A biosensing method for the direct serological detection of liver diseases by integrating a SERS-based sensor and a CNN classifier

肝癌 血清学 肝细胞癌 卷积神经网络 计算机科学 人工智能 癌症研究 医学 抗体 免疫学
作者
Ningtao Cheng,Dajing Chen,Bin Lou,Jing Fu,Hongyang Wang
出处
期刊:Biosensors and Bioelectronics [Elsevier BV]
卷期号:186: 113246-113246 被引量:50
标识
DOI:10.1016/j.bios.2021.113246
摘要

Direct serological detection, due to its clinical facility and testing economy, affords prominent clinical values to the early detection of cancer. Surface-enhanced Raman spectroscopy (SERS)-based sensors have shown great promise in realizing this form of detection. Detecting liver cancer early with such a form, especially in terms of monitoring the pathogenic progression from hepatic inflammations to cancer, is the most effective clinical path to reducing the mortality rate. However, the methodology investigation for this purpose remains a formidable challenge. We fabricated a SERS-based sensor, consisting of Au-Ag nanocomplex-decorated ZnO nanopillars on paper. The sensor has an analytic enhancement factor of 1.02 × 107, which is enough to sense the biomolecular information of liver diseases through direct serum SERS analysis. A convolutional neural network (CNN) classifier for recognizing serum SERS spectra was constructed by deep learning. Integrating this sensor with the CNN, we established an intelligent biosensing method and realized direct serological detection of liver diseases within 1 min. As a proof-of-concept, the method achieved a prediction accuracy of 97.78% on an independent test dataset randomly sampled from 30 normal controls, 30 hepatocellular carcinoma (HCC) cases, and 30 hepatitis B (HB) patients. The results suggest this method can be developed for detecting liver diseases clinically and is worthy of exploration as a means of liver cancer surveillance. The presented sensor holds potential for clinical translation to the direct serological detection of diseases.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
1秒前
潇洒的天与完成签到,获得积分10
2秒前
hannah完成签到,获得积分10
3秒前
ajing完成签到,获得积分10
4秒前
丫丫完成签到,获得积分10
4秒前
zhanjl13完成签到,获得积分10
4秒前
CXS完成签到,获得积分10
5秒前
随风完成签到,获得积分10
5秒前
wang完成签到,获得积分20
9秒前
ee完成签到,获得积分10
9秒前
斯文的若颜完成签到,获得积分10
10秒前
楚楚楚完成签到,获得积分10
13秒前
斑马完成签到,获得积分10
14秒前
MRJJJJ完成签到,获得积分10
15秒前
16秒前
老迟到的友菱完成签到,获得积分10
19秒前
zzf完成签到,获得积分10
20秒前
zy0411完成签到,获得积分10
20秒前
太清完成签到 ,获得积分10
20秒前
22秒前
酷炫的大碗完成签到,获得积分10
22秒前
zzf发布了新的文献求助10
23秒前
114555完成签到,获得积分10
25秒前
YOUYOU完成签到,获得积分10
25秒前
谨慎纸飞机完成签到,获得积分10
26秒前
江鹿柒柒完成签到,获得积分10
26秒前
xrkxrk完成签到 ,获得积分0
27秒前
kelaibing完成签到,获得积分10
27秒前
岁末完成签到 ,获得积分10
28秒前
小张完成签到 ,获得积分10
29秒前
29秒前
乐观鸣凤完成签到,获得积分10
30秒前
运敬完成签到 ,获得积分10
30秒前
强公子完成签到,获得积分10
31秒前
32秒前
35秒前
冷酷的闹闹完成签到 ,获得积分10
35秒前
孙孙孙啊完成签到,获得积分10
35秒前
顺心的芝麻完成签到 ,获得积分10
36秒前
hebhm发布了新的文献求助10
36秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
Cognitive Neuroscience: The Biology of the Mind 1000
Technical Brochure TB 814: LPIT applications in HV gas insulated switchgear 1000
Immigrant Incorporation in East Asian Democracies 600
Nucleophilic substitution in azasydnone-modified dinitroanisoles 500
不知道标题是什么 500
A Preliminary Study on Correlation Between Independent Components of Facial Thermal Images and Subjective Assessment of Chronic Stress 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3968578
求助须知:如何正确求助?哪些是违规求助? 3513400
关于积分的说明 11167585
捐赠科研通 3248853
什么是DOI,文献DOI怎么找? 1794499
邀请新用户注册赠送积分活动 875131
科研通“疑难数据库(出版商)”最低求助积分说明 804664