FX5, a non-steroidal glucocorticoid receptor antagonist, ameliorates diabetic cognitive impairment in mice
Bibliographic record
Abstract
Abstract Diabetic cognitive impairment (DCI) is a common diabetic complication characterized by learning and memory deficits. In diabetic patients, hyperactivated hypothalamic-pituitary-adrenal (HPA) axis leads to abnormal increase of glucocorticoids (GCs), which causes the damage of hippocampal neurons and cognitive impairment. In this study we investigated the cognition-improving effects of a non-steroidal glucocorticoid receptor (GR) antagonist 5-chloro-N-[4-chloro-3-(trifluoromethyl) phenyl]thiophene-2-sulfonamide (FX5) in diabetic mice. Four weeks after T1DM or T2DM was induced, the mice were administered FX5 (20, 40 mg·kg−1·d−1,i.g.) for 8 weeks. Cognitive impairment was assessed in open field test, novel object recognition test, Y-maze test, and Morris water maze test. We showed that FX5 administration significantly ameliorated the cognitive impairments in both type 1 and 2 diabetic mice. Similar cognitive improvement was observed in diabetic mice following brain GR-specific knockdown by injecting AAV-si-GR. Moreover, AAV-si-GRinjection occluded the cognition-improving effects of FX5, suggesting that FX5 functioning as a non-steroidal GR antagonist. In PA-treated primary neurons (as DCI model in vitro), we demonstrated that FX5 (2, 5, 10 μM) dose-dependently ameliorated synaptic impairment via upregulating GR/BDNF/TrkB/CREB pathway, protected against neuronal apoptosis through repressing GR/PI3K/AKT/GSK3β-mediated tauopathy and subsequent endoplasmic reticulum stress. In LPS-treated primary microglia, FX5 dose-dependently inhibited inflammation through GR/NF-κB/NLRP3/ASC/Caspase-1 pathway. These beneficial effects were also observed in the hippocampus of diabetic mice following FX5 administration. Collectively, we have elucidated the mechanisms underlying the beneficial effects of non-steroidal GR antagonist FX5 on DCI and highlighted the potential of FX5 in the treatment of the disease.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".