Investigating the Effect of Δ9-Tetrahydrocannabinol on Brain-derived Neurotropic Factor Levels in Depressed Male Rats
Bibliographic record
Abstract
Introduction: Despite the legalization of cannabis in Canada, researchers have yet to understand the extent of its side effects on neural development. This is especially true when considering the impact of cannabis on those with mental illnesses including depression. One vital protein involved in the growth and differentiation of neurons in the brain is the brain-derived neurotrophic factor (BDNF). The aim of our proposed study is to evaluate how the neural health of depressed individuals is impacted by Δ9-tetrahydrocannabinol (THC) consumption, the primary psychoactive component of cannabis, in comparison to those without depression by comparing their respective BDNF levels. Methods: To test this, we will use olfactory bulbectomised rats as models for depression and measure the BDNF concentration in their frontal cortices, hippocampi, and amygdalae following controlled exposure to THC for 21 days via intraperitoneal injections. BDNF will be detected via Western blot and quantified using BDNF-specific ELISAs. Conclusion: Our study could influence how society views the risks associated with the use of cannabis amongst individuals with depression. Given that BDNF baseline levels are lower in individuals with depression versus those without, the potentially significant effect of BDNF on overall neural development could possibly be impacted by recurrent THC dosages. Our study could impact the frequency of usage of THC amongst depressed individuals.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".