Evidence map›Paper›PMID 40523769›Full record

ReviewZhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences2025

Non-bioartificial liver support system in the treatment of pediatric acute liver failure.

Na Tian, Qiang Sun

Abstract readReview
In one paragraph

Review in Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences, 2025. 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

2 authors.

Na TianBlood Purification Center, Beijing Children's Hospital Affiliated to Capital Medical University, Beijing 100045, China. putini@126.com.
Qiang SunBlood Purification Center, Beijing Children's Hospital Affiliated to Capital Medical University, Beijing 100045, China. sunqiang@bch.com.cn.

Funding

the Artificial Liver Special Fund of Beijing Liver-Gallbladder Xiangzhao Public Welfare Foundation iGandanF-1082022-RGG028
6 · The paper itself

Abstract

Pediatric acute liver failure (PALF) is a severe and rare clinical syndrome characterized by rapid progression and high mortality. Current main treatment strategies include medical therapy, artificial liver support, and liver transplantation. Given the limited efficacy of medical treatment and the challenges of liver transplantation, such as donor scarcity and high costs, the non-biological artificial liver (NBAL) support system has become a widely used and effective alternative in clinical practice. It provides critical time for liver function recovery or as a bridging therapy to transplantation. Common NBAL modalities include plasma exchange (PE), plasma adsorption (PA), albumin dialysis (AD), and various combination therapies. Therapeutic PE removes toxins by replacing plasma and is suitable as adjuvant therapy in liver failure; high-volume PE is used in acute liver failure but is costly. PA and double plasma molecular adsorption systems remove specific toxins while reducing plasma consumption. AD systems eliminate macromolecular toxins through different mechanisms. Hybrid blood purification therapies combine multiple modes to enhance solute clearance efficiency. Elucidating the clinical characteristics and applications of various NBAL techniques in pediatric acute liver failure may provide valuable guidance for the use of NBAL support systems in pediatric clinical practice.

Indexed as

Liver, ArtificialLiver Failure, AcuteChildHumansPlasma Exchangeacute liver failureartificial liverblood purificationnon-bioartificialpediatrics

Identifiers

PMID40523769
PMCPMC13345953

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.