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Record W3135361800 · doi:10.1117/12.2577914

Label-free histopathological assessment of tissues with photoacoustic remote sensing (PARS) microscopy

2021· article· en· W3135361800 on OpenAlexaff
Benjamin R. Ecclestone, Saad Abbasi, Deepak Dinakaran, Muba Taher, Kevan Bell, Frank K.H. van Landeghem, John R. Mackey, Parsin Haji Reza

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPhotoacoustic and Ultrasonic Imaging
Canadian institutionsUniversity of AlbertaUniversity of Waterloo
Fundersnot available
KeywordsMicroscopyPhotoacoustic imaging in biomedicineAcoustic microscopyBiomedical engineeringMaterials sciencePathologyMedicineOpticsPhysics

Abstract

fetched live from OpenAlex

Here we explore the use of Photoacoustic Remote Sensing (PARS™) microscopy, a recently developed non-contact photoacoustic imaging modality, for visualizing subcellular structures label-free in tissues. Operating in an all-optical reflection-mode architecture PARS captures optical absorption contrast within bulk tissue samples. Presented here, by visualizing endogenous optical absorption of DNA and cytochromes, cellular morphology is captured with contrast analogous to the industry standard hematoxylin and eosin (H&E) staining. Subcellular features are recovered from human and murine, brain and gastrointestinal tissues. This work represents a significant step towards the development of a real-time microscopy system for label-free histopathological assessment of tissues in-situ.

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)0.000
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: none
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.012
GPT teacher head0.257
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 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
Published2021
Admission routes1
Has abstractyes

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