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Record W3213332348 · doi:10.1016/j.pacs.2021.100315

Multispectral optoacoustic tomography for non-invasive disease phenotyping in pediatric spinal muscular atrophy patients

2021· article· en· W3213332348 on OpenAlexafffund
Adrian P. Regensburger, Alexandra L. Wagner, Vera Danko, Jörg Jüngert, Anna Federle, Daniel Klett, Stephanie Schuessler, Adrian Büehler, Markus F. Neurath, Andreas Roos, Hanns Lochmüller, Joachim Woelfle, Regina Trollmann, Maximilian J. Waldner, Ferdinand Knieling

Bibliographic record

VenuePhotoacoustics · 2021
Typearticle
Languageen
FieldMedicine
TopicNeurogenetic and Muscular Disorders Research
Canadian institutionsUniversity of OttawaOttawa HospitalChildren's Hospital of Eastern Ontario
FundersHorizon 2020Canadian Institutes of Health ResearchCanada Foundation for InnovationInterdisziplinäres Zentrum für Klinische Forschung, Universitätsklinikum WürzburgHORIZON EUROPE Framework ProgrammeCanada Excellence Research Chairs, Government of CanadaErlangen Graduate School of Advanced Optical TechnologiesHorizon 2020 Framework ProgrammeElse Kröner-Fresenius-StiftungCanada Research ChairsFriedrich-Alexander-Universität Erlangen-NürnbergEuropean CommissionDeutsche ForschungsgemeinschaftMuscular Dystrophy Canada
KeywordsSMA*Spinal muscular atrophyMedicineRadiologyPathologyDisease

Abstract

fetched live from OpenAlex

Proximal spinal muscular atrophy (SMA) is a rare progressive, life limiting genetic motor neuron disease. While promising causal therapies are available, meaningful prognostic biomarkers for therapeutic monitoring are missing. We demonstrate handheld Multispectral Optoacoustic Tomography (MSOT) as a novel non-invasive imaging approach to visualize and quantify muscle wasting in pediatric SMA. While MSOT signals were distributed homogeneously in muscles of healthy volunteers (HVs), SMA patients showed moth-eaten optoacoustic signal patterns. Further signal quantification revealed greatest differences between groups at the isosbestic point for hemoglobin (SWL 800 nm). The SWL 800 nm signal intensities further correlated with clinical phenotype tested by standard motor outcome measures. Therefore, handheld MSOT could enable non-invasive assessment of disease burden in SMA patients.

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: Observational · Consensus signal: none
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
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.282
Teacher spread0.269 · 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 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

Citations34
Published2021
Admission routes2
Has abstractyes

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