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Record W4399459425 · doi:10.1002/nano.202400004

Rhabdomyosarcoma targeting with tuned porous silicon nanoparticles

2024· article· en· W4399459425 on OpenAlexfundno aff
Sofia Dominguez‐Gil, Rita Sala, Victoria Judith Morel, Christophe Nguyen, Khaled El Cheikh, Alain Morère, Jean‐Olivier Durand, Jochen Rößler, Michele Bernasconi, Frédérique Cunin, Magali Gary‐Bobo

Bibliographic record

VenueNano Select · 2024
Typearticle
Languageen
FieldEngineering
TopicNanoplatforms for cancer theranostics
Canadian institutionsnot available
FundersUniversité de MontpellierAgence Nationale de la RechercheMach-Gaensslen Foundation of Canada
KeywordsPorous siliconRhabdomyosarcomaPhototoxicityPorphyrinCytotoxicityNanoparticleMaterials scienceMannoseTwo-photon excitation microscopySiliconBiophysicsCancer researchChemistryNanotechnologySarcomaIn vitroOptoelectronicsMedicineFluorescencePhotochemistryBiochemistryPathologyOptics

Abstract

fetched live from OpenAlex

Abstract We describe porous silicon nanoparticles (pSiNP) chemically functionalized with an analog of mannose 6‐phosphate (AMFA) and a porphyrin derivative to target aggressive pediatric Rhabdomyosarcoma (RMS) tumor cells. Our findings demonstrate that the pSiNP@AMFA@porphyrin nanosystems are efficiently internalized by RMS cells, which overexpress mannose 6‐phosphate receptors, and induce cytotoxicity and phototoxicity when exposed to two‐photon excitation light. These results provide an interesting potential for targeting and treating RMS pediatric tumors.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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)0.0000.000

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 head0.005
GPT teacher head0.194
Teacher spread0.189 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations1
Published2024
Admission routes1
Has abstractyes

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