An integrative approach for compressive quality control of RespireAidTM, a traditional Chinese medicine formula against SARS-CoV-2
Bibliographic record
Abstract
RespireAid™ (NRICM101) is an effective anti-SARS-CoV-2 traditional Chinese medicine formula and has been licensed as a drug or dietary supplement in Taiwan, Luxembourg, Australia, Singapore, Cambodia, Philippines, and Canada. In this study, we provided integrated quality control strategy to analyze the ingredient of RespireAid™. In addition, the lot-to-lot efficacy stabilities were also evaluated. We found that RespireAid™ comprised of monosaccharides and disaccharides (34.0%), maltodextrin (23.5%), inorganic elements and ash (12.2%), oligosaccharides and polysaccharides (11.4%), principal components (4.4%), moisture (4.0%), amino acids (3.5%), β-Cyclodextrin (0.25%), menthol (0.25%), and nucleotides (0.14%), while the remainder was unidentified (6.36%). This is the first time that the chemical composition of a complex traditional Chinese medicine was clarified using various analytical instruments. The lot-to-lot anti-oxidation and anti-inflammation efficacies of RespireAid™ were consistent, with average 50% scavenging concentrations of 0.22 ± 0.02 mg/mL and 5.76 ± 0.59 mg/mL, respectively. From a comprehensive quality control strategy point of view, RespireAid™, designed from a traditional Chinese medicine formula, displayed high quality, transparency, and efficacy. This integrated strategy provides a clear and reliable way to evaluate the quality of complex traditional Chinese medicines.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".