In recent years, there has been a significant surge in discussions surrounding artificial intelligence (AI), along with a corresponding increase in its practical applications in various facets of everyday life, including the medical industry. Notably, even in the highly specialized realm of neurosurgery, AI has been utilized for differential diagnosis, pre-operative evaluation, and improving surgical precision. Many of these applications have begun to mitigate risks of intraoperative and postoperative complications and post-operative care. This article aims to present an overview of the principal published papers on the significant themes of tumor, spine, epilepsy, and vascular issues, wherein AI has been applied to assess its potential applications within neurosurgery. The method involved identifying high-cited seminal papers using PubMed and Google Scholar, conducting a comprehensive review of various study types, and summarizing machine learning applications to enhance understanding among clinicians for future utilization. Recent studies demonstrate that machine learning (ML) holds significant potential in neuro-oncological care, spine surgery, epilepsy management, and other neurosurgical applications. ML techniques have proven effective in tumor identification, surgical outcomes prediction, seizure outcome prediction, aneurysm prediction, and more, highlighting its broad impact and potential in improving patient management and outcomes in neurosurgery. This review will encompass the current state of research, as well as predictions for the future of AI within neurosurgery.

Epigenetic reprogramming predicts the long-term functional health effects of health-related metabolic disease. This epigenetic reprogramming is activated by exogenous or endogenous insults, leading to altered healthy and different disease states. The epigenetic and environmental changes involve a roadmap of epigenetic networking, such as dietary components and exercise on epigenetic imprinting and restoring epigenome patterns laid down during embryonic development, which are paramount to establishing youthful cell type and health. Nutrition and exercise are among the most well-known environmental epigenetic factors influencing the proper developmental and functional lifestyle, with potential beneficial or detrimental effects on health status. The diet and exercise strategies applied from conception could represent an innovative epigenetic target for preventing and treating human diseases. Here, we describe the potential role of diet and exercise as therapeutic epigenetic strategies for health and diseases, highlighting putative future perspectives in this field.

Primary care has the potential to be transformed by artificial intelligence (AI) and, in particular, machine learning (ML). This review summarizes the potential of ML and its subsets in influencing two domains of primary care: pre-operative care and screening. ML can be utilized in preoperative treatment to forecast postoperative results and assist physicians in selecting surgical interventions. Clinicians can modify their strategy to reduce risk and enhance outcomes using ML algorithms to examine patient data and discover factors that increase the risk of worsened health outcomes. ML can also enhance the precision and effectiveness of screening tests. Healthcare professionals can identify diseases at an early and curable stage by using ML models to examine medical pictures, diagnostic modalities, and spot patterns that may suggest disease or anomalies. Before the onset of symptoms, ML can be used to identify people at an increased risk of developing specific disorders or diseases. ML algorithms can assess patient data such as medical history, genetics, and lifestyle factors to identify those at higher risk. This enables targeted interventions such as lifestyle adjustments or early screening. In general, using ML in primary care offers the potential to enhance patient outcomes, reduce healthcare costs, and boost productivity.

The recent report of the first pig kidney transplant in a living human brings hope to thousands of people with end‐stage kidney failure. The scientific community views this early success with caution as kidney xenotransplantation exhibits many challenges and barriers. One of these is coagulation dysregulation. This includes (i) pig von Willebrand Factor (vWF) interaction with human platelets, which can induce abnormal clotting responses, heightening the risk of graft failure, (ii) the inefficiency of pig thrombomodulin in activating human protein C, which emphasizes the species‐specific variations that aggravate coagulation challenges, and (iii) the development of thrombotic microangiopathy in the pig grafts and the occurrence of systemic consumptive coagulopathy in the recipients. Indeed, coagulation dysregulation largely results from differences in endothelial cell response and incompatibilities between pig and human coagulation–anticoagulation pathways. These barriers can be resolved by modifications to pig vWF and the expression of human thrombomodulin and endothelial protein C receptors in pig cells, serving as strategic interventions to align the coagulation systems of the two species more closely. These coagulation challenges have clinical implications in how they affect graft survival and patient outcome. Genetic engineering of the organ‐source pig and the administration of various drugs have assisted in correcting this coagulation dysregulation. Hence, comprehending and controlling coagulation dysregulation is crucial for progress in xenotransplantation as a viable option for treating patients with terminal kidney disease.