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Lithium alters regional rat brain myo-inositol at 2 and 4 weeks: an ex-vivo magnetic resonance spectroscopy study at 18.8 T

2006· article· en· W2066122790 on OpenAlexaff
Brent M McGrath, Andrew J. Greenshaw, Ryan T. McKay, Carolyn M. Slupsky, Peter H. Silverstone

Bibliographic record

VenueNeuroreport · 2006
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsThe Metabolomics Innovation CentreUniversity of Alberta
Fundersnot available
KeywordsEx vivoInositolIn vivoLithium (medication)Nuclear magnetic resonance spectroscopyNuclear magnetic resonanceCentral nervous systemMagnetic resonance imagingNeuroscienceChemistryFunctional magnetic resonance spectroscopy of the brainBiophysicsEndocrinologyBiochemistryInternal medicineBiologyFunctional magnetic resonance imagingMedicineIn vitroPhysicsReceptor

Abstract

fetched live from OpenAlex

Lithium has been the mainstay of treatment for bipolar disorder. Early studies suggest that lithium acts via inositol depletion. This study assesses the effect of 1, 2 and 4 weeks of lithium treatment on myo-inositol concentrations across several brain regions. Thirty-six Sprague-Dawley rats were treated for 2 weeks with an intraperitoneal injection of either 1 mmol/kg/day, twice daily lithium chloride (n=18) or placebo (2 ml/kg of saline) (n=18). The rats were separated into three groups: 1 week, 2 weeks and 4 weeks. Brains were dissected into prefrontal, temporal and occipital cortical areas, as well as hippocampus, and analyzed at 18.8 T. Myo-inositol was quantified using the Chenomx Profiler software. Lithium did not alter myo-inositol concentrations at 1 week. A significant reduction exists in myo-inositol concentrations in lithium-treated rats at 2 and 4 weeks, across all four brain regions. Studies suggest brain region-specific alterations in myo-inositol concentrations among bipolar patients. Our findings suggest that lithium-induced reduction of myo-inositol is more global.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.320
Threshold uncertainty score0.861

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 head0.021
GPT teacher head0.317
Teacher spread0.296 · 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 designObservational
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

Citations11
Published2006
Admission routes1
Has abstractyes

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