Sustainable deathstyles? The geography of green burials in Britain

In the context of a wider literature on ‘deathscapes’, we map the emergence of a new mode of burial and remembrance in Britain. Since a ‘green’ burial ground was established in Carlisle in 1993, sites for so-called ‘green, ‘natural’ or ‘woodland’ funerals have proliferated. There are now over 270 such sites in Britain. Drawing on a postal and email survey sent to all managers/owners and visits to 15 green burial grounds (enabling observations and semi-structured interviews with their managers), we chart their growth, establishment and regulation and describe the landscapes associated with them. This requires, and leads to, wider reflections on nature, capital, consumption, culture and the body

Evolution of the neobladder: A critical review of open and intracorporeal neobladder reconstruction techniques

Orthotopic neobladder is an attractive alternative to the ileal conduit following radical cystectomy. Robotic cystectomy is gaining popularity although the uptake of neobladder reconstruction is low, with the majority of cases being constructed extracorporeally via a mini-laparotomy. Minimally invasive cystectomy using the robotic platform facilitates intracorporeal neobladder reconstruction and several techniques have been described. This review discusses issues relating to patient selection, and describes existing techniques of open surgical neobladder reconstruction and their evolution to suit an intracorporeal approach. A Medline search for publications from January 1970 to September 2015 with the following keyword search criteria was performed: radical cystectomy, robotic cystectomy, intracorporeal, neobladder, orthotopic bladder reconstruction, surgical technique, patient selection and ureteric–ileal anastomosis

Training in Robotic Surgery—an Overview

PURPOSE OF REVIEW: There has been a rapid and widespread adoption of the robotic surgical system with a lag in the development of a comprehensive training and credentialing framework. A literature search on robotic surgical training techniques and benchmarks was conducted to provide an evidence-based road map for the development of a robotic surgical skills for the novice robotic surgeon. RECENT FINDINGS: A structured training curriculum is suggested incorporating evidence-based training techniques and benchmarks for progress. This usually involves sequential progression from observation, case assisting, acquisition of basic robotic skills in the dry and wet lab setting along with achievement of individual and team-based non-technical skills, modular console training under supervision, and finally independent practice. SUMMARY: Robotic surgical training must be based on demonstration of proficiency and safety in executing basic robotic skills and procedural tasks prior to independent practice

Visual kinematic force estimation in robot-assisted surgery – application to knot tying

Robot-assisted surgery has potential advantages but lacks force feedback, which can lead to errors such as broken stitches or tissue damage. More experienced surgeons can judge the tool-tissue forces visually and an automated way of capturing this skill is desirable. Methods to measure force tend to involve complex measurement devices or visual tracking of tissue deformation. We investigate whether surgical forces can be estimated simply from the discrepancy between kinematic and visual measurement of the tool position. We show that combined visual and kinematic force estimation can be achieved without external measurements or modelling of tissue deformation. After initial alignment when no force is applied to the tool, visual and kinematic estimates of tool position diverge under force. We plot visual/kinematic displacement with force using vision and marker-based tracking. We demonstrate the ability to discern the forces involved in knot tying and visualize the displacement force using the publicly available JIGSAWS dataset as well as clinical examples of knot tying with the da Vinci surgical system. The ability to visualize or feel forces using this method may offer an advantage to those learning robotic surgery as well as adding to the information available to more experienced surgeons

The impact of simulated motion blur on lesion detection performance in full field digital mammography

