Killer Beacons for Combined Cancer Imaging and Therapy
Bibliographic record
Abstract
Precisely localizing therapeutic agents in neoplastic areas would greatly improve their efficacy for killing tumor cells and reduce their toxicity to normal cells. Photodynamic therapy (PDT) is a promising cancer treatment modality, and near-infrared fluorescence imaging (NIRF-I) is a sensitive and noninvasive approach for in vivo cancer detection. This review focuses on the current efforts to engineer single molecule constructs that allow these two modalities to be combined to achieve a high level of selectivity for cancer treatment. The primary component of these so called killer beacons is a fluorescent photosensitizer responsible for both imaging and therapy. By attaching other components, e.g. various DNA- or peptide-based linkers, quenchers or cancer cell-specific delivery vehicles, their primary diagnostic and therapeutic functions as well as their target specificity and pharmacological properties can be modulated. This modular design makes these agents customizable, offering the ability to assemble a few simple and often interchangeable functional modules into beacons with totally different functions. This review will summarize following three types of killer beacons: photodynamic molecular beacons, traceable beacons and beacons with built-in apoptosis sensor. Despite the rapid progress in killer beacon development, numerous challenges remain before these beacons can be translated into clinics, such as photobleaching, delivery efficiency and cancer-specificity. In this review we outline the basic principles of killer beacons, the current achievements and future directions, including possible cancer targets and different therapeutic applications.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".