Molecular mechanisms of Fe<sup>2+</sup>‐induced β‐lactoglobulin cold gelation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
To get more insight into the mechanisms of cold gelation of beta-lactoglobulin (beta-lg), macroscopic and molecular structural changes during Fe(2+)-induced gelation of beta-lg were investigated using Fourier transform-infrared (FTIR) spectroscopy and rheological methods. The FTIR spectroscopy results show that, upon the preheating treatment (first step of gel process), native globular proteins are denatured and aggregated molecules are found in solution. The spectra are similar to those of gels obtained in the second step of the process upon incorporation of Fe, which suggests that aggregated molecules formed during the preheating treatment constitute the structural basis of the aggregation. However, the rheological data show that the aggregation is achieved via two molecular mechanisms, both of which are modulated by the iron concentration. At 30 mM of iron, gel formation is essentially controlled by van der Waals interactions, while at 10 mM of iron, hydrophobic interactions predominate. At the two concentrations, disulfide bonds contribute to gel consolidation, the effect being more pronounced at 10 mM of iron. These mechanisms lead to the formation of gels of different microstructures. At the highest iron concentration, a strong and rapid decrease in the repulsion forces is produced, resulting in random aggregation. At the lowest iron concentration, the iron diminishes the superficial charge of both molecules and aggregated molecules, facilitating the interaction among hydrophobic regions and leading to the growth of the aggregation in the preferential direction and to filamentous gel formation. This study provides a comprehensive view of the different modes of gelation.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it