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Record W4409711670 · doi:10.1038/s41467-026-72488-4

Shock-Scattering Micro-Histotripsy-Aided Fine Needle Aspiration for Enhanced Biomarker Profiling and Cytopathology

2025· preprint· en· W4409711670 on OpenAlexafffund
Joy Wang, Pradyumna Kedarisetti, E. D. McAlister, Matthew Mallay, Jeffrey Woodacre, Benjamin Adam, Remegio Maglantay, Juan Jovel, Frank Wuest, Jeremy Brown, Roger J. Zemp

Bibliographic record

VenueNature Communications · 2025
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced Biosensing Techniques and Applications
Canadian institutionsDalhousie UniversityUniversity of CalgaryUniversity of Alberta
FundersCanadian Cancer Society Research InstituteAlberta Cancer Foundation
KeywordsCytopathologyProfiling (computer programming)BiomarkerFine-needle aspirationMedicineRadiologyPathologyBiopsyComputer scienceCytologyChemistry

Abstract

fetched live from OpenAlex

Core needle biopsy is the gold standard procedure for sampling tissues for pathology-based diagnostics. However, it produces significant tissue damage and may lead to undue pain and risk of complications such as infections. Alternatives such as fine needle aspiration and liquid biopsy have not yet achieved the same widespread utility owing to the limited abundance of cells and relevant biomarkers in extracted samples. Here we introduce a shock-scattering micro-histotripsy-aided fine needle aspiration technology which uses cavitation to liquefy nano-liter to micro-liter volumes to produce tissue homogenates with both intact and lysed cells. It permits not only conventional cytopathology with high success but sufficient high-quality tissue homogenates to enable reliable ancillary testing such as genetic biomarker profiling and even whole genome sequencing with improved quality compared to formalin-fixed samples. Our approach represents an advance in tissue diagnostics with orders of magnitude less damage than core-needle biopsy procedures.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.409
Threshold uncertainty score1.000

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 science0.0010.002
Research integrity0.0010.001
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 head0.026
GPT teacher head0.344
Teacher spread0.318 · 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.

Study designBench or experimental
Domainnot available
GenreMethods

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
Published2025
Admission routes2
Has abstractyes

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