Novel Treatment Strategies for Major Depressive Disorder: Investigating Ketamine’s Antidepressant Effects and the Role of the c-Jun N-Terminal Kinase Pathway
Bibliographic record
Abstract
Current first-line treatments for depression, namely monoamine-based drugs such as SSRIs, take weeks to show any clinical effects, and they are only effective in 60-70% of patients.There is therefore an urgent need to develop more rapid and efficacious treatments.Ketamine, an N-methyl-D-aspartate (NMDA) receptor antagonist often used as a dissociative anesthetic, has been found to have rapid (within hours) antidepressant effects, even in historically treatment-resistant patients.Nevertheless, ketamine has its own limitations, such as unwanted side effects and abuse potential.The overarching goal of this thesis was to gain a better understanding of the antidepressant mechanisms of ketamine.Interestingly, we found that ketamine did not impact the typical stress hormone, corticosterone, nor did it modulate brain-region specific monoamine changes that were induced by acute (restraint) or systemic (lipopolysaccharide; LPS) stressors.However, ketamine did have anti-inflammatory actions, reducing interleukin-1 beta (IL-1β) and tumor necrosis factor alpha (TNF-α), and further still, repeated ketamine treatment promoted adult neurogenesis within the hippocampus.Notably, repeated ketamine also had an antidepressant-like behavioral effect that was still detectable 8 days after the final ketamine injection.In terms of potential mechanistic factors, ketamine increased active levels of the signaling factor c-Jun N-terminal kinase (JNK) within the cortex, and inhibition of JNK itself increased corticosterone levels.Intriguingly, JNK inhibition also modulated some stress-induced behavioral and monoaminergic changes, implying a diverse role for the protein.Overall, the data support a role for ketamine in neuroplasticity and immune function, and set the stage for future investigations into the pathways (i.e.JNK) associated with its antidepressant effects.
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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 | 0.001 |
| 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".