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Record W1486618514 · doi:10.1002/mrm.24539

Simultaneous in vivo pH and temperature mapping using a PARACEST‐MRI contrast agent

2012· article· en· W1486618514 on OpenAlexafffund
Nevin McVicar, Alex X. Li, Mojmı́r Suchý, Robert H. E. Hudson, Ravi S. Menon, Robert Bartha

Bibliographic record

VenueMagnetic Resonance in Medicine · 2012
Typearticle
Languageen
FieldMaterials Science
TopicLanthanide and Transition Metal Complexes
Canadian institutionsWestern University
FundersCanadian Institutes of Health ResearchOntario Institute for Cancer Research
KeywordsChemistryIn vivoNuclear magnetic resonanceAnalytical Chemistry (journal)Saturation (graph theory)Chromatography

Abstract

fetched live from OpenAlex

Altered tissue temperature and/or pH is a common feature in pathological conditions, where metabolic demand exceeds oxygen supply such as in tumors and following stroke. Therefore, in vivo tissue temperature and pH may become valuable biomarkers for disease detection and the monitoring of disease progression or treatment response in conditions with altered metabolic demand. In this study, pH is measured using the amide protons of a thulium (Tm(3+)) complex with a DOTAM-Glycine-Lysine (ligand: Tm(3+)-DOTAM-Gly-Lys). The pH was uniquely determined from the linewidth of the asymmetry curve of the chemical exchange saturation transfer spectrum, independent of contrast agent concentration, or temperature for a given saturation pulse. pH maps with an inter-pixel standard deviation of less than 0.1 pH units were obtained in 10 mM Tm(3+)-DOTAM-Gly-Lys solutions with pH ranging from 6.0 to 8.0 pH units at 37°C. Temperature maps were simultaneously obtained using the chemical shift of the chemical exchange saturation transfer peak. Temperature and pH maps are demonstrated in the mouse leg (N = 3), where the mean and standard deviation for pH was 7.2 ± 0.2 pH unit and temperature was 37.4 ± 0.5°C.

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 score0.003

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 integrity0.0000.001
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 head0.026
GPT teacher head0.270
Teacher spread0.244 · 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

Citations70
Published2012
Admission routes2
Has abstractyes

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