Introduction: The ureteral ostia may not be easily identified in urological endoscopic procedures, leading to an incomplete diagnosis of urinary tract diseases or a predisposition to iatrogenic lesions. The purpose of...Introduction: The ureteral ostia may not be easily identified in urological endoscopic procedures, leading to an incomplete diagnosis of urinary tract diseases or a predisposition to iatrogenic lesions. The purpose of our study is to evaluate the anatomical distribution of ureteralostia in normal bladders and those with thickened walls. Materials and Methods: We dissected 30 vesical-prostate blocks from human cadavers and identified the ostia of the bladder trigone. A computerized morphometric analysis was performed to measure the thickness of the detrusor muscle, the distances between the ureteral ostia themselves and the distances between each ureteral ostium (left—LUO and right—RUO) and the internal urethral ostium (IUO). The angle formed between the IUO and LUO/RUO was also recorded as well as the volume of the prostates. Results: Fifteen bladders with a non-thickened detrusor (6 mm) were identified. The average prostatic volume of the dissected blocks was 23.7 cm3. The distance between ureteral ostia, the distance from IUO to LUO, the distance from IUO to RUO and the angle formed between IUO and LUO/RUO in normal and thickened bladder were, respectively, 1.9 cm/2.2 cm (p = 0.09), 1.6 cm/1.6 cm (p = 0.82), 1.6 cm/1.7 cm (p = 0.79) and 77/91 (p = 0.17). Conclusions: Our study shows that there is no significant difference in the position of bladder ostia in healthy and thickened bladders. We believe that our findings may facilitate locating the ureteral orifices in situations where endoscopic identification is difficult.展开更多
Each year,accidents involving ships result in significant loss of life,environmental pollution and economic losses.The promotion of navigation safety through risk reduction requires methods to assess the spatial distr...Each year,accidents involving ships result in significant loss of life,environmental pollution and economic losses.The promotion of navigation safety through risk reduction requires methods to assess the spatial distribution of the relative likelihood of occurrence.Yet,such methods necessitate the integration of large volumes of heterogenous datasets which are not well suited to traditional data structures.This paper proposes the use of the Discrete Global Grid System(DGGS)as an efficient and advantageous structure to integrate vessel traffic,metocean,bathymetric,infrastructure and other relevant maritime datasets to predict the occurrence of ship groundings.Massive and heterogenous datasets are well suited for machine learning algorithms and this paper develops a spatial maritime risk model based on a DGGS utilising such an approach.A Random Forest algorithm is developed to predict the frequency and spatial distribution of groundings while achieving an R2 of 0.55 and a mean squared error of 0.002.The resulting risk maps are useful for decision-makers in planning the allocation of mitigation measures,targeted to regions with the highest risk.Further work is identified to expand the applications and insights which could be achieved through establishing a DGGS as a global maritime spatial data structure.展开更多
文摘Introduction: The ureteral ostia may not be easily identified in urological endoscopic procedures, leading to an incomplete diagnosis of urinary tract diseases or a predisposition to iatrogenic lesions. The purpose of our study is to evaluate the anatomical distribution of ureteralostia in normal bladders and those with thickened walls. Materials and Methods: We dissected 30 vesical-prostate blocks from human cadavers and identified the ostia of the bladder trigone. A computerized morphometric analysis was performed to measure the thickness of the detrusor muscle, the distances between the ureteral ostia themselves and the distances between each ureteral ostium (left—LUO and right—RUO) and the internal urethral ostium (IUO). The angle formed between the IUO and LUO/RUO was also recorded as well as the volume of the prostates. Results: Fifteen bladders with a non-thickened detrusor (6 mm) were identified. The average prostatic volume of the dissected blocks was 23.7 cm3. The distance between ureteral ostia, the distance from IUO to LUO, the distance from IUO to RUO and the angle formed between IUO and LUO/RUO in normal and thickened bladder were, respectively, 1.9 cm/2.2 cm (p = 0.09), 1.6 cm/1.6 cm (p = 0.82), 1.6 cm/1.7 cm (p = 0.79) and 77/91 (p = 0.17). Conclusions: Our study shows that there is no significant difference in the position of bladder ostia in healthy and thickened bladders. We believe that our findings may facilitate locating the ureteral orifices in situations where endoscopic identification is difficult.
基金This work is partly funded by the University of Southampton’s Marine and Maritime Institute(SMMI)and the European Research Council under the European Union’s Horizon 2020 research and innovation program(grant agreement number:723526:SEDNA).
文摘Each year,accidents involving ships result in significant loss of life,environmental pollution and economic losses.The promotion of navigation safety through risk reduction requires methods to assess the spatial distribution of the relative likelihood of occurrence.Yet,such methods necessitate the integration of large volumes of heterogenous datasets which are not well suited to traditional data structures.This paper proposes the use of the Discrete Global Grid System(DGGS)as an efficient and advantageous structure to integrate vessel traffic,metocean,bathymetric,infrastructure and other relevant maritime datasets to predict the occurrence of ship groundings.Massive and heterogenous datasets are well suited for machine learning algorithms and this paper develops a spatial maritime risk model based on a DGGS utilising such an approach.A Random Forest algorithm is developed to predict the frequency and spatial distribution of groundings while achieving an R2 of 0.55 and a mean squared error of 0.002.The resulting risk maps are useful for decision-makers in planning the allocation of mitigation measures,targeted to regions with the highest risk.Further work is identified to expand the applications and insights which could be achieved through establishing a DGGS as a global maritime spatial data structure.
