MétaCan
Menu
Back to cohort
Record W2898786937 · doi:10.1039/c8mt00230d

Elemental characterisation of the pyramidal neuron layer within the rat and mouse hippocampus

2018· article· en· W2898786937 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueMetallomics · 2018
Typearticle
Languageen
FieldNursing
TopicTrace Elements in Health
Canadian institutionsSaskatoon Medical ImagingUniversity of SaskatchewanCanadian Light Source (Canada)Bentley (Canada)
FundersNational Institute of Environmental Health SciencesNatural Sciences and Engineering Research Council of CanadaDepartment of Health and Aged Care, Australian GovernmentCanada Research ChairsDementia AustraliaAustralian SynchrotronDementia Australia Research FoundationCanadian Institutes of Health ResearchNational Health and Medical Research CouncilSaskatchewan Health Research FoundationAustralian GovernmentHeart and Stroke Foundation of CanadaAustralian National Fabrication FacilityMedical Research CouncilAustralian Nuclear Science and Technology Organisation
KeywordsHippocampal formationHippocampusPyramidal cellLayer (electronics)NeuronFluorescence microscopeFluorescencePyramidal tractsElemental analysisChemistryBiophysicsNeuroscienceBiologyOpticsInorganic chemistryPhysics

Abstract

fetched live from OpenAlex

A unique combination of sensitivity, resolution, and penetration make X-ray fluorescence imaging (XFI) ideally suited to investigate trace elemental distributions in the biological context. XFI has gained widespread use as an analytical technique in the biological sciences, and in particular enables exciting new avenues of research in the field of neuroscience. In this study, elemental mapping by XFI was applied to characterise the elemental content within neuronal cell layers of hippocampal sub-regions of mice and rats. Although classical histochemical methods for metal detection exist, such approaches are typically limited to qualitative analysis. Specifically, histochemical methods are not uniformly sensitive to all chemical forms of a metal, often displaying variable sensitivity to specific "pools" or chemical forms of a metal. In addition, histochemical methods require fixation and extensive chemical treatment of samples, creating the strong likelihood for metal redistribution, leaching, or contamination. Direct quantitative elemental mapping of total elemental pools, in situ within ex vivo tissue sections, without the need for chemical fixation or addition of staining reagents is not possible with traditional histochemical methods; however, such a capability, which is provided by XFI, can offer an enormous analytical advantage. The results we report herein demonstrate the analytical advantage of XFI elemental mapping for direct, label-free metal quantification, in situ within ex vivo brain tissue sections. Specifically, we definitively characterise for the first time, the abundance of Fe within the pyramidal cell layers of the hippocampus. Localisation of Fe to this cell layer is not reproducibly achieved with classical Perls histochemical Fe stains. The ability of XFI to directly quantify neuronal elemental (P, S, Cl, K, Ca, Fe, Cu, Zn) distributions, revealed unique profiles of Fe and Zn within anatomical sub-regions of the hippocampus i.e., cornu ammonis 1, 2 or 3 (CA1, CA2 or CA3) sub-regions. Interestingly, our study reveals a unique Fe gradient across neuron populations within the non-degenerating and pathology free rat hippocampus, which curiously mirrors the pattern of region-specific vulnerability of the hippocampus that has previously been established to occur in various neurodegenerative diseases.

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.

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 score0.158
Threshold uncertainty score0.295

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)0.0000.000

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 head0.022
GPT teacher head0.278
Teacher spread0.256 · 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