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Record W2326178444 · doi:10.1097/mpa.0000000000000096

Biliopancreatic Route for Effective Viral Transduction of Pancreatic Islets

2014· article· en· W2326178444 on OpenAlexafffund
Kate Banks, Tairan Qin, Tao Liang, An Jing Wang, Herbert Y. Gaisano

Bibliographic record

VenuePancreas · 2014
Typearticle
Languageen
FieldMedicine
TopicPancreatic function and diabetes
Canadian institutionsUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsTransduction (biophysics)Pancreatic isletsIsletMedicineSignal transductionInternal medicineCell biologyInsulinBiologyBiochemistry

Abstract

fetched live from OpenAlex

OBJECTIVE: Pancreatic islets are notoriously difficult to efficiently transduce genes with viruses whether in vivo or ex vivo, the latter only transducing superficial layers of the islet. To improve efficiency of transduction, we explored surgical approaches to virus delivery in vivo. METHODS: A technique was developed for retrograde surgical perfusion into the rat biliopancreatic duct with a test adenovirus containing a construct coexpressing green fluorescent protein, the latter for detection of infected cells. RESULTS: Pancreatic islets isolated after acute pancreatic infusion and cultured for 2 days showed expression in the entire islet and in almost all islets. When rats were recovered from the surgery, and then islets isolated at 1 and 8 weeks after surgery, we continued to see extensive islet green fluorescent protein expression, albeit at more reduced levels at 8 weeks. CONCLUSIONS: This strategy of surgical pancreatic ductal perfusion of viruses is an effective way to transduce or reduce gene expression in pancreatic islets for both acute and chronic study.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.009
GPT teacher head0.249
Teacher spread0.240 · 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 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

Citations3
Published2014
Admission routes2
Has abstractyes

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