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Record W2070388688 · doi:10.1002/nbm.1547

A proton NMR study on the hydration of normal <i>versus</i> psoriatic stratum corneum: linking distinguishable reservoirs to anatomical structures

2010· article· en· W2070388688 on OpenAlexafffund
Cornelia Laule, Sumia Tahir, Charmaine L. L. Chia, Irene M. Vavasour, Neil Kitson, Alex L. MacKay

Bibliographic record

VenueNMR in Biomedicine · 2010
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicAdvancements in Transdermal Drug Delivery
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCorneocyteStratum corneumChemistryProton NMRProtonBiophysicsAnalytical Chemistry (journal)ChromatographyStereochemistry

Abstract

fetched live from OpenAlex

The NMR behaviour of normal and psoriatic stratum corneum (SC) was investigated as a function of hydration with the aim of obtaining a better understanding of the role of water in the SC structure. Time domain NMR techniques were employed to identify the signal from water and that from nonaqueous components of the SC, such as lipids and proteins. The signals were investigated as a function of water content. The free induction decay was separated into mobile signal (from water and mobile lipids) and solid signal (from protein and 'solid' lipids). Spin-spin relaxation (T(2)) measurements further separated the mobile domains within the SC. The results suggested that, when water is added to dry SC, it first enters the corneocytes; then, at a hydration of 0.24-0.33 g H(2)O/g SC (normal SC) or 0.12-0.24 g H(2)O/g SC (psoriatic SC), water begins to accumulate in hydrated lipid regions. Water was found to exchange between these two domains on the time scale of a few hundred milliseconds. When compared with normal SC, psoriatic SC had a looser corneocyte structure, a larger mobile lipid component at low hydration and a smaller capacity for corneocyte water.

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 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 score0.164
Threshold uncertainty score0.704

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.093
GPT teacher head0.438
Teacher spread0.345 · 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.

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

Citations8
Published2010
Admission routes2
Has abstractyes

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