Immunosenescence and Multiple Sclerosis: A Literature Review
Bibliographic record
Abstract
Introduction: Multiple sclerosis is a chronic inflammatory disorder characterized by the demyelination of central nervous system neurons, giving rise to various motor and non-motor impairments. Aging has been strongly associated with inflammation and immunosenescence, and it is believed that the dysfunction of regulatory T-cells is the central complication in the maintenance of peripheral immunity. CD4+ T-cells and Th17 cells seem to play a crucial role in autoimmune inflammation and are important in the pathophysiology underlying multiple sclerosis. In this systematic review, the link between aging and T-cell function will be explored as well as its implication in MS pathophysiology. Methods: A literature review was conducted using databases such as PubMed, NCBI, and Scopus. Relevant primary literature describing theories or results of an experiment and review papers were selected. Data from primary articles were analyzed to explore the association between aging and MS, as well as its contribution to immunosenescence. Results: There exists a strong association between aging and the pathophysiology of MS which was suggested by a multitude of laboratory studies. Animal models of experimental autoimmune encephalomyelitis have demonstrated the immunological mechanisms of this disease by highlighting differences in T-cell presence and function in healthy people versus MS patients. Discussion: According to numerous studies, chronic inflammation is recognized as a sign of aging, rendering it one of the key contributors to neurodegenerative diseases like MS. The implication of regulatory T-cells in MS is crucial due to its necessity for the maintenance of immunosuppressive activity, which has been found to deteriorate with age. Myelin antigens supplied by microglial cells reactivate autoreactive CD4+ T-cells infiltrating the CNS, producing a cascade of immunological responses that lead to demyelination and tissue death. Conclusion: This literature review finds that MS is largely T-cell mediated and that the aging process heightens chronic inflammation, leading to the destruction of neurons in the CNS.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.019 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".