The ambivalent effect of Fe <sub>3</sub> O <sub>4</sub> nanoparticles on the urea-induced unfolding and dilution-based refolding of lysozyme
Bibliographic record
Abstract
) nanoparticles (MNPs), it is essential to identify the influence of these nanoparticles on basic biological processes. Therefore, in this research, the effect of MNPs on the structure and activity of hen egg white lysozyme (HEWL) (EC 3.2.1.1) as a model protein was examined using tryptophan intrinsic fluorescence, UV/Vis, and circular dichroism spectroscopy. Moreover, enzyme activities were analyzed by a turbidometric approach in the presence of MNPs at concentrations providing MNPs/HEWL ratios in the range of 0.04-1.25. As-synthesized MNPS were characterized by Fourier transform infrared spectroscopy, x-ray diffraction, scanning electron microscopy, transmission electron microscopy, vibrating sample magnetometry and the zeta potential of MNPs was measured to be -29 mV. The goal of this work was investigating the ordering or disordering effect of MNPs on protein structure at ratios lower or higher than 0.918 as concentration ratio of threshold (CRT), respectively, in order to answer the question: 'How can the denaturation and refolding of a model protein (HEWL) be affected by MNPs?' As has been reported recently, the protein folding, helicity, and half-life were improved at CRT to make the protein more disordered upon interaction with MNPs. The disordering effect of urea at >CRT and even at <CRT in the denaturation buffer (urea 6 M) increased and at
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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) | 0.001 | 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".