The feasibility of <i>in vivo</i> detection of lanthanum using a <sup>241</sup> Am K x-ray fluorescence system
Bibliographic record
Abstract
Abstract Objective: Lanthanum (La) is commonly used in phosphate binders in the form of lanthanum carbonate in patients with end-stage kidney disease undergoing hemodialysis treatments. With this administration, there is the potential for La storage in the body with bone being the main site of concern. However, the long-term effects of residual La in the body on bone health are not yet known. In this work, we investigate the feasibility of using a K x-ray fluorescence (K-XRF) spectroscopy system to measure bone La in vivo . Approach: A series of hydroxyapatite (HAp) bone mineral phantoms were created to represent human bone. A 1.09 GBq 241 Am source was used to excite the HAp phantoms doped with various known concentrations of La placed in a 90° geometry relative to the photon source and high-purity germanium (HPGe) detector. Main results: For a detector live time of 2000 s, the minimum detection limit was calculated to be 1.7 µ g La g −1 Ca or 0.7 µ g La g −1 HAp and is comparable to previously reported in vivo bone La concentrations. Significance: The technique developed in this study shows promising results and provides an alternative method to invasive biopsy sampling techniques to monitor the accumulation of bone La. To the best of our knowledge, this is the first reported work that seeks to non-invasively measure bone La via in vivo XRF.
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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".