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Record W21073952 · doi:10.1016/j.trim.2010.11.003

Missed opportunities: Managing achievement and difference in an urban public college preparatory magnet high school

2004· article· en· W21073952 on OpenAlexfundno aff
Joseph Daniel Cytrynbaum

Bibliographic record

VenueTransplant Immunology · 2004
Typearticle
Languageen
FieldSocial Sciences
TopicEducation Systems and Policy
Canadian institutionsnot available
FundersCanadian Institutes of Health Research
KeywordsMathematics educationPreparatory schoolAcademic achievementPolitical sciencePedagogyMedical educationSociologyPsychologyMedicinePrimary education

Abstract

fetched live from OpenAlex

Regulatory T (Treg) cells play an important role in the regulation of immune responses but whether Treg will induce tolerance in transplant recipients in the clinic remains unknown. Our previous studies have shown that TCRαβ(+)CD3(+)CD4⁻CD8⁻NK1.1⁻ (double negative, DN) T cells suppress T cell responses and prolong allograft survival in a single locus MHC-mismatched mouse model. In this study, we investigated the role of DNT cells in a more robust, fully MHC-mismatched BALB/c to C57BL/6 transplantation model, which may be more clinically relevant. Adoptive transfer of DNT cells in combination with short-term rapamycin treatment (days 1-9) induced long-term heart allograft survival (101±31 vs. 39±13 days rapamycin alone, p<0.01). Furthermore adoptive transfer DNT cells augmented CD4+Foxp3+ Treg cells accumulation in transplant recipients while depletion of CD4(+) Treg cells by anti-CD25 inhibited the effect of DNT cells on long-term graft survival (48±12 days vs. 101±31 days, p<0.001). In conclusion, DNT cells combined with short-term immunosuppression can prolong allograft survival, which may be through the accumulation of CD4(+)Foxp3(+) Treg cells in the recipient. Our result suggests that allograft tolerance may require the co-existence of different type Treg cell phenotypes which are affected by current immunosuppression.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.821
Threshold uncertainty score0.999

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 head0.064
GPT teacher head0.302
Teacher spread0.237 · 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 designTheoretical or conceptual
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
Published2004
Admission routes1
Has abstractyes

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