Globally, the discipline of neurosurgery has evolved remarkably fast. Despite being one of the latest medical specialties, which appeared only around hundred years ago, it has witnessed innovations in the aspects of diagnostics methods, macro and micro surgical techniques, and treatment modalities. Unfortunately, this development is not evenly distributed between developed and developing countries. The same is the case with neurosurgical education and training, which developed from only traditional apprentice programs in the past to more structured, competence-based programs with various teaching methods being utilized, in recent times. A similar gap can be observed between developed and developing counties when it comes to neurosurgical education. Fortunately, most of the scholars working in this field do understand the coherent relationship between neurosurgical education and neurosurgical practice. In context to this understanding, a symposium was organized during the World Federation of Neurological Surgeons (WFNS) Special World Congress Beijing 2019. This symposium was the brain child of Prof. Yoko Kato—one of the eminent leaders in neurosurgery and an inspiration for female neurosurgeons. Invited speakers from different continents presented the stages of development of neurosurgical education in their respective countries. This paper summarizes the outcome of these presentations, with particular emphasis on and the challenges faced by developing countries in terms of neurosurgical education and strategies to cope with these challenges.

Endoscopic retrograde cholangiopancreatography (ERCP) is the gold standard procedure for the treatment of bile duct stones, and most stones are successfully removed with accessories such as biliary baskets or extraction balloons. Impaction of a biliary basket is not an uncommon complication of this procedure, being reported in 0.8%–5.9% of cases. Mechanical lithotripsy usually solves the problem by crushing the stone, followed by extraction of the stone fragments. However, on rare occasions, fracture of the basket occurs during mechanical lithotripsy, and this can pose a special management problem, depending on where the breakage occurs [1–3]. We report the successful management of an impacted biliary basket after breakage of the basket handle cord during extra-endoscopic mechanical lithotripsy. A 35-year-old man, who had undergone early laparoscopic cholecystectomy in a small regional hospital 1 month before, presented with jaundice and upper right quadrant pain on admission. Laboratory tests revealed obstructive jaundice with raised γ-glutamyl transpeptidase (γGT) and alkaline phosphatase (ALP) and a total bilirubin concentration of 11.11mg/ dL. Apart from a mildly dilated common bile duct (CBD) with stones, computed tomography of the abdomen revealed no E-Videos

The paper proposes a novel framework for registering and segmenting 3D point clouds of large-scale natural terrain and complex environments coming from a multisensor heterogeneous robotics system, consisting of unmanned aerial and ground vehicles. This framework involves data acquisition and pre-processing, 3D heterogeneous registration and integrated multi-sensor based segmentation modules. The first module provides robust and accurate homogeneous registrations of 3D environmental models based on sensors’ measurements acquired from the ground (UGV) and aerial (UAV) robots. For 3D UGV registration, we proposed a novel local minima escape ICP (LME-ICP) method, which is based on the well known iterative closest point (ICP) algorithm extending it by the introduction of our local minima estimation and local minima escape mechanisms. It did not require any prior known pose estimation information acquired from sensing systems like odometry, global positioning system (GPS), or inertial measurement units (IMU). The 3D UAV registration has been performed using the Structure from Motion (SfM) approach. In order to improve and speed up the process of outliers removal for large-scale outdoor environments, we introduced the Fast Cluster Statistical Outlier Removal (FCSOR) method. This method was used to filter out the noise and to downsample the input data, which will spare computational and memory resources for further processing steps. Then, we co-registered a point cloud acquired from a laser ranger (UGV) and a point cloud generated from images (UAV) generated by the SfM method. The 3D heterogeneous module consists of a semi-automated 3D scan registration system, developed with the aim to overcome the shortcomings of the existing fully automated 3D registration approaches. This semi-automated registration system is based on the novel Scale Invariant Registration Method (SIRM). The SIRM provides the initial scaling between two heterogenous point clouds and provides an adaptive mechanism for tuning the mean scale, based on the difference between two consecutive estimated point clouds’ alignment error values. Once aligned, the resulting homogeneous ground-aerial point cloud is further processed by a segmentation module. For this purpose, we have proposed a system for integrated multi-sensor based segmentation of 3D point clouds. This system followed a two steps sequence: ground-object segmentation and color-based region-growing segmentation. The experimental validation of the proposed 3D heterogeneous registration and integrated segmentation framework was performed on large-scale datasets representing unstructured outdoor environments, demonstrating the potential and benefits of the proposed semi-automated 3D registration system in real-world environments.

