Prevalence of haptic feedback in robot-mediated surgery : a systematic review of literature

© 2017 Springer-Verlag. This is a post-peer-review, pre-copyedit version of an article published in Journal of Robotic Surgery. The final authenticated version is available online at: https://doi.org/10.1007/s11701-017-0763-4With the successful uptake and inclusion of robotic systems in minimally invasive surgery and with the increasing application of robotic surgery (RS) in numerous surgical specialities worldwide, there is now a need to develop and enhance the technology further. One such improvement is the implementation and amalgamation of haptic feedback technology into RS which will permit the operating surgeon on the console to receive haptic information on the type of tissue being operated on. The main advantage of using this is to allow the operating surgeon to feel and control the amount of force applied to different tissues during surgery thus minimising the risk of tissue damage due to both the direct and indirect effects of excessive tissue force or tension being applied during RS. We performed a two-rater systematic review to identify the latest developments and potential avenues of improving technology in the application and implementation of haptic feedback technology to the operating surgeon on the console during RS. This review provides a summary of technological enhancements in RS, considering different stages of work, from proof of concept to cadaver tissue testing, surgery in animals, and finally real implementation in surgical practice. We identify that at the time of this review, while there is a unanimous agreement regarding need for haptic and tactile feedback, there are no solutions or products available that address this need. There is a scope and need for new developments in haptic augmentation for robot-mediated surgery with the aim of improving patient care and robotic surgical technology further.Peer reviewe

Mitochondrial DNA regulates TNF-alpha mRNA stability

Sepsis is defined as potentially fatal systemic inflammation, caused by an infection. It is the leading cause of ICU mortality and the 10th leading cause of death in the United States. Several models exist to mimic this disorder, and have demonstrated differential mortality rates between the models as well as the individual animals. Previous studies have shown that elevated levels of plasma mitochondrial DNA (mtDNA) correlated with mortality in septic patients, and cell-free mitochondrial DNA can elicit toll-like receptor mediated immune responses similar to LPS-mediated septicemia. However, the role of mtDNA in the pathophysiology sepsis is still unknown. The focus of this study was to create sepsis in a mouse model using the murine Cecal Ligation and Puncture (CLP) model, and measure plasma mtDNA levels. After CLP was performed on experimental mice, blood plasma was collected 24 hours later. Elevated amounts of circulating mtDNA were detectable in the plasma using real time PCR and cytochrome B2 as a marker of mitochondria. These data were correlated with plasma IL-6 levels, which were used to predict mortality within 5 days of CLP to stratify mice into two populations of those predicted to live or die following the procedure. We also aimed to investigate the effect of mtDNA and mitochondrial debris on naïve mouse macrophages in an in vitro study of the regulation of inflammatory cytokines interleukin-6 (IL-6), tumor necrosis factor alpha (TNF-α), and interleukin-1 beta (IL-1β). In order to observe the effects of mtDNA on murine macrophages, mitochondria was purified from mouse liver and used to stimulate these cells alongside positive control, LPS. Stimulation with mtDNA and mitochondrial debris resulted in increased levels of TNF-α mRNA in lysed cells as well as their surrounding media as compared to control cells, as well as increased transcript half life as measured over four hours post stimulation with transcription inhibitor actinomycin D. The increases in mRNA half-life elicited by mtDNA were comparable to those observed after LPS addition. Stimulation also caused increased binding of TNF-α mRNA to the RNA binding protein, AUF1, as measured by immunoprecipitation of RNA-protein complexes and assayed for TNF-α binding by PCR. These results demonstrate that mitochondrial damage-associated molecular patterns regulate TNF-α mRNA expression at the post-transcriptional level through AUF1, an mRNA destabilizing factor. This is a novel mechanism that likely contributes to sepsis pathophysiology, and demonstrates the involvement of the mitochondrial fission and fusion balance and its regulation in the sepsis innate immune response

Echocardiography

The book "Echocardiography - New Techniques" brings worldwide contributions from highly acclaimed clinical and imaging science investigators, and representatives from academic medical centers. Each chapter is designed and written to be accessible to those with a basic knowledge of echocardiography. Additionally, the chapters are meant to be stimulating and educational to the experts and investigators in the field of echocardiography. This book is aimed primarily at cardiology fellows on their basic echocardiography rotation, fellows in general internal medicine, radiology and emergency medicine, and experts in the arena of echocardiography. Over the last few decades, the rate of technological advancements has developed dramatically, resulting in new techniques and improved echocardiographic imaging. The authors of this book focused on presenting the most advanced techniques useful in today's research and in daily clinical practice. These advanced techniques are utilized in the detection of different cardiac pathologies in patients, in contributing to their clinical decision, as well as follow-up and outcome predictions. In addition to the advanced techniques covered, this book expounds upon several special pathologies with respect to the functions of echocardiography

Quantifying atherosclerosis in vasculature using ultrasound imaging

Cerebrovascular disease accounts for approximately 30% of the global burden associated with cardiovascular diseases [1]. According to the World Stroke Organisation, there are approximately 13.7 million new stroke cases annually, and just under six million people will die from stroke each year [2]. The underlying cause of this disease is atherosclerosis – a vascular pathology which is characterised by thickening and hardening of blood vessel walls. When fatty substances such as cholesterol accumulate on the inner linings of an artery, they cause a progressive narrowing of the lumen referred to as a stenosis. Localisation and grading of the severity of a stenosis, is important for practitioners to assess the risk of rupture which leads to stroke. Ultrasound imaging is popular for this purpose. It is low cost, non-invasive, and permits a quick assessment of vessel geometry and stenosis by measuring the intima media thickness. Research is showing that 3D monitoring of plaque progression may provide a better indication of sites which are at risk of rupture. Various metrics have been proposed. From these, the quantification of plaques by measuring vessel wall volume (VWV) using the segmented media-adventitia boundaries (MAB) and lumen-intima boundaries (LIB) has been shown to be sensitive to temporal changes in carotid plaque burden. Thus, methods to segment these boundaries are required to help generate VWV measurements with high accuracy, less user interaction and increased robustness to variability in di↵erent user acquisition protocols.ii This work proposes three novel methods to address these requirements, to ultimately produce a highly accurate, fully automated segmentation algorithm which works on intensity-invariant data. The first method proposed was that of generating a novel, intensity-invariant representation of ultrasound data by creating phase-congruency maps from raw unprocessed radio-frequency ultrasound information. Experiments carried out showed that this representation retained the necessary anatomical structural information to facilitate segmentation, while concurrently being invariant to changes in amplitude from the user. The second method proposed was the novel application of Deep Convolutional Networks (DCN) to carotid ultrasound images to achieve fully automatic delineation of the MAB boundaries, in addition to the use of a novel fusion of amplitude and phase congruency data as an image source. Experiments carried out showed that the DCN produces highly accurate and automated results, and that the fusion of amplitude and phase yield superior results to either one alone. The third method proposed was a new geometrically constrained objective function for the network's Stochastic Gradient Descent optimisation, thus tuning it to the segmentation problem at hand, while also developing the network further to concurrently delineate both the MAB and LIB to produce vessel wall contours. Experiments carried out here also show that the novel geometric constraints improve the segmentation results on both MAB and LIB contours. In conclusion, the presented work provides significant novel contributions to field of Carotid Ultrasound segmentation, and with future work, this could lead to implementations which facilitate plaque progression analysis for the end�user