Evidence map›Paper›PMID 41924278›Full record

ReviewFrontiers in immunology2026

Interferon therapy for chronic hepatitis B in children: an immunological perspective.

Yinan Zhao, Yige Wang, Guoying Yu

Abstract readReview
In one paragraph

Review in Frontiers in immunology, 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

3 authors.

Yinan ZhaoInfectious Diseases Major, Qinghai University, Xining, China.
Yige WangInfectious Diseases Major, Qinghai University, Xining, China.
Guoying YuDepartment of Hepatology II, Fourth People's Hospital of Qinghai Province, Xining, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Chronic hepatitis B (CHB) remains a significant global health issue, particularly in children, where the virus can lead to long-term hepatic damage and immune system dysregulation. Interferon (IFN) therapy, such as pegylated interferon (PEG-IFN), has served as a fundamental approach in the management of CHB by facilitating immune modulation and viral suppression. Nevertheless, the application of this treatment in children presents unique challenges, including heterogeneous immune responses, potential adverse effects, and constraints regarding long-term efficacy. This perspective discusses the immunological mechanisms associated with IFN therapy in pediatric CHB, emphasizing its potential to enhance immune-mediated clearance and suppress viral activity. We additionally examine the primary clinical challenges, including treatment resistance, adverse effects, and the necessity for personalized approaches to optimize therapeutic outcomes. Furthermore, this study examines prospective developments in IFN therapy, encompassing innovations in drug formulations, combination treatment strategies, and the implementation of personalized medicine approaches. Despite the challenges associated with IFN therapy, it continues to be a promising treatment modality. Furthermore, ongoing research into its combination with other immunomodulatory agents holds potential for developing more effective and sustainable management strategies for children with CHB.

Indexed as

Antiviral AgentsHepatitis B, ChronicHepatitis B virusInterferonsChildDrug Therapy, CombinationHumansPrecision MedicineTreatment OutcomeAntiviral AgentsInterferonsadverse effectschildrenchronic hepatitis Bimmune modulationinterferon therapypersonalized medicinetherapeutic challenges

Identifiers

PMID41924278
PMCPMC13035511

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.