Evidence map›Paper›PMID 41169375›Full record

ReviewFrontiers in immunology2025

Reversing inflammatory diseases via trained immunity: mechanisms, challenges, and prospects.

Wanli Xu, Zhilin Guo, Tingyun Xu, Junjie Chen, Leyi Chen, Wenan Xu

Abstract readReview
In one paragraph

Review in Frontiers in immunology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 1 pooled it
–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

12 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Epithelial-Dermal Immune Memory: TrackingInternational journal of molecular sciences · 2026
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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

6 authors.

Wanli XuShenzhen Clinical College of Stomatology, School of Stomatology, Southern Medical University, Shenzhen, Guangdong, China.
Zhilin GuoSchool of Stomatology, Southern Medical University, Guangzhou, Guangdong, China.
Tingyun XuShenzhen Clinical College of Stomatology, School of Stomatology, Southern Medical University, Shenzhen, Guangdong, China.
Junjie ChenShenzhen Clinical College of Stomatology, School of Stomatology, Southern Medical University, Shenzhen, Guangdong, China.
Leyi ChenShenzhen Clinical College of Stomatology, School of Stomatology, Southern Medical University, Shenzhen, Guangdong, China.
Wenan XuShenzhen Clinical College of Stomatology, School of Stomatology, Southern Medical University, Shenzhen, Guangdong, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Chronic inflammatory diseases are widespread and often accompanied by comorbidities, making treatment challenging. Current immunosuppressive and anti-inflammatory therapies have limited efficacy and significant side effects, and are insufficient to address the complexity of coexisting conditions. This review explores recent advances in innate immune memory, also known as trained immunity, and its potential role in inflammatory diseases. We hypothesize that targeting the regulatory mechanisms of trained immunity may lead to novel therapeutic strategies that more effectively control inflammation and improve disease outcomes. Finally, we highlight that the interplay between trained immunity and inflammatory diseases remains incompletely understood, and further research is needed to elucidate its mechanisms and clinical translational potential.

Indexed as

Immunity, InnateImmunologic MemoryInflammationAnimalsHumansTrained Immunityepigenetic reprogrammingimmune systeminflammationinflammatory diseasesinnate immune memorytrained immunity

Identifiers

PMID41169375
PMCPMC12568716

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.