Bibliographic record
Abstract
Prion diseases share common features on a sub-cellular level with many neurodegenerative diseases including Alzheimer disease; the most prevalent neurodegenerative disease world-wide. The most obvious similarity is the accumulation of misfolded forms of the host proteins which forms aggregates in the brains of patients. Remarkably, one of the earliest pathological changes detected in degenerating brain tissue, well before clinical symptoms are observed, is synaptic dysfunction and loss. This pathology was recently shown to be reversible in early stages of mouse prion disease suggesting that synaptic regeneration and reestablishment of neuronal function is possible. Determination of the molecular events that underlie synapse degeneration and how this eventually results in neuronal loss is therefore a research priority that may contribute to the search for new therapeutic interventions for neurodegenerative disorders. Functional genomic studies using unbiased whole genome expression analyses represent one method that can provide insights into these perplexing processes. However, transcriptional profiles from brain tissues are representative of a heterogeneous mixture of cell types that effectively mask the expression of low abundance transcripts, or molecular changes that occur only in a small population of affected neurons. One method that was recently applied to address these challenges was laser capture microdissection which was used to effectively isolate the CA1 neuronal rich region of the hippocampus prior to RNA extraction. Profiling of both mRNAs and microRNAs revealed previously unidentified neuronal-specific genes and expression signatures that are relevant to understanding the pathophysiological processes involved in preclinical stages of prion disease. In this review we will highlight these molecular signatures and discuss their implications with respect to prion-induced neurodegeneration.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".