Investigating the Effects of Venlafaxine on Oxidative Stress and Exploring its Relationship to Neurodegenerative Disease
Bibliographic record
Abstract
The rise in presence of pharmaceuticals in wastewater effluent has had behavioural effects on fish, which this study aimed to investigate.⁴ The pharmaceutical studied was venlafaxine (VEN), a commonly prescribed antidepressant.⁴ Previous studies have shown that venlafaxine contributes to oxidative stress.³ Notably, oxidative stress has been linked to neurodegeneration and plays a role in the pathogenesis of neurodegenerative diseases, such as Alzheimer’s, and Parkinson’s in humans.¹ This study applied a novel approach through in vitro methods to directly expose zebrafish brains to varying VEN concentrations (0.01, 0.1, and 1 μg/L) and quantify oxidative stress. Zebrafish brains were used due to their neuroanatomical similarity to humans.¹⁴ Results demonstrated decreases in cell viability, and increases in antioxidant enzyme activity (catalase and superoxide dismutase) with rising concentrations of VEN. These findings demonstrate increases in oxidative stress, which was indirectly measured by antioxidant enzyme activity, as well as the effects of VEN on cell viability and protein amounts in the brain cells. These results indicate a possible link between venlafaxine exposure and increased risk of neurodegeneration in humans. Further investigation should be done to examine the association between venlafaxine concentrations in water and increased incidence of neurodegenerative disease in mammalian brains.
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) | 0.002 | 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".