The Impact of Psilocybin on High Glucose/Lipid-Induced Changes in INS-1 Cell Viability and Dedifferentiation
Bibliographic record
Abstract
Serotonin emerges as a pivotal factor influencing the growth and functionality of β-cells. Psilocybin, a natural compound derived from mushrooms of the Psilocybe genus, exerts agonistic effects on the serotonin 5-HT2A and 5-HT2B receptors, thereby mimicking serotonin’s behavior. This study investigates the potential impacts of psilocybin on β-cell viability, dedifferentiation, and function using an in vitro system. The INS-1 832/13 Rat Insulinoma cell line underwent psilocybin pretreatment, followed by exposure to high glucose-high lipid (HG-HL) conditions for specific time periods. After being harvested from treated cells, total transcript and cellular protein were utilized for further investigation. Our findings implied that psilocybin administration effectively mitigates HG-HL-stimulated β-cell loss, potentially mediated through the modulation of apoptotic biomarkers, which is possibly related to the mitigation of TXNIP, STAT-1, and STAT-3 phosphorylation. Furthermore, psilocybin exhibits the capacity to modulate the expression of key genes associated with β-cell dedifferentiation, including Pou5f1 and Nanog, indicating its potential in attenuating β-cell dedifferentiation. This research lays the groundwork for further exploration into the therapeutic potential of psilocybin in Type II diabetes intervention.
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) | 0.001 | 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".