Evidence map›Paper›PMID 42709246›Full record

ReviewClinical and experimental medicine2026

Cell-based therapies of autoimmune diseases in the context of artificial intelligence development.

Diya Dinesh, Snigdha Suman Das, Barshana Bhattacharya, K Sreedhara Ranganath Pai

Abstract readReview
In one paragraph

Review in Clinical and experimental medicine, 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

4 authors.

Diya DineshDepartment of Pharmacology, Manipal College of Pharmaceutical Sciences, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education (MAHE), Manipal, 576104, Karnataka, India.
Snigdha Suman DasDepartment of Pharmacology, Manipal College of Pharmaceutical Sciences, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education (MAHE), Manipal, 576104, Karnataka, India.
Barshana BhattacharyaDepartment of Pharmacology, Manipal College of Pharmaceutical Sciences, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education (MAHE), Manipal, 576104, Karnataka, India.
K Sreedhara Ranganath PaiDepartment of Pharmacology, Manipal College of Pharmaceutical Sciences, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education (MAHE), Manipal, 576104, Karnataka, India. ksr.pai@manipal.edu.ORCID http://orcid.org/0000-0002-2017-9533

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Autoimmune diseases are comprised of several chronic inflammatory conditions characterized by activation of autoreactive T and B lymphocytes and produces autoantibodies. While most of the current treatment options mainly rely on non-specific immunosuppression, stem cell based therapies can be a promising candidate for promoting "immune reset" and restore long term tolerance against these complex diseases. Stem cell based therapy eliminate autoreactive clones and reconstitute immune tolerance. Besides, mesenchymal stromal cells (MSCs; also referred to as mesenchymal stem cells) have immunomodulatory effects via paracrine signalling, including anti-inflammatory cytokine and extracellular vesicles secretion. However, successful clinical translation is limited by complex high-dimensional datasets and patient heterogeneity. In this review, we have focused on the convergent journey of Artificial Intelligence (AI) and stem cell based therapies, which mainly evolved in recent times. We also discussed how machine learning and deep learning methods facilitate the analysis of single-cell multi-omics, spatial transcriptomics, and easily identify disease states. Furthermore, the role of AI in optimizing the design and potency of therapeutic modalities, including regulatory T cells (Tregs), Chimeric Antigen Receptor (CAR)-T cells, and mesenchymal stromal cells (MSCs) have also been explored. Altogether, this review highlights the emerging role of AI-guided approaches in bridging mechanistic immunology with future precision stem cell therapies, while several computational and preclinical studies are promising, prospective clinical validation remains necessary before widespread clinical implementation .

Indexed as

Artificial IntelligenceAutoimmune DiseasesCell- and Tissue-Based TherapyAnimalsHumansMesenchymal Stem CellsArtificial Intelligence (AI)Autoimmune diseaseImmunologyStem cell researchTranscriptomics

Identifiers

PMID42709246
PMCPMC13553666

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.