Exploring the Theranostic Potential of Gas Vesicle Protein Nanobubbles
Bibliographic record
Abstract
Halophilic archaeabacterial gas vesicles (GVs) are a class of protein nanobubbles isolated from buoyant, aquatic microorganisms. The hollow chamber of GVs enables them to be readily visualized by ultrasound (US) waves. Extensive research has gone into characterizing the imaging or therapeutic functions of GVs, but no work has sought to combine these functions to develop GVs as multifunctional theranostics. We hypothesized that GVs can serve as a scaffold to integrate therapeutics or tumor targeting ligands through covalent or noncovalent approaches. This thesis investigates the strategy of chemically conjugating therapeutics to the free amino groups on the GV shell, and explores the non-covalent complexation of GVs with bispecific linkers to introduce tumor-targeting function to GVs. The first study demonstrated that covalently modifying the surface of GVs with the photosensitizer called chlorin e6 (Ce6) transformed them into light-sensitive, photoactive nanobubbles (Ce6-GVs) with potent cancer-cell killing effects. The second study used bispecific adapters to noncovalently link monoclonal antibodies that bound Ce6-GVs, in order to target Ce6-GVs to cancers overexpressing the tumor antigen called carcinoembryonic antigen (CEA). The third study focused on the surface modification of Ce6-GVs using GV specific antibodies covalently modified with another tumor targeting ligand called folate. Overall, the results of these studies support the hypothesis that GVs can be repurposed into nanoscale theranostics for cancer. GVs can be manipulated in diverse ways for diagnostic or imaging purposes, which warrants further investigation on GVs and other ligands or tools that may be useful to develop them into beneficial biologics.
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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".