Daniel Wayne Eller

All Publications

• Long-Chain Fatty Acids as Drivers of Neuroinflammation in Neurodegeneration: Mechanistic Links to Lipid Peroxidation, Ferroptosis, and Mitochondrial Dysfunction. Nutrients Christodoulou, R. C., Lorentzen, L., Eller, D., Vassiliou, E. 2026; 18 (9)

  Abstract

  Background: Neurodegenerative diseases (NDs) are mainly considered disorders marked by severe immunometabolic imbalance, characterized by ongoing neuroinflammation and glial activation. While mitochondrial dysfunction and oxidative stress are well-known features, the upstream metabolic factors linking these pathological processes remain poorly understood. Methods: In this review, we examined recent preclinical and clinical studies exploring the connections between lipid metabolism, glial immunometabolism, and regulated cell death pathways. Our focus was on how long-chain fatty acids (LCFAs) facilitate communication among mitochondria, reactive oxygen species (ROS), and ferroptosis in Alzheimer's disease (AD), Parkinson's disease (PD), and amyotrophic lateral sclerosis (ALS). Results: New evidence shifts LCFAs from merely being passive indicators of cellular damage to active, upstream regulators of the neuroimmune response. Existing research shows that excess LCFA intake can overload astrocytic mitochondrial oxidative phosphorylation, leading to abnormal lipid droplet buildup and reactive astrogliosis. This lipid-driven reactivity promotes microglial polarization toward a persistent pro-inflammatory state. Notably, high levels of specific LCFAs, especially arachidonic acid, increase ROS production and lipid peroxidation. This lipotoxic environment ultimately triggers ferroptosis, an iron-dependent form of cell death shared across multiple NDs. Conclusions: The harmful interaction among mitochondrial dysfunction, lipid peroxidation, and ferroptosis is driven by an imbalance in LCFA levels. Addressing current challenges, such as the complex effects of polyunsaturated fatty acid supplementation, requires advanced techniques like single-cell multi-omics and artificial intelligence. Understanding this intricate lipidomic-transcriptomic crosstalk is crucial for moving toward personalized neuroimmunometabolism and developing new treatments to prevent ferroptosis.

  View details for DOI 10.3390/nu18091392

  View details for PubMedID 42123994

• Multimodal artificial intelligence in glioma management: integrating neuroimaging and hematologic biomarkers for precision oncology Frontiers in Oncology Christodoulou, R. C., Papageorgiou, P. S., Eller, D. W., Vassiliou, E., Papageorgiou, S. G., Solomou, E., Georgiou, M. 2026; 16: 13

  Abstract

  Gliomas are biologically heterogeneous primary brain tumors that remain challenging to diagnose, prognosticate, and monitor noninvasively, owing to marked intratumoral heterogeneity, treatment-related imaging changes, and limited accessibility of tissue biomarkers. Despite advances in molecular classification, clinical decision-making still relies heavily on neuroimaging, highlighting the need for integrative, data-driven approaches.This narrative review examines how artificial intelligence (AI) can integrate multimodal neuroimaging with hematologic and other liquid biomarkers to support clinical decision-making in glioma management.We synthesize recent advances in machine learning (ML) and deep learning (DL) applied to MRI and PET for glioma detection, segmentation, molecular phenotype inference, and outcome prediction. We review both segmentation-based and segmentation-free modeling paradigms, highlighting their respective assumptions, advantages, and limitations. Advanced imaging techniques, including diffusion (DWI, DTI) and perfusion imaging, MR spectroscopy, and metabolic and amino acid PET, are discussed as sources of biologically specific signals that extend beyond conventional structural imaging. We further examine blood-derived biomarkers, such as inflammatory and immune mediators, circulating nucleic acids, and extracellular vesicle cargo, which provide complementary insights into tumor-host interactions and enable longitudinal assessment. Emerging generative and systems-level modeling approaches are also reviewed in the context of multimodal data integration and clinical application.Multimodal AI has the potential to integrate spatial imaging phenotypes with systemic biological signals to improve noninvasive diagnosis, molecular risk stratification, and treatment monitoring in gliomas. Translation to clinical practice will depend on appropriate methodological design choices, standardized workflows, rigorous external validation, uncertainty-aware decision support, and continuous performance monitoring in real-world settings.

