Bacteriophages as Targeted Therapeutic Vehicles: Challenges and Opportunities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Bacteriophages, with their distinctive ability to selectively target host bacteria, stand out as a compelling tool in the realm of drug and gene delivery. Their assembly from proteins and nucleic acids, coupled with their modifiable and biologically unique properties, enables them to serve as efficient and safe delivery systems. Unlike conventional nanocarriers, which face limitations such as non-specific targeting, cytotoxicity, and reduced transfection efficiency in vivo, engineered phages exhibit promising potential to overcome these hurdles and improve delivery outcomes. This review highlights the potential of bacteriophage-based systems as innovative and efficient systems for delivering therapeutic agents. It explores strategies for engineering bacteriophage, categorizes the principal types of phages employed for drug and gene delivery, and evaluates their applications in disease therapy. It provides intriguing details of the use of natural and engineered phages in the therapy of diseases such as cancer, bacterial and viral infections, veterinary diseases, and neurological disorders, as well as the use of phage display technology in generating monoclonal antibodies against various human diseases. Additionally, the use of CRISPR-Cas9 technology in generating genetically engineered phages is elucidated. Furthermore, it provides a critical analysis of the challenges and limitations associated with phage-based delivery systems, offering insights for overcoming these obstacles. By showcasing the advancements in phage engineering and their integration into nanotechnology, this study underscores the potential of bacteriophage-based delivery systems to revolutionize therapeutic approaches and inspire future innovations in medicine.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 it