ArticleCell journal2022
Cell-Free Treatments: A New Generation of Targeted Therapies for Treatment of Ischemic Heart Disease.
Article in Cell journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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.
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.
Who cites it
3 citing papers in PubMed, 2 citations in OpenAlex.
- Engineered ATP-loaded extracellular vesicles: a dual-functional strategy for improving myocardial infarction therapy.Drug delivery and translational research · 2026Article
- Therapeutic Properties of M2 Macrophages in Chronic Wounds: An Innovative Area of Biomaterial-Assisted M2 Macrophage Targeted Therapy.Stem cell reviews and reports · 2025Review
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Although recent progress in medicine has substantially reduced cardiovascular diseases (CVDs)-related mortalities, current therapeutics have failed miserably to be beneficial for all patients with CVDs. A wide array of evidence suggests that newly-introduced cell-free treatments (CFTs) have more reliable results in the improvement of cardiac function. The main regeneration activity of CFTs protocols is based on bypassing cells and using paracrine factors. In this article, we aim to compare various stem cell secretomes, a part of a CFTs strategy, to generalize their effective clinical outcomes for patients with CVDs. Data for this review article were collected from 70 published articles (original, review, randomized clinical trials (RCTs), and case reports/series studies done on human and animals) obtained from Cochrane, Science Direct, PubMed, Scopus, Elsevier, and Google Scholar) from 2015 to April 2020 using six keywords. Full-text/full-length articles, abstract, section of book, chapter, and conference papers in English language were included. Studies with irrelevant/insufficient/data, or undefined practical methods were excluded. CFTs approaches involved in growth factors (GFs); gene-based therapies; microRNAs (miRNAs); extracellular vesicles (EVs) [exosomes (EXs) and microvesicles (MVs)]; and conditioned media (CM). EXs and CM have shown more remarkable results than stem cell therapy (SCT). GF-based therapies have useful results as well as side effects like pathologic angiogenesis. Cell source, cell's aging and CM affect secretomes. Genetic manipulation of stem cells can change the secretome's components. Growing progression to end stage heart failure (HF), propounds CFTs as an advantageous method with practical and clinical values for replacement of injured myocardium, and induction of neovascularization. To elucidate the secrets behind amplifying the expansion rate of cells, increasing life-expectancy, and improving quality of life (QOL) for patients with ischemic heart diseases (IHDs), collaboration among cell biologist, basic medical scientists, and cardiologists is highly recommended.
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Registered trials
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.