Contact

  • Academic
    gnageeb@stanford.edu
    University - Student Department: School of Medicine Position: Graduate, Medicine

All Publications

  • Nutritional Interventions in Osteoarthritis: Mechanisms, Clinical Evidence, and Translational Opportunities. Nutrients Patel, M., Betanzos, G., Troka, M., Modi, J., Nageeb, G., Kaye, A. D., Abd-Elsayed, A. 2026; 18 (2)

    Abstract

    Osteoarthritis (OA) is a leading cause of chronic pain worldwide. This is driven by progressive cartilage degradation, inflammation, oxidative stress, and metabolic dysfunction. Current pharmacologic interventions mostly lead to symptomatic relief without actually affecting disease progression. Thus, there is a growing interest in the development of new interventional methods. Our review seeks to synthesize preclinical, translational, and clinical evidence on the impact nutritional methods have on OA management. Whole-diet approaches, such as Mediterranean and plant-based, have been linked to reduced pain, increased physical function, and positive biomarker changes. Bioactive compounds, including curcumin, polyphenols, omega-3 fatty acids, and select herbal extracts, have shown anti-inflammatory, antioxidant, and chondroprotective effects via NF-κB, Nrf2, AMPK, and SIRT1 pathways. This review particularly focuses on plant-derived substances. Emerging nanoparticle technology with regard to advanced delivery systems shows initial promise in nutraceutical pharmacokinetics and tissue targeting. Overall, nutritional interventions are adjunct interventions to OA management. Although these are not full treatment replacements, dietary modifications and targeted nutraceutical strategies with improved delivery systems may lead to more preventive, personalized, and holistic OA management and care.

    View details for DOI 10.3390/nu18020244

    View details for PubMedID 41599857

    View details for PubMedCentralID PMC12844890

  • Risk-stratified management of ankylosing spondylitis-related spinal fractures-a meta-synthesis of contemporary surgical and nonsurgical strategies: a narrative review. Journal of spine surgery (Hong Kong) Abikenari, M. A., Yoo, K. H., Liu, J., Ha, J., Nageeb, G., Jain, B., Park, L., Hani, U., Veeravagu, A. 2025; 11 (4): 1095-1110

    Abstract

    Ankylosing spondylitis (AS) spinal fractures pose unique diagnostic and therapeutic challenges due to the altered biomechanics, rigid ankylosed spine, and risk for extensive neurologic injury. The optimal practice is not established with rising clinical occurrences. This article aims to review the current literature regarding diagnosis, classification, and operative and non-operative treatment paradigms of spinal fractures due to AS in adults and present a cohesive perspective to facilitate evidence-based clinical practice.A narrative systematic review was conducted on the basis of the PubMed database, including English-language papers from January 2000 to May 2025. Keywords included "AS", "spinal fracture", "vertebral trauma", "surgical management", and "neurological outcomes". Studies identified were evaluated based on clinical relevance, level of evidence, and representation of evolving concepts in diagnosis and management.The review discusses the specific biomechanical frailties of the ankylosed spine, recent classification methods like AO Spine and Denis classifications, and recent imaging modalities for diagnosis. It highlights operative decision-making approaches, posterior-only, anterior, and combination, in fracture morphology, neurologic status, and patient comorbidities. It discusses perioperative concerns such as positioning issues, blood loss, and complications like hardware failure and infection. Four summary tables provide insight into imaging preference, surgical interventions, outcomes, and complication profiles.Prompt diagnosis and personalized treatment of AS-related spinal fractures are essential to reducing morbidity and mortality. Emerging literature supports the use of posterior-only methods in selected cases, but highly context-specific surgical choices must remain. The review stresses the importance of prospective studies as a guide to standard treatment protocols and improved outcomes for this difficult patient group.

    View details for DOI 10.21037/jss-25-119

    View details for PubMedID 41509825

    View details for PubMedCentralID PMC12775638

  • Molecular and biophysical remodeling of the blood-brain barrier in glioblastoma: mechanistic drivers of tumor-neurovascular crosstalk FRONTIERS IN PHYSICS Abikenari, M., Sjoholm, M., Liu, J., Nageeb, G., Ha, J. H., Wu, J., Ren, A., Sayadi, J., Lim, J., Cho, K., Verma, R., Medikonda, R., Banu, M., Lim, M. 2025; 13

    View details for DOI 10.3389/fphy.2025.1723329

    View details for Web of Science ID 001651720600001

  • 'Time is brain': Enhancing stroke knowledge and emergency response readiness in seniors HEALTH EDUCATION JOURNAL Sadeghzadeh, S., Adu-Brimpong, J., Abikenari, M. A., Bhatia, A., Harary, P. M., Bhanot, P., Muller, R. K., Escalante, R., Schonfeld, E., Jain, B., Nageeb, G., Wu, A., Hsin, G. S., Vora, N., Dodd, R. L. 2025

    View details for DOI 10.1177/00178969251371488

    View details for Web of Science ID 001586652700001

  • Publicly Available Datasets for Artificial Intelligence in Neurosurgery: A Systematic Review. Journal of clinical medicine Chan, B., Kim, B., Schonfeld, E., Nageeb, G., Pant, A., Sjoholm, A., Medikonda, R., Hani, U., Veeravagu, A. 2025; 14 (16)

    Abstract

    Introduction: The advancement of artificial intelligence (AI) in neurosurgery is dependent on high quality, large, labeled datasets. Labeled neurosurgical datasets are rare, driven by the high expertise required for labeling neurosurgical data. A comprehensive resource overviewing available datasets for AI in neurosurgery is essential to identify areas for potential model building and areas of needed data construction. Methods: We conducted a systematic review according to PRISMA guidelines to identify publicly available neurosurgical datasets suitable for machine learning. A PubMed search on 8 February 2025, yielded 267 articles, of which 86 met inclusion criteria. Each study was reviewed to extract dataset characteristics, model development details, validation status, availability, and citation impact. Results: Among the 86 included studies, 83.7% focused on spine pathology, with tumor (3.5%), vascular (4.7%), and trauma (7.0%) comprising the remaining. The majority of datasets were image-based, particularly X-ray (37.2%), MRI (29.1%), and CT (20.9%). Label types included segmentation (36.0%), diagnosis (26.7%), and detection/localization (20.9%), with only 2.3% including outcome labels. While 97.7% of studies reported training a model, only 22.6% performed external validation, 20.2% shared code, and just 7.1% provided public applications. Accuracy was the most frequently reported performance metric, even for segmentation tasks, where only 60% of studies used the Dice score metric. Studies often lacked task-appropriate evaluation metrics. Conclusions: We conducted a systematic review to capture all publicly accessible datasets that can be applied to build AI models for neurosurgery. Current datasets are heavily skewed towards spine imaging and lack both clinical patient specific and outcomes information. Provided baseline models from these datasets are limited by poor external validation, lack of reproducibility, and reliance on suboptimal evaluation metrics. Future efforts should prioritize developing multi-institutional datasets with outcome labels, validated models, public access, and domain diversity to accelerate the safe and effective integration of AI into neurosurgical care.

    View details for DOI 10.3390/jcm14165674

    View details for PubMedID 40869500