Deposition of Antibody Modified Upconversion Nanoparticles on Glass by a Laser-Assisted Method to Improve the Performance of Cell Culture
Bibliographic record
Abstract
A suitable surface is vital for maintaining or even promoting cells’ function and communication. Recently, studies show that nanostructured coatings could have a potential in improving cell adhesion. However, it hardly minimizes the contamination by using traditional solution-coating technology. Matrix-assisted pulsed laser evaporation (MAPLE) technique is a contamination-free process and demonstrates an efficient process to deposit biopolymer without damaging their backbone on the surface of various substrates. Here, upconversion nanoparticles (NaGdF 4 : Yb 3+ , Er 3+ ) with/without immunoglobulin G (IgG) modification were produced by a one-pot synthesis method. The average size of the upconversion nanoparticles (UCNPs) is 50 ± 8 nm. IgG bio-conjugated on the surface of UCNPs can be directly observed by transmission electron microscope (TEM). MAPLE system utilizing a Nd:YAG laser (λ = 532 nm, ν = 10 Hz) is applied to deposit UCNPs with/without IgG modification on the glass bottom of culture dish. In addition, the behaviors of human umbilical vein endothelial cells (HUVECs) cultured on the culture dishes coated with UCNPs with/without IgG have been studied as compared to the control sample, glass coated with gelatin. No toxic effect is imposed on cells. The results of this work indicate that the deposition of UCNPs with/without antibody by the MAPLE technique could enhance the adhesion and proliferation of cells.
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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".