Primary splenic peripheral nerve sheath tumour in a dog

An 8-year-old crossbred dog was presented with a one-month history of progressive weakness, respiratory impairment and abdominal distension. Surgical exploration revealed the presence of a splenic mass that infiltrated the mesentery and was adherent to the stomach and pancreas. The mass was composed of highly cellular areas of spindle-shaped cells arranged in interlacing bundles, streams, whorls and storiform patterns (Antoni A pattern) and less cellular areas with more loosely arranged spindle to oval cells (Antoni B pattern). The majority of neoplastic cells expressed vimentin, S-100 and glial fibrillary acidic protein (GFAP), but did not express desmin, alpha-smooth muscle actin or factor VIII. These morphological and immunohistochemical findings characterized the lesion as a malignant peripheral nerve sheath tumour (PNST). Primary splenic PNST has not been documented previously in the dog.

Dirichlet-Neumann inverse spectral problem for a star graph of Stieltjes strings

We solve two inverse spectral problems for star graphs of Stieltjes strings with Dirichlet and Neumann boundary conditions, respectively, at a selected vertex called root. The root is either the central vertex or, in the more challenging problem, a pendant vertex of the star graph. At all other pendant vertices Dirichlet conditions are imposed; at the central vertex, at which a mass may be placed, continuity and Kirchhoff conditions are assumed. We derive conditions on two sets of real numbers to be the spectra of the above Dirichlet and Neumann problems. Our solution for the inverse problems is constructive: we establish algorithms to recover the mass distribution on the star graph (i.e. the point masses and lengths of subintervals between them) from these two spectra and from the lengths of the separate strings. If the root is a pendant vertex, the two spectra uniquely determine the parameters on the main string (i.e. the string incident to the root) if the length of the main string is known. The mass distribution on the other edges need not be unique; the reason for this is the non-uniqueness caused by the non-strict interlacing of the given data in the case when the root is the central vertex. Finally, we relate of our results to tree-patterned matrix inverse problems.