Evidence map›Paper›PMID 39604584›Full record

ArticleNPJ digital medicine2024

A virtual scalable model of the Hepatic Lobule for acetaminophen hepatotoxicity prediction.

Stelian Camara Dit Pinto, Jalal Cherkaoui, Debarshi Ghosh, Valentine Cazaubon, Kenza E Benzeroual, Steven M Levine, Mohammed Cherkaoui, Gagan K Sood, Sharmila Anandasabapathy, Sadhna Dhingra and 2 more

Abstract read
In one paragraph

Article in NPJ digital medicine, 2024. 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. Article
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

12 authors.

Stelian Camara Dit PintoDepartment of Computer Science, Digital Engineering and Artificial Intelligence, Long Island University, Brooklyn, NY, USA.ORCID http://orcid.org/0000-0003-2155-1672
Jalal CherkaouiDepartment of Computer Science, Digital Engineering and Artificial Intelligence, Long Island University, Brooklyn, NY, USA.
Debarshi GhoshDepartment of Computer Science, Digital Engineering and Artificial Intelligence, Long Island University, Brooklyn, NY, USA.
Valentine CazaubonDepartment of Computer Science, Digital Engineering and Artificial Intelligence, Long Island University, Brooklyn, NY, USA.
Kenza E BenzeroualArnold & Marie Schwartz College of Pharmacy and Health Sciences, Long Island University, Brooklyn, NY, USA.
Steven M LevineDassault Systèmes, Vélizy-Villacoublay, France.
Mohammed CherkaouiDepartment of Computer Science, Digital Engineering and Artificial Intelligence, Long Island University, Brooklyn, NY, USA.
Gagan K SoodDivision of Gastroenterology, Baylor College of Medicine, Houston, TX, USA.
Sharmila AnandasabapathyDivision of Gastroenterology, Baylor College of Medicine, Houston, TX, USA.
Sadhna DhingraDepartment of Pathology and Genomic Medicine, Houston Methodist Hospital, Houston, TX, USA.
John M VierlingDepartments of Medicine and Surgery, Baylor College of Medicine, Houston, TX, USA.
Nicolas R GalloDepartment of Computer Science, Digital Engineering and Artificial Intelligence, Long Island University, Brooklyn, NY, USA. nicolas.gallo@liu.edu.ORCID http://orcid.org/0009-0004-7967-4266

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Addressing drug-induced liver injury is crucial in drug development, often causing Phase III trial failures and market withdrawals. Traditional animal models fail to predict human liver toxicity accurately. Virtual twins of human organs present a promising solution. We introduce the Virtual Hepatic Lobule, a foundational element of the Living Liver, a multi-scale liver virtual twin. This model integrates blood flow dynamics and an acetaminophen-induced injury model to predict hepatocyte injury patterns specific to patients. By incorporating metabolic zonation, our predictions align with clinical zonal hepatotoxicity observations. This methodology advances the development of a human liver virtual twin, aiding in the prediction and validation of drug-induced liver injuries.

Identifiers

PMID39604584
PMCPMC11603025

What Socratic holds

Textmetadata
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 Socratic graph.