Establishing antitussive pseudoephedrine alkaloid preparations from ecuadorian yasuni canopy emergent cecropia latex collections at research tower platforms
Bibliographic record
Abstract
Can canopy-emergent latex sources from tropical Cecropia species offer a viable route to antitussive pseudoephedrine alkaloid preparations? This research addressed that question by collecting latex from Cecropia peltata trees at tower platforms in Ecuador's Yasuni National Park during the wet season of 2022. Fresh latex was stabilized with citric acid buffer on-site, then transported under cold-chain conditions to the Saskatoon School of Phytopharmacology for phytochemical workup. Sequential extraction with ethanol, methanol, and aqueous solvents produced five distinct fractions. Each fraction was screened for pseudoephedrine content by high-performance liquid chromatography coupled with diode-array detection. The ethanol fraction gave the highest pseudoephedrine yield at 3.87 mg/g dry latex, followed by the methanol fraction at 2.14 mg/g. Antitussive activity was tested in a citric acid-induced cough model adapted from earlier guinea pig protocols. The ethanol fraction suppressed cough frequency by 92.7% at 50 mg/kg body weight, outperforming the reference drug dextromethorphan at an equal dose. Thin-layer chromatography and infrared spectroscopy confirmed alkaloid identity. Results point toward Cecropia latex as a promising raw material for antitussive preparations, though standardization and toxicology work remain necessary before any clinical translation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".