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Record W2065429247 · doi:10.3109/08982100902913204

Role of nanocarrier systems in cancer nanotherapy

2009· review· en· W2065429247 on OpenAlex
M. R. Mozafari, Abbas Pardakhty, S H Azarmi, J. A. Jazayeri, Ali Nokhodchi, Abdelwahab Omri

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Liposome Research · 2009
Typereview
Languageen
FieldMaterials Science
TopicNanoparticle-Based Drug Delivery
Canadian institutionsLaurentian UniversityUniversity of Alberta
FundersDongbei University of Finance and Economics
KeywordsNanocarriersCancerCancer therapyNanomedicineNanotechnologyDrugMedicineMonoclonal antibodyCancer researchPharmacologyMaterials scienceNanoparticleAntibodyImmunologyInternal medicine

Abstract

fetched live from OpenAlex

Cancer continues to be a major cause of morbidity and mortality worldwide. While discovery of new drugs and cancer chemotherapy opened a new era for the treatment of tumors, optimized concentration of drug at the target site is only possible at the expense of severe side effects. Nanoscale carrier systems have the potential to limit drug toxicity and achieve tumor localization. When linked with tumor-targeting moieties, such as tumor-specific ligands or monoclonal antibodies, the nanocarriers can be used to target cancer-specific receptors, tumor antigens, and tumor vasculatures with high affinity and precision. This article is an overview of advances and prospects in the applications of nanocarrier technology in cancer therapy. Applications of nanoliposomes, dendrimers, and nanoparticles in cancer therapy are explained, along with their preparation methods and targeting strategies.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.936
Threshold uncertainty score0.828

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.106
GPT teacher head0.427
Teacher spread0.321 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it