Andres Mauricio Alvarez Pinzon

Introduction: Primary central nervous system lymphoma (PCNSL) in patients with HIV/AIDS is difficult to treat due to severe immunosuppression, opportunistic infections, and reduced marrow reserve, which limit tolerance to intensive chemotherapy and radiotherapy. Radiosurgery offers a targeted treatment option, but its success depends on immune recovery, particularly adequate CD4 restoration through HAART. Additionally, molecular mediators such as MYC, CD44, and RGS13 influence tumor biology, immune evasion, and radiosensitivity, underscoring the need for an integrated therapeutic approach. Methods: We retrospectively analyzed 89 patients with HIV/AIDS-associated PCNSL who underwent stereotactic radiosurgery. Clinical variables included demographics, CD4 count at diagnosis and prior to radiosurgery, ART status, treatment combinations, and survival outcomes. Subgroup analyses stratified outcomes by CD4 thresholds (< 100 vs ≥ 100 cells/µL) and timing of HAART initiation. In addition, we explored the functional relevance of MYC, CD44, and RGS13 as described in prior patent publications and translational studies, focusing on their implications for proliferation, survival signaling, and therapeutic resistance. Results: Patients treated with radiosurgery alongside HAART had significantly better survival than those receiving radiosurgery alone. A CD4 count ≥ 100 cells/µL before treatment was key for durable response, local control, and survival. Early HAART initiation promoted immune recovery and enhanced radiosurgical benefit, consistent with guideline recommendations on optimizing immunity before cytotoxic therapy. Molecular findings linked MYC and CD44 to resistance and aggressiveness, while RGS13 may influence B-cell signaling and radiosensitivity. Conclusion: The study shows that immune restoration through HAART is essential for successful radiosurgery in HIV/AIDS-related PCNSL. Optimal outcomes require early HAART initiation and CD4 recovery (≥ 100 cells/µL), while radiosurgery alone is insufficient. Future trials should integrate radiosurgery with immune-based and molecularly guided strategies to improve survival.

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Epilepsy remains a major therapeutic challenge, with approximately one-third of patients experiencing drug-resistant epilepsy (DRE) despite the availability of multiple antiseizure medications (ASMs). This review aims to evaluate emerging ASMs—cenobamate, fenfluramine, ganaxolone, ezogabine (retigabine), and perampanel—with a focus on their mechanisms of action, pharmacological profiles, and potential role in precision medicine. A comprehensive literature search was conducted using PubMed, Scopus, and Web of Science to identify preclinical and clinical studies evaluating the pharmacodynamics, pharmacokinetics, efficacy, and safety of the selected ASMs. Relevant trials, reviews, and mechanistic studies were reviewed to synthesize the current understanding of their application in DRE and specific epilepsy syndromes. Each ASM demonstrated unique mechanisms targeting hyperexcitability, including the modulation of γ-aminobutyric acid receptor A (GABA-A) receptors, sodium and potassium channels, α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor (AMPA receptors), and serotonin systems. These mechanisms correspond with specific pathophysiological features in syndromes such as Dravet and Lennox–Gastaut. Evidence from clinical trials supports their use as adjunctive therapies with generally favorable tolerability, though adverse events and variable efficacy profiles were noted. The mechanistic diversity of these emerging ASMs supports their value in personalized epilepsy management, particularly in treatment-resistant cases. While the promise of precision medicine is evident, further studies are required to address challenges related to long-term safety, cost, and equitable access.

Background: Disparities in neuro-oncological care between high-income and low- and middle-income countries (LMICs) are well documented, yet region-specific data from Latin America remain limited. This review evaluates epidemiologic trends, access to care, and systemic challenges in brain tumor management across Latin American LMICs, using Argentina as a case study. Methods: A systematic review of peer-reviewed literature was conducted focusing on brain tumor incidence, mortality, risk factors, and availability of diagnostics and treatments in Latin America. Socioeconomic, cultural, and systemic barriers were also analyzed. Results: Latin America exhibits some of the highest global brain tumor mortality rates, with Brazil reporting age-standardized rates exceeding 4.5 per 100,000. Glioblastomas are frequently diagnosed at younger ages, often in the fifth decade of life, compared to the global average. Meningioma incidence has increased by 15–20% over the last decade, yet region-wide data remain fragmented. Access to neuroimaging, neurosurgery, radiotherapy, and chemotherapy is limited, with up to 60% of patients relying solely on under-resourced public health systems. Less than 30% of hospitals in rural areas have MRI availability, and continuous professional training is infrequent. Innovative adaptations, such as awake craniotomy, are used in some LMIC centers in response to equipment scarcity. Conclusions: Brain tumor care in Latin America is hindered by limited epidemiological data, restricted access to diagnostics and treatment, and insufficient workforce training. Targeted investments in healthcare infrastructure, international educational collaborations, and policy-level reforms are critical to reducing disparities and improving outcomes in neuro-oncology across the region.

