Direct Determination of Selenium in Serum Matrix by Electrothermal Atomic Absorption Spectrometry: Application on Healthy Individuals from Algeria
Bibliographic record
Abstract
Selenium is a trace element implicated significantly in oxidative stress in biological systems. In this work, a direct method for selenium determination in serum samples by electrothermal atomic absorption spectrometry with Zeeman background correction is proposed.Serum samples were five-fold diluted in a 0.14% HNO3 and 0.2% Triton X-100 solution. Aliquots of 20 µL of the diluted serum samples were directly introduced into transversely heated graphite tubes. A total of 5 µg Pd and 3 µg Mg(NO3)2 was used as chemical modifier. Optimization of heating program was conducted by varying pyrolysis and atomization characteristic values. The optimal conditions were found to be 1400 and 2000 °C for pyrolysis and atomization temperatures respectively and 11 seconds for pyrolysis hold time.The standard additions method was employed for calibration. Intra-day and inter-day validation using quality control samples at each point of the addition calibration curve were performed. Good accuracy, precision and recovery were achieved with the proposed method (less than 5% for accuracy and precision). A characteristic mass of 92.550 pg, a limit of detection of 4.010 ppb and a limit of quantification of 13.375 ppb in undiluted serum samples were obtained.Several reference values of serum selenium concentrations were reported. As an application of the present method, selenium levels were determined in serum samples collected from 20 healthy individuals from Northern-Center of Algeria. Selenium concentrations ranged from 86.883 to 133.955 ppb with a mean value of 110.824±11.898 ppb.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".