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Aug 2026
Dale E. BredesenCorresponding author
Background/Objectives The etiology and pathophysiology of neurodegenerative diseases are incompletely understood, and the treatments largely ineffective. A unifying theory that explains otherwise poorly understood observations may help to enhance therapeutic outcomes. Methods Here is proposed a unifying theory of neurodegenerative disease, the Pr2 theory, describing the origin, pathophysiology, optimal evaluation and treatment of patients, explanation of observations not explained by previous theories, and implications for further research and translation. The theory proposes that neurodegenerative diseases represent network insufficiencies, networks jeopardized by antagonistic pleiotropy. Through repeated evolutionary selection for performance over durability, highly functional and finely tuned neural subnetworks subserving key survival features such as lethal force and behavioral modification have evolved at the expense of durability and reserve. The Pr2 theory focuses on the physiology associated with the causes of neurodegeneration rather than the pathology that is largely reactive but has been the focus of the lion’s share of the research and attempted treatment of neurodegenerative diseases. Results Initial translation of the Pr2 theory to a precision medicine approach to cognitive decline has led to superior outcomes compared to other therapeutic approaches, and at least in some cases, sustained improvement for over a decade has been achieved. Conclusions The proposed theory explains key features of neurodegenerative disease pathophysiology and suggests novel therapeutic targets. Initial predictions have been validated in two proof-of-concept trials and one randomized controlled trial for patients with mild cognitive impairment or early-stage dementia.
Apr 2024 DOI 10.14302/issn.2998-4211.jalr-24-4926
Bahadur Khan FaizaCorresponding author
This article has been retracted on 20 March 2025. VIEW THE RETRACTION NOTICE (https://doi.org/10.14302/issn.2998-4211.jalr-25-5855) The research is focused on neuroinflammation a normal physiological process which is known to be associated with neurodegenerative diseases could be the potential targeted therapy via the microglia cells, it starts with defining Alzheimer’s; a neurodegenerative disease which causes deposition of Aβ (amyloid beta) protein in the cerebral cortex as well as NFT (neurofibrillary tangles) in the hippocampus and basal ganglia. The paper then describes process of neuroinflammation, microglia’s role, apolipoprotein E4 gene in relation to Alzheimer’s, which leads to different stem cell research and how pruning microglia as well as targeting microglia receptors in the brain is being used in current research trials, we included multiple meta-analysis showing microglia receptors being targeted currently by emerging drugs like propofol, antibodies CSF1R inhibitor etc, which are currently under trial phase, the research ends with concluding potential diagnostic markers like sirt1 considered to be an anti-aging protein which can be used as therapeutic interventions and Lps effect on Sirt 1. A Microglia initiated target therapy in Neuroinflammation for Alzheimer’s Patients.
Jan 2019 DOI 10.14302/issn.2641-5526.jmid-18-2529
Juan Luis Fernández-MartínezCorresponding author
Discrimination of case-control status based on gene expression differences has potential to identify novel pathways relevant to neurodegenerative diseases including Parkinson’s disease (PD). In this paper we applied two different novel algorithms to predict dysregulated pathways of gene expression across several different regions of the brain in PD and controls. The Fisher’s ratio sampler uses the Fisher’s ratio of the most discriminatory genes as prior probability distribution to sample the genetic networks and their likelihood (accuracy) was established via Leave-One-Out-Cross Validation (LOOCV). The holdout sampler finds the minimum-scale signatures corresponding to different random holdouts, establishing their likelihood using the validation dataset in each holdout. Phenotype prediction problems have by genesis a very high underdetermined character. We used both approaches to sample different lists of genes that optimally discriminate PD from controls and subsequently used gene ontology to identify pathways affected by disease. Both algorithms identified common pathways of Insulin signaling, FOXA1 Transcription Factor Network, HIF-1 Signaling, p53 Signaling and Chromatin Regulation/Acetylation. This analysis provides new therapeutic targets to treat PD.