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Record W2118857462 · doi:10.1002/bit.22634

Effect of cell lysates on retroviral transduction efficiency of cells in suspension culture

2009· article· en· W2118857462 on OpenAlexaff
Pascal Beauchesne, Katherine J. Bruce, Bruce D. Bowen, James M. Piret

Bibliographic record

VenueBiotechnology and Bioengineering · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicVirus-based gene therapy research
Canadian institutionsCanada's Michael Smith Genome Sciences CentreUniversity of British Columbia
Fundersnot available
KeywordsTransduction (biophysics)Cell cultureProtamine sulfateLysisCell growthBiologyCellProtamineCell biologyTropismMolecular biologyChemistryBiochemistryVirologyVirusGeneticsHeparin

Abstract

fetched live from OpenAlex

Recombinant retroviruses are effective vectors able to integrate transgenes into the target cell's genome to achieve longer-term expression. This study investigates the effect of cell lysis products, a common cell culture by-product, on the transduction of suspension cells by gammaretroviral vectors. Cell lysates derived from human and murine suspension cell lines significantly increased the transduction of human TF-1 and K-562 cell lines by gibbon ape leukemia virus-pseudotyped retroviral vectors without altering tropism. The transduction efficiency of TF-1 cells increased as a function of lysate concentration and decreased with increasing target cell concentrations. This was adequately predicted using a saturation equation based on the lysed-to-target cell concentration ratio, R, where: Fold increase = 1+Fold_(Max) (R/(K_(L)+R)). Lysate completely masked the effects of fibronectin when the two were added in combination. With protamine sulfate, the transduction efficiency was increased by lysate to 58% from 20% for protamine sulfate alone. Overall, the presence of cell lysate significantly influenced the outcome of the transduction process, either alone or in the presence of protamine sulfate or fibronectin.

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 score0.014
Threshold uncertainty score0.351

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 head0.004
GPT teacher head0.232
Teacher spread0.228 · 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

Citations0
Published2009
Admission routes1
Has abstractyes

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