Evidence map›Paper›PMID 40206438›Full record

ReviewLiver research (Beijing, China)2025

Bioengineered extracellular vesicles: The path to precision medicine in liver diseases.

Ashmit Mittal, Vibhuti R Jakhmola, Sukriti Baweja

Abstract readReview
In one paragraph

Review in Liver research (Beijing, China), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
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.

Ashmit MittalDepartment of Molecular and Cellular Medicine, Institute of Liver and Biliary Sciences, New Delhi, India.
Vibhuti R JakhmolaDepartment of Molecular and Cellular Medicine, Institute of Liver and Biliary Sciences, New Delhi, India.
Sukriti BawejaDepartment of Molecular and Cellular Medicine, Institute of Liver and Biliary Sciences, New Delhi, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Extracellular vesicles (EVs) are membrane-bound entities secreted by each cell, categorized as, exosomes, microvesicles or apoptotic bodies based on their size and biogenesis. They serve as promising vectors for drug delivery due to their capacity to carry diverse molecular signatures reflective of their cell of origin. EV research has significantly advanced since their serendipitous discovery, with recent studies focusing on their roles in various diseases and their potential for targeted therapy. In liver diseases, EVs are particularly promising for precision medicine, providing diagnostic and therapeutic potential in conditions such as metabolic dysfunction-associated steatotic liver disease and metabolic dysfunction-associated steatohepatitis, hepatocellular carcinoma, alcoholic liver disease, liver fibrosis, and acute liver failure. Despite challenges in isolation and characterization, engineered EVs have shown efficacy in delivering therapeutic agents with improved targeting and reduced side effects. As research progresses, EVs hold great promise to revolutionize precision medicine in liver diseases, offering targeted, efficient, and versatile therapeutic options. In this review, we summarize various techniques for loading EVs with therapeutic cargo including both passive and active methods, and the potential of bioengineered EVs loaded with various molecules, such as miRNAs, proteins, and anti-inflammatory drugs in ameliorating clinical pathologies of liver diseases.

Indexed as

BioengineeringDrug deliveryExosomesExtracellular vesicles (EVs)Liver diseasesMicrovesiclesPrecision medicineTherapeutic agents

Identifiers

PMID40206438
PMCPMC11977285

What OpenQuestion holds

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LicenceCC BY-NC-ND
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.