Advances of the Nanotechnology in Targeted Nanomedicines for Treatment of Bone Cancers and Diseases
Bibliographic record
Abstract
The ever-evolving field of nanotechnology has been applied over the years as an amazing potential tool in bone disorders through the development of targeted drug nanosystems. Bone diseases can be referred as any bone condition able to cause morbidity or even mortality to the host. Osteosarcoma has been most investigated condition for management and treatment by nanomedicine, and several nanomaterials such as nanoparticles, nanocapsules, nanospheres, nanodiamonds are being used for drug delivery. Likewise, other bone diseases, such as osteoporosis, osteonecrosis, osteoarthritis, bone tuberculosis have received nanotherapy with a large rate of success. The nanomedicine seems to make up a targeting lack by conventional therapy and chemotherapy, besides appointing to a trend of conservative clinical treatment, especially for severe disorders. Additionally, nanomedicine has advanced over the years, therefore, there is a strong need to accelerate its application in bone diseases reducing the mortality rate related to these conditions. This review article provides an overview on the advances of nanotechnology over the last two decades and highlights the pathways of investigations for targeted delivery nanoensembles.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".