Brain tumour homogenates analysed by surface-enhanced Raman spectroscopy: Discrimination among healthy and cancer cells

星形细胞瘤 主成分分析 病理 拉曼光谱 脑组织 脑瘤 脑癌 癌症 医学 胶质瘤 生物医学工程 计算机科学 癌症研究 内科学 人工智能 光学 物理
作者
Aneta Aniela Kowalska,Sylwia M. Berus,Marcin Kadej,Agnieszka Kamińska,Alicja M. Kmiecik,Katarzyna Ratajczak-Wielgomas,Tomasz Jurek,Łukasz Zadka
出处
期刊:Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy [Elsevier]
被引量:7
标识
DOI:10.1016/j.saa.2019.117769
摘要

Abstract One of the biggest challenge for modern medicine is to make a discrimination among healthy and cancerous tissues. Therefore, nowadays big effort of scientist are devoted to find a new way for as fast as possible diagnosis with as much as possible accuracy in distinguishing healthy from cancerous tissues. That issues are probably the most important in the case of brain tumours, when the diagnosis time plays a great role. Herein we present the surface-enhanced Raman spectroscopy (SERS) together with the principal component analysis (PCA) used to identify the spectra of different brain specimens, healthy and tumour tissues homogenates. The presented analyses include three sets of brain tissues as control samples taken from healthy objects (one set consists of samples from four brain lobes and both hemispheres; eight samples) and the brain tumours from five patients (two Anaplastic Astrocytoma