Screening BVOCs in Cypress Cones to Improve Anxiety and Insomnia and Target Prediction
Bibliographic record
Abstract
The Cypress ( Platycladus orientalis (L.) Franco) cones have a unique aroma and are commonly used in pillow fillings to alleviate anxiety and insomnia. In this study, gas chromatography-mass spectrometry (GC-MS) was employed to extract and analyze the biogenic volatile organic compounds (BVOCs) from Cypress cone shells, identifying a total of 28 components, with terpenoids comprising over 99% of the total. α-Pinene was the predominant component, accounting for 67% of the total content (176.328 μg/g). Target prediction identified significant interactions between 14 BVOCs and 19 protein targets, with (-)-α-Pinene, limonene, bornyl acetate, and 3-carene being potential key components for alleviating anxiety and insomnia. The primary targets were VDR (Vitamin D receptor), AchE (Acetylcholinesterase), CTSD (Cathepsin D), TRPV1 (Transient Receptor Potential Cation Channel Subfamily V Member 1), CNR2 (Cannabinoid receptor 2), and PPARα (Peroxisome proliferator-activated receptor alpha), which are mainly involved in neurotransmission and circadian rhythm regulation. Molecular docking simulations showed that the binding of α-Pinene and β-caryophyllene to PPARα and CNR2 proteins was primarily driven by hydrophobic forces, with binding energies ranging from -5.17 to -7.83 kcal/mol, suggesting that these BVOCs might alleviate anxiety and insomnia by influencing related functional proteins. This study reveals the potential mechanisms by which Cypress cone shells may help alleviate anxiety and insomnia.
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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 | 0.000 | 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".