Comparative study of serum sample preparation methods in aggregation-based plasmonic sensing
Bibliographic record
Abstract
The use of nanoparticle-based colorimetric methods has received considerable attention in a broad range of clinical and biomedical applications due to their high sensitivity, low cost, extreme simplicity and excellent analytical performance. However, the formation of a protein corona has severely limited the application of nanoparticles (NPs) in clinical samples, which can confer colloidal stability to serum-exposed nanoparticles compared to pristine particles. To address this challenge, dialysis, ultrafiltration and phenol : chloroform : isopentanol extraction methods were compared aiming at facile and routine protein separation methods to eliminate the formation of protein corona on NPs and the development of a sensitive and simple therapeutic drug monitoring (TDM) assay for the detection of aminoglycoside antibiotics in serum. Based on the comparison of the sensitivity of the localized surface plasmon resonance (LSPR) aggregation assay in pure water, untreated serum and serum after the different sample preparation methods, we revealed by Coomassie blue staining that proteins in the serum were the predominant interfering molecules to degrade the sensitivity of serum-based aggregation assays. Using dialysis, naked eye semi-quantification was achieved at the clinical level for amikacin, tobramycin and streptomycin. The dialysis efficiency and dialysis coefficient of amikacin were also measured to prove the efficacy of dialysis as a fast and efficient protein-removal method. This strategy is expected to be applicable universally as a pretreatment for the assay of small molecules with plasmonic assays in crude biological samples.
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.
How this classification was reachedexpand
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 | 0.001 |
| 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 teacher head, 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".