Targeted Therapeutic Nanoparticles: An Immense Promise to Fight against Cancer
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
In nanomedicine, targeted therapeutic nanoparticle (NP) is a virtual outcome of nanotechnology taking the advantage of cancer propagation pattern. Tying up all elements such as therapeutic or imaging agent, targeting ligand, and cross-linking agent with the NPs is the key concept to deliver the payload selectively where it intends to reach. The microenvironment of tumor tissues in lymphatic vessels can also help targeted NPs to achieve their anticipated accumulation depending on the formulation objectives. This review accumulates the application of poly(lactic-co-glycolic acid) (PLGA) and polyethylene glycol (PEG) based NP systems, with a specific perspective in cancer. Nowadays, PLGA, PEG, or their combinations are the mostly used polymers to serve the purpose of targeted therapeutic NPs. Their unique physicochemical properties along with their biological activities are also discussed. Depending on the biological effects from parameters associated with existing NPs, several advantages and limitations have been explored in teaming up all the essential facts to give birth to targeted therapeutic NPs. Therefore, the current article will provide a comprehensive review of various approaches to fabricate a targeted system to achieve appropriate physicochemical properties. Based on such findings, researchers can realize the benefits and challenges for the next generation of delivery systems.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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