Background: 5-Aminolevulinic acid (5-ALA) serves as a precursor in the heme biosynthesis pathway, resulting in the selective accumulation of protoporphyrin IX (PpIX) within glioma cells. This property facilitates fluorescence-guided resection (FGR) in high-grade gliomas (HGGs), enhancing surgical precision and oncological results. Nonetheless, its clinical implementation is restricted by factors such as accessibility, cost, and technical limitations. Methods: A systematic review of PubMed literature (2019–2024) was conducted to assess the efficacy of 5-ALA in HGG surgery compared to conventional white light microscopy. Studies focusing on non-neurosurgical applications, pediatric populations, and non-HGG indications were excluded. Results: Nineteen articles met the criteria. Recent studies indicate that 5-ALA-guided resection significantly enhances gross total resection (GTR) rates compared to white light surgery (75.4% vs. 54.3%, p < 0.001). Patients receiving 5-ALA-assisted resection exhibit enhanced progression-free survival (PFS) at 6 months (median 8.1 months compared to 5.4 months, p = 0.002) and overall survival (OS) (median 15.2 months versus 12.3 months, p = 0.008). The necessity for specialized neurosurgical microscopes equipped with blue light filters restricts accessibility, especially in low-resource environments. Recent advancements in fluorescence-enhancing technologies, particularly loupe-based systems, have demonstrated increases in fluorescence intensity by up to tenfold through direct emission. Sodium fluorescein, originally designed for ophthalmological use, has been adapted for enhancing contrast in intracranial tumors; however, its non-specific binding to serum albumin restricts its accuracy in glioma resection. Conclusions: Recent publications demonstrate that 5-ALA fluorescence-guided surgery significantly improves gross total resection rates and survival outcomes in patients with high-grade gliomas. Although it offers clinical advantages, cost and equipment constraints continue to pose substantial obstacles to broad implementation. Additional research is required to enhance fluorescence-guided techniques and increase accessibility in resource-constrained environments.

Parkinson’s disease (PD) is a progressive neurodegenerative disorder marked by motor and non-motor dysfunctions that severely compromise patients’ quality of life. While pharmacological treatments provide symptomatic relief in the early stages, advanced PD often requires neurosurgical interventions, such as deep brain stimulation (DBS) and focused ultrasound (FUS), for effective symptom management. A significant challenge in optimizing these therapeutic strategies is the early identification and recruitment of suitable candidates for clinical trials. This review explores the role of artificial intelligence (AI) in advancing neurosurgical and neuroscience interventions for PD, highlighting the ways in which AI-driven platforms are transforming clinical trial design and patient selection. Machine learning (ML) algorithms and big data analytics enable precise patient stratification, risk assessment, and outcome prediction, accelerating the development of novel therapeutic approaches. These innovations improve trial efficiency, broaden treatment options, and enhance patient outcomes. However, integrating AI into clinical trial frameworks presents challenges such as data standardization, regulatory hurdles, and the need for extensive validation. Addressing these obstacles will require collaboration among neurosurgeons, neuroscientists, AI specialists, and regulatory bodies to establish ethical and effective guidelines for AI-driven technologies in PD neurosurgical research. This paper emphasizes the transformative potential of AI and technological innovation in shaping the future of PD neurosurgery, ultimately enhancing therapeutic efficacy and patient care.

