A Novel ELISA System for Measuring Modified LDL-Adiponectin Complex
Bibliographic record
Abstract
AIM: Adiponectin is an anti-diabetic and anti-atherogenic protein secreted primarily from adipose tissue. Adiponectin and modified LDL (mLDL) form a complex to modulate their biological activity. To elucidate the significance of the complex formation, we analyzed its effects on vascular tissue and developed and verified novel quantifying methods for adiponectin. METHODS: To study the significance of the mLDL-adiponectin complex (MAC) formation, we used the wire-myography method on rat mesenteric artery. We developed a method to measure MAC by using LOX-1 as the capture protein and anti-adiponectin antibody for detection. We compared serum MAC levels between hemodialysis patients and control subjects. RESULTS: Administering mLDL alone to rat mesenteric artery impaired endothelium-dependent vasorelaxation, whereas simultaneously administering adiponectin with mLDL protected rat mesenteric artery from the mLDL-induced impairment of vasorelaxation. This finding indicates MAC formation prevents endothelium from mLDL-induced dysfunction in tissue. Using our novel ELISA for MAC, we found that MAC was increasingly detectable depending on the doses of mLDL and adiponectin in vitro. In serum, hemodialysis patients showed a significantly higher ratio of MAC-high patients (higher than the median level of MAC) than did healthy controls. Furthermore, the MAC-high hemodialysis group had lower mLDL activity measured as LOX-1 ligand containing apoB. CONCLUSION: Using our ELISA, we detected MAC in human serum that protected blood vessels from the deleterious effects of oxidized LDL.
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".