Nanosponges as an Emerging Platform for Cancer Treatment and Diagnosis
Bibliographic record
Abstract
Abstract Nanosponges are unique sponge‐like carrier systems with numerous cavities and a mesh‐like network throughout their surface. They are categorized into three types based on the material of construction, i.e., polymer‐, inorganic‐, and bio‐derived material‐based nanosponges. Unlike conventional nanoparticles, nanosponges bear countless interconnected voids and mesh‐like network that enables maximum drug, imaging probe, or photosensitizer loading via absorption, encapsulation, conjugation, or complexion. The voids can be further sealed using suitable lipids or polymers to avoid leakage of drugs at the non‐target site, protect therapeutics from biological conditions, and control the release of therapeutics for a prolonged period. Recently, nanosponges have grasped the attention of researchers in the area of cancer by overcoming the drawbacks associated with the conventional delivery of immunotherapeutic, targeted therapeutic, and chemotherapeutic agents. Interestingly, several reports have also witnessed their potential in cancer theranostics. Owing to this supremacy, nanosponges could be an apt platform for cancer therapy and diagnosis. The current review discusses the general aspects, types, and preparation methods of nanosponges. In addition, the ambit of nanosponges in treating different types of cancer is meticulously discussed, along with detailed descriptions of their corresponding patents. Furthermore, the role of nanosponges in phototherapy and cancer theranostics are selectively canvassed.
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) | 0.000 | 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 | 0.000 | 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".