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Record W3001516313 · doi:10.3389/fnins.2019.01415

A Review of ex vivo Elemental Mapping Methods to Directly Image Changes in the Homeostasis of Diffusible Ions (Na+, K+, Mg2 +, Ca2 +, Cl–) Within Brain Tissue

2020· review· en· W3001516313 on OpenAlexaff
David Hartnell, Wendy Andrews, Nicole M. Smith, Haibo Jiang, Erin J. McAllum, Ramesh Rajan, Frederick Colbourne, Melinda Fitzgerald, Virginie Lam, Ryusuke Takechi, M. Jake Pushie, Michael Kelly, Mark J. Hackett

Bibliographic record

VenueFrontiers in Neuroscience · 2020
Typereview
Languageen
FieldNursing
TopicTrace Elements in Health
Canadian institutionsUniversity of SaskatchewanUniversity of AlbertaWomen and Children’s Health Research Institute
FundersDementia AustraliaDementia Australia Research Foundation
KeywordsHomeostasisIonMass spectrometryChemistryEx vivoMicroscopyAnalytical Chemistry (journal)BiophysicsInductively coupled plasma mass spectrometryBiologyBiochemistryPathologyEnvironmental chemistryMedicineCell biologyChromatographyIn vitro

Abstract

fetched live from OpenAlex

Diffusible ions (Na+, K+, Mg2+, Ca2+, Cl-) are vital for healthy function of all cells, especially brain cells. Unfortunately, the diffusible nature of these ions renders them difficult to study with traditional microscopy approaches. This mini-review examines the recent progress in the field, using direct elemental mapping techniques to study ion homeostasis during normal brain physiology and pathophysiology. The mini-review examines the advantages and limitations of specific techniques: proton induced X-ray emission (PIXE), X-ray fluorescence microscopy (XFM), secondary ion mass spectrometry (SIMS), laser-ablation inductively coupled plasma mass spectrometry (LA-ICP-MS), and the sample preparation requirements to study diffusible ions with these methods.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.003

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 head0.073
GPT teacher head0.429
Teacher spread0.356 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations18
Published2020
Admission routes1
Has abstractyes

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