Recent Outcomes and Challenges of Artificial Intelligence, Machine Learning, and Deep Learning in Neurosurgery.

Neurosurgeons receive extensive technical training, which equips them with the knowledge and skills to specialise in various fields and manage the massive amounts of information and decision-making required throughout the various stages of neurosurgery, including preoperative, intraoperative, and postoperative care and recovery. Over the past few years, artificial intelligence (AI) has become more useful in neurosurgery. AI has the potential to improve patient outcomes by augmenting the capabilities of neurosurgeons and ultimately improving diagnostic and prognostic outcomes as well as decision-making during surgical procedures. By incorporating AI into both interventional and non-interventional therapies, neurosurgeons may provide the best care for their patients. AI, machine learning (ML), and deep learning (DL) have made significant progress in the field of neurosurgery. These cutting-edge methods have enhanced patient outcomes, reduced complications, and improved surgical planning.

Hereditary spastic paraplegia: Novel insights into the pathogenesis and management.

Hereditary spastic paraplegia is a genetically heterogeneous neurodegenerative disorder characterised primarily by muscle stiffness in the lower limbs. Neurodegenerative disorders are conditions that result from cellular and metabolic abnormalities, many of which have strong genetic ties. While ageing is a known contributor to these changes, certain neurodegenerative disorders can manifest early in life, progressively affecting a person's quality of life. Hereditary spastic paraplegia is one such condition that can appear in individuals of any age. In hereditary spastic paraplegia, a distinctive feature is the degeneration of long nerve fibres in the corticospinal tract of the lower limbs. This degeneration is linked to various cellular and metabolic processes, including mitochondrial dysfunction, remodelling of the endoplasmic reticulum membrane, autophagy, abnormal myelination processes and alterations in lipid metabolism. Additionally, hereditary spastic paraplegia affects processes like endosome membrane trafficking, oxidative stress and mitochondrial DNA polymorphisms. Disease-causing genetic loci and associated genes influence the progression and severity of hereditary spastic paraplegia, potentially affecting various cellular and metabolic functions. Although hereditary spastic paraplegia does not reduce a person's lifespan, it significantly impairs their quality of life as they age, particularly with more severe symptoms. Regrettably, there are currently no treatments available to halt or reverse the pathological progression of hereditary spastic paraplegia. This review aims to explore the metabolic mechanisms underlying the pathophysiology of hereditary spastic paraplegia, emphasising the interactions of various genes identified in recent network studies. By comprehending these associations, targeted molecular therapies that address these biochemical processes can be developed to enhance treatment strategies for hereditary spastic paraplegia and guide clinical practice effectively.