Причины и последствия геномной нестабильности при психических и нейродегенеративных заболеваниях
Bibliographic record
Abstract
Each neuron has 100-10000 connections (synapses) with other neural cells, therefore genome pathologies affecting a small proportion of brain cells are capable of causing dysfunction of the entire central nervous system (CNS). Recently, genome and chromosome instability has been uncovered in neurodegeneration (Alzheimer's disease, ataxia telangiectasia). Somatic tissue-specific mosaicism was observed in the brain of individuals with neuropsychiatric diseases including schizophrenia, autism, intellectual disability, and epilepsy. The study of genetic processes in neurons allows determination of a certain number of genetic pathways and candidate processes, modifications of which can cause impaired genome stability. Brain-specific somatic mutations generally occur at the earliest stages of development. Accordingly, genome variability and somatic mosaicism are expected to be mediated by cell cycle regulation, DNA repair, DNA replication, and programmed cell death in the brain. Endomitosis, endoreduplication, and abortive entrance to the cell cycle are also commonly observed in neurodegeneration. Brain-specific genome instability maybe a key element in the pathogenic cascade of neurodegeneration. Here we review the current state of knowledge concerning somatic genome variations in neurodegenerative and psychiatric diseases and analyze the causes and consequences of genomic instability in the CNS.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".