Nanoparticles for Improved Therapeutics and Imaging in Cancer Therapy
Bibliographic record
Abstract
Nanotechnology involves creation and utilization of materials, devices or systems on the nanometer scale. The field of nanotechnology is currently undergoing explosive development on many fronts. The technology is expected to generate innovations and play a critical role in drug delivery and imaging. There has been tremendous progress made in the use of polymer and lipid based nanoparticles (NPs) for drug delivery and imaging. Recently, more attention has been given to incorporating inorganic NPs such as gold and magnetic NPs with both imaging and therapeutic capabilities into polymer and lipid based NPs for improved therapy and imaging in cancer treatment. In this review article, the recent progress in the development of multiplex polymer, lipid, and inorganic NPs towards optimizing techniques for drug delivery and multimodal imaging will be discussed along with the relevant patents. Keywords: Semimetal Nanowires, Superlattices, Anodic Alumina Membranes, nanowires, nanotubes, nanobelts, nanoplates, Bi-based nanowires, Semimetal, Bi, Sb, pulsed electrodeposition, thermoelectric, L-point conduction, T-point valence band, Wiedemann-Franz law, thermopower (S2), –, Sb nanowire system, template-based synthesis, chemical vapor deposition, high-density thermoelectric materials nanowire arrays, glass matrix, cylindrical pores, the anodic alumina membranes, Anisotropic thermal expansion, packing densities, electric-field-effect, single-nanowire device, thermoelectric power, Bi-Sb Superlattice Nanowires, Growth Kinetics, Thermal Expansion
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".