The Role of Voltage-Gated Calcium Channels in Neurotransmitter Phenotype Specification: Coexpression and Functional Analysis in Xenopus laevis

Calcium activity has been implicated in many neurodevelopmental events, including the specification of neurotransmitter phenotypes. Higher levels of calcium activity lead to an increased number of inhibitory neural phenotypes, whereas lower levels of calcium activity lead to excitatory neural phenotypes. Voltage-gated calcium channels (VGCCs) allow for rapid calcium entry and are expressed during early neural stages, making them likely regulators of activity-dependent neurotransmitter phenotype specification. To test this hypothesis, multiplex fluorescent in situ hybridization was used to characterize the coexpression of eight VGCC alpha 1 subunits with the excitatory and inhibitory neural markers xVGlut1 and xVIAAT in Xenopus laevis embryos. VGCC coexpression was higher with xVGlut1 than xVIAAT, especially in the hindbrain, spinal cord, and cranial nerves. Calcium activity was also analyzed on a single-cell level, and spike frequency was correlated with the expression of VGCC alpha 1 subunits in cell culture. Cells expressing Ca(V)2.1 and Ca(V)2.2 displayed increased calcium spiking compared with cells not expressing this marker. The VGCC antagonist diltiazem and agonist (-)BayK 8644 were used to manipulate calcium activity. Diltiazem exposure increased the number of glutamatergic cells and decreased the number of gamma-aminobutyric acid (GABA)ergic cells, whereas (2) BayK 8644 exposure decreased the number of glutamatergic cells without having an effect on the number of GABAergic cells. Given that the expression and functional manipulation of VGCCs are correlated with neurotransmitter phenotype in some, but not all, experiments, VGCCs likely act in combination with a variety of other signaling factors to determine neuronal phenotype specification. (C) 2014 Wiley Periodicals, Inc

Biofilm formation in total hip arthroplasty: Prevention and treatment

Biomaterials science is a very active area of research, which has allowed the successful use of implants in the orthopaedic field for over a century. However, implant infection remains a clinical concern as it is associated with extensive patient morbidity and a high economic burden, which is predicted to increase due to an ageing population. Bacteria are able to adhere, colonise and develop into biofilms on the surface of biomaterials making associated infections physiologically different to other post-surgical infections. Unfortunately, biofilms exert increased protection from the host immune system and an increased resistance to antibiotic therapy in comparison to their planktonic counterparts. The aim of this review is to assess the current knowledge on treatments, pathogenesis and the prevention of infections associated with orthopaedic implants, with a focus on total hip arthroplasty

Registration of Untracked 2D Laparoscopic Ultrasound to CT Images of the Liver using Multi-Labelled Content-Based Image Retrieval

Laparoscopic Ultrasound (LUS) is recommended as a standard-of-care when performing laparoscopic liver resections as it images sub-surface structures such as tumours and major vessels. Given that LUS probes are difficult to handle and some tumours are iso-echoic, registration of LUS images to a pre-operative CT has been proposed as an image-guidance method. This registration problem is particularly challenging due to the small field of view of LUS, and usually depends on both a manual initialisation and tracking to compose a volume, hindering clinical translation. In this paper, we extend a proposed registration approach using Content-Based Image Retrieval (CBIR), removing the requirement for tracking or manual initialisation. Pre-operatively, a set of possible LUS planes is simulated from CT and a descriptor generated for each image. Then, a Bayesian framework is employed to estimate the most likely sequence of CT simulations that matches a series of LUS images. We extend our CBIR formulation to use multiple labelled objects and constrain the registration by separating liver vessels into portal vein and hepatic vein branches. The value of this new labeled approach is demonstrated in retrospective data from 5 patients. Results show that, by including a series of 5 untracked images in time, a single LUS image can be registered with accuracies ranging from 5.7 to 16.4 mm with a success rate of 78%. Initialisation of the LUS to CT registration with the proposed framework could potentially enable the clinical translation of these image fusion techniques

Vessel segmentation for automatic registration of untracked laparoscopic ultrasound to CT of the liver