Objective: Motion blur is a known phenomenon in full-field digital mammography, but the impact on lesion detection is unknown. This is the first study to investigate detection performance with varying magnitudes of simulated motion blur. Method: Seven observers (15±5 years’ reporting experience) evaluated 248 cases (62 containing malignant masses, 62 containing malignant microcalcifications and 124 normal cases) for three conditions: no blurring (0 mm) and two magnitudes of simulated blurring (0.7 mm and 1.5 mm). Abnormal cases were biopsy proven. Mathematical simulation was used to provide a pixel shift in order to simulate motion blur. A free-response observer study was conducted to compare lesion detection performance for the three conditions. The equally weighted jackknife alternative free-response receiver operating characteristic (wJAFROC) was used as the figure of merit. Test alpha was set at 0.05 to control probability of Type I error. Results: wJAFROC analysis found a statistically significant difference in lesion detection performance for both masses (F(2,22) = 6.01, P=0.0084) and microcalcifications (F(2,49) = 23.14, P<0.0001). The figures of merit reduced as the magnitude of simulated blurring increased. Statistical differences were found between some of the pairs investigated for the detection of masses (0.0mm v 0.7mm, and 0.0mm v 1.5mm) and all pairs for microcalcifications (0.0 mm v 0.7 mm, 0.0 mm v 1.5 mm, and 0.7 mm v 1.5 mm). No difference was detected between 0.7 mm and 1.5 mm for masses. Conclusion: Mathematical simulation of motion blur caused a statistically significant reduction in lesion detection performance. These false negative decisions could have implications for clinical practice. Advances in knowledge: This research demonstrates for the first time that motion blur has a negative and statistically significant impact on lesion detection performance digital mammography

The role of circulating tumour cells and nucleic acids in blood for the detection of bladder cancer: A systematic review

BACKGROUND: Blood-based biomarkers are a neglected resource in bladder cancer, where the mainstay of focus has been on urinary biomarkers. However, blood-based biomarkers are gaining popularity in other solid cancers, particularly circulating tumour cells (CTCs) and circulating nucleic acids. In this systematic review, we identify and discuss the diagnostic value of CTC, cell-free DNA and RNA based biomarkers in bladder cancer. METHODS: A MEDLINE/Pubmed systematic search was performed using the following keywords: (bladder cancer) AND (blood OR plasma OR serum) AND biomarker AND (DNA OR RNA OR cfDNA OR cell-free DNA OR RNA OR CTC). All studies including blood-based biomarkers based on DNA, RNA and CTCs were reviewed. Of the included studies, studies reporting sensitivity, specificity and/or AUC/ROC values were further described. RESULTS: Systematic searched yielded 47 studies that were eligible, of which 21, 19 and 3 studies reported DNA, RNA and CTC biomarkers respectively. 15 of these studies included sensitivity, specificity and/or AUC/ROC values. Biomarkers sensitivity and specificity ranged widely at 2.4-97.6% and 43.3-100% respectively. Median number of patients recruited in the studies was 56 (IQR 41-90). Only 3 studies included an independent validation cohort. The highest sensitivity and specificity pairing achieved in the validation cohort was 80.0% and 89.1% respectively. CONCLUSIONS: This systematic review provides a comprehensive overview of the blood-based CTC and nucleic acid biomarkers that have been investigated. An overlap in interest of targets between studies suggests that these could be promising biomarkers, but few biomarkers achieve high sensitivity and specificity, and fewer still have been validated independently

Evolving robotic surgery training and improving patient safety, with the integration of novel technologies

INTRODUCTION: Robot-assisted surgery is becoming increasingly adopted by multiple surgical specialties. There is evidence of inherent risks of utilising new technologies that are unfamiliar early in the learning curve. The development of standardised and validated training programmes is crucial to deliver safe introduction. In this review, we aim to evaluate the current evidence and opportunities to integrate novel technologies into modern digitalised robotic training curricula. METHODS: A systematic literature review of the current evidence for novel technologies in surgical training was conducted online and relevant publications and information were identified. Evaluation was made on how these technologies could further enable digitalisation of training. RESULTS: Overall, the quality of available studies was found to be low with current available evidence consisting largely of expert opinion, consensus statements and small qualitative studies. The review identified that there are several novel technologies already being utilised in robotic surgery training. There is also a trend towards standardised validated robotic training curricula. Currently, the majority of the validated curricula do not incorporate novel technologies and training is delivered with more traditional methods that includes centralisation of training services with wet laboratories that have access to cadavers and dedicated training robots. CONCLUSIONS: Improvements to training standards and understanding performance data have good potential to significantly lower complications in patients. Digitalisation automates data collection and brings data together for analysis. Machine learning has potential to develop automated performance feedback for trainees. Digitalised training aims to build on the current gold standards and to further improve the 'continuum of training' by integrating PBP training, 3D-printed models, telementoring, telemetry and machine learning