Evidence map›Paper›PMID 35340253›Full record

ArticleJournal of healthcare engineering2022

Neuronal Apoptosis in Patients with Liver Cirrhosis and Neuronal Epileptiform Discharge Model Based upon Multi-Modal Fusion Deep Learning.

Nannan Chi, Xiuping Wang, Yun Yu, Manman Wu, Jianan Yu

RetractedOpen access · hybridAbstract readRetracted Publication
In one paragraph

Article in Journal of healthcare engineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.3field-weighted citation impact, top 44% of its field
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

1 citing paper in PubMed, 4 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors at 2 institutions in 1 country.

Nannan ChiDigestive Department, the First Affiliated Hospital of Jiamusi University, Jiamusi 154000, Heilongjiang, China.
Xiuping WangDepartment of Neurology, the First Affiliated Hospital of Jiamusi University, Jiamusi 154000, Heilongjiang, China.
Yun Yu3 Medical Education Department, the First Affiliated Hospital of Jiamusi University, Jiamusi 154000, Heilongjiang, China.
Manman WuGraduate Department, Jiamusi University, Jiamusi 154000, Heilongjiang, China.
Jianan YuDepartment of Neurology, the First Affiliated Hospital of Jiamusi University, Jiamusi 154000, Heilongjiang, China.ORCID 0000-0001-9219-7848
First Affiliated Hospital of Jiamusi University · CNJiamusi University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Neurons refer to nerve cells. Each neuron is connected with thousands of other neurons to form a corresponding functional area and carry out complex communication with other functional areas. Its importance to the human body is self-evident. There are also many scholars studying the mechanism of apoptosis. This paper proposes a study of neuronal apoptosis in patients with liver cirrhosis and neuronal epileptiform discharge models based on multi-modal fusion deep learning, aiming to study the influencing factors of abnormal neuronal discharge in the brain. The method in this paper is to study multi-modal information fusion methods, perform Bayesian inference, and analyze multi-modal medical data. The function of these research methods is to obtain the relationship between the independence of information and the intersection of information among modalities. In the neuronal epileptiform discharge model, the mRNA expression level of the necroptotic signaling pathway related protein was detected, and the mechanism of neuronal necrosis in patients with liver cirrhosis was explored. Experiments show that the neuron recognition rate has been increased from 67.2% to 84.5%, and the time has been reduced, proving the effectiveness of deep learning.

Indexed as

Deep LearningApoptosisBayes TheoremHumansLiver CirrhosisNeurons

Identifiers

PMID35340253
PMCPMC8947874
OpenAlexW4220670055

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.