Neurosurgical cadaveric and in vivo large animal training models for cranial and spinal approaches and techniques — a systematic review of the current literature

Introduction. Due to its high complexity, neurosurgery consists of a demanding learning curve that requires intense training and a deep knowledge of neuroanatomy. Microsurgical skill development can be achieved through various models of simulation, but as human cadaveric models are not always accessible, cadaveric animal models can provide a reliable environment in which to enhance the acquisition of surgical dexterity. The aim of this review was to analyse the current role of animal brains in laboratory training and to assess their correspondence to the procedures performed in humans. Material and methods. A Pubmed literature search was performed to identify all the articles concerning training cranial and spinal techniques on large animal heads. The search terms were ‘training model’, and ‘neurosurgery’ in association with ‘animal’, ‘sheep’, ‘cow’, and ‘swine’. The exclusion criteria were articles that were on human brains, experimental fundamental research, or on virtual simulators. Results. The search retrieved 119 articles, of which 25 were relevant to the purpose of this review. Owing to their similar neuroanatomy, bovine, porcine and ovine models prove to be reliable structures in simulating neurosurgical procedures. On bovine skulls, an interhemispheric transcalosal and retrosigmoid approach along with different approaches to the Circle of Willis can be recreated. Ovine model procedures have varied from lumbar discectomies on sheep spines to craniosynostosis surgery, whereas in ex vivo swine models, cadaveric dissections of lateral sulcus, median and posterior fossa have been achieved. Conclusions. Laboratory training models enhance surgical advancements by familiarising trainee surgeons with certain neuroanatomical structures and promoting greater surgical dexterity. The accessibility of animal brains allows trainee surgeons to exercise techniques outside the operating theatre, thus optimising outcomes in human surgical procedures

Brachioradial pruritus secondary to cervical disc protrusion - a case report

Brachioradial pruritus (BRP) is a rare chronic neuropathy of the skin of the arms and forearms that presents with itching, burning or tingling, with no associated dermatological features. Sun exposure and cervical spine pathology have been described as causes for BRP; however, the exact aetiology is often unclear. Herein, we discuss the case of a 63-year-old female patient who presented with BRP with a C5-C6 distribution. Physical examination excluded skin conditions, thus magnetic resonance imaging was done and revealed a C5-C6 disc protrusion. Anterior cervical discectomy and fusion were performed leading to the resolution of symptoms. The case emphasizes the beneficial role of anterior cervical discectomy and fusion as a last resort in patients with refractory pruritus of discogenic cause

Syndrome of inappropriate antidiuretic hormone secretion after functional endoscopic sinus surgery

Functional endoscopic sinus surgery (FESS) is effective in cases of sinusitis where pharmacological treatment has not been successful. Patients undergoing FESS have reported an 85% improvement in symptoms as measured by the quality of life scores. Despite its convincing therapeutic benefit, complications sometimes occur with potentially dire consequences. We report the case of a 69-year-old patient who underwent FESS for recurrent frontal sinusitis and developed a syndrome of inappropriate antidiuretic hormone secretion (SIADH) on Day 3 post-operatively. To our knowledge, this is the first documented case of SIADH arising after an endoscopic intervention for frontal sinusitis

Reliable Learning with PDE-Based CNNs and DenseNets for Detecting COVID-19, Pneumonia, and Tuberculosis from Chest X-Ray Images

It has recently been shown that the interpretation by partial differential equations (PDEs) of a class of convolutional neural networks (CNNs) supports definition of architectures such as parabolic and hyperbolic networks. These networks have provable properties regarding the stability against the perturbations of the input features. Aiming for robustness, we tackle the problem of detecting changes in chest X-ray images that may be suggestive of COVID-19 with parabolic and hyperbolic CNNs and with domain-specific transfer learning. To this end, we compile public data on patients diagnosed with COVID-19, pneumonia, and tuberculosis, along with normal chest X-ray images. The negative impact of the small number of COVID-19 images is reduced by applying transfer learning in several ways. For the parabolic and hyperbolic networks, we pretrain the networks on normal and pneumonia images and further use the obtained weights as the initializers for the networks to discriminate between COVID-19, pneumonia, tuberculosis, and normal aspects. For DenseNets, we apply transfer learning twice. First, the ImageNet pretrained weights are used to train on the CheXpert dataset, which includes 14 common radiological observations (e.g., lung opacity, cardiomegaly, fracture, support devices). Then, the weights are used to initialize the network which detects COVID-19 and the three other classes. The resulting networks are compared in terms of how well they adapt to the small number of COVID-19 images. According to our quantitative and qualitative analysis, the resulting networks are more reliable compared to those obtained by direct training on the targeted dataset