes in medical imaging.

Magnetic resonance and computed tomography produce sets of tomograms which are termed discrete 3D scenes. Usually, discrete 3D scenes are analyzed in two dimensions by observing each tomogram on a screen so that the three-dimensional information contained in the scene can be recovered only partially and qualitatively. The three-dimensional reconstruction of the shape of biological structures from discrete 3D scenes would allow a complete and quantitative recovery of the available information, but this task has proved hard for conventional processing techniques. In this paper we present a system architecture based on neural networks for the fully automated segmentation and recognition of structures of interest in discrete 3D scenes. The system includes a retina and two main processing modules, an Attention-Focuser System and a Region-Finder System, which have been implemented by using feed-forward nets trained with the back-propagation algorithm. This architecture has been tested on computer-simulated structures and has been applied to the reconstruction of the spinal cord and the brain from sets of tomograms.