Evidence map›Paper›PMID 42840945›Full record

ReviewFrontiers in immunology2026

Myasthenia gravis: from recognition of heterogeneity to a paradigm shift toward precision therapy.

Zhao-Qing Li, Ting-Yue Deng, Wen-Jun Qiao, Wei-Feng Xie, Yan-Peng Huang, Fan-Yu Liu, Qi Qin, Jing-Sheng Zhang, Le Guan, Qing-Feng Wang

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

10 authors.

Zhao-Qing Li *Liaoning University of Traditional Chinese Medicine, Shenyang, China.
Ting-Yue Deng *The Affiliated Hospital of Liaoning University of Traditional Chinese Medicine, Shenyang, China.
Wen-Jun QiaoThe Affiliated Hospital of Liaoning University of Traditional Chinese Medicine, Shenyang, China.
Wei-Feng XieThe Affiliated Hospital of Liaoning University of Traditional Chinese Medicine, Shenyang, China.
Yan-Peng HuangThe Affiliated Hospital of Liaoning University of Traditional Chinese Medicine, Shenyang, China.
Fan-Yu LiuThe Affiliated Hospital of Liaoning University of Traditional Chinese Medicine, Shenyang, China.
Qi QinThe Affiliated Hospital of Liaoning University of Traditional Chinese Medicine, Shenyang, China.
Jing-Sheng ZhangThe Affiliated Hospital of Liaoning University of Traditional Chinese Medicine, Shenyang, China.
Le GuanLiaoning University of Traditional Chinese Medicine, Shenyang, China.
Qing-Feng WangLiaoning University of Traditional Chinese Medicine, Shenyang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Myasthenia gravis (MG) is a heterogeneous neuroimmune disorder in which pathogenic autoantibodies impair neuromuscular junction transmission and produce fluctuating skeletal muscle weakness. Although conventional therapy with acetylcholinesterase inhibitors, corticosteroids, and broad immunosuppressants remains effective for many patients, its limitations are increasingly evident, including delayed onset, cumulative toxicity, and inadequate control in refractory disease. These challenges have accelerated the transition from non-selective immunosuppression to biologic and other targeted therapies. Recent advances in MG have revealed that treatment response is strongly shaped by disease heterogeneity across molecular, cellular, and structural levels. Antibody-defined subtypes, including acetylcholine receptor (AChR)-positive, muscle-specific tyrosine kinase (MuSK)-positive, low-density lipoprotein receptor-related protein 4 (LRP4)-positive, and seronegative MG, differ in immune mechanism, complement dependence, and therapeutic vulnerability. On this basis, neonatal Fc receptor (FcRn) antagonists, complement inhibitors, and B-cell-directed therapies have emerged as major biologic classes that increasingly enable mechanism-informed treatment selection. At the same time, resistance remains an important clinical problem, arising from persistent autoreactive immune compartments, pathway-level escape, and irreversible neuromuscular junction damage that may sustain disability despite immunological control. In this review, we discuss how immunological heterogeneity provides the biological rationale for targeted therapy in MG and synthesize current evidence for established and emerging biologics, including FcRn antagonists, complement inhibitors, B-cell- and plasma-cell-directed strategies, and cell-based immunotherapies. We further examine the major barriers that now define the field, particularly long-term safety, infection risk, treatment resistance, incomplete biomarker frameworks, and the distinction between active immune refractoriness and fixed structural impairment. Finally, we outline how biomarker-guided stratification, dynamic monitoring, and integration of neuroprotective or regenerative approaches may help move MG management from empiric escalation toward safer and more precise individualized care.

Indexed as

Myasthenia GravisPrecision MedicineAnimalsAutoantibodiesB-LymphocytesHistocompatibility Antigens Class IHumansLDL-Receptor Related ProteinsReceptor Protein-Tyrosine KinasesReceptors, CholinergicReceptors, FcAutoantibodiesFc receptor, neonatalHistocompatibility Antigens Class ILDL-Receptor Related ProteinsLRP4 protein, humanMUSK protein, humanReceptor Protein-Tyrosine KinasesReceptors, CholinergicReceptors, Fcbiologicsbiomarkerscomplement inhibitorsFcRn antagonistsmyasthenia gravisneuroimmune diseaseprecision medicinerefractory myasthenia gravis

Identifiers

PMID42840945
PMCPMC13640129

What Socratic holds

Textmetadata
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 Socratic graph.