MicroRNA in neurodegenerative drug discovery: the way forward?
Bibliographic record
Abstract
Neurodegenerative diseases occur when neuronal cells in the brain or spinal cord progressively lose function and eventually die. Pathological analysis of these tissues reveals changes that include the loss of synapses, tangles of misfolded protein and immune cell activation, even during very early stages of disease well before debilitating clinical signs are apparent. This suggests that if neurodegeneration is treated early enough, drugs designed to delay the progress of these diseases by either repairing the early damage and loss of neurons, or protecting neuron functionality from further insult, may be efficacious. MicroRNAs (miRNAs) are small non-coding RNAs that can post-transcriptionally regulate gene expression. They are particularly numerous within neurons where many are expressed with high specificity, which suggests that they have important roles in the healthy brain. Indeed, miRNAs are essential for the post-mitotic survival of neurons, implying a crucial role in survival and neuroprotection. This has focused attention on exploring the use of miRNA-based drugs as a means to correct cellular abnormalities and maintain neuronal function in neurodegenerative diseases. These efforts are spurred on by the rapid progress to clinical trials for a number of miRNA-based therapies for other diseases such as cardiovascular diseases, fibrosis and cancer.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.013 | 0.019 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".