Determination of fingerprint by Hplc-Uv of improved traditional medicines to combat the marketing of substandard and falsified medicines
Bibliographic record
Abstract
Improved traditional medicines (ITMs) are increasingly used worldwide, but quality control still poses challenges due to the lack of official analytical methods. This makes it impossible to guarantee their quality, efficacy, and safety. The primary aim of this study was to develop an analytical method using High-Performance Liquid Chromatography (HPLC) to characterise the chemical profiles of improved traditional medicines (ITMs) marketed in the Democratic Republic of Congo. This was followed by the validation of the developed method and its routine application. Chromatographic separation was performed using the XBridge C18 column (250 × 4.6 mm internal diameter; 5 µm particle size), maintained at 25 °C. The mobile phase consisted of a gradient mixture of mobile phases A (acetonitrile) and B (0.05 % aqueous trifluoroacetic acid solution), pumped at 1.0 mL/min. UV detection was carried out at 220 nm. A generic method was developed that proved to be specific, linear (R² > 0.990), accurate (RSD < 10 %), and precise. The validated method was successfully applied to 12 real samples marketed in Kinshasa (capital of DR Congo). The validated HPLC method proves to be a reliable tool for ITM quality control. This method allows for the simultaneous analysis of multiple biomarkers in an ITM. Its routine use would support the harmonization and safety of these plant-based products, which are widely utilized in the DRC, and encourage scientifically supervised traditional medicine.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".