Biorecognition through Layer-by-Layer Polyelectrolyte Assembly: In-Situ Hybridization on Living Cells
Bibliographic record
Abstract
Encapsulated cells were formed from the assembly of cationic and anionic alternating layers using a number of polyelectrolyte-based systems. Chitosan, alginate, hyaluronic acid, and oligonucleotides were used as polyelectrolytes to encapsulate individual E. coli cells, which were used as a model. Zeta potential measurements taken for both chitosan/alginate and chitosan/hyaluronic acid systems indicate successful layer-by-layer (LbL) deposition and gave full reversal of the surface change eight times. Layer adsorption was further observed by fluorescence microscopy, and, through a newly developed protocol for sample preparation, transmission electron microscopy micrographs clearly showed the presence of LbL assembly on the outer layer of the cell membrane, in the nanometer range. A second generation of E. coli cells could be grown from encapsulated first generation cells, demonstrating that the cellular activity was not affected by the presence of polyelectrolyte multilayers. Hybridization between attached oligonucleotide sequences and the complementary sequence was demonstrated by both fluorescence spectroscopy and microscopy. Fluorescence energy transfer data recorded after hybrid formation showed that at a molar ratio of 10:20 (donor:acceptor), Q and I were 92.3% and 52.5%, respectively, which suggests that fluorescein fluorescence was quenched by 92.3% and that the fluorescence of rhodamine was enhanced by 52.5%. Oligonucleotide incorporation was stabilized by deposition of four alternating layers, hence offering not only the potential use of the encapsulated cell as a bio-recognition system but also its application in a number of fields such as oligonucleotide delivery, gene therapy, and the use of DNA as an immunocompatible coating.
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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) | 0.001 | 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 | 0.001 | 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".