Bibliographic record
Abstract
Epigenetic modifications happen in a dynamic and continuous fashion during life. These include molecular modifications of DNA, RNA, and proteins (histones) during cellular renewal and differentiation, and organ development throughout development and life. It is not surprising that deregulation of such fundamental mechanisms could lead to cellular abnormalities and organ disfunction, causing human disease. While all body parts and organs have their own regulatory processes, the mammalian brain and central nervous system appear to be the most complex part of the body. Accordingly, neurons that are the nerve cells in the central and peripheral nervous system have distinct characteristics that makes them especially important. Of note, change in epigenetic modifications in the central or peripheral nervous system may lead to neurological disorders. This special topic on "Epigenetic modification in neurological diseases" includes four peer-reviewed articles published in Frontiers in Neuroscience.Focusing on the role of non-coding regulatory RNA molecules in Alzheimer's disease (AD), Canoy and collogues discuss recent advances in AD pathology [1]. In this systemic review, the authors cover a range of topics on long non-coding RNA (lncRNA) molecules, microRNAs (miRNA), circular RNAs (circRNA), as well as piwi-interacting RNAs (piRNA). In addition to the role of these regulatory RNAs in AD pathology, their potential application as diagnostic markers as well as possible therapeutic targets are also discussed. In this regard, the authors cover implication of these regulators RNAs in different cellular processes including cell propagation and cell death, apoptosis, autophagy, tau phosphorylation, amyloid-beta aggregation, oxidative stress, and neuroinflammation. The authors further explore the workflow of their approach in screening literature and databases that is clearly demonstrated in the first Figure of this article. Additional illustrations are used to present early onset versus late onset, and not-defined cases of Alzheimer's disease along with extra information within the three Figures and one Table of this comprehensive review [1].The article by Tian et al., presents an interesting concept on the impact of elevated hemoglobin A1c (HbA1c) with increased risk for impaired cognition in a sex-dependent manner [2]. While the link between epigenetic modifications such as DNA methylation with that of HbA1c in type 1 diabetes has been reported [3], this study highlights the potential effects of higher levels of HbA1c in cognitive impairment. The authors explain how they have analyzed independent datasets from UK Biobank cohort with and without neuroimaging information, as the gene-outcome and gene-exposure groups, respectively. The authors describe certain types of analysis to include HbA1c levels, brain age gap, and fractional anisotropy. The detailed Figure 1 of this article captures the systemic analysis and study design of the authors and is complemented with two Tables and additional Figures to represent the results [2].A systemic review by Yuan et al., provides engaging contents on the link between epigenetics and depression [4]. This study benefits from the Web of Science core dataset that covers depression epigenetics published studies between 01/2002-to-06/2023. The authors used certain key words in their literature search, highlighting that the link between epigenetics and adolescent depression requires further investigations. The authors discuss the nature of depression, their approach and strategy in dataset screen and analysis, and present their results. Multiple Figures and Tables are The authors make a case by evidence from epidemiological studies to support such association, while acknowledging the need for molecular investigations. The authors explain how certain neurodegenerative diseases such as AD and Parkinson's disease (PD) may have epigenetic components. In the other hand the authors explain that individuals suffering from TBI may show short-term or long-term change of epigenetic mechanisms in the brain cells that could potentially be a link to the risk of experiencing AD, PD, and/or dementia. In this article, the authors discuss epigenetic modifications such as DNA methylation and histone post-translational modification(s) like acetylation. The authors further discuss the link between TBI pathology and epigenetic modifications, presenting their hypothesis that these could potentially be considered a risk for neurodegenerative disorders. They also provide insightful opinion on how certain interventions could prevent the development of neurodegenerative disorders. Their discussions of such therapeutic intermediation include the use of methyl donors, inhibition of chronic neuroinflammation, as well as the application of inhibitors for histone acetylation or methylation. This article is complemented with a Table that covers
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 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".