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Record W2508336542 · doi:10.1002/nbm.3589

Neurochemical profile of the human cervical spinal cord determined by MRS

2016· article· en· W2508336542 on OpenAlexaff
Andreas Hock, Bertram J. Wilm, Giorgia Zandomeneghi, Garyfalia Ampanozi, Sabine Franckenberg, Niklaus Zoelch, Patrik O. Wyss, Nicola De Zanche, Jurek A. Nordmeyer‐Massner, Thomas Kræmer, Michael J. Thali, Matthias Ernst, Spyros Kollias, A Henning

Bibliographic record

VenueNMR in Biomedicine · 2016
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsUniversity of Alberta
FundersSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
KeywordsNeurochemicalSpinal cordNeuroscienceAnatomyMedicineChemistryBiology

Abstract

fetched live from OpenAlex

MRS enables insight into the chemical composition of central nervous system tissue. However, technical challenges degrade the data quality when applied to the human spinal cord. Therefore, to date detection of only the most prominent metabolite resonances has been reported in the healthy human spinal cord. The aim of this investigation is to provide an extended metabolic profile including neurotransmitters and antioxidants in addition to metabolites involved in the energy and membrane metabolism of the human cervical spinal cord in vivo. To achieve this, data quality was improved by using a custom-made, cervical detector array together with constructive averaging of a high number of echo signals, which is enabled by the metabolite cycling technique at 3T. In addition, the improved spinal cord spectra were extensively cross-validated, in vivo, post-mortem in situ and ex vivo. Reliable identification of up to nine metabolites was achieved in group analyses for the first time. Distinct features of the spinal cord neurochemical profile, in comparison with the brain neurotransmission system, include decreased concentrations of the sum of glutamate and glutamate and increased concentrations of aspartate, γ-amino-butyric acid, scyllo-inositol and the sum of myo-inositol and glycine.

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)0.000
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 score0.150
Threshold uncertainty score0.205

Codex and Gemma teacher scores by category

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 head0.023
GPT teacher head0.353
Teacher spread0.330 · 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

Citations5
Published2016
Admission routes1
Has abstractyes

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