MétaCan
Menu
Back to cohort
Record W2891435568 · doi:10.1093/qjmed/hcw128.002

Using Maldi-imaging to investigate region-specific proteome in IPF Lung

2016· article· en· W2891435568 on OpenAlexaff
Darryl A. Knight, Christopher Grainge, James C. Hogg, Jun Han, Christoph H. Borchers, David W. Waters, Michael Schuliga, Bart Vanaudenaerde, Steven E. Mutsaers, Cecilia M. Prêle, Geoff Laurent, Janette K. Burgess

Bibliographic record

VenueQJM · 2016
Typearticle
Languageen
FieldMedicine
TopicPulmonary Hypertension Research and Treatments
Canadian institutionsUniversity of VictoriaUniversity of British Columbia
Fundersnot available
KeywordsProteomeMALDI imagingLungComputational biologyMedicineChemistryMatrix-assisted laser desorption/ionizationBiologyInternal medicineBiochemistry

Abstract

fetched live from OpenAlex

RATIONALE: Previous studies have compared gene or protein expression from biopsies or random samples of IPF lung to normal or disease controls. Regional variation and typical progression of IPF provides a unique opportunity to investigate disease pathogenesis. HYPOTHESIS: Region-specific cues from the microenvironment are central to the pathogenesis of IPF. Our aim was to examine the feasibility of conducting a qualitative and quantitative analysis of extracellular matrix proteins in human lung tissue using peptide sequencing and Maldi-imaging. METHODS: Lung were air inflated to 20cm H20 pressure and snap frozen in liquid N2 fumes. Frozen slices were cut with a band saw and 2x2cm tissue cores prepared from 8 different regions. For Maldi imaging, sections (20 μm) were thaw mounted on indium tin oxide coated microscopic slides and coated with sinapinic acid. MS data were recorded on an Apex-Qe 12T hybrid quadrupole-FTICR mass spectrometer equipped with an Apollo dual-mode electrospray ionization Maldi ion source. Maldi imaging data were acquired with image pixel resolution of 200μm.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.272
Threshold uncertainty score0.280

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 head0.090
GPT teacher head0.335
Teacher spread0.245 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueQJMSame topicPulmonary Hypertension Research and TreatmentsFrench-language works237,207