Safety Evaluation of Serendipity Berry Sweet Protein From <i>Komagataella phaffii</i>
Bibliographic record
Abstract
Serendipity Berry Sweet Protein (sweelin) is a novel hyper-sweet thermophilic protein designed using Artificial Intelligence Computational Protein Design (AI-CPD) to improve the stability and sensory profile of the protein found in serendipity berry (Dioscoreophyllum cumminsii). sweelin is produced through precision fermentation by expression in Komagataella phaffii. The safety of sweelin was investigated through an evaluation of its genotoxicity, mutagenicity, systemic toxicity and digestibility potential in in vitro and in vivo models. sweelin was not genotoxic in in vitro reverse mutation and mammalian micronucleus assays and was not associated with systemic toxicity in a 90-day dietary toxicity study in rats. The no-observed-adverse-effect level for sweelin in Sprague Dawley rats was established as 14,300 ppm, the highest dose tested. This dose level corresponds to dietary intakes of 838.3 and 946.0 mg/kg body weight/day in male and female rats, respectively. sweelin was demonstrated to be readily digestible in an in vitro semi-dynamic model of the gastrointestinal tract. The results support the safety of sweelin as a food ingredient for sweetening purposes.
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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 | 0.000 | 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".