Systemically transferred hematopoietic stem cells home to the subretinal space and express RPE-65 in a mouse model of retinal pigment epithelium damage

Stem cell regeneration of damaged tissue has recently been reported in many different organs. Since the loss of retinal pigment epithelium (RPE) in the eye is associated with a major cause of visual loss - specifically, age-related macular degeneration - we investigated whether hematopoietic stem cells (HSC) given systemically can home to the damaged subretinal space and express markers of RPE lineage. Green fluorescent protein (GFP) cells of bone marrow origin were used in a sodium iodate (NaIO(3)) model of RPE damage in the mouse. The optimal time for adoptive transfer of bone marrow-derived stem cells relative to the time of injury and the optimal cell type [whole bone marrow, mobilized peripheral blood, HSC, facilitating cells (FC)] were determined by counting the number of GFP(+) cells in whole eye flat mounts. Immunocytochemistry was performed to identify the bone marrow origin of the cells in the RPE using antibodies for CD45, Sca-1, and c-kit, as well as the expression of the RPE-specific marker, RPE-65. The time at which bone marrow-derived cells were adoptively transferred relative to the time of NaIO(3) injection did not significantly influence the number of cells that homed to the subretinal space. At both one and two weeks after intravenous (i.v.) injection, GFP(+) cells of bone marrow origin were observed in the damaged subretinal space, at sites of RPE loss, but not in the normal subretinal space. The combined transplantation of HSC+FC cells appeared to favor the survival of the homed stem cells at two weeks, and RPE-65 was expressed by adoptively transferred HSC by four weeks. We have shown that systemically injected HSC homed to the subretinal space in the presence of RPE damage and that FC promoted survival of these cells. Furthermore, the RPE-specific marker RPE-65 was expressed on adoptively transferred HSC in the denuded areas.

Cerebellar cortical degeneration with selective granule cell loss in Bavarian mountain dogs

Three Bavarian mountain dogs aged between 18 and 20 months, not related to each other, were presented with chronic signs of cerebellar dysfunction. On sagittal T2-weighted magnetic resonance imaging brain images, the tentative diagnosis of cerebellar hypoplasia was established based on an enlarged cerebrospinal fluid space around the cerebellum and an increased cerebrospinal fluid signal between the folia. Post-mortem examination was performed in one dog and did show an overall reduction of cerebellar size. On histopathologic examination, a selective loss of cerebellar granule cells with sparing of Purkinje cells was evident. Therefore, the Bavarian mountain dog is a breed where cerebellar cortical degeneration caused by the rather exceptional selective granule cell loss can be seen as cause of chronic, slowly progressive cerebellar dysfunction starting at an age of several months.

Streak detection algorithm for space debris detection on optical images.

Any image processing object detection algorithm somehow tries to integrate the object light (Recognition Step) and applies statistical criteria to distinguish objects of interest from other objects or from pure background (Decision Step). There are various possibilities how these two basic steps can be realized, as can be seen in the different proposed detection methods in the literature. An ideal detection algorithm should provide high recognition sensitiv ity with high decision accuracy and require a reasonable computation effort . In reality, a gain in sensitivity is usually only possible with a loss in decision accuracy and with a higher computational effort. So, automatic detection of faint streaks is still a challenge. This paper presents a detection algorithm using spatial filters simulating the geometrical form of possible streaks on a CCD image. This is realized by image convolution. The goal of this method is to generate a more or less perfect match between a streak and a filter by varying the length and orientation of the filters. The convolution answers are accepted or rejected according to an overall threshold given by the ackground statistics. This approach yields as a first result a huge amount of accepted answers due to filters partially covering streaks or remaining stars. To avoid this, a set of additional acceptance criteria has been included in the detection method. All criteria parameters are justified by background and streak statistics and they affect the detection sensitivity only marginally. Tests on images containing simulated streaks and on real images containing satellite streaks show a very promising sensitivity, reliability and running speed for this detection method. Since all method parameters are based on statistics, the true alarm, as well as the false alarm probability, are well controllable. Moreover, the proposed method does not pose any extraordinary demands on the computer hardware and on the image acquisition process.