Focused ultrasound as a treatment modality for gliomas.

Ultrasound waves were initially used as a diagnostic tool that provided critical insights into several pathological conditions (e.g., gallstones, ascites, pneumothorax, etc.) at the bedside. Over the past decade, advancements in technology have led to the use of ultrasound waves in treating many neurological conditions, such as essential tremor and Parkinson's disease, with high specificity. The convergence of ultrasound waves at a specific region of interest/target while avoiding surrounding tissue has led to the coined term "focused ultrasound (FUS)." In tumor research, ultrasound technology was initially used as an intraoperative guidance tool for tumor resection. However, in recent years, there has been growing interest in utilizing FUS as a therapeutic tool in the management of brain tumors such as gliomas. This mini-review highlights the current knowledge surrounding using FUS as a treatment modality for gliomas. Furthermore, we discuss the utility of FUS in enhanced drug delivery to the central nervous system (CNS) and highlight promising clinical trials that utilize FUS as a treatment modality for gliomas.

Augmented surgical decision-making for glioblastoma: integrating AI tools into education and practice.

Surgical decision-making for glioblastoma poses significant challenges due to its complexity and variability. This study investigates the potential of artificial intelligence (AI) tools in improving "decision-making processes" for glioblastoma surgery. A systematic review of literature identified 10 relevant studies, primarily focused on predicting resectability and surgery-related neurological outcomes. AI tools, especially rooted in radiomics and connectomics, exhibited promise in predicting resection extent through precise tumor segmentation and tumor-network relationships. However, they demonstrated limited effectiveness in predicting postoperative neurological due to dynamic and less quantifiable nature of patient-related factors. Recognizing these challenges, including limited datasets and the interpretability requirement in medical applications, underscores the need for standardization, algorithm optimization, and addressing variability in model performance and then further validation in clinical settings. While AI holds potential, it currently does not possess the capacity to emulate the nuanced decision-making process utilized by experienced neurosurgeons in the comprehensive approach to glioblastoma surgery.

A Scalable Histological Method to Embed and Section Multiple Brains Simultaneously.

The preparation and processing of rodent brains for evaluation by immunohistochemistry is time-consuming. A large number of mouse brains are routinely used in experiments in neuroscience laboratories to evaluate several models of human diseases. Thus, methods are needed to reduce the time associated with processing brains for histology. A scalable method was developed to embed, section, and stain multiple mouse brains using supplies found in any common histology laboratory. Section collection schemes can be scaled to provide identical bregma locations between adjacent sections for immunohistochemistry, facilitating comprehensive, high-quality immunohistochemistry. As a result, sectioning and staining times are considerably reduced as sections from multiple blocks are stained simultaneously. This method improves on previous procedures and allows multiple embedding and subsequent immunostaining of brains easily with a dramatically reduced time requirement. Furthermore, we expand this method for use in numerous mouse tissues, rat brain tissue, and post-mortem human brain and arterial tissues. In summary, this procedure allows the processing of many rodent or human tissues from perfusion through microscopy in 10 days or less.