Furosemide as a Probe Molecule for the Treatment of Neuroinflammation in Alzheimer’s Disease
Bibliographic record
Abstract
The accumulation and deposition of β-amyloid (Aβ) is one postulated cause of Alzheimer’s disease (AD). In addition to its direct toxicity on neurons, Aβ may induce neuroinflammation through the concomitant activation of microglia. Emerging evidence suggests that microglia-mediated neuroinflammation plays an important role in the pathogenesis of AD. As brain macrophages, microglia engulf misfolded-Aβ by phagocytosis. However, the accumulated toxic Aβ may paradoxically “hyper-activate” microglia into a neurotoxic proinflammatory and less phagocytotic phenotype, contributing to neuronal death. This study reports that the known drug furosemide is a potential probe molecule for reducing AD-neuroinflammation. Our data demonstrate that furosemide inhibits the secretion of proinflammatory TNF-α, IL-6, and nitric oxide; downregulates the mRNA level of Cd86 and the protein expression of COX-2, iNOS; promotes phagocytic activity; and enhances the expression of anti-inflammatory IL-1RA and arginase. Our mechanism of action studies further demonstrate that furosemide reduces LPS-induced upregulation of endoplasmic reticulum (ER) stress marker genes, including Grp78, Atf4, Chop, tXbp1, and sXbp1 . These data support the observation that furosemide is a known drug with the capacity to downregulate the proinflammatory microglial M1 phenotype and upregulate the anti-inflammatory M2 phenotype, a potentially powerful and beneficial pharmacologic effect for inflammatory diseases such as AD.
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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 | 0.001 |
| 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".