The Involvement of TNF-α in Cognitive Dysfunction Associated with Major Depressive Disorder: An Opportunity for Domain Specific Treatments
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Major depressive disorder is a highly prevalent, chronic and recurring disorder, associated with substantial impairment in cognitive and interpersonal functions. Accumulating evidence suggests that inflammatory processes play an important role in the etio-pathogenesis, phenomenology, comorbidity and treatment of MDD. Suboptimal remission rates and the persistence of cognitive deficits contribute to functional impairment in MDD inviting the need for the development of mechanistically novel and domain specific treatment approaches. The MEDLINE/ Pubmed database was searched from inception to February, 9th, 2014 with combinations of the following search terms: 'TNF-alpha', 'depression', 'infliximab', 'etanercept', 'adalimumab', 'golimumab' and 'certolizumab'. Preclinical and clinical evidence linking TNF-α to MDD pathophysiology were reviewed as well as the current status of TNF-α modulators as novel agents for the treatment of MDD. Experimental models and clinical studies provide encouraging preliminary evidence for the efficacy of TNF- α antagonists in mitigating depressive symptoms and improving cognitive deficits. Further studies are warranted to confirm these data in larger randomized controlled trials in primary psychiatric populations. Translational research provides a promising perspective that may aid the development and/or repurposing of mechanism-based treatments for depressive symptoms and cognitive impairment in MDD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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