Insight into synergetic effects of serum albumin and glucose on the biodegradation behavior of WE43 alloy in simulated body fluid
Bibliographic record
Abstract
Abstract The biodegradation rate of Mg alloy medical devices, such as screws and plates for temporary bone fracture fixation or coronary angioplasty stents, is an increasingly important area of study. In vitro models of the corrosion behavior of these devices use revised simulated body fluid (m-SBF) based on a healthy individual’s blood chemistry. Therefore, model outputs have limited application to patients with altered blood plasma glucose or protein concentrations. This work studies the biodegradation behavior of Mg alloy WE43 in m-SBF modified with varying concentrations of glucose and bovine serum albumin (BSA) to (1) mimic a range of disease states and (2) determine the contributions of each biomolecule to corrosion. Measurements include the Mg ion release rate, electrolyte pH, the extent of hydrogen evolution (as a proxy for corrosion rate), surface morphology, and corrosion product composition and effects. BSA (0.1 g l–1) suppresses the rate of hydrogen evolution (about 30%) after 24 h and—to a lesser degree—Mg2+ release in both the presence and absence of glucose. This effect gets more pronounced with time, possibly due to BSA adsorption on the Mg surface. Electrochemical studies confirm that adding glucose (2 g l–1) to the solution containing BSA (0.1 g l–1) caused a decrease in corrosion resistance (by around 40%), and concomitant increase in the hydrogen evolution rate (from 10.32 to 11.04 mg cm–2 d–1) to levels far beyond the tolerance limits of live tissues.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".