On the importance of the innervation of the human cervical longitudinal ligaments at vertebral level.

PURPOSE: In our aging society, the prevalence of degenerative spinal diseases rose drastically within the last years. However, up till now, the origin of cervical pain is incompletely understood. While animal and small cadaver studies indicate that a complex system of sensory and nociceptive nerve fibers in the anterior (ALL) and posterior longitudinal ligament (PLL) at the level of the intervertebral disc might be involved, there is a lack of data exploring whether such a network exists and is equally distributed within the cervical vertebrae (VB). We, therefore, aimed to investigate the spatial distribution of the mentioned nerve networks in human tissue. METHODS: We performed macroscopic (Sihler staining, Spalteholz technique, and Plastination) and microscopic (immunohistochemistry for PGP 9.5 and CGRP) studies to characterize spatial differences in sensory and nociceptive innervation patterns. Therefore, 23 human body donors were dissected from level C3-C6. RESULTS: We could show that there is a focal increase in sensory and nociceptive nerve fibers at the level of C4 and C5 for both ALL and PLL, while we observed less nerve fiber density at the level of C3 and C6. An anatomical vicinity between nerve and vessels was observed. CONCLUSION: To our knowledge, these findings for the first time report spatial differences in sensory and nociceptive nerve fibers in the human cervical spine at VB level. The interconnection between nerves and vessels supports the importance of the perivascular plexus. These findings might be of special interest for clinical practice as many patients suffer from pain after cervical spine surgery.

Assessment of ApoC1, LuzP6, C12orf75 and OCC-1 in cystic glioblastoma using MALDI-TOF mass spectrometry, immunohistochemistry and qRT-PCR.

Mass spectrometric analysis of glioblastoma cyst fluids has disclosed a protein peak with m/z 6424-6433. Among the proteins, potentially generating this peak are ApoC1 and LuzP6. To further elucidate protein expression of glioblastoma cells, we analyzed MALDI-TOF results of cyst fluid, performed immunohistochemistry and mRNA analysis. MALDI-TOF protein extraction from 24 glioblastoma cyst fluids was performed with a weak cation exchange. 50 glioblastoma samples were stained with two custom-made antibodies against LuzP6 and commercial antibodies against ApoC1, C12orf75 and OCC-1 and analyzed. For mRNA detection, 16 tissue samples were stored in RNAlater, extracted using the miRNeasy kit and reversely transcribed. For 12 patients, synopsis of results from all three examinations was possible. MALDI-TOF confirmed the peak at 6433 Da in 75% of samples. Immunohistochemically, LuzP6 was detected in 92% (LuzP61-29) and 96% (LuzP630-58) of samples and ApoC1 in 66%. Mean mRNA levels were highest for ApoC1, followed by LuzP6. No correlation between mRNA expression, immunohistochemical staining and intensity of the MALDI-TOF peaks was found. An unequivocal identification of one protein as the source for the 6433 peak is not possible, but our results point to ApoC1 and LuzP6 as the underlying proteins.

Rule-based Cervical Spine Defect Classification Using Medical Narratives.

Classifying the defects occurring at the cervical spine provides the basis for surgical treatment planning and therapy recommendation. This process requires evidence from patient records. Further, the degree of a defect needs to be encoded in a standardized from to facilitate data exchange and multimodal interoperability. In this paper, a concept for automatic defect classification based on information extracted from textual data of patient records is presented. In a retrospective study, the classifier is applied to clinical documents and the classification results are evaluated.

VolHOG: a volumetric object recognition approach based on bivariate histograms of oriented gradients for vertebra detection in cervical spine MRI.

PURPOSE: The automatic recognition of vertebrae in volumetric images is an important step toward automatic spinal diagnosis and therapy support systems. There are many applications such as the detection of pathologies and segmentation which would benefit from automatic initialization by the detection of vertebrae. One possible application is the initialization of local vertebral segmentation methods, eliminating the need for manual initialization by a human operator. Automating the initialization process would optimize the clinical workflow. However, automatic vertebra recognition in magnetic resonance (MR) images is a challenging task due to noise in images, pathological deformations of the spine, and image contrast variations. METHODS: This work presents a fully automatic algorithm for 3D cervical vertebra detection in MR images. We propose a machine learning method for cervical vertebra detection based on new features combined with a linear support vector machine for classification. An algorithm for bivariate gradient orientation histogram generation from three-dimensional raster image data is introduced which allows us to describe three-dimensional objects using the authors' proposed bivariate histograms. RESULTS: A detailed performance evaluation on 21 T2-weighted MR images of the cervical vertebral region is given. A single model for cervical vertebrae C3-C7 is generated and evaluated. The results show that the generic model performs equally well for each of the cervical vertebrae C3-C7. The algorithm's performance is also evaluated on images containing various levels of artificial noise. The results indicate that the proposed algorithm achieves good results despite the presence of severe image noise. CONCLUSIONS: The proposed detection method delivers accurate locations of cervical vertebrae in MR images which can be used in diagnosis and therapy. In order to achieve absolute comparability with the results of future work, the authors are following an open data approach by making the image dataset used in their performance evaluation available to the public.