  View details for DOI 10.3389/fonc.2026.1812518

  View details for PubMedCentralID PMC13135974

• Metabolic Dysfunction in Alzheimer's Disease: Brain Glucose Hypometabolism as an Early Precursor to Amyloid and Tau Pathology. Journal of clinical medicine Christodoulou, R. C., Eller, D., Papageorgiou, P. S., Angelopoulou, E., Vassiliou, E., Papageorgiou, S. G. 2026; 15 (5)

  Abstract

  Objective: Alzheimer's disease (AD) is traditionally characterized by amyloid-β and tau pathology; however, accumulating evidence indicates that metabolic and inflammatory dysfunctions are early, central contributors to disease development. This narrative review explores how metabolic disturbances influence AD pathophysiology. Methods: A comprehensive literature search was performed on PubMed, Embase, and Scopus. Selected studies were original studies or reviews published in English within the past five years involving human subjects. Case reports, case series, editorials, and non-human studies were excluded. A total of 64 articles were reviewed and summarized. Results: Cerebral glucose hypometabolism, mitochondrial impairment, insulin resistance, oxidative stress, and neuroinflammation were observed throughout the AD spectrum. These metabolic changes often appeared before significant amyloid accumulation and were more closely linked to tau pathology and cognitive decline. Early microglial activation was linked to transient glucose hypermetabolism, progressing to glucose hypometabolism and neurodegeneration as the disease advanced. Conclusions: AD is associated with a gradual breakdown of metabolic and inflammatory homeostasis, which occurs before and promotes the development of traditional neuropathological features. Addressing early metabolic vulnerabilities may be essential for effective disease intervention and prevention.

  View details for DOI 10.3390/jcm15051884

  View details for PubMedID 41827301

  View details for PubMedCentralID PMC12986246

• From Lesion to Decision: AI for ARIA Detection and Predictive Imaging in Alzheimer's Disease. Biomedicines Christodoulou, R. C., Papageorgiou, P. S., Sarquis, M. D., Rivera, L., Morales Gonzalez, C., Eller, D., Rivera, G., Petrou, V., Vamvouras, G., Vassiliou, E., Papageorgiou, S. G., Georgiou, M. F. 2025; 13 (11)

  Abstract

  Background: Alzheimer's disease (AD) remains the leading cause of dementia worldwide, with anti-amyloid monoclonal antibodies (MABs) marking a significant advance as the first disease-modifying therapies. Their use, however, is limited by amyloid-related imaging abnormalities (ARIA), which appear as vasogenic edema or effusion (ARIA-E) and hemosiderin-related changes (ARIA-H) on MRI. Variability in imaging protocols, subtle early findings, and the lack of standardized risk models challenge detection and management. Methods: This narrative review summarizes current artificial intelligence (AI) applications for ARIA detection and risk prediction. A comprehensive literature search across PubMed, Embase, and Scopus identified studies focusing on MRI-based AI analysis, lesion quantification, and predictive modeling. Results: The evidence is organized into six thematic domains: ARIA definitions, imaging challenges, foundations of AI in neuroimaging, detection tools, predictive frameworks, and future perspectives. Conclusions: AI offers promising avenues to standardize ARIA evaluation, improve lesion quantification, and enable individualized risk prediction. Progress will depend on multicenter datasets, shared frameworks, and prospective validation. Ultimately, AI-driven neuroimaging may transform how treatment-related complications are monitored in the era of anti-amyloid therapy.

  View details for DOI 10.3390/biomedicines13112739

  View details for PubMedID 41301832

  View details for PubMedCentralID PMC12650076

• The impact of library visits on undergraduate student GPA: The vital role of the library as a contributor to student success JOURNAL OF ACADEMIC LIBRARIANSHIP Bloom, M., Eller, D., Hall, M., Lang, A., Sample, A. 2025; 51 (3)

  View details for DOI 10.1016/j.acalib.2025.103040

  View details for Web of Science ID 001448042700001

• Shrunken Median Location Effect Estimates: An Application to Immuno-Oncology Journal of Probability and Statistics Holmes, T. H., Eller, D. W., Barnett, J., Maecker, H. T. 2025; 2025 (1): 6

  View details for DOI 10.1155/jpas/1856034

• A Theory for Statistically Optimized Bridging Specimens Holmes, T., Barnett, J., Eller, D., Maecker, H. SSRN. 2025

  Abstract

  In immune assays, at times the same specimen or set of specimens are run in all batches. These are bridging specimens that can be used to adjust for batch effects. In this paper, we provide complete theory on the use of bridging specimens to adjust for batch effects, encompassing design considerations, a linear model that includes bridging specimens as covariates, accounting for measurement error in bridging specimens, and borrowing information across analytes for efficiency gains. This theory is outlined so that specific methods can be built on a solid theoretical foundation.

• Bibliometrics and Pentecostal Scholarship A Review of Trends in Pneuma PNEUMA Eller, D. W., Isgrigg, D. D. 2023; 45 (1): 78-101

  View details for DOI 10.1163/15700747-bja10084

  View details for Web of Science ID 001021542500006

• New Horizons for the Individual Research Consultation: Critical Hermeneutics and Habermas’ Goal of Intersubjective Agreement InterActions: UCLA Journal of Education and Information Studies Eller, D. W. 2023; 18 (1): 16

  View details for DOI 10.5070/D418159162

• Transparency and the future of semantic searching in academic libraries Information Services and Use Eller, D. W. 2022; 42 (3-4)

  View details for DOI 10.3233/ISU-220175