Simple and Sensitive Method of Fluorometry for Determination of Total Antioxidant Capacity
Bibliographic record
Abstract
Background and Objective: Oxidative stress plays an important role in the pathogensis of various diseases, including lung cancer, chronic obstructive pulmonary disease, and atherosclerosis.Total antioxidant capacity plays a significant role in the body's antioxidant defense, so its assessment is a matter of the utmost importance.Currently, assessment of this capacity is performed in various scientific fields by expensive imported kits.The aim of the present study was to design a sensitive fluorometric method for the assessment of total antioxidant capacity and improvement of sensitivity, accuracy, and speed of the measurement. Methods:The sensitivity and intra-and inter-assay accuracy, verification by recovery and parallelism tests, method comparison, and correlation and coherence evaluation were performed.To increase the accuracy and speed of reading, the assay was performed in a microplate and reading was done using a fluorometer plate.Result: In accuracy assessment, intra-and inter-assay coefficient of variation was calculated to be 4.1-5.7 and 4.4-7.5, respectively.In validity evaluation, the recovery percentage was calculated to be 90-109, the recovery percentage range was 90 to 109.Comparison of the results of this method on 50 serum samples with common colorimetric method, indicated a good correlation (0.93).The sensitivity of the studied method was 0.01 mM/l.Conclusion: Microplate-reader fluorometry, in addition to increasing the speed of measurement, has enough efficacy, accuracy, and sensitivity to assess total antioxidant capacity and could be an appropriate alternative for current colorimetric methods.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".