Clearing the Fog: A Review of Antipsychotics for Parkinson's-Related Hallucinations: A Focus on Pimavanserin, Quetiapine and Clozapine.

Parkinson's disease is a progressive neurodegenerative disorder characterized by motor and non-motor symptoms, including hallucinations. The use of antipsychotic medications is a common strategy to manage hallucinations associated with Parkinson's disease psychosis (PDP). However, careful consideration is necessary when selecting the most appropriate drug due to the potential risks associated with the available treatment options. Atypical antipsychotics (AAPs), such as Pimavanserin and Clozapine, have effectively controlled PDP symptoms. On the contrary, the support for utilizing quetiapine is not as substantial as other antipsychotics because research studies specifically investigating its application are still emerging and relatively recent. The broad mechanisms of action of AAPs, involving dopamine and serotonin receptors, provide improved outcomes and fewer side effects than typical antipsychotics. Conversely, other antipsychotics, including risperidone, olanzapine, aripiprazole, ziprasidone, and lurasidone, have been found to worsen motor symptoms and are generally not recommended for PDP. While AAPs offer favorable benefits, they are associated with specific adverse effects. Extrapyramidal symptoms, somnolence, hypotension, constipation, and cognitive impairment are commonly observed with AAP use. Clozapine, in particular, carries a risk of agranulocytosis, necessitating close monitoring of blood counts. Pimavanserin, a selective serotonin inverse agonist, avoids receptor-related side effects but has been linked to corrected QT (QTc) interval prolongation, while quetiapine has been reported to be associated with an increased risk of mortality. This review aims to analyze the benefits, risks, and mechanisms of action of antipsychotic medications to assist clinicians in making informed decisions and enhance patient care.

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.