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Record W2335791358 · doi:10.1385/1592598536

Laser Capture Microdissection

2005· book· en· W2335791358 on OpenAlexfundno aff
Graeme I. Murray, Stephanie Curran

Bibliographic record

VenueHumana Press eBooks · 2005
Typebook
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMolecular Biology Techniques and Applications
Canadian institutionsnot available
FundersUniformed Services University of the Health SciencesSchool of Medicine, New York UniversityUniversité Pierre et Marie CurieAarhus UniversitetshospitalUniversität RegensburgKarolinska InstitutetInstitut National de la Santé et de la Recherche MédicaleBrigham and Women's HospitalUniversity of AberdeenUniversität BaselYale UniversityInstitute of GeneticsDirectorate for Biological SciencesAlvin J. Siteman Cancer CenterAarhus Universitet
KeywordsLaser capture microdissectionMicrodissectionLaserComputer scienceBiologyOpticsPhysicsGenetics

Abstract

fetched live from OpenAlex

Laser capture microdissection (LCM) is a recent technique used in the isolation of specific cell populations from a diverse background of cell types, cytological preparations, or live cell culture via direct visualization of the cell. It is based on the adherence of visually selected cells to transparent thermoplastic membrane (ethylene vinyl acetate polymer) which overlies the dehydrated tissue section and is focally melted by triggering of a low energy infrared laser pulse. DNA, mRNA, and protein can be extracted successfully from captured tissue fragments, down to the single cell level. It plays an important role in genomics, pro­ teomics, diagnostic techniques and therapy. This current paper is an attempt to review the role of LCM in molecular diag nosis which can further be applied in the field of head and neck pathology.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.050
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 science0.0000.000
Research integrity0.0010.000
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 head0.017
GPT teacher head0.260
Teacher spread0.243 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations17
Published2005
Admission routes1
Has abstractyes

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