Therapeutic Strategies Targeting Oxidative Stress and Inflammation: A Narrative Review
Bibliographic record
Abstract
Oxidative stress and inflammation are deeply interconnected processes implicated in the onset and progression of numerous chronic diseases. Despite promising mechanistic insights, conventional antioxidant and anti-inflammatory therapies such as NSAIDs, corticosteroids, and dietary antioxidants have shown limited and inconsistent success in long-term clinical applications due to challenges with efficacy, safety, and bioavailability. This review explores the molecular interplay between redox imbalance and inflammatory signaling and highlights why conventional therapeutic translation has often been inconsistent. It further examines emerging strategies that aim to overcome these limitations, including mitochondrial-targeted antioxidants, Nrf2 activators, immunometabolic modulators, redox enzyme mimetics, and advanced delivery platforms such as nanoparticle-enabled delivery. Natural polyphenols, nutraceuticals, and regenerative approaches, including stem cell-derived exosomes, are also considered for their dual anti-inflammatory and antioxidant potential. By integrating recent preclinical and clinical evidence, this review underscores the need for multimodal, personalized interventions that target the redox-inflammatory axis more precisely. These advances offer renewed promise for addressing complex diseases rooted in chronic inflammation and oxidative stress.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".