A rapid bioassay method for the determination of 90Sr in human urine sample
Bibliographic record
Abstract
A rapid bioassay method has been developed for the determination of (90)Sr in human urine samples. The method is based on on-cartridge decolourisation of urine sample, separation of (90)Y from (90)Sr on an anion exchange resin column and by determination of (90)Sr using a liquid scintillation analyser (LSA). Separation of (90)Y from (90)Sr was achieved through selective complexation of yttrium with phosphate and subsequent retention of the anionic yttrium phosphate species on anion exchange resin. A total recovery of 97 +/- 2 % was obtained for strontium with three washes. The minimum detectable activity for the method was 0.2 Bq or 40 Bq l(-1). Measurement accuracy (relative bias, B(r)) and repeatability (relative precision, S(B)) of the method for the determination of (90)Sr were found to be -1 and 4.7 %, respectively. Excellent linearity (r(2) > 0.999) was established over an activity range from 3.25 x 10(2) to 3.25 x 10(4) Bq l(-1). The method was also found to be very robust (S(B) < 5 %) against the matrix effect from different urine samples. Performance of the rapid bioassay method for sensitivity, accuracy and repeatability evaluated against the performance criteria for radiobioassay (ANSI N13.30) was found to be in compliant. Considering the simplicity, excellent analytical figures of merit, fast sample turnaround time (<1 h) and cost efficiency (<30 USD per sample) of the developed method, it is very promising as a rapid bioassay method for supporting the medical response to an emergency where internal contamination of (90)Sr is involved.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".