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Record W2192466573 · doi:10.1002/jcb.25456

DNA Superresolution Structure of Reed–Sternberg Cells Differs Between Long‐Lasting Remission Versus Relapsing Hodgkin's Lymphoma Patients

2015· article· en· W2192466573 on OpenAlexafffund
Christiaan H. Righolt, Hans Knecht, Sabine Mai

Bibliographic record

VenueJournal of Cellular Biochemistry · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced Fluorescence Microscopy Techniques
Canadian institutionsMcGill UniversityUniversity of ManitobaJewish General HospitalCancerCare Manitoba
FundersCanadian Cancer SocietyCanada Foundation for Innovation
KeywordsReed–Sternberg cellLymphomaDNADAPIHodgkin lymphomaClassical Hodgkin lymphomaPathologyCell cultureBiologyCancer researchMolecular biologyChemistryMedicineStainingGenetics

Abstract

fetched live from OpenAlex

Recent developments in microscopy have led to superresolution microscopy images of cells. Structured illumination microscopy was used before to reveal new details in the DNA structure and the structure of the DNA-free space in the DAPI-stained cell nuclei of the Hodgkin's lymphoma HDLM-2 cell line. This study extends this technology to primary pre-treatment classical Hodgkin's lymphoma samples of ten patients. Significant differences in both the DNA structure and the structure of the DNA-free space were detected between lymphocytes and malignant cells. Both types of structures were similar for lymphocytes of different patients. When the patients were un-blinded and grouped based on their clinical outcome, either non-relapsed or relapsed, a significant difference in the DNA structure of their Reed-Sternberg (RS) cells was found. Since, RS cells develop from mono-nucleated Hodgkin (H) cells, these data suggest distinct architectural restructuring of nuclei during RS cell formation in patients going to long-lasting remission versus relapse. J. Cell. Biochem. 117: 1633-1637, 2016. © 2015 Wiley Periodicals, Inc.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
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 integrity0.0000.000
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 head0.012
GPT teacher head0.256
Teacher spread0.244 · 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
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

Citations16
Published2015
Admission routes2
Has abstractyes

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