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Record W1989331780 · doi:10.1016/j.bbmt.2010.09.021

Microchimerism in Salivary Glands after Blood- and Marrow-Derived Stem Cell Transplantation

2010· article· en· W1989331780 on OpenAlexafffund
Simon D. Tran, Robert S. Redman, A. John Barrett, Steven Z. Pavletic, Sharon Key, Younan Liu, Ashley Carpenter, Hieu Nguyen, Yoshinori Sumita, Bruce J. Baum, Stanley R. Pillemer, Éva Mezey

Bibliographic record

VenueBiology of Blood and Marrow Transplantation · 2010
Typearticle
Languageen
FieldMedicine
TopicPrenatal Screening and Diagnostics
Canadian institutionsMcGill University
FundersNational Institute of Dental and Craniofacial ResearchNational Institutes of HealthNational Cancer InstituteNational Heart, Lung, and Blood InstituteCanadian Institutes of Health ResearchU.S. Department of Veterans Affairs
KeywordsMedicineMicrochimerismStem cellTransplantationImmunologyPathologyInternal medicineFetusPregnancyCell biologyBiologyGenetics

Abstract

fetched live from OpenAlex

Blood- and marrow-derived stem cells (BMDSCs) provide disease-ameliorating effects for cardiovascular and autoimmune diseases. Microchimerism from donor BMDSCs has been reported in several recipient tissues. We hypothesized that this finding suggests a potential use of BMDSCs in the treatment of salivary dysfunctions. We investigated the presence of Y chromosome-positive cells in salivary gland biopsies of 5 females who had received a marrow or blood stem cell transplant from male donors. One to 16 years after transplantation, all recipients exhibited scattered Y chromosome-positive cells in the acini, ducts, and stroma of their salivary glands (mean of 1.01%). Potentially, these cells can be markers of transplantation tolerance, contribute to neoplastic epithelial tissues, or engraft at sites of injury. In addition, transplantation of BMDSCs could be used for treatment of Sjögren's syndrome and salivary glands damaged by therapeutic irradiation for cancers of the head and neck.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.259
Threshold uncertainty score0.567

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 head0.006
GPT teacher head0.216
Teacher spread0.211 · 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

Citations33
Published2010
Admission routes2
Has abstractyes

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