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Record W3034797383 · doi:10.1002/jsp2.1098

Immunohistochemical analysis of protein expression in formalin fixed paraffin embedded human intervertebral disc tissues

2020· article· en· W3034797383 on OpenAlexfundno aff
Abbie L. A. Binch, Joseph Snuggs, Christine L. Le Maitre

Bibliographic record

VenueJOR Spine · 2020
Typearticle
Languageen
FieldMedicine
TopicSpine and Intervertebral Disc Pathology
Canadian institutionsnot available
FundersH2020 European Research CouncilMedical Research CouncilVersus ArthritisHorizon 2020 Framework ProgrammeMedical Research Council CanadaEuropean Commission
KeywordsImmunohistochemistryIntervertebral discPathologyProtein expressionAnatomyBiologyMedicineGeneGenetics

Abstract

fetched live from OpenAlex

Immunohistochemistry (IHC) is a useful technique for the localization and semiquantification of protein expression within tissues. Adult human intervertebral disc (IVD) tissues contain a large amount of auto-fluorescence which often makes immunofluorescence techniques inappropriate on tissue samples but can be applied to isolated cell samples. Thus, IHC remains one of, if not the most common application for protein detection within IVD tissue. Immunostaining localizes antigen expression through specific epitope-antibody interactions. Within the field of IVD research, IHC is commonly used on fresh frozen and paraffin embedded tissues to elucidate the expression of antigens. Here, we discuss the principles of IHC applied to formalin fixed paraffin embedded IVD tissue and supply optimized protocols for antibodies used within our group to guide research within the IVD field.

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)0.002
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.002

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 head0.031
GPT teacher head0.335
Teacher spread0.304 · 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 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

Citations25
Published2020
Admission routes1
Has abstractyes

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