Biophysical Insights into β-Lactamase-Gold Nanoparticle Conjugates: Pioneering Diagnostic Solutions for Antimicrobial Resistance
Bibliographic record
Abstract
With the increasing mortality attributed to antibiotic resistance, the development of a rapid and early detection biosensor for β-lactamase enzyme identification has become imperative.The exceptional optical and electromagnetic characteristics of gold nanoparticles have rendered them as a prime candidate for biosensing applications.This research seeks to establish a foundation for the nanosensor development by investigating intricate interaction between gold nanoparticles and the β-lactamase enzyme, focusing on functional and conformational dynamics following conjugation.UV-visible spectroscopy has been employed to examine the stability of bioconjugates and influence of pH on their conformational state by observing changes in the localized surface resonance plasmon band (LSRP).The minor red shift of the LSPR peak following β-lactamase conjugation confirms protein conjugation and indicates the absence of gold nanoparticle aggregation due to the protein.The fluorescence quenching of tryptophan residues in β-lactamase, in the presence of gold nanoparticles, is utilized to ascertain the binding of protein onto the surface.Circular dichroism spectroscopy further provided insight into the structural integrity of bioconjugate.Intact α-helix and β-sheet peak in CD spectra confirmed that the interaction of gold nanoparticle surface did not cause unfolding or denaturation of protein.This research provides essential insights into the interaction between β-lactamase and gold nanoparticles, facilitating the advancement of sophisticated nanosensors that may be utilized in early pathogen detection and the monitoring of antimicrobial resistance.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".