Epigenetic Modifications lead towards Neurodegeneration
Bibliographic record
Abstract
Background: The foremost factor involved in Neurodegeneration is the impact of epigenetic modifications; through its nature to epigenetically mark the neuron-associated genes, also, by affecting cognitive functions and damaging neurons that promote mutations. Due to these changes in the genes; neurodegenerative diseases are developed. This review will assess epigenetic modifications that switch “on” & “off” the genes associated with neurons that lead towards neurodegeneration in humans. Methodology: This systematic review is based on Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines to conduct a search strategy and for the preparation of the manuscript. A search engine (PubMed) was used and the article reference list was searched for relevant primary research articles. 100 out of 22278 studies dated from January/2000 to February/2019 met the inclusion criteria. Two quality assessments were piloted and included: (1) Authors evaluation and (2) Risk of bias. Results: Quality of interventions provided was rated “good”, Risk of bias in studies was rated “fair” and the team of authors approved included papers. Furthermore, 13 out of 100 studies critical appraisal analysis demonstrated the relationship between epigenetic alterations and neurodegeneration and the rest of the studies described neuro-epigenetics, epigenetic remodeling and epigenetic mechanisms. Conclusion: Exogenous influence like aviation stress or co-factors, such as nutrition and physical stress plays a major role in silencing the “gene switching” proteins of epigenetic marks and influences the onset and progression of neurodegeneration. Furthermore, intervention in epigenetics might help promote brain health.
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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 | 0.014 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".