Determination of fingerprint by Hplc-Uv of improved traditional medicines to combat the marketing of substandard and falsified medicines
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it