A new look at auranofin, dextromethorphan and rosiglitazone for reduction of glia-mediated inflammation in neurodegenerative diseases
Bibliographic record
Abstract
Neurodegenerative disorders including Alzheimer's disease are characterized by chronic inflammation in the central nervous system. The two main glial types involved in inflammatory reactions are microglia and astrocytes. While these cells normally protect neurons by providing nutrients and growth factors, disease specific stimuli can induce glial secretion of neurotoxins. It has been hypothesized that reducing glia-mediated inflammation could diminish neuronal loss. This hypothesis is supported by observations that chronic use of non-steroidal anti-inflammatory drugs (NSAIDs) is linked with lower incidences of neurodegenerative disease. It is possible that the NSAIDs are not potent enough to appreciably reduce chronic neuroinflammation after disease processes are fully established. Gold thiol compounds, including auranofin, comprise another class of medications effective at reducing peripheral inflammation. We have demonstrated that auranofin inhibits human microglia- and astrocyte-mediated neurotoxicity. Other drugs which are currently used to treat peripheral inflammatory conditions could be helpful in neurodegenerative disease. Three different classes of anti-inflammatory compounds, which have a potential to inhibit neuroinflammation are highlighted below.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".