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365.4: Exploring immune maturation in childhood transplantation: Influence of age, organ type, thymus excision, and immunosuppression on lymphocyte populations.

2024· article· en· W4402798370 on OpenAlexaffabout
Angela Hamie, Lavinia Ionescu, Maryna Yaskina, Seema Mital, Bethany J. Foster, Upton Allen, Robert J. Ingham, Simon Urschel

Bibliographic record

VenueTransplantation · 2024
Typearticle
Languageen
FieldMedicine
TopicOrgan Transplantation Techniques and Outcomes
Canadian institutionsUniversity of Alberta HospitalMcGill University Health CentreMontreal Children's HospitalUniversity of TorontoSickKids FoundationHospital for Sick ChildrenWomen and Children’s Health Research InstituteUniversity of Alberta
Fundersnot available
KeywordsImmunosuppressionImmune systemImmunologyLymphocyteOrgan transplantationTransplantationBiologyMedicineInternal medicine

Abstract

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Introduction: Younger children receiving solid organ transplantation (Tx) exhibit lower rejection rates than older individuals, likely related to immune immaturity. Standard immunosuppression protocols inadequately address individual needs and may result in avoidable adverse effects such as infection and post-transplant lymphoproliferative disorder. Thymus excision in heart Tx further alters the immune system and is associated with atopic disorders. We hypothesized that the composition of the adaptive immune system in childhood Tx is affected by age, organ, thymus excision and immunosuppression, thereby influencing the clinical outcomes of Tx. Methods: In a national multicenter collaboration (CNTRP-POSITIVE), we included children listed for heart, lung and kidney solid organ Tx. Peripheral blood mononuclear cells (PBMC) were isolated from pre-Tx, 3-month, and 12-month post-Tx blood samples. The adaptive immune system was characterized by flow cytometric deep phenotyping and stimulation assays, grouped by stages of immune maturation (0-2, 2-<10, and 10-18 years of age). Results: Samples were analyzed from 115 children pre-transplant, 89 at 3 months, and 69 at 12 months. Patients receiving heart or liver Tx were younger than those receiving kidneys (n = 28/36/51; median ages: 1.6/1.7/10.3 years, respectively). The CD4+ T cell count decreased significantly at 3 months post-Tx compared to pre-Tx, most pronounced in < 2 year olds (p<0.001). A recovery of CD4+ T cell levels to pre-Tx levels at 12 months was observed in kidney and liver, but not in heart recipients (p<0001). In contrast, regulatory T cells (Tregs) increased 3 months post-Tx (p= 0.0126), returning to baseline at 12 months (p= 0.8337). CD19+ B cell count remained similar post-Tx with the exception of a significant decrease at 12 months post-Tx in the < 2 years age group (p < 0001). Transitional B cells showed a decrease at 3 months post-Tx in kidney recipients, but increased in heart recipients in trend (p = 0.0584), while memory B cells changed with age but independent of immune suppression. Thymoglobulin induction therapy had profound persistent effects on T but also B cells and subpopulations while basiliximab reduced B cells but did not affect immune maturation. Conclusion: Children receiving heart Tx showed a persistent inability to recover CD4 counts at 12 months post-Tx, unlike kidney and liver recipients, likely related to thymus excision at or before Tx. Children < 2 years show the strongest effect of Tx on CD4 T cell and CD19 B cell populations. The increase in Tregs at 3 months post-Tx across all variables supports their role in balancing an exaggerated immune response, leading to a more equilibrated state by 12 months post-Tx. Aggressive induction with Thymoglobulin results in extensive and persistent alteration of the lymphocyte composition, while basiliximab affects only B cell proportions. These findings will help to guide adaption of patient management to personalized needs. Canadian National Transplant Research Program.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.030
GPT teacher head0.297
Teacher spread0.267 · 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 designObservational
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".

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Citations0
Published2024
Admission routes2
Has abstractyes

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