5 research outputs found

    Dysregulation of locus coeruleus development in congenital central hypoventilation syndrome.

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    Human congenital central hypoventilation syndrome (CCHS), resulting from mutations in transcription factor PHOX2B, manifests with impaired responses to hypoxemia and hypercapnia especially during sleep. To identify brainstem structures developmentally affected in CCHS, we analyzed two postmortem neonatal-lethal cases with confirmed polyalanine repeat expansion (PARM) or Non-PARM (PHOX2B∆8) mutation of PHOX2B. Both human cases showed neuronal losses within the locus coeruleus (LC), which is important for central noradrenergic signaling. Using a conditionally active transgenic mouse model of the PHOX2B∆8 mutation, we found that early embryonic expression (<E10.5) caused failure of LC neuronal specification and perinatal respiratory lethality. In contrast, later onset (E11.5) of PHOX2B∆8 expression was not deleterious to LC development and perinatal respiratory lethality was rescued, despite failure of chemosensor retrotrapezoid nucleus formation. Our findings indicate that early-onset mutant PHOX2B expression inhibits LC neuronal development in CCHS. They further suggest that such mutations result in dysregulation of central noradrenergic signaling, and therefore, potential for early pharmacologic intervention in humans with CCHS

    Intraoperative neuropathology of glioma recurrence: Cell detection and classification

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    Intraoperative neuropathology of glioma recurrence represents significant visual challenges to pathologists as they carry significant clinical implications. For example, rendering a diagnosis of recurrent glioma can help the surgeon decide to perform more aggressive resection if surgically appropriate. In addition, the success of recent clinical trials for intraoperative administration of therapies, such as inoculation with oncolytic viruses, may suggest that refinement of the intraoperative diagnosis during neurosurgery is an emerging need for pathologists. Typically, these diagnoses require rapid/STAT processing lasting only 20-30 minutes after receipt from neurosurgery. In this relatively short time frame, only dyes, such as hematoxylin and eosin (H and E), can be implemented. The visual challenge lies in the fact that these patients have undergone chemotherapy and radiation, both of which induce cytological atypia in astrocytes, and pathologists are unable to implement helpful biomarkers in their diagnoses. Therefore, there is a need to help pathologists differentiate between astrocytes that are cytologically atypical due to treatment versus infiltrating, recurrent, neoplastic astrocytes. This study focuses on classification of neoplastic versus non-neoplastic astrocytes with the long term goal of providing a better neuropathological computer-aided consultation via classification of cells into reactive gliosis versus recurrent glioma. We present a method to detect cells in H and E stained digitized slides of intraoperative cytologic preparations. The method uses a combination of the ‘value’ component of the HSV color space and ‘b*’ component of the CIE L*a*b* color space to create an enhanced image that suppresses the background while revealing cells on an image. A composite image is formed based on the morphological closing of the hue-luminance combined image. Geometrical and textural features extracted from Discrete Wavelet Frames and combined to classify cells into neoplastic and non-neoplastic categories. Experimental results show that there is a strong consensus between the proposed method’s cell detection markings with those of the pathologist’s. Experiments on 48 images from six patients resulted in F1-score as high as 87.48%, 88.08% and 86.12% for Reader 1, Reader 2 and the reader consensus, respectively. Classification results showed that for both read

    Classification of glioblastoma and metastasis for neuropathology intraoperative diagnosis:A multi-resolution textural approach to model the background

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    Brain cancer surgery requires intraoperative consultation by neuropathology to guide surgical decisions regarding the extent to which the tumor undergoes gross total resection. In this context, the differential diagnosis between glioblastoma and metastatic cancer is challenging as the decision must be made during surgery in a short time-frame (typically 30 minutes). We propose a method to classify glioblastoma versus metastatic cancer based on extracting textural features from the non-nuclei region of cytologic preparations. For glioblastoma, these regions of interest are filled with glial processes between the nuclei, which appear as anisotropic thin linear structures. For metastasis, these regions correspond to a more homogeneous appearance, thus suitable texture features can be extracted from these regions to distinguish between the two tissue types. In our work, we use the Discrete Wavelet Frames to characterize the underlying texture due to its multi-resolution capability in modeling underlying texture. The textural characterization is carried out in primarily the non-nuclei regions after nuclei regions are segmented by adapting our visually meaningful decomposition segmentation algorithm to this problem. k-nearest neighbor method was then used to classify the features into glioblastoma or metastasis cancer class. Experiment on 53 images (29 glioblastomas and 24 metastases) resulted in average accuracy as high as 89.7% for glioblastoma, 87.5% for metastasis and 88.7% overall. Further studies are underway to incorporate nuclei region features into classification on an expanded dataset, as well as expanding the classification to more types of cancers
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