Synergistically Enhancing the Therapeutic Effect on Cancer, via Asymmetric Bioinspired Materials
Bibliographic record
Abstract
The undesirable side effects of conventional chemotherapy are one of the major problems associated with cancer treatment. Recently, with the development of novel nanomaterials, tumor-targeted therapies have been invented in order to achieve more specific cancer treatment with reduced unfavorable side effects of chemotherapic agents on human cells. However, the clinical application of nanomedicines has some shortages, such as the reduced ability to cross biological barriers and undesirable side effects in normal cells. In this order, bioinspired materials are developed to minimize the related side effects due to their excellent biocompatibility and higher accumulation therapies. As bioinspired and biomimetic materials are mainly composed of a nanometric functional agent and a biologic component, they can possess both the physicochemical properties of nanomaterials and the advantages of biologic agents, such as prolonged circulation time, enhanced biocompatibility, immune modulation, and specific targeting for cancerous cells. Among the nanomaterials, asymmetric nanomaterials have gained attention as they provide a larger surface area with more active functional sites compared to symmetric nanomaterials. Additionally, the asymmetric nanomaterials are able to function as two or more distinct components due to their asymmetric structure. The mentioned properties result in unique physiochemical properties of asymmetric nanomaterials, which makes them desirable materials for anti-cancer drug delivery systems or cancer bio-imaging systems. In this review, we discuss the use of bioinspired and biomimetic materials in the treatment of cancer, with a special focus on asymmetric nanoparticle anti-cancer agents.
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) | 0.000 | 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 | 0.001 | 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".