The Role of Brain Derived Neurotrophic Factor (BDNF) in Regulating α-synuclein Pathology in Primary Microglia and Neurons.
Bibliographic record
Abstract
-synuclein rich Lewy bodies are a prominent pathological feature of Parkinson's disease (PD) that involves accumulation in both neurons and microglia.With no current cure, there is a need for novel therapies addressing the damage rather than the symptoms of PD as the pathological aggregates cause the death of dopaminergic neurons through inflammatory mechanisms.Evidence has suggested BDNF, a common neurotrophin with essential roles in cell maintenance, neurogenesis, and plasticity could offer neuroprotective effects potentially mitigating the damage observed.However, very little is known regarding its impact upon microglial cells, which are implicated in the neuroinflammatory processes evident in PD.We hypothesized that the administration of exogenous α-syn preformed fibrils (PFFs) will induce a cytotoxic environment associated with increased reactive microglia and that BDNF would reduce this effect by modulating the microglial phenotype.To this end, we found that BDNF when paired with α-syn PFF did decrease the level of cell death and accumulation of α-syn PFFs in selectively treated microglia and neuron co-cultures.BDNF also induced microglial morphological changes over time and cell dependently modulated levels of the antiinflammatory cytokine, IL-4 in cocultures.We currently provide a novel in vitro model that allows for tracking -syn spread and pathology between primary cortical microglia and neurons.The data suggest that highly plastic microglial states occur over time and critically interact with -syn bearing neurons and that BDNF pre-treatment modifies this interaction.
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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 | 0.000 |
| 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) | 0.001 | 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 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".