Neuroinflammation and neuroprogression in depression: Effects of alternative drug treatments
Bibliographic record
Abstract
Given that available antidepressant pharmacotherapies are not optimally effective, there is a need for alternative treatment options that are rooted in a comprehensive understanding of the illness's pathophysiology. Major depressive disorder (MDD) has been historically attributed to monoamine, i.e., serotonin (5-hydroxytryptamine, 5-HT) imbalance and some brain morphological pathologies that have directed treatment towards particular medications that are only minimally effective. MDD pathophysiologies have now been regarded as linked to chronic inflammation and MDD can be treated with compounds that have anti-inflammatory properties. Individuals vulnerable to MDD have increased baseline neuroinflammatory response that is exacerbated by psychogenic stress. When pro-inflammatory mechanisms are chronically hyperactive, dysfunction of brain-related processes occur. We propose that inflammation is one of the primary mechanisms that trigger biological changes leading to MDD. Inflammatory resolution occurs when homeostasis is achieved after an inflammatory response. However, cascading biological events are likely to prevent resolution from occurring and worsen both inflammation and MDD. Novel and alternative pharmacotherapies-e.g., ketamine, cannabinoids, and psychedelics-provide a richer mechanistic perspective on the role of neuroinflammation and neuroprogression by means of rapid, short-term, and long-term symptom relief potentially based on their anti-inflammatory properties. These drugs ultimately decrease proinflammatory cytokine levels that correspond with improved symptoms. However, it is unclear what differentiates these compounds from others in their mechanistic efficacy. Thus, a closer investigation into their anti-inflammatory effects is imperative in order to better elucidate the link between MDD and inflammation, as well as uncover the mechanisms involved in long-term symptom reduction of MDD.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".