PURPOSE: Registration of Laparoscopic Ultrasound (LUS) to a pre-operative scan such as Computed Tomography (CT) using blood vessel information has been proposed as a method to enable image-guidance for laparoscopic liver resection. Currently, there are solutions for this problem that can potentially enable clinical translation by bypassing the need for a manual initialisation and tracking information. However, no reliable framework for the segmentation of vessels in 2D untracked LUS images has been presented. METHODS: We propose the use of 2D UNet for the segmentation of liver vessels in 2D LUS images. We integrate these results in a previously developed registration method, and show the feasibility of a fully automatic initialisation to the LUS to CT registration problem without a tracking device. RESULTS: We validate our segmentation using LUS data from 6 patients. We test multiple models by placing patient datasets into different combinations of training, testing and hold-out, and obtain mean Dice scores ranging from 0.543 to 0.706. Using these segmentations, we obtain registration accuracies between 6.3 and 16.6 mm in 50% of cases. CONCLUSIONS: We demonstrate the first instance of deep learning (DL) for the segmentation of liver vessels in LUS. Our results show the feasibility of UNet in detecting multiple vessel instances in 2D LUS images, and potentially automating a LUS to CT registration pipeline

A critical role for the chromatin remodeller CHD7 in anterior mesoderm during cardiovascular development

CHARGE syndrome is caused by spontaneous loss-of-function mutations to the ATP-dependant chromatin remodeller chromodomain-helicase-DNA-binding protein 7 (CHD7). It is characterised by a distinct pattern of congenital anomalies, including cardiovascular malformations. Disruption to the neural crest lineage has previously been emphasised in the aetiology of this developmental disorder. We present evidence for an additional requirement for CHD7 activity in the Mesp1-expressing anterior mesoderm during heart development. Conditional ablation of Chd7 in this lineage results in major structural cardiovascular defects akin to those seen in CHARGE patients, as well as a striking loss of cardiac innervation and embryonic lethality. Genome-wide transcriptional analysis identified aberrant expression of key components of the Class 3 Semaphorin and Slit-Robo signalling pathways in Chd7(fl/fl);Mesp1-Cre mutant hearts. CHD7 localises at the Sema3c promoter in vivo, with alteration of the local chromatin structure seen following Chd7 ablation, suggestive of direct transcriptional regulation. Furthermore, we uncover a novel role for CHD7 activity upstream of critical calcium handling genes, and demonstrate an associated functional defect in the ability of cardiomyocytes to undergo excitation-contraction coupling. This work therefore reveals the importance of CHD7 in the cardiogenic mesoderm for multiple processes during cardiovascular development

Assessment of Electromagnetic Tracking Accuracy for Endoscopic Ultrasound

Endoscopic ultrasound (EUS) is a minimally-invasive imaging technique that can be technically difficult to perform due to the small field of view and uncertainty in the endoscope position. Electromagnetic (EM) tracking is emerging as an important technology in guiding endoscopic interventions and for training in endotherapy by providing information on endoscope location by fusion with pre-operative images. However, the accuracy of EM tracking could be compromised by the endoscopic ultrasound transducer. In this work, we quantify the precision and accuracy of EM tracking sensors inserted into the working channel of a flexible endoscope, with the ultrasound transducer turned on and off. The EUS device was found to have little (no significant) effect on static tracking accuracy although jitter increased significantly. A significant change in the measured distance between sensors arranged in a fixed geometry was found during a dynamic acquisition. In conclusion, EM tracking accuracy was not found to be significantly affected by the flexible endoscope

In vivo estimation of target registration errors during augmented reality laparoscopic surgery

PURPOSE: Successful use of augmented reality for laparoscopic surgery requires that the surgeon has a thorough understanding of the likely accuracy of any overlay. Whilst the accuracy of such systems can be estimated in the laboratory, it is difficult to extend such methods to the in vivo clinical setting. Herein we describe a novel method that enables the surgeon to estimate in vivo errors during use. We show that the method enables quantitative evaluation of in vivo data gathered with the SmartLiver image guidance system. METHODS: The SmartLiver system utilises an intuitive display to enable the surgeon to compare the positions of landmarks visible in both a projected model and in the live video stream. From this the surgeon can estimate the system accuracy when using the system to locate subsurface targets not visible in the live video. Visible landmarks may be either point or line features. We test the validity of the algorithm using an anatomically representative liver phantom, applying simulated perturbations to achieve clinically realistic overlay errors. We then apply the algorithm to in vivo data. RESULTS: The phantom results show that using projected errors of surface features provides a reliable predictor of subsurface target registration error for a representative human liver shape. Applying the algorithm to in vivo data gathered with the SmartLiver image-guided surgery system shows that the system is capable of accuracies around 12 mm; however, achieving this reliably remains a significant challenge. CONCLUSION: We present an in vivo quantitative evaluation of the SmartLiver image-guided surgery system, together with a validation of the evaluation algorithm. This is the first quantitative in vivo analysis of an augmented reality system for laparoscopic surgery

