Anti-inflammatory effects of moxifloxacin and levofloxacin on cadmium-activated human astrocytes: Inhibition of proinflammatory cytokine release, TLR4/STAT3, and ERK/NF-κB signaling pathway
Bibliographic record
Abstract
Cadmium is a non-essential element and neurotoxin that causes neuroinflammation, which leads to neurodegenerative diseases and brain cancer. To date, there are no specific or effective therapeutic agents to control inflammation and alleviate cadmium-induced progressive destruction of brain cells. Fluoroquinolones (FQs), widely used antimicrobials with effective blood-brain barrier penetration, show promise in being repurposed as anti-inflammatory drugs. Therefore, we aimed to test the efficacy of repurposed FQs for the treatment of cadmium-induced inflammation using cultures of U-87 MG human astrocytes and primary human astrocytes. Both FQs abrogated cadmium-induced interleukin (IL)-6 and IL-8 release from human astrocytes in a concentration and time-dependent manner, although levofloxacin had a stronger inhibitory effect than moxifloxacin. The downregulation of inflammatory cytokine release occurred with a concomitant reduction in cadmium-induced elevations in p65 nuclear factor-κB (NF-κB) and extracellular signal-regulated kinases (ERKs) 1/2 phosphorylation. Additionally, levofloxacin treatment significantly alleviated cadmium-induced activation of phosphorylated NF-κB translocation and toll-like receptor (TLR)-4/signal transducer and activator of transcription (STAT) 3 signaling. Transcriptome analysis revealed that modulation of inflammation-related pathways was the most enriched after FQ treatment. Our data suggest that FQs, particularly levofloxacin, attenuate the inflammatory process mediated by cadmium in human astrocytes. These effects may be mediated, at least in part, by inhibition of immune pathways regulated by TLR4, STAT3, ERK MAPK, and NF-κB.
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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) | 0.001 | 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".