Increased serum advanced glycation end products are associated with impairment in HDL antioxidative capacity in diabetic nephropathy
Bibliographic record
Abstract
Sir, I read with interest the paper by Zhou et al. [ 1 ] in which an inverse relationship between organophosphatase activity and concentration of advanced glycation end products was reported. The organophosphatase assay used in that study is the commercially available EnzChek® Paraoxonase Assay Kit from Invitrogen™. The samples used for the assay were the supernatant from serum after precipitation of the non-high-density lipoproteins using dextran-sulfate magnesium. Somewhat surprisingly, there was no citation for the use of this novel combination of reagent and sample for measuring human serum paraoxonase-1 activity. I have been unable to find any published reports validating this assay for the measurement of human serum paraoxonase-1 activity. This contrasts with other published works with novel paraoxonase-1 substrates, such as that by Gaidukov and Tawfik [ 2 ]. The EnzChek® assay has been used in one other study, where the results were corroborated by measurement of enzyme mass, but not by serum arylesterase activity [ 3 ]. Soukharev and Hammond report the development of a fluorogenic organophosphatase substrate [ 4 ] that is hydrolysed by purified human paraoxonase 1. However, no data have been published validating the substrate used in that paper, 7-diethylphospho-6,8-difluor-4-methylumbelliferyl (DEPFMU), in the context of a human serum matrix or the serum matrix remaining after divalent cation/polyvalent anion precipitations. I would propose that the results of this type of assay be referred to as organophosphatase activity in the same way as results for phenyl acetate hydrolysis are referred to as arylesterase activity. Referring to this activity as paraoxonase would require rigorous validation and comparison with serum hydrolysis of paraoxon. I would also make an appeal that the introduction of novel assays be accompanied by the validation data with conditions described in sufficient detail to allow replication in subsequent studies. Conflict of interest statement . None declared.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.006 | 0.005 |
| 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".