MétaCan
Menu
Back to cohort
Record W4408565902 · doi:10.1021/acsabm.5c00123

Synergy of Microfluidics and Nanomaterials: A Revolutionary Approach for Cancer Management

2025· review· en· W4408565902 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

VenueACS Applied Bio Materials · 2025
Typereview
Languageen
FieldEngineering
TopicInnovative Microfluidic and Catalytic Techniques Innovation
Canadian institutionsImpact
Fundersnot available
KeywordsMicrofluidicsNanotechnologyNanomaterialsEngineeringMaterials science

Abstract

fetched live from OpenAlex

Cancer affects millions of individuals every year and is the second most common cause of death. Various therapeutic strategies are explored for the management of cancer including radiation therapy and chemotherapy with or without surgical procedures. However, the drawbacks like poor cancer cell targeting and higher toxicity for healthy cells need the advancement of the therapeutic strategy. The exploration of nanomedicine achieves targeted distribution, and the adoption of microfluidics technology for the preparation of the nanoparticulate system has enhanced the efficacy and uniformity of the nanocarriers. The overview of the existing designs of the microfluidics device assisted in the preparation of the nanoparticles, and various nanodelivery systems formulated using the microfluidic device including liposomes, lipidic nanocarriers, quantum dots, polymeric nanoparticles, and metallic nanocarriers are discussed in this review. Further, the challenges associated with the fabrication of the microfluidics device and the fabrication of microfluidics device-based nanoparticles are detailed here.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.670
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.020
GPT teacher head0.278
Teacher spread0.257 · 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