Evidence map›Paper›PMID 42676618›Full record

ReviewMedical review (2021)2026

Immune reset: what do we learn from bone marrow transplantation.

Huidong Guo, Xiao-Jun Huang

Abstract readReview
In one paragraph

Review in Medical review (2021), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Huidong GuoPeking University People's Hospital, Peking University Institute of Hematology, National Clinical Research Center for Hematologic Disease, Beijing Key Laboratory of Cell and Gene Therapy for Hematologic Malignancies, Collaborative Innovation Center of Hematology, Peking University, Beijing, China.ORCID https://orcid.org/0000-0003-3851-3656
Xiao-Jun HuangPeking University People's Hospital, Peking University Institute of Hematology, National Clinical Research Center for Hematologic Disease, Beijing Key Laboratory of Cell and Gene Therapy for Hematologic Malignancies, Collaborative Innovation Center of Hematology, Peking University, Beijing, China.ORCID https://orcid.org/0000-0002-2145-6643

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Bone marrow transplantation (BMT) has traditionally been viewed as a curative strategy for hematologic malignancies through replacement of the diseased hematopoietic system. However, accumulating evidence indicates that BMT represents a far more profound biological process: an enforced collapse and subsequent reconstruction of the hematopoietic and immune system. This process provides a unique human model to investigate how immune homeostasis can be reprogrammed beyond physiological conditions. Importantly, post-transplant immunity does not revert to a pre-existing state but instead converges on a newly defined equilibrium shaped by conditioning-induced injury, donor-recipient immune interactions, stromal regeneration, and therapeutic interventions. In this review, we conceptualize BMT as an immune reset and synthesize emerging insights into the mechanisms that govern tolerance reinstallation and immune stability after transplantation. By reframing BMT as a model of immune reset rather than simple immune reconstitution, we highlight general principles of immune homeostasis restoration that extend beyond transplantation and may inform therapeutic strategies for autoimmunity, immune aging, and immune-mediated diseases.

Indexed as

bone marrow transplantconditioningimmune homeostasisimmune tolerance

Identifiers

PMID42676618
PMCPMC13526690

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.