Revolutionising Cancer Diagnosis and Treatment: A Review on Advancements in Nanomaterial-based Theranostics
Bibliographic record
Abstract
The persistent struggle against cancer has given rise to the development of nanotheranostics, a domain that integrates therapeutic and diagnostic capabilities within nanoscale structures. This paper explores advancements in nanomaterials and nanoparticles for cancer nanotheranostics, focusing on their design, significance, and applications. The incorporation of biocompatible nanoparticles in cancer therapy offers personalised, targeted approaches while minimising side effects. The use of nanomaterials such as metals, polymers, and lipids enable precise drug delivery and imaging. Various imaging modalities, including ultrasound and fluorescence, complement therapeutic strategies for enhanced precision. Critical parameters for nanomaterial selection and design are discussed, emphasising biocompatibility, targeting efficiency, and drug delivery capacity. Biocompatibility ensures safe interactions within biological systems, requiring mitigation of toxicological concerns through strategies like anti-inflammatory peptides or ligand-functionalization. Targeting efficiency combines passive and active targeting to enhance specificity, reshaping cancer diagnostics and therapy. Drug delivery capacity is achieved through engineered core-shell structures with distinct properties, including liposomes, micelles, and dendrimers, each tailored for targeted therapy and imaging. This paper also discusses the advancements in the field of cancer treatment using nanotheranostics and its economic impact on the Canadian healthcare systems while following the ethical guidelines towards patients’ consent, privacy, and the proper use of emerging technologies.
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 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".