A Systematic Review of Delta-9-Tetrahydrocannabinol (∆9-THC) in Astrocytic Markers
Bibliographic record
Abstract
BACKGROUND: Astrocytic reactivity in substance use disorders (SUDs) has been extensively studied, yet the molecular effect of delta-9-tetrahydrocannabinol (∆9-THC, the main psychoactive compound in cannabis) on glial cells, especially astrocytes, remains poorly understood. Exploring ∆9-THC's impact on astrocytic markers can provide insight into its effects on brain functions such as homeostasis, synaptic transmission, and response to neuronal injury. This systematic review synthesizes findings from studies investigating ∆9-THC's impact on astrocytic markers. METHODS: A systematic review was conducted using EMBASE, Medline, and PsychoInfo via the OvidSP platform. Studies reporting astrocytic markers following ∆9-THC exposure in animals and humans were included. Data were extracted from twelve eligible full-text articles, and the risk of bias was assessed using the Systematic Review Center for Laboratory Animal Experimentation. RESULTS: This research identified several astrocytic markers, including glial fibrillary acidic protein (GFAP), nestin, and glutamate-aspartate transporter (GLAST). Both GFAP and nestin expressions increased in adulthood following adolescence and adult ∆9-THC exposure. An increase in GLAST expression was also noted during early development after ∆9-THC exposure. CONCLUSIONS: This review indicates varying levels of astrocytic reactivity to ∆9-THC across different developmental stages, including adolescence and adulthood. ∆9-THC appears to impact maturation, particularly during early developmental stages, and exhibits sex-dependent effects.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".