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Record W4399776544 · doi:10.1002/slct.202400450

Comprehensive Review of Mesoporous Silica Nanoparticles: Drug Loading, Release, and Applications as Hemostatic Agents

2024· article· en· W4399776544 on OpenAlex

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

VenueChemistrySelect · 2024
Typearticle
Languageen
FieldMaterials Science
TopicNanoparticle-Based Drug Delivery
Canadian institutionsMemorial University of Newfoundland
FundersMustansiriyah University
KeywordsMesoporous silicaHemostatic AgentDrugNanoparticleNanotechnologyMesoporous materialMaterials scienceChemistryPharmacologyMedicineHemostasisSurgeryOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Recently, mesoporous silica nanoparticles (MSNs) have emerged as promising candidates in the field of hemorrhage control owing to their extended pore size, high surface area, and excellent biocompatibility. These characteristics directly influence the toxicity of cells, the loading of therapeutic agents, and the release of active ions during the hemostasis process. Therefore, understanding the fundamentals of tuning these characteristics is important to design these types of carriers. While several literature reviews have explored the role of MSNs in hemorrhage control, comprehensive studies focusing on their general characteristics and specific applications remain scarce. This review concentrates on the principles of synthesizing mesoporous silica, the general types of MSNs, techniques for loading drugs methods onto the site of injury, release kinetics models, biocompatibility, toxicity, and the unique properties of MSNs. Furthermore, the article examines the mechanism of action of MSNs as nanomaterial hemostatic agents.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.069
Threshold uncertainty score0.759

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.012
GPT teacher head0.273
Teacher spread0.260 · 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