HPTLC Method for the Ultrasensitive Detection of Triamterene in Plasma
Bibliographic record
Abstract
A high-performance thin-layer chromatographic (HPTLC) method has developed for the selective detection of a diuretic drug, triamterene (TRIAM), in pure form, tablets and human plasma. The method was based on chromatographic separation of TRIAM using HPTLC plates, precoated with silica gel, and a mobile phase consisted of ethyl acetate: dimethylformamide: ammonia (7.0: 2.7: 0.3, by volume). The native fluorescence signal of TRIAM was detected at 440 nm and used to quantify TRIAM using the proposed method, improving the method sensitivity to ~250-folds in comparison to that reported in previous HPTLC studies. The developed method enabled the detection of TRIAM in pure drug and biological samples (human plasma) within linear concentrations ranged from 0.8 to 60 ng/band or 1.0 to 60 ng/band for pure drug and plasma samples, respectively. Furthermore, the method was validated according to the official guidelines to permit its applicability in quality control and clinical laboratories.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".