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Abstract This review assesses laser-based diagnostic modalities for intraoperative tissue diagnosis in neurosurgical oncology, emphasizing their utility in delineating tumor margins. Technologies such as optical coherence tomography, photoacoustic imaging, and confocal microscopy are scrutinized for their capability to enhance intraoperative discernment of neoplastic versus healthy tissue. We discuss the technical advancements, limitations related to depth penetration and resolution, and innovative approaches to mitigate these challenges. Economic and regulatory considerations pertinent to the clinical adoption of these technologies are also examined. The review highlights current clinical trials and research initiatives aiming to validate and standardize these applications. It concludes by highlighting the importance of ongoing research, cross-disciplinary cooperation, and professional training to integrate laser-based diagnostics into neurosurgical practice, with the ultimate goal of optimizing patient outcomes in brain tumor resection.

In this narrative review, we explore the evolving role of machine learning (ML) in the diagnosis, prognosis, and clinical management of traumatic brain injury (TBI). The increasing prevalence of TBI necessitates advanced techniques for timely and accurate diagnosis, and ML offers promising tools to meet this challenge. Current research predominantly focuses on integrating clinical data, patient demographics, lab results, and imaging findings, but there remains a gap in fully harnessing the potential of image features. While advancements have been made in areas such as subdural hematoma segmentation and prognosis prediction, the translation of these techniques into clinical practice is still in its infancy. This is further compounded by challenges related to data privacy, clinician trust, and the interoperability of various health systems. Despite these hurdles, FDA-approved ML applications for TBI and their subsequent promising results underscore the potential of ML in revolutionizing TBI care. This review concludes by emphasizing the importance of bridging the gap between theoretical research and real-world clinical application and the necessity of addressing the ethical and privacy implications of integrating ML into healthcare.

Purpose: Intraocular pressure (IOP) is the main modifiable risk factor for glaucoma. Current therapies target the anterior outflow of aqueous humor or its production. This study aims to demonstrate eplerenone could reduce IOP through a possible posterior outflow path via retinal pigment epithelium (RPE). Methods: In this retrospective study, IOP changes in patients undergoing eplerenone treatment were investigated. Inclusion criteria were IOP data immediately before and during treatment. Exclusion criteria included ophthalmic procedures, changes in topical glaucoma treatment, or taking systemic medications affecting IOP. After reviewing 162 charts, 41 subjects were eligible. Pearson correlation test was used to investigate the correlation between continuous IOP and eplerenone dosage. Results: The mean ± SD IOP before eplerenone treatment was 14.31 ± 3.73 mmHg and decreased to 13.50 ± 4.04 mmHg; however, this was not statistically significant (P = 0.39). In subset of patients with eplerenone dose of more than 25 mg/day and baseline IOP equal to or less than 15 mmHg, the mean IOP before eplerenone treatment was 12.33 ± 2.59 mmHg and decreased to 10.33 ± 2.99, which is a trend toward IOP reduction with a 16% reduction in IOP (P = 0.055). Conclusion: A possible dose-dependent decrease in IOP with eplerenone provides indirect evidence for the posterior flow model and suggests the mineralocorticoid receptors (MRs) in RPE play a role in the posterior flow of aqueous humor. It can be deduced that the RPE pumps responsible for the posterior flow of aqueous humor are MR-regulated and their function can be enhanced with MR antagonists.