Current lung cancer screening guidelines in the United States fail to identify many individuals at risk of developing the disease. Additionally, existing healthcare infrastructure has been leveraged to establish IPN clinics, a promising approach to addressing the limitations of current screening guidelines. Early-stage lung cancer is frequently diagnosed because of the incidental detection of pulmonary nodules on clinically indicated chest CT scans, particularly in the absence of formal screening programs. While artificial intelligence (AI) systems for lung cancer detection have demonstrated significant advancements in medicine, their clinical validation in screening settings remains limited. This review will discuss the pivotal trials underpinning the United States Preventive Services Task Force (USPSTF) recommendations for lung cancer screening, which have shaped the current guidelines for at-risk populations. We will explore recent studies investigating the role of AI in enhancing lung cancer screening efforts, highlighting how AI has the potential to improve early detection, streamline workflows, and reduce false positives and negatives in screening processes. This review will present the lung cancer screening rates at our institution, with a specific focus on the validation and integration of AI-driven technologies into our established screening programs. Using AI algorithms, we have validated enhanced detection capabilities through retrospective analysis of historical patient data, demonstrating significant improvements in identifying high-risk individuals and early-stage malignancies. These AI models, validated through rigorous cross-validation methods and clinical trials, have proven to outperform traditional screening approaches in sensitivity and specificity. The integration of these AI technologies within the lung cancer screening framework not only optimizes existing programs but also expands access to screening, improving early detection rates and ultimately leading to better patient outcomes. Through continuous validation and refinement, we aim to solidify AI’s role in transforming lung cancer detection and patient care. Through ongoing validation and implementation, AI can play a crucial role in transforming lung cancer screening practices, ultimately contributing to earlier diagnosis and improved patient survival.

Parkinson’s disease (PD) is a chronic, progressive neurodegenerative disorder that significantly impacts patients’ quality of life due to both motor and non-motor symptoms. While pharmacological interventions can alleviate symptoms in the early stages, advanced cases often become refractory to medication, requiring additional therapeutic approaches. Neurosurgical interventions such as deep brain stimulation (DBS) and focused ultrasound (FUS) have emerged as effective treatments for patients with medication-resistant motor symptoms. However, one of the biggest challenges in advancing these therapies is the early identification and recruitment of suitable candidates for clinical trials, essential for refining and expanding these treatments. This review explores the integration of artificial intelligence (AI) and other technological innovations in enhancing clinical trial processes for neurosurgical treatments in PD. AI platforms are revolutionizing patient selection for trials by providing advanced tools for patient stratification, risk assessment, and trial design optimization. These technologies improve trial efficiency and facilitate more personalized treatment strategies that enhance surgical outcomes and minimize adverse effects. Despite the potential of AI to transform neurosurgical care, challenges remain, particularly concerning the standardization of AI applications and their seamless integration into clinical practice. Multidisciplinary collaboration will be crucial to overcoming these obstacles and establishing evidence-based guidelines for the safe and effective use of AI in neurosurgical trials. Through continued innovation and interdisciplinary efforts, AI has the potential to revolutionize patient care and research in PD, leading to significant improvements in neurosurgical interventions and patient outcomes.

Background: Hypothalamic gliomas, though rare, present significant challenges in neuro-oncology due to their critical location and high morbidity risk. Pilocytic astrocytoma is the most common subtype, requiring a delicate balance between tumor control and preservation of neurological function. This study explores radiosurgery as a viable treatment option for hypothalamic gliomas, with a focus on low-grade lesions. Methods: A comprehensive literature review was conducted using the PubMed database to compare the outcomes of surgical and non-surgical interventions for hypothalamic gliomas. The purpose of case report and clinical guidelines is to present a case report and critically compare its outcomes with the data identified in the literature. We report the case of a 25-year-old male presenting with hypernatremia, diabetes insipidus, and altered mental status. Imaging revealed a hypothalamic lesion consistent clinically with a low-grade astrocytoma. Given the tumor’s proximity to critical neurovascular structures, gamma knife radiosurgery was chosen as the intervention. Post treatment, tumor reduction and neurological improvement were observed and documented. Results: The case demonstrated a significant decrease in tumor size and resolution of neurological symptoms post radiosurgery. The management of hypothalamic gliomas remains contentious due to the complex anatomy of the hypothalamus. While surgical resection remains common, it carries a high risk of morbidity. Radiosurgery offers a minimally invasive alternative, effectively controlling tumor growth while reducing neurological risk. The favorable outcomes in our case, supported by the literature, highlight the efficacy of radiosurgery, particularly for low-grade astrocytomas. Conclusions: Gamma knife radiosurgery presents a promising alternative to conventional surgery for hypothalamic gliomas, particularly low-grade lesions such as pilocytic astrocytomas. The reduction in surgical risk and positive clinical outcomes make radiosurgery a valuable tool in the management of these challenging tumors.