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Record W3046718334 · doi:10.1039/d0tb00987c

Advances in local and systemic drug delivery systems for post-surgical cancer treatment

2020· review· en· W3046718334 on OpenAlexaff
Md Aquib, Ajkia Zaman Juthi, Muhammad Asim Farooq, Manasik Gumah Ali, Alhamzah Hasan Waheed Janabi, Sneha Bavi, Parikshit Banerjee, Raghunath B. Bhosale, Rohit Bavi, Bo Wang

Bibliographic record

VenueJournal of Materials Chemistry B · 2020
Typereview
Languageen
FieldMaterials Science
TopicNanoparticle-Based Drug Delivery
Canadian institutionsGovernment of Northwest Territories
Fundersnot available
KeywordsDrug deliveryDrugCancer drugsRepresentation (politics)CancerSystemic therapyMedicineIntensive care medicinePharmacologyMaterials scienceNanotechnologyInternal medicinePolitical science

Abstract

fetched live from OpenAlex

Surgery is considered to be the favored approach for the treatment of most solid tumor malignancies. The quality of life among cancer patients has significantly improved due to advancements in instrumentation and surgical techniques; however, the recurrence of tumors and metastasis after operation remains challenging and results in a decreased quality of life and an increase in the mortality rate. Therefore, there is a need to explore applicable approaches to eradicate the circulating tumor cells and any residual tumor at the surgical site to inhibit the recurrence of the tumor and reduce the threat of distant metastasis. Recently drug delivery systems have been used to deliver immunotherapy or chemotherapy agents, which could augment the efficacy of surgical resection. In this review, we have summarized the efficacy and the recent progress of controlled drug delivery systems based approaches for post-surgical cancer treatment. Clinical translation challenges and opportunities have also been discussed.

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.755
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.018
GPT teacher head0.290
Teacher spread0.272 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreReview

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".

Quick stats

Citations47
Published2020
Admission routes1
Has abstractyes

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