Development of an ic-CLEIA for precise detection of 3-CQA in herbs and patent medicines: ensuring quality control and therapeutic efficacy
Bibliographic record
Abstract
Background: 3-caffeoylquinic acid (3-CQA), a member of the chlorogenic acid family, possesses diverse pharmacological properties, such as scavenging, antioxidant, and antiapoptotic activity, rendering substantial value to alimentary consumables and therapeutic substances. However, the pervasiveness of non-standard practices, notably the misuse and abuse of indigenous botanicals, coupled with the inherent susceptibility of 3-CQA to degradation under light and heat exposure, engenders discernible disparateness in the quality profiles of the same kinds of herbs. Consequently, precise quantification of 3-CQA becomes imperative. Methods: In this context, an artificial antigen was synthesized as a specific conjugate of 3-CQA and bovine serum albumin (3-CQA-BSA), followed by the generation of a monoclonal antibody (mAb) against the conjugate. Through optimization, a mAb-based indirect competitive chemiluminescence enzyme immunoassay (ic-CLEIA) was developed. Results: and the calibration range of 2.97 ng/mL and 0.64-13.75 ng/mL, respectively, outperforming the conventional enzyme-linked immunosorbent assay (ELISA). Notably, the ic-CLEIA displayed 10.71% cross-reactivity with 3,5-dicaffeoylquinic acid, alongside minimal cross-reactivity toward other isomeric counterparts and analogs. Validation experiments on herbs and Chinese patent medicines using ic-CLEIA, confirmed by high-performance liquid chromatography (HPLC) analysis, revealed a robust correlation coefficient of 0.9667 between the two modalities. Conclusion: These findings unequivocally demonstrated that the proposed ic-CLEIA represents a viable and reliable analytical method for 3-CQA determination. This method holds significant potential for ensuring the quality control and therapeutic efficacy germane to herbs and patent medicines, spanning diverse therapeutic milieus and applications.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".