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Record W2981087014 · doi:10.1039/c9md00400a

Recent developments on zinc(<scp>ii</scp>) metal–organic framework nanocarriers for physiological pH-responsive drug delivery

2019· review· en· W2981087014 on OpenAlexaff
Weicong Liu, Ying Pan, Weiwei Xiao, Hongjia Xu, Dong Liu, Fei Ren, Xinsheng Peng

Bibliographic record

VenueMedChemComm · 2019
Typereview
Languageen
FieldChemistry
TopicMetal-Organic Frameworks: Synthesis and Applications
Canadian institutionsCentre for Drug Research and Development
FundersShenzhen Peacock PlanNatural Science Foundation of Guangdong Province
KeywordsNanocarriersZincChemistryDrug deliveryDrugMetalNanotechnologyPharmacologyMaterials scienceOrganic chemistryMedicine

Abstract

fetched live from OpenAlex

The high storage capacities and excellent biocompatibilities of zinc(ii) metal-organic frameworks (Zn-MOFs) have made them outstanding candidates as drug delivery carriers. Recent studies on the pH-responsive processes based on carrier-drug interactions have proven them to be the most efficient and effective way to control the release profiles of drugs. To satisfy the ever-growing demand in cancer therapy, great efforts are being devoted to the development of methods to precisely control drug release and achieve targeted use of an active substance at the right time and place. In this review article, we discuss the diverse stimuli based on Zn-MOFs carriers that have been achieved upon external activation from single pH-stimulus-responsive or/and multiple pH-stimuli-responsive viewpoints. Also, the perspectives and future challenges in this type of carrier system are 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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.057
GPT teacher head0.308
Teacher spread0.251 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations62
Published2019
Admission routes1
Has abstractyes

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