Critical Influences of Plasma pH on Human Protein Properties for Modeling Considerations: Size, Charge, Conformation, Hydrophobicity, and Denaturation
Bibliographic record
Abstract
The fouling of biomaterials (e.g., membranes) by plasma proteins has always garnered attention because it renders biomedical devices ineffective and can jeopardize the patient’s well-being. Modeling the fouling process sheds light on its mechanisms and helps improve the biocompatibility of biomaterials. Assuming proteins to be hard spheres with uniform surface properties reduces the modeling complexity, but it seriously deviates from the accurate, real perspective. One reason for the inaccuracy is that proteins’ properties tend to change as environmental factors such as pH and ionic strength are varied. This study critically reviews the pH-induced changes in protein properties, namely size, charge, conformity, hydrophobicity, and denaturation. Though these properties may be interrelated, they are addressed individually to allow for a thorough discussion. The study illustrates the necessity of incorporating the protein property changes resulting from pH alteration to better explain and model the fouling process. The discussion is focused on human serum albumin and fibrinogen. Human serum albumin is the most abundant plasma protein, while fibrinogen plays a major role in blood clotting and triggering of the thrombogenic response.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".