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Record W2075758721 · doi:10.1039/c3tb20476f

New nanomaterials for the improvement of psoriatic lesions

2013· article· en· W2075758721 on OpenAlexfundno aff
Maria Crişan, Luminița David, Bianca Moldovan, Adriana Vulcu, Simina Dreve, Maria Perde‐Schrepler, Corina Tatomir, Gabriela Adriana Filip, Pompei Bolfă, Marcela Achim, Ioana Chiorean, Irina Kacsó, Camelia Berghian-Groșan, Liliana Olenic

Bibliographic record

VenueJournal of Materials Chemistry B · 2013
Typearticle
Languageen
FieldMaterials Science
TopicNanoparticles: synthesis and applications
Canadian institutionsnot available
FundersRyerson University
KeywordsNanomaterialsThermogravimetric analysisFourier transform infrared spectroscopySambucus nigraMaterials scienceNuclear chemistrySpectroscopyTransmission electron microscopyChemistryNanotechnologyChemical engineeringOrganic chemistryBiochemistry

Abstract

fetched live from OpenAlex

The main purpose of the present paper is to emphasize the non-invasive effect of some new prepared nanomaterials on skin diseases (psoriasis) together with the procedures to obtain them. These new materials are based on gold nanoparticles and natural compounds extracted from native plants of the Adoxaceae family (European cranberrybush -Viburnum opulus L. and European black elderberry -Sambucus nigra L.) and possess a known anti-inflammatory activity mainly due to their high content of anthocyanins and other polyphenols. The nanomaterials were characterized by transmission electron microscopy (TEM), UV-Vis spectroscopy, Fourier-transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD), energy-dispersive X-ray spectroscopy (EDX) and thermogravimetric analysis (TGA). Studies in vivo and in vitro were made in order to determine the toxicity of the products. Based on the obtained nanomaterials, specific dermatological creams were prepared. Their effect on psoriatic lesions, in comparison with the hydrocortisone creams, was studied.

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 categoriesInsufficient payload (model declined to judge)
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 score0.005
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0050.000

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 head0.015
GPT teacher head0.245
Teacher spread0.231 · 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
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

Citations34
Published2013
Admission routes1
Has abstractyes

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