Evidence map›Paper›PMID 41900461›Full record

ArticleMicroorganisms2026

Machine Learning-Enhanced MALDI-TOF Mass Spectrometry for Screening HBsAg-Positive Patients.

Tiantian Zhang, Shixuan Huang, Junxun Li, Yuwei Wu, Xinyu Zhao, He Gao, Juan Yang, Lingshuang Yang, Lulu Cao, Xinqiang Xie and 5 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Microorganisms, 2026. 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

15 authors.

Tiantian ZhangSchool of Food and Biological Engineering, Shaanxi University of Science and Technology, Xi'an 710021, China.
Shixuan HuangState Key Laboratory of Applied Microbiology Southern China, Guangdong Provincial Key Laboratory of Microbial Safety and Health, National Health Commission Science and Technology Innovation Platform for Nutrition and Safety of Microbial Food, Key Laboratory of Big Data Technologies for Food Microbiological Safety, State Administration for Market Regulation, Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou 510070, China.ORCID 0009-0004-5850-3425
Junxun LiDepartment of Medical Laboratory, First Affiliated Hospital, Sun Yat-Sen University, Guangzhou 510080, China.ORCID 0000-0003-0636-9506
Yuwei WuState Key Laboratory of Applied Microbiology Southern China, Guangdong Provincial Key Laboratory of Microbial Safety and Health, National Health Commission Science and Technology Innovation Platform for Nutrition and Safety of Microbial Food, Key Laboratory of Big Data Technologies for Food Microbiological Safety, State Administration for Market Regulation, Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou 510070, China.
Xinyu ZhaoState Key Laboratory of Applied Microbiology Southern China, Guangdong Provincial Key Laboratory of Microbial Safety and Health, National Health Commission Science and Technology Innovation Platform for Nutrition and Safety of Microbial Food, Key Laboratory of Big Data Technologies for Food Microbiological Safety, State Administration for Market Regulation, Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou 510070, China.
He GaoState Key Laboratory of Applied Microbiology Southern China, Guangdong Provincial Key Laboratory of Microbial Safety and Health, National Health Commission Science and Technology Innovation Platform for Nutrition and Safety of Microbial Food, Key Laboratory of Big Data Technologies for Food Microbiological Safety, State Administration for Market Regulation, Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou 510070, China.
Juan YangState Key Laboratory of Applied Microbiology Southern China, Guangdong Provincial Key Laboratory of Microbial Safety and Health, National Health Commission Science and Technology Innovation Platform for Nutrition and Safety of Microbial Food, Key Laboratory of Big Data Technologies for Food Microbiological Safety, State Administration for Market Regulation, Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou 510070, China.
Lingshuang YangState Key Laboratory of Applied Microbiology Southern China, Guangdong Provincial Key Laboratory of Microbial Safety and Health, National Health Commission Science and Technology Innovation Platform for Nutrition and Safety of Microbial Food, Key Laboratory of Big Data Technologies for Food Microbiological Safety, State Administration for Market Regulation, Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou 510070, China.
Lulu CaoState Key Laboratory of Applied Microbiology Southern China, Guangdong Provincial Key Laboratory of Microbial Safety and Health, National Health Commission Science and Technology Innovation Platform for Nutrition and Safety of Microbial Food, Key Laboratory of Big Data Technologies for Food Microbiological Safety, State Administration for Market Regulation, Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou 510070, China.
Xinqiang XieState Key Laboratory of Applied Microbiology Southern China, Guangdong Provincial Key Laboratory of Microbial Safety and Health, National Health Commission Science and Technology Innovation Platform for Nutrition and Safety of Microbial Food, Key Laboratory of Big Data Technologies for Food Microbiological Safety, State Administration for Market Regulation, Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou 510070, China.ORCID 0000-0003-3943-7755
Hui ZhaoState Key Laboratory of Applied Microbiology Southern China, Guangdong Provincial Key Laboratory of Microbial Safety and Health, National Health Commission Science and Technology Innovation Platform for Nutrition and Safety of Microbial Food, Key Laboratory of Big Data Technologies for Food Microbiological Safety, State Administration for Market Regulation, Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou 510070, China.
Jing ChengDepartment of Medical Laboratory, First Affiliated Hospital, Sun Yat-Sen University, Guangzhou 510080, China.
Hongxia TanDepartment of Medical Laboratory, First Affiliated Hospital, Sun Yat-Sen University, Guangzhou 510080, China.
Ying LiState Key Laboratory of Applied Microbiology Southern China, Guangdong Provincial Key Laboratory of Microbial Safety and Health, National Health Commission Science and Technology Innovation Platform for Nutrition and Safety of Microbial Food, Key Laboratory of Big Data Technologies for Food Microbiological Safety, State Administration for Market Regulation, Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou 510070, China.ORCID 0000-0003-4012-6954
Qingping WuSchool of Food and Biological Engineering, Shaanxi University of Science and Technology, Xi'an 710021, China.ORCID 0000-0001-6503-359X

Funding

National Key Research and Development Program of China 2023YFF1104100National Natural Science Foundation of China 32202380Project of Guangdong Academy of Sciences 2022GDASZH-2022010101
6 · The paper itself

Abstract

Hepatitis B virus (HBV) remains a major global public health challenge, and its early screening is essential for controlling transmission and improving treatment outcomes. We analyzed serum samples from 422 participants via Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) to establish a screening model for hepatitis B surface antigen (HBsAg)-positive status. Following multi-bin preprocessing and single-sample spectral aggregation, we assessed three machine learning algorithms-random forest, deep neural network, and light gradient boosting machine (LightGBM). Among them, the LightGBM model achieved the best performance, with an optimized F1 score of 0.87 and an area under the receiver operating characteristic curve (AUC) of 0.94. A 100-iteration ensemble feature stabilization strategy identified twelve distinct

Indexed as

hepatitis B surface antigenhepatitis B virusmachine learningmatrix-assisted laser desorption/ionization time-of-flightserum

Identifiers

PMID41900461
PMCPMC13028653

What OpenQuestion holds

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Registered trials

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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.