Single-Domain Antibody Functionalized CdSe/ZnS Quantum Dots for Cellular Imaging of Cancer Cells
Bibliographic record
Abstract
Photoluminescent (PL) semiconductor nanocrystals, when spherical in shape also termed as quantum dots (QDs), have attracted significant attention in biolabeling and bioimaging applications. Usually, such bio-oriented applications require targeting to the site of interest, and the use of antibodies is acknowledged as one common strategy for specific and compelling targeting. Conventional antibodies and some of their derivatives have been tested as targeting agents; to the best of our knowledge, the present study is the first with the use of single-domain antibodies (sdAbs) to overcome some disadvantages related to issues such as stability, aggregation, and production cost. Our sdAbs, which are small but fully functional recognition proteins and derived from camelid species, are superior to all of the above antibody choices. This manuscript addresses our efforts on the synthesis and targeting of bioconjugated PL QDs with a sdAb named EG2, which binds strongly to epidermal growth factor receptor (EGFR), a protein of which is widely known as a tumor marker. PEGylation was performed at the same time. The entity of our PEGylated-and-sdAb-conjugated QDs, presented as proof of principle, is robust in specific labeling of EGFR on in vitro grown SK-BR3 and MDA-MB468 human breast-cancer cells.
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 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".