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Record W3106788650 · doi:10.1016/j.xpro.2020.100163

Transduction of Primary AML Cells with Lentiviral Vector for In Vitro Study or In Vivo Engraftment

2020· article· en· W3106788650 on OpenAlexfundno aff
Aaron D. Schimmer, Rashim Pal Singh, Ayesh K. Seneviratne, Geethu Emily Thomas, Neil MacLean, Rose Hurren

Bibliographic record

VenueSTAR Protocols · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Interference and Gene Delivery
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchOtsuka PharmaceuticalPrincess Margaret Cancer FoundationTakeda Pharmaceutical CompanyNovartis
KeywordsTransduction (biophysics)Small hairpin RNAViral vectorIn vivoBiologyStem cellBone marrowImmune systemMyeloid leukemiaCell biologyHaematopoiesisCancer researchImmunologyCell cultureGeneGenetics

Abstract

fetched live from OpenAlex

We describe a method to silence genes in primary acute myeloid leukemia cells by transducing them with shRNA in lentiviral vectors. The transduction of primary non-adherent cells is particularly challenging. The protocol will aid in performing such experiments and is particularly helpful to prepare cells for in vivo engraftment studies. Use of a special medium supplemented with cytokines preserves the viability of the leukemic stem cells and their ability to engraft the marrow of immune-deficient mice. For complete details on the use and execution of this protocol, please refer to Singh et al. (2020).

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 categoriesnone
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 score0.006
Threshold uncertainty score0.342

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 head0.029
GPT teacher head0.284
Teacher spread0.255 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations2
Published2020
Admission routes1
Has abstractyes

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