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Record W2058265311 · doi:10.1080/10731190500289834

Transplantation of Bioencapsulated Bone Marrow Stem Cells Improve Hepatic Regeneration and Survival of 90% Hepatectomized Rats: A Preliminary Report

2005· article· en· W2058265311 on OpenAlexaff
Zun Chang Liu, Thomas Ming Swi Chang

Bibliographic record

VenueArtificial Cells Blood Substitutes and Biotechnology · 2005
Typearticle
Languageen
FieldMedicine
TopicPancreatic function and diabetes
Canadian institutionsMcGill University
Fundersnot available
KeywordsBone marrowBone Marrow Stem CellStem cellHepatocyteTransplantationRegeneration (biology)Liver regenerationMedicinePathologyBiologyInternal medicineIn vitroCell biologyBiochemistry

Abstract

fetched live from OpenAlex

We transplanted bioencapsulated bone marrow stem cells intraperitoneally into 90% hepatectomized rats and found that this increases both the rates of hepatic regeneration and survival of the animals. Bone marrow cells isolated from Wistar rats were bioencapsulated using alginate-polylysine-alginate method. These bioencapsulated bone marrow cells were transplanted intraperitoneally into 90% hepatectomized syngeneic wistar rats. Control groups included 90% hepatectomized group receiving intraperitoneal injection of either empty microcapsules or free bone marrow cells. Unlike the control groups, transplantation of bioencapsulated bone marrow cells improved the survival of 90% hepatectomized rats, with an efficacy similar to that of bioencapsulated hepatocytes or free hepatocytes. These results suggest that syngeneic bioencapsulated bone marrow stem cells can increase the survival rates of 90% hepatectomized rats. We also discuss the potential for a new alternative to hepatocyte transplantation for cellular therapy of acute liver failure. In particular, bone marrow stem cells can be obtained from the same patient with no immunorejection, whereas in hepatocyte transplant, immunosuppressant will be needed to prevent immunorejection of the donor hepatocytes.

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 score0.008
Threshold uncertainty score0.610

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.218
Teacher spread0.207 · 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

Citations20
Published2005
Admission routes1
Has abstractyes

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