The decision-making trial and evaluation laboratory (DEMATEL) method is one of the most significant multi-criteria techniques for defining the relationships among criteria and for defining the weight coefficients of criteria. Since multi-criteria models are very often used in management and decision-making under conditions of uncertainty, the fuzzy DEMATEL model has been extended in this paper by D numbers (fuzzy DEMATEL-D). The aim of this research was to develop a multi-criteria methodology that enables the objective processing of fuzzy linguistic information in the pairwise comparison of criteria. This aim was achieved through the development of the fuzzy DEMATEL-D method. Combining D numbers with trapezoidal fuzzy linguistic variables (LVs) allows for the additional processing of uncertainties and ambiguities that exist in experts’ preferences when comparing criteria with each other. In addition, the fuzzy DEMATEL-D methodology has a unique reasoning algorithm that allows for the rational processing of uncertainties when using fuzzy linguistic expressions for pairwise comparisons of criteria. The fuzzy DEMATEL-D methodology provides an original uncertainty management framework that is rational and concise. In order to illustrate the effectiveness of the proposed methodology, a case study with the application of the proposed multi-criteria methodology is presented.

The aim of this paper is to compare air quality in Sarajevo in March 2019 and March 2020 with outbreak of the novel coronavirus SARS-CoV-2 in Sarajevo and Bosnia and Herzegovina. First preventive and protective measures were issued at the end of second week of March, while on 21 March 2020 an order imposing complete ban of movement of citizens from late afternoon until early in the morning next day was issued. This was rare opportunity to compare air quality in Sarajevo having same causes of air pollution for one part of March 2019 and March 2020 and different causes of air pollution during the lockdown and ban of movement caused by SARS-CoV-2. Statistical hypothesis testing is used to compare values during March 2019 and March 2020 before the lockdown (the first phase) and during the lockdown (the second phase). Complete and comprehensive analysis is performed for both phases of March 2019 and March 2020, before the lockdown and during the lockdown. It is shown that there are statistical evidences that during the lockdown period mean concentration values of O3 and NO2 are smaller than mean values during the same period in March 2019, while mean concentration value of PM10 is greater than mean value during the same period in March 2019. Also, statistical hypothesis testing is used to compare concentration of air pollutants before and during lockdown period in March 2020. It is shown that mean concentration values of PM10 and O3 are greater during lockdown period, while mean concentration value of NO2 before the lockdown in March 2020 is greater than during the lockdown period. Coefficients of correlation as the measure of the strength of linear association between air pollutants PM10, O3 and NO2 and meteorological parameters air temperature, humidity and pressure, wind speed and wind direction are calculated as well.

The objective of this study was to determine the minimum inhibitory concentrations (MICs) of nine antimicrobials (enrofloxacin, ciprofloxacin, norfloxacin, gentamicin, spectinomycin, oxytetracycline, tylosin, florfenicol, and tiamulin) against 24 Mycoplasma ovipneumoniae isolates obtained from sheep and goats and to compare the resulting antimicrobial profiles. Enrofloxacin and ciprofloxacin had the lowest MIC50 values (<0.03 μg/mL) and MIC90 values (0.25 μg/mL) for all tested isolates. The highest MIC50 value (2 μg/mL) was obtained for florfenicol, while oxytetracycline and tylosin exhibited the highest MIC90 values (16 μg/mL). The MIC values for all fluoroquinolones and oxytetracycline were significantly lower for sheep isolates. Sheep isolates were considerably more susceptible to norfloxacin and tylosin than were goat isolates. This study demonstrated differences in antimicrobial susceptibilities between sheep and goat isolates, revealing M. ovipneumoniae in goat isolates to be less susceptible. The results suggest a possible link between antimicrobial profiles of M. ovipneumoniae isolates and their host ruminant species.