Determination of nikethamide by micellar electrokinetic chromatography
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
Abstract A simple, fast, sensitive and reproducible micellar electrokinetic chromatography (MEKC)–UV method for the determination of nikethamide (NKD) in human urine and pharmaceutical formulation has been developed and validated. The method exhibits high trueness, good precision, short analysis time and low reagent consumption. NKD is an organic compound belonging to the psychoactive stimulants used as an analeptic drugs. The proposed analytical procedure consists of few steps: dilution of urine or drug in distilled water, centrifugation for 2 min (12,000 g ), separation by MEKC and ultraviolet‐absorbance detection of NKD at 260 nm. The background electrolyte used was 0.035 mol/L pH 9 borate buffer with the addition of 0.05 mol/L sodium dodecyl sulfate and 6.5% ACN. Effective separation was achieved within 5.5 min under a voltage of 21 kV (~90 μA) using a standard fused‐silica capillary (effective length 51 cm, 75 μm i.d.). The determined limit of detection for NKD in urine was 1 μmol/L (0.18 μg/mL). The calibration curve obtained for NKD in urine showed linearity in the range 4–280 μmol/L (0.71–49.90 μg/mL), with R 2 0.9998. The RSD of the points of the calibration curve varied from 5.4 to 9.5%. The analytical procedure was successfully applied to analysis of pharmaceutical formulation and spiked urine samples from healthy volunteers.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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