NEURO-INFLAMMATION AND ITS IMPACT ON NEURODEGENERATIVE DISEASES: A SYSTEMATIC REVIEW
Bibliographic record
Abstract
Background: Neurodegenerative disorders, including Alzheimer’s disease, Parkinson’s disease, and Amyotrophic Lateral Sclerosis (ALS), involve gradual loss of neurons and functional impairment. There is new proof that neuro-inflammation is critical to the onset and progression of these diseases. The damage to neural tissue as well as the sustained degeneration of neural tissues is caused due to pro-inflammatory cytokines along with microglial activation and immune signaling pathways. This systematic review aims to integrate the existing information about the role of inflammation in neurodegenerative diseases, especially focusing on its mechanisms and possibilities for treatment. Objective: To systematically review existing literature to assess the impact of neuro-inflammation in the main neurodegenerative illness in relation to their pathogenesis. This review attempts to find main predictors of inflammation, explain the primary processes, and evaluate the effects of changing immunological defense responses of the central nervous system on treatment outcomes. Methods: A systematic search was done on PubMed, Scopus, Web of Science, and Google Scholar from the year 2010 to 2025. The inclusion criteria focused on studies dealing with neurodegenerative diseases and had relevant inflammatory biomarkers or mechanisms. The data collected included study design, disease focus, demographics of the markers (cytokines, TLR4, microglial activation), mechanistic pathways, and therapeutic approaches. The quality of studies was evaluated using the Newcastle-Ottawa Scale. Findings through the studies were qualitatively analyzed to identify prevailing themes. Results: There were 123 eligible studies identified through the search. Euro-inflammation was persistently associated with disease evolution in all three paramount disorders. The most frequently examined factors explain neuro degeneration as cytokines (e.g., IL-1β, TNF-α.) and overstimulation of microglia. TLR4 signaling and inflammasome pathways were often related to oxidative stress, synaptic pathology, and neuronal death. About 80% of studies found that modifying inflammatory responses is likely to improve the course of the disease. The results indicate that targeting neuroinflammatory processes has the potential for effective diagnosis and treatment. Conclusions: From this systematic review, hyper inflammation is one of the most important phenomena during the progression of neurodegenerative disorders. Inflammation-related neurodegeneration such as cytokine release and microglial activity provides damage to the neurons, constituting a rational basis for therapy. Greater focus on unmasking treatment will premise strategies based on timing of the intervention, controlling inflammation, and the use of multidisciplinary approaches into practice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".