Hand-eye calibration for rigid laparoscopes using an invariant point

PURPOSE: Laparoscopic liver resection has significant advantages over open surgery due to less patient trauma and faster recovery times, yet it can be difficult due to the restricted field of view and lack of haptic feedback. Image guidance provides a potential solution but one current challenge is in accurate "hand-eye" calibration, which determines the position and orientation of the laparoscope camera relative to the tracking markers. METHODS: In this paper, we propose a simple and clinically feasible calibration method based on a single invariant point. The method requires no additional hardware, can be constructed by theatre staff during surgical setup, requires minimal image processing and can be visualised in real time. Real-time visualisation allows the surgical team to assess the calibration accuracy before use in surgery. In addition, in the laboratory, we have developed a laparoscope with an electromagnetic tracking sensor attached to the camera end and an optical tracking marker attached to the distal end. This enables a comparison of tracking performance. RESULTS: We have evaluated our method in the laboratory and compared it to two widely used methods, "Tsai's method" and "direct" calibration. The new method is of comparable accuracy to existing methods, and we show RMS projected error due to calibration of 1.95 mm for optical tracking and 0.85 mm for EM tracking, versus 4.13 and 1.00 mm respectively, using existing methods. The new method has also been shown to be workable under sterile conditions in the operating room. CONCLUSION: We have proposed a new method of hand-eye calibration, based on a single invariant point. Initial experience has shown that the method provides visual feedback, satisfactory accuracy and can be performed during surgery. We also show that an EM sensor placed near the camera would provide significantly improved image overlay accuracy

Global rigid registration of CT to video in laparoscopic liver surgery

PURPOSE: Image-guidance systems have the potential to aid in laparoscopic interventions by providing sub-surface structure information and tumour localisation. The registration of a preoperative 3D image with the intraoperative laparoscopic video feed is an important component of image guidance, which should be fast, robust and cause minimal disruption to the surgical procedure. Most methods for rigid and non-rigid registration require a good initial alignment. However, in most research systems for abdominal surgery, the user has to manually rotate and translate the models, which is usually difficult to perform quickly and intuitively. METHODS: We propose a fast, global method for the initial rigid alignment between a 3D mesh derived from a preoperative CT of the liver and a surface reconstruction of the intraoperative scene. We formulate the shape matching problem as a quadratic assignment problem which minimises the dissimilarity between feature descriptors while enforcing geometrical consistency between all the feature points. We incorporate a novel constraint based on the liver contours which deals specifically with the challenges introduced by laparoscopic data. RESULTS: We validate our proposed method on synthetic data, on a liver phantom and on retrospective clinical data acquired during a laparoscopic liver resection. We show robustness over reduced partial size and increasing levels of deformation. Our results on the phantom and on the real data show good initial alignment, which can successfully converge to the correct position using fine alignment techniques. Furthermore, since we can pre-process the CT scan before surgery, the proposed method runs faster than current algorithms. CONCLUSION: The proposed shape matching method can provide a fast, global initial registration, which can be further refined by fine alignment methods. This approach will lead to a more usable and intuitive image-guidance system for laparoscopic liver surgery

Intelligent viewpoint selection for efficient CT to video registration in laparoscopic liver surgery

PURPOSE: Minimally invasive surgery offers advantages over open surgery due to a shorter recovery time, less pain and trauma for the patient. However, inherent challenges such as lack of tactile feedback and difficulty in controlling bleeding lower the percentage of suitable cases. Augmented reality can show a better visualisation of sub-surface structures and tumour locations by fusing pre-operative CT data with real-time laparoscopic video. Such augmented reality visualisation requires a fast and robust video to CT registration that minimises interruption to the surgical procedure. METHODS: We propose to use view planning for efficient rigid registration. Given the trocar position, a set of camera positions are sampled and scored based on the corresponding liver surface properties. We implement a simulation framework to validate the proof of concept using a segmented CT model from a human patient. Furthermore, we apply the proposed method on clinical data acquired during a human liver resection. RESULTS: The first experiment motivates the viewpoint scoring strategy and investigates reliable liver regions for accurate registrations in an intuitive visualisation. The second experiment shows wider basins of convergence for higher scoring viewpoints. The third experiment shows that a comparable registration performance can be achieved by at least two merged high scoring views and four low scoring views. Hence, the focus could change from the acquisition of a large liver surface to a small number of distinctive patches, thereby giving a more explicit protocol for surface reconstruction. We discuss the application of the proposed method on clinical data and show initial results. CONCLUSION: The proposed simulation framework shows promising results to motivate more research into a comprehensive view planning method for efficient registration in laparoscopic liver surgery