Thiazolidinediones: novel treatments for cognitive deficits in mood disorders?
Bibliographic record
Abstract
The aim of this review is to provide a rationale for evaluating thiazolidinediones (TZDs) as putative treatments for cognitive deficits in individuals with mood disorders. A MedLine search of all English-language articles published between January 1966 and August 2006 was conducted. The search terms were: the non-proprietary names of TZDs (e.g., rosiglitazone and pioglitazone), peroxisome proliferator-activated receptor, cognition, neuroprotection, inflammation, oxidative stress, cellular metabolism and excitotoxicity cross-referenced with the individual names of mood (e.g., major depressive disorder and bipolar disorder) and dementing disorders (e.g., Alzheimer's disease) as defined in the Diagnostic and Statistical Manual of Mental Disorders third edition, revised/fourth edition, text revision (DSM-III-R/IV-TR). The search was augmented with a manual review of article reference lists. Articles selected for review were based on adequacy of sample size, the use of standardized experimental procedures, validated assessment measures and overall manuscript quality. Contemporary pathophysiologic models of mood disorders emphasize alterations in neuronal plasticity, metabolism and cytoarchitecture with associated regional abnormalities in neuronal (and glial) density and morphology. These abnormalities are hypothesized to subserve cognitive deficits and other clinical features of mood disorders. TZDs may attenuate, abrogate and/or reverse the neurotoxic effects of depressive illness by means of disparate mechanisms, notably insulin signaling, anti-inflammation, glucocorticoid activity and cellular metabolism. Extant data provide the basis for formulating a hypothesis that TZDs may be salutary for cognitive deficits and several aspects of somatic health (e.g., cardiovascular disease) associated with mood disorders.
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.
How this classification was reachedexpand
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".