Functional Gonadotroph Adenomas in Male Patients: Clinical Characteristics, Surgical Outcomes, and Hormonal Follow-up.

Background And Objectives: Functional gonadotroph adenomas (FGAs) are rare pituitary neuroendocrine tumors derived from the steroidogenic factor 1 (SF-1) lineage that present clinically with elevated serum levels of follicle-stimulating hormone (FSH) and/or luteinizing hormone (LH). While previous research has predominantly featured female patients, this study specifically aims to better understand the clinical characteristics and postoperative outcomes of FGAs in male patients.

Methods: A retrospective review of medical records from a single institution, encompassing 1306 pituitary adenomas, identified 12 male patients who underwent transsphenoidal resection for FGAs.

Predicting therapeutic clinical trial enrollment for adult patients with low- and high-grade glioma using supervised machine learning.

Therapeutic clinical trial enrollment does not match glioma incidence across demographics. Traditional statistical methods have identified independent predictors of trial enrollment; however, our understanding of the interactions between these factors remains limited. To test the interactive effects of demographic, socioeconomic, and oncologic variables on trial enrollment, we designed boosted neural networks (BNNs) for all glioma patients ( = 1042), women ( = 445, 42.

Gabapentinoids confer survival benefit in human glioblastoma.

Neuronal-glioma interactions are increasingly recognized as critical in the development and progression of central nervous system tumors. Recent research highlights that gliomas can integrate into neural circuits through various mechanisms, including the synaptogenic factor thrombospondin-1 (TSP-1). This new mechanistic understanding of cancer neuroscience allows for novel insights into target discovery.

Enhancing neuro-oncology care through equity-driven applications of artificial intelligence.

The disease course and clinical outcome for brain tumor patients depend not only on the molecular and histological features of the tumor but also on the patient's demographics and social determinants of health. While current investigations in neuro-oncology have broadly utilized artificial intelligence (AI) to enrich tumor diagnosis and more accurately predict treatment response, postoperative complications, and survival, equity-driven applications of AI have been limited. However, AI applications to advance health equity in the broader medical field have the potential to serve as practical blueprints to address known disparities in neuro-oncologic care.

The complexity of online patient education materials for wound care strategies: A readability analysis.

Background: In the United States, over 6 million people are affected by chronic wounds. Patients often rely on the Internet for treatment information; however, these educational materials typically exceed the average eighth-grade health literacy level. This study aimed to assess the readability and language accessibility of online patient education materials on wound care strategies.

The Association Between Task Complexity and Cortical Language Mapping Accuracy.

Background And Objectives: Direct cortical stimulation (DCS) mapping enables the identification of functional language regions within and around gliomas before tumor resection. Intraoperative mapping is required because glioma-infiltrated cortex engages in synchronous activity during task performance in a manner similar to normal-appearing cortex but has decreased ability to encode information for complex tasks. It is unknown whether task complexity influenced DCS mapping results.

A population study of clinical trial accrual for women and minorities in neuro-oncology following the NIH Revitalization Act.

Background: The NIH Revitalization Act, implemented 29 years ago, set to improve the representation of women and minorities in clinical trials. In this study, we investigate progress made in all phase therapeutic clinical trials for neuroepithelial CNS tumors stratified by demographic-specific age-adjusted disease incidence and mortality. Additionally, we identify workforce characteristics associated with clinical trials meeting established accrual benchmarks.