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Record W2010595926 · doi:10.1117/12.842321

Lung alveolar wall disruption in three-dimensional space identified using second-harmonic generation and multiphoton excitation fluorescence

2010· article· en· W2010595926 on OpenAlexafffund
Thomas Abraham, James C. Hogg

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2010
Typearticle
Languageen
FieldMedicine
TopicTissue Engineering and Regenerative Medicine
Canadian institutionsUniversity of British Columbia
FundersCentre for Hip Health and MobilityUniversity of British Columbia
KeywordsElastinLungExtracellular matrixAlveolar WallFluorescencePathologyMatrix (chemical analysis)Lung transplantationMaterials scienceMicroscopyFluorescence microscopeChemistryBiophysicsBiomedical engineeringMedicineOpticsComposite materialBiologyPhysics

Abstract

fetched live from OpenAlex

Second harmonic generation and multiphoton excited fluorescence microscopy methods were used to examine structural remodeling of the extracellular matrix in human lung alveolar walls undergoing emphysematous destruction. Fresh lung samples removed from a patient undergoing lung transplantation for very severe chronic obstructive pulmonary disease were compared to similar samples from an unused donor lung that served as a control. The generated spatially resolved 3D images show the spatial distribution of collagen, elastin and other endogenously fluorescent tissue components such as macrophages. In the case of control lung tissue, we found well ordered alveolar walls with composite type structure made up of collagen matrix and relatively fine elastic fibers. In contrast, lung tissue undergoing emphysematous destruction was highly disorganized with increased alveolar wall thickness compared to control lung tissue.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.016
GPT teacher head0.259
Teacher spread0.242 · 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 designBench or experimental
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
Published2010
Admission routes2
Has abstractyes

Explore more

Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicTissue Engineering and Regenerative MedicineFrench-language works237,207