Evaluating the Binding of Selected Biomolecules to Cranberry Derived Proanthocyanidins Using the Quartz Crystal Microbalance
Bibliographic record
Abstract
Despite cranberry being associated with the prevention of bacterial infections for over a century, our understanding of the bioavailability and mechanisms by which cranberry prevents infection is limited. This study investigates the interactions between cranberry proanthocyanidins (CPAC) and human serum proteins (albumin, α-1-acid glycoprotein, and fibrinogen) that may be encountered during CPAC metabolism following ingestion. To better understand how CPAC might interfere with bacterial infection, we also examined the interactions between CPAC and selected bacterial virulence factors; namely, lipopolysaccharide (LPS) and rhamnolipid. The binding of CPAC to the serum proteins, rhamnolipids and LPS from Escherichia coli O111:B4 can be described by Langmuir-type isotherms, allowing the determination of the apparent adsorption affinity constants, with CPAC interacting most strongly with fibrinogen with a binding constant of 2.2 × 10(8) M(-1). These binding interactions will limit the bioavailability of the CPAC at the site of action, an important consideration in designing further clinical trials. Furthermore, CPAC interacts with Pseudomonas aeruginosa 10 LPS, E. coli O111:B4 LPS, and P. aeruginosa rhamnolipids in fundamentally different manners, supporting the theory that cranberry prevents bacterial infections via multiple mechanisms.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".