Evidence map›Paper›PMID 39737504›Full record

SynthesisThe Indian journal of medical research2024

Stem cell therapy approaches for non-malignant diseases & non-haematological diseases in India: A systematic review.

Chandrashekhar Chavan, Suman Ray, Chandra Mohan Kumar

Abstract readSystematic Review
In one paragraph

Synthesis in The Indian journal of medical research, 2024. 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.

Chandrashekhar ChavanDepartment of Inclusive Health, CSIR-National Institute of Science Communication and Policy Research, New Delhi, India.
Suman RayDepartment of Inclusive Health, CSIR-National Institute of Science Communication and Policy Research, New Delhi, India.
Chandra Mohan KumarDepartment of Pediatrics, All India Institute of Medical Sciences, Patna, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background & objectives Our study aims to provide the diversity of stem cell use for non-malignant, non-haematological diseases in India through the lens of clinical trials. Methods A PRISMA approach was used to evaluate the safety and efficacy of stem cell use for the period 2001-2021 in India. The outcomes were measured using each disease category, types of stem cells, the origin of stem cells, safety, and efficacy. Results Of the 9206 studies screened, 61 studies that were relevant to stem cell use for non-malignant diseases were included for analysis. Autologous stem cells (75%) were used predominantly compared to allogenic stem cells (18.33%), followed by mixed type (6.67%). Use of bone marrow-derived stem cells (51%) was dominant, followed by melanocytes (19%), adipose (7%), haematopoietic (12%), and (11%) other types of stem cells. The study revealed 37 randomized clinical trial studies conducted in the government research hospital compared to the non-government. Interpretation & conclusions Maintaining the gold standard for stem cell therapy requires randomized clinical trials with large sample sizes, control groups, failures, adverse effects, etc. It is important to have a monitoring and regulation system in stem cell clinical research activities with enough preclinical data and repeated exchanges between the bench and the bedside.

Indexed as

Stem Cell TransplantationCell- and Tissue-Based TherapyHumansIndiaNoncommunicable DiseasesAllogeneic stem cellsautologous stem cellsclinical outcomesefficacynon-malignant diseasesafetystem cell therapy

Identifiers

PMID39737504
PMCPMC11683502

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Registered trials

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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.