Evidence map›Paper›PMID 36369430›Full record

ArticleHepatology international2023

Fully connected neural network-based serum surface-enhanced Raman spectroscopy accurately identifies non-alcoholic steatohepatitis.

Feng Gao, De-Chan Lu, Tian-Lei Zheng, Shi Geng, Jun-Cheng Sha, Ou-Yang Huang, Liang-Jie Tang, Pei-Wu Zhu, Yang-Yang Li, Li-Li Chen and 5 more

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In one paragraph

Article in Hepatology international, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
–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

4 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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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

15 authors.

Feng Gao *Department of Gastroenterology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
De-Chan Lu *Key Laboratory of Opto-Electronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory for Photonics Technology, Fujian Normal University, Fuzhou, 350000, China.
Tian-Lei Zheng *Artificial Intelligence Unit, Department of Medical Equipment Management, Affiliated Hospital of Xuzhou Medical University, Xuzhou, China.
Shi GengArtificial Intelligence Unit, Department of Medical Equipment Management, Affiliated Hospital of Xuzhou Medical University, Xuzhou, China.
Jun-Cheng ShaInterventional Radiology, Affiliated Hospital of Xuzhou Medical University, Xuzhou, China.
Ou-Yang HuangNAFLD Research Center, Department of Hepatology, The First Affiliated Hospital of Wenzhou Medical University, No. 2 Fuxue Lane, Wenzhou, 325000, China.
Liang-Jie TangNAFLD Research Center, Department of Hepatology, The First Affiliated Hospital of Wenzhou Medical University, No. 2 Fuxue Lane, Wenzhou, 325000, China.
Pei-Wu ZhuDepartment of Laboratory Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
Yang-Yang LiDepartment of Pathology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
Li-Li ChenNAFLD Research Center, Department of Hepatology, The First Affiliated Hospital of Wenzhou Medical University, No. 2 Fuxue Lane, Wenzhou, 325000, China.
Giovanni TargherSection of Endocrinology, Diabetes and Metabolism, Department of Medicine, University and Azienda Ospedaliera Universitaria Integrata of Verona, Verona, Italy.
Christopher D ByrneSouthampton National Institute for Health Research Biomedical Research Centre, University Hospital Southampton, Southampton General Hospital, Southampton, UK.
Zu-Fang Huang *Key Laboratory of Opto-Electronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory for Photonics Technology, Fujian Normal University, Fuzhou, 350000, China. zfhuang@fjnu.edu.cn.
Ming-Hua Zheng *NAFLD Research Center, Department of Hepatology, The First Affiliated Hospital of Wenzhou Medical University, No. 2 Fuxue Lane, Wenzhou, 325000, China. zhengmh@wmu.edu.cn.ORCID http://orcid.org/0000-0003-4984-2631
CHESS-MAFLD consortium

Funding

National Natural Science Foundation of China 82070588
6 · The paper itself

Abstract

background/purpose of the studyThere is a need to find a standardized and low-risk diagnostic tool that can non-invasively detect non-alcoholic steatohepatitis (NASH). Surface enhanced Raman spectroscopy (SERS), which is a technique combining Raman spectroscopy (RS) with nanotechnology, has recently received considerable attention due to its potential for improving medical diagnostics. We aimed to investigate combining SERS and neural network approaches, using a liver biopsy dataset to develop and validate a new diagnostic model for non-invasively identifying NASH.

methodsSilver nanoparticles as the SERS-active nanostructures were mixed with blood serum to enhance the Raman scattering signals. The spectral data set was used to train the NASH classification model by a neural network primarily consisting of a fully connected residual module.

resultsData on 261 Chinese individuals with biopsy-proven NAFLD were included and a prediction model for NASH was built based on SERS spectra and neural network approaches. The model yielded an AUROC of 0.83 (95% confidence interval [CI] 0.70-0.92) in the validation set, which was better than AUROCs of both serum CK-18-M30 levels (AUROC 0.63, 95% CI 0.48-0.76, p = 0.044) and the HAIR score (AUROC 0.65, 95% CI 0.51-0.77, p = 0.040). Subgroup analyses showed that the model performed well in different patient subgroups.

conclusionsFully connected neural network-based serum SERS analysis is a rapid and practical tool for the non-invasive identification of NASH. The online calculator website for the estimated risk of NASH is freely available to healthcare providers and researchers ( http://www.pan-chess.cn/calculator/RAMAN_score ).

Indexed as

Metal NanoparticlesNon-alcoholic Fatty Liver DiseaseBiomarkersBiopsyHumansLiverNeural Networks, ComputerSerumSilverSpectrum Analysis, RamanBiomarkersSilverNAFLD (non-alcoholic fatty liver disease)NASH (non-alcoholic steatohepatitis)RamanSERS (surface-enhanced Raman spectroscopy)

Identifiers

PMID36369430
PMCPMC9651904

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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.