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Record W1982656232 · doi:10.1038/srep07613

Decreased deformability of lymphocytes in chronic lymphocytic leukemia

2015· article· en· W1982656232 on OpenAlexafffund
Yi Zheng, Jun Wen, John Nguyen, Mark A. Cachia, Chen Wang, Yu Sun

Bibliographic record

VenueScientific Reports · 2015
Typearticle
Languageen
FieldMedicine
TopicChronic Lymphocytic Leukemia Research
Canadian institutionsMount Sinai HospitalUniversity of Toronto
FundersGrand Challenges CanadaNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsChronic lymphocytic leukemiaStiffnessMedicineMetastasisLeukemiaCancerCellCancer researchPathologyImmunologyChemistryMaterials scienceInternal medicine

Abstract

fetched live from OpenAlex

This paper reports the first study of stiffness/deformability changes of lymphocytes in chronic lymphocytic leukemia (CLL) patients, demonstrating that at the single cell level, leukemic metastasis progresses are accompanied by biophysical property alterations. A microfluidic device was utilized to electrically measure cell volume and transit time of single lymphocytes from healthy and CLL patients. The results from testing thousands of cells reveal that lymphocytes from CLL patients have higher stiffness (i.e., lower deformability), as compared to lymphocytes in healthy samples, which was also confirmed by AFM indentation tests. This observation is in sharp contrast to the known knowledge on other types of metastatic cells (e.g., breast and lung cancer cells) whose stiffness becomes lower as metastasis progresses.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.035
GPT teacher head0.313
Teacher spread0.277 · 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

Citations53
Published2015
Admission routes2
Has abstractyes

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