The neuronal nucleus: a new battlefield in fight against neurodegeneration
Bibliographic record
Abstract
Aging is an inevitable fact of life which brings along a series of age-associated diseases. Although medical innovations and patient care improvement have increased our life expectancy, the rate of age-associated diseases have also increased. Nervous system is specifically prone to these diseases that cause neuronal loss in different anatomical regions. Alzheimer's disease is the best-known example of age-associated illnesses and is diagnosed by accumulation of intracellular Neurofibrillary tangles and extracellular Amyloid Plaques resulting in dementia. However, therapeutic attempts aiming at the removal of these plaques and tangles to reverse the cognitive decline have generally failed in human patients and may compromise the patient's health. We have learnt that interruption of neuronal housekeeping systems such as autophagy contributes to formation of these aggregates, and therefore understanding the underlying mechanisms that lead to failure of these endogenous protective systems may provide valuable information and novel therapies. The house keeping systems are delicately regulated through gene expression and chromatin modifications in the nucleus, however, the contribution of this largest cellular organelle in pathophysiology of the disease has been overlooked. During the last few years, a wealth of information on neuronal nucleus has emerged that provides a strong rationale for examining its contribution to the pathophysiology of the disease. In this research perspective, I have attempted to summarize the latest research on neuronal nucleus, with a special focus on nuclear lamina damage and its downstream events to rationalize the need for focusing on the neuronal nucleus as a therapeutic target.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".