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Eponyms highlight the contributions made to medicine over the years, and celebrate individuals for their work involving diseases, pathologies, and anatomical landmarks. We have compiled an in-depth report of eponyms used in skull base neurosurgery, as well as the historical contexts of the personalities behind the names. A literature search identified 36 eponyms of the bones, foramina and ligaments of the skull base named after anatomists and physician-scientists. The 36 eponymous structures pinpointed include Arnold's canal, the foramen of Arnold, Bill's bar, Bertin's bones, Civinini's canal, Civinini's ligament, Civinini's process, sinodural angle of Citelli, Clivus of Blumenbach, Dorello's canal, the Eustachian tube, the eponymous cavernous sinus triangles of Parkinson, Kawase, Mullan, Dolenc, Glasscock and Hakuba, the Fallopian canal, the Glasserian fissure, Gruber's ligament, Haller cells, the spine of Henle, Highmore's antrum, the foramen of Huschke, Hyrtl's fissure, the Ingrassia process, Jacobson's canal, the MacEwen triangle, Meckel's cave, the Onodi air cell, the Pacchionian foramen, Fossa of Rosenmuller, the foramen of Vesalius, the Vidian canal, Trautman's triangle and the annular tendon of Zinn. Knowledge of the relevant eponyms enables succinct descriptions of important skull base structures, provides an understanding of associated clinical implications, and reminds us of the vast history of contributions to neurosurgery made by prominent figures in the field.

Intracranial aneurysms are a common asymptomatic vascular pathology, the rupture of which is a devastating event with a significant risk of morbidity and mortality. Aneurysm detection and risk stratification before rupture events are, therefore, imperative to guide prophylactic measures. Artificial intelligence has shown great promise in the management pathway of aneurysms, through automated detection, the prediction of rupture risk, and outcome prediction after treatment. The complementary use of these programs, in addition to clinical practice, has demonstrated high diagnostic and prognostic accuracy, with the potential to improve patient outcomes. In the present review, we explored the role and limitations of deep learning, a subfield of artificial intelligence, in the aneurysm patient journey. We have also briefly summarized the application of deep learning models in automated detection and prediction in cerebral arteriovenous malformations and Moyamoya disease.

Over the years, Ghana has been challenged with erratic power cuts and load shedding, and waste-to-energy (WtE) technologies have been identified as one of the solutions to remedy the situation. In the current study, a multicriteria decision analysis (MCDA) is performed on four different WtE technologies viz anaerobic digestion, gasification, plasma arc gasification, and pyrolysis to identify the alternative with the most techno-economic advantage for investment in Ghana. The goal of the current study is achieved using a fuzzy TOPSIS approach. Five academic and field experts were employed to judge all four alternatives according to ten selected techno-economic criteria. The present study reveals that for all the alternatives under consideration, the most feasible WtE technology for investment in Ghana follows the order; gasification > anaerobic digestion > pyrolysis > plasma arc gasification. The most influential technical and economic criteria are energy generation per annum and initial investment, respectively. Sensitivity analysis shows a high degree of consistency, robustness, and stability in the obtained results. The current work recommends that the integration of anaerobic digestion and gasification should be promoted as it has the potential to offer a well-balanced WtE technology under both technical and economic conditions compared to the stand-alone systems. Findings from the current study could ease the decision-making of potential WtE technology investors in Ghana.