Neuroprotection: Pro-survival and Anti-neurotoxic Mechanisms as Therapeutic Strategies in Neurodegeneration
Bibliographic record
Abstract
Neurotrophins (NTs) are a subset of the neurotrophic factor family. These growth factors were originally named based on the nerve growth functional assays used to identify them. NTs act as paracrine or autocrine factors for cells expressing NT receptors. The receptors and their function have been studied primarily in cells of the nervous system, but are also present in the cardiovascular, endocrine, and immune systems, as well as in many neoplastic cells. The signals activated by NTs can be varied, depending on cellular stage and context, healthy or disease states, and depending on whether the specific NTs and their receptors are expressed in the relevant cells. In the healthy central and peripheral adult nervous systems, NTs drive neuronal survival, phenotype, synaptic maintenance, and function. Deficiencies of the NT/NT receptor axis are causally associated with disease onset or disease progression. Paradoxically, NTs can also drive synaptic loss and neuronal death. In the embryonic stage this activity is essential for proper developmental pruning of the nervous system, but in the adult it can be associated with neurodegenerative disease. Given their key role in neuronal survival and death, NTs and NT receptors have long been considered therapeutic targets to achieve neuroprotection. The first neuroprotective approaches consisted of enhancing neuronal survival signals using NTs. Later strategies selectively targeted receptors to induce survival signals specifically, while avoiding activation of death signals. Recently, the concept of selectively targeting receptors to reduce neuronal death signals has emerged. Here, we review the rationale of each neuroprotective strategy with respect to the complex cell biology and pharmacology of each target receptor.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".