Bibliographic record
Abstract
gene in humans have been shown to predict non-responsiveness to antidepressant drugs; suggesting that FGF2 is required for antidepressants to work. In this study, we hypothesized that antidepressants will not work in rodents that lack the FGF2 gene. Hence, we tested antidepressant treatment in transgenic mice that had the FGF2 gene knocked out. Chronic unpredictable stress (CUS) has been used for several decades to produce a reliable depressive and anxious phenotype in mice. This study followed a CUS paradigm and used fluoxetine (Prozac) as antidepressant treatment. Mice received daily fluoxetine administration beginning on week three of CUS and continued until the end of week five to provide an antidepressant effect and reverse the effects of stress. To test for levels of anxiety and depression, a battery of behavioral tests was conducted which began from the least stressful (i.e. sucrose preference test, open field maze, elevated plus maze) to the most stressful test (forced swim test) to prevent testing carry-over effects. AnyMaze software was used to measure behavior in the open field and elevated plus mazes by recording the amount of time each mouse spent in certain parts of the maze. Future studies will examine brain changes associated with FGF2 gene deletion – particularly in astrocyte cells – which might be necessary for successful antidepressant action. Hopefully, this will elucidate novel therapeutic targets for antidepressant and anti-anxiety medication.
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 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".