High‐performance liquid chromatography (<scp>HPLC</scp>) as a means of assessing the presence of uric acid in archeological human remains: Challenges and future directions
Bibliographic record
Abstract
OBJECTIVES: This research aimed to replicate the Swinson, D., Snaith, J., Buckberry, J., & Brickley, M. (2010). High performance liquid chromatography (HPLC) in the investigation of gout in paleopathology. International Journal of Osteoarchaeology, 20, 135-143. https://doi.org/10.1002/oa.1009 method for detecting uric acid in archeological human remains to investigate gout in past populations and to improve the original High Performance Liquid Chromatography-ultraviolet (HPLC-UV) method by using HPLC-mass spectrometry (HPLC-MS), a more sensitive, compound-specific detection method. MATERIALS AND METHODS: We used reference samples of uric acid to create a dilution series to assess the limits of quantification and detection. Samples from individuals with and without gout lesions were taken from foot bones and ribs from the English cemeteries of Tanyard, Hickleton, Gloucester, and Lincoln. RESULTS: We could not replicate the results of Swinson and colleagues using HPLC-UV. Tests using a dilution series of uric acid showed HPLC-MS was approximately 100× more sensitive than HPLC-UV, with the additional benefit of being compound specific. A newly developed hydrophilic interaction chromatography (HILIC) method improved retention characteristics. Fourteen samples from eight individuals, five with skeletal lesions consistent with gout, were analyzed with the final method. None showed evidence of uric acid despite the newly developed method's improved sensitivity and specificity. DISCUSSION: The lack of detectable uric acid extracted from these samples suggests that (1) urate crystals were not present in any of the bone samples, regardless of gout status; (2) urate crystals did not survive these specific archeological conditions; or (3) the concentration of uric acid in our bone extracts was low, and thus larger samples would be required.
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 | 0.013 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".