Expression, Characterization, and Evaluation of a RANK-Binding Single Chain Fraction Variable: An Osteoclast Targeting Drug Delivery Strategy
Bibliographic record
Abstract
A single chain Fraction variable (scFv) employs antibody-like target recognition specificity. Osteoclasts, responsible for bone resorption, express Receptor Activator of Nuclear factor Kappa B (RANK) receptors. This study aimed to express, characterize, and evaluate scFv against RANK receptors that may serve as a platform to target osteoclasts. Using phage display technology, scFv against RANK receptor was expressed and characterized by DNA sequencing, sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE), matrix-assisted laser desorption-ionization time-of-flight (MALDI TOF), enzyme-linked immunosorbent assay (ELISA), Western blot, and immunocytochemistry. The potential for cytotoxicity was evaluated using an MTT (3-[4,5-dimethylthiazol-2-yl]-2,5-diphenyl tetrazolium bromide) assay, and its cross reactivity was evaluated using ELISA. Osteoclast-like cells were generated from RAW 264.7 cells, and the osteoclast targeting ability of scFv was evaluated using immunocytochemistry. ScFv's antiresorptive efficacy was studied using a tartrate-resistant acid phosphatase (TRAP) assay and resorption assay. Anti-RANK scFv was successfully expressed and characterized. No cross reactivity with other tumor necrosis factor receptor (TNFR) members and no cytotoxic effect on a non-RANK bearing cell line were observed. It showed specificity toward a RANK receptor and an inhibitory effect on osteoclast activity. With the increase in development trends for biologics as therapeutics and growing knowledge on the importance of osteoclast targeted therapy, this study may provide a drug delivery strategy to target osteoclasts, thereby leading to a promising therapy for resorptive bone diseases.
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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) | 0.000 | 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 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".