The emergence of genome-wide association studies (GWAS) has identified genetic traits and polymorphisms that are associated with the progression of nonalcoholic fatty liver disease (NAFLD). Phospholipase domain-containing 3 and transmembrane 6 superfamily member 2 are genes commonly associated with NAFLD phenotypes. However, there are fewer studies and replicability in lesser-known genes such as LYPLAL1 and glucokinase regulator (GCKR). With the advent of artificial intelligence (AI) in clinical genetics, studies have utilized AI algorithms to identify phenotypes through electronic health records and utilize convolution neural networks to improve the accuracy of variant identification, predict the deleterious effects of variants, and conduct phenotype-to-genotype mapping. Natural language processing (NLP) and machine-learning (ML) algorithms are popular tools in GWAS studies and connect electronic health record phenotypes to genetic diagnoses using a combination of international classification disease (ICD)-based approaches. However, there are still limitations to machine-learning - and NLP-based models, such as the lack of replicability in larger cohorts and underpowered sample sizes, which prevent the accurate prediction of genetic variants that may increase the risk of NAFLD and its progression to advanced-stage liver fibrosis. This may be largely due to the lack of understanding of the clinical consequence in the majority of pathogenic variants. Though the concept of evolution-based AI models and evolutionary algorithms is relatively new, combining current international classification disease -based NLP models with phylogenetic and evolutionary data can improve prediction accuracy and create valuable connections between variants and their pathogenicity in NAFLD. Such developments can improve risk stratification within clinical genetics and research while overcoming limitations in GWAS studies that prevent community-wide interpretations.
Robotic surgery has applications in many medical specialties, including urology, general surgery, and surgical oncology. In the context of a widespread resource and personnel shortage in Low- and Middle-Income Countries (LMICs), the use of robotics in surgery may help to reduce physician burnout, surgical site infections, and hospital stays. However, a lack of haptic feedback and potential socioeconomic factors such as high implementation costs and a lack of trained personnel may limit its accessibility and application. Specific improvements focused on improved financial and technical support to LMICs can help improve access and have the potential to transform the surgical experience for both surgeons and patients in LMICs. This review focuses on the evolution of robotic surgery, with an emphasis on challenges and recommendations to facilitate wider implementation and improved patient outcomes.
Glioblastoma Multiforme (GBM) is a debilitating type of brain cancer with a high mortality rate. Despite current treatment options such as surgery, radiotherapy, and the use of temozolomide and bevacizumab, it is considered incurable. Various methods, such as drug repositioning, have been used to increase the number of available treatments. Drug repositioning is the use of FDA-approved drugs to treat other diseases. This is possible because the drugs used for this purpose have polypharmacological effects. This means that these medications can bind to multiple targets, resulting in multiple mechanisms of action. Antipsychotics are one type of drug used to treat GBM. Antipsychotics are a broad class of drugs that can be further subdivided into typical and atypical classes. Typical antipsychotics include chlorpromazine, trifluoperazine, and pimozide. This class of antipsychotics was developed early on and primarily works on dopamine D2 receptors, though it can also work on others. Olanzapine and Quetiapine are examples of atypical antipsychotics, a category that was created later. These medications have a high affinity for serotonin receptors such as 5- HT2, but they can also act on dopamine and H1 receptors. Antipsychotic medications, in the case of GBM, also have other effects that can affect multiple pathways due to their polypharmacological effects. These include NF-B suppression, cyclin deregulation, and -catenin phosphorylation, among others. This review will delve deeper into the polypharmacological, the multiple effects of antipsychotics in the treatment of GBM, and an outlook for the field's future progression.
With increasing prevalence and an expected rise in disease burden, cancer is a cause of concern for African healthcare. The cancer burden in Africa is expected to rise to 2.1 million new cases per year and 1.4 million deaths annually by the year 2040. Even though efforts are being made to improve the standard of oncology service delivery in Africa, the current state of cancer care is not yet on par with the rise in the cancer burden. Cutting-edge technologies and innovations are being developed across the globe to augment the battle against cancer; however, many of them are beyond the reach of African countries. Modern oncology innovations targeted to ward Africa would be promising to address the high cancer mortality rates. The innovations should be cost-effective and widely accessible to tackle the rapidly rising mortality rate on the African continent. Though it may seem promising, a multidisciplinary approach is required to overcome the challenges associated with the development and implementation of modern oncology innovations in Africa.
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.
The circadian rhythm (CR) is a fundamental biological process regulated by the Earth's rotation and solar cycles. It plays a critical role in various bodily functions, and its dysregulation can have systemic effects. These effects impact metabolism, redox homeostasis, cell cycle regulation, gut microbiota, cognition, and immune response. Immune mediators, cycle proteins, and hormones exhibit circadian oscillations, supporting optimal immune function and defence against pathogens. Sleep deprivation and disruptions challenge the regulatory mechanisms, making immune responses vulnerable. Altered CR pathways have been implicated in diseases such as diabetes, neurological conditions, and systemic autoimmune diseases (SADs). SADs involve abnormal immune responses to self-antigens, with genetic and environmental factors disrupting self-tolerance and contributing to conditions like Systemic Lupus Erythematosus, Rheumatoid Arthritis, and Inflammatory Myositis. Dysregulated CR may lead to increased production of pro-inflammatory cytokines, contributing to the systemic responses observed in SADs. Sleep disturbances significantly impact the quality of life of patients with SADs; however, they are often overlooked. The relationship between sleep and autoimmune conditions, whether causal or consequential to CR dysregulation, remains unclear. Chrono-immunology investigates the role of CR in immunity, offering potential for targeted therapies in autoimmune conditions. This paper provides an overview of the connections between sleep and autoimmune conditions, highlighting the importance of recognizing sleep disturbances in SADs and the need for further research into the complex relationship between the CR and autoimmune diseases.
Alzheimer's disease (AD) constitutes a multifactorial neurodegenerative pathology characterized by cognitive deterioration, personality alterations, and behavioral shifts. The ongoing brain impairment process poses significant challenges for therapeutic interventions due to activating multiple neurotoxic pathways. Current pharmacological interventions have shown limited efficacy and are associated with significant side effects. Approaches focusing on the early interference with disease pathways, before activation of broad neurotoxic processes, could be promising to slow down symptomatic progression of the disease. Curcumin-an integral component of traditional medicine in numerous cultures worldwide-has garnered interest as a promising AD treatment. Current research indicates that curcumin may exhibit therapeutic potential in neurodegenerative pathologies, attributed to its potent anti-inflammatory and antioxidant properties. Additionally, curcumin and its derivatives have demonstrated an ability to modulate cellular pathways via epigenetic mechanisms. This article aims to raise awareness of the neuroprotective properties of curcuminoids that could provide therapeutic benefits in AD. The paper provides a comprehensive overview of the neuroprotective efficacy of curcumin against signaling pathways that could be involved in AD and summarizes recent evidence of the biological efficiency of curcumins in vivo.