Nanomedicine Based Approaches to Cancer Diagonsis and Therapy
Bibliographic record
Abstract
Pharmaceutical nanoparticles were first described in 1970s, and the term “nanotechnology” is now commonly used to refer to the fabrication of new materials with nanoscale dimensions between 1 and 100 nm (Thrall 2004). Several types of nanometer scale systems such as nanoparticles, nanospheres, nanotubes, nanogels and molecular conjugates are being investigated (Lemieux et al. 2000;Liu et al. 2007;Ravi et al. 2004). The field of nanomedicine aims to use the properties and physical characteristics of nanomaterials which have been extensively investigated as novel intravascular or cellular probes for both diagnostic (imaging) and therapeutic purposes (drug/gene delivery). The sub-micron size of nanoparticle delivery systems confers distinct advantages as compared to large sized systems including targeted delivery, higher and deeper tissue penetrability, greater cellular uptake and greater ability to cross the blood-brain barrier (Kreuter et al. 1995;Vinogradov et al. 2002;Vogt et al. 2006). Therapeutic transgene(s) encoded by plasmid or chemically modified DNA can be dissolved, entrapped, chemically conjugated, encapsulated or adsorbed to the surface of nanoparticles. There are, broadly, two main types of nanosized particles with different inner structures: A. Nanoparticle/Nanosphere: Matrix composed of entangled oligomer or polymer units; and B. Nanocapsule: Reservoir consisting of a hydrophobic core surrounded by a polymer wall. Lipids can also be used to generate liposomes or micelles (discussed in detail later). These nanodevices can confer protection to the DNA against a variety of degradative and destabilizing factors, and enhance delivery efficiency to the cells while minimizing the toxic effects. Nanoparticles are expected to play a critical role in the innovation and development of future cancer treatment modalities. Recent research has developed functional nanoparticles that are covalently linked to biological molecules such as peptides, proteins, nucleic acids, or small-molecule ligands (Alivisatos 2004;Chan et al. 2002;Michalet et al. 2005). Medical applications have also appeared, such as the use of superparamagnetic iron oxide nanoparticles as a contrast agent in the detection of lymph node prostate cancer (Harisinghani et al. 2003) and the use of polymeric nanoparticles for targeted gene delivery to tumor vasculatures (Hood et al. 2002). Target-specific drug/gene delivery and early diagnosis is currently a high priority R&D area, and one in which nanomedicine will inevitably make critical contributions. Current modalities of diagnosis and treatment of various diseases, especially cancer, have major limitations such as poor sensitivity or
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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".