RIPK1 inhibition mitigates neuroinflammation and rescues depressive-like behaviors in a mouse model of LPS-induced depression
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Depression is often linked to inflammation in the brain. Researchers have been exploring ways to reduce this inflammation to improve depression symptoms. One potential target is a protein called RIPK1, which is known to contribute to brain inflammation. However, it's unclear how RIPK1 influences depression. Our study aims to determine whether RIPK1 inhibition could alleviate neuroinflammation-associated depression and elucidate its underlying mechanisms. METHODS: To investigate our research objectives, we established a neuroinflammation mouse model by administering LPS. Behavioral and biochemical assessments were conducted on these mice. The findings were subsequently validated through in vitro experiments. RESULTS: . Remarkably, Necrostatin (Nec-1 S), a RIPK1 inhibitor, mitigated these changes. We further found altered expression and phosphorylation of eIF4E, PI3K/AKT/mTOR, and synaptic proteins in hippocampal tissues, BV2, and N2a cells post-LPS treatment, which Nec-1 S also ameliorated. Importantly, eIF4E inhibition reversed some of the beneficial effects of Nec-1 S, suggesting a complex interaction between RIPK1 and eIF4E in LPS-induced neuroinflammation. Moreover, citronellol, a RIPK1 agonist, significantly altered eIF4E phosphorylation, indicating RIPK1's potential upstream regulatory role in eIF4E and its contribution to neuroinflammation-associated depression. CONCLUSION: These findings propose RIPK1 as a pivotal mediator in regulating neuroinflammation and neural plasticity, highlighting its significance as a potential therapeutic target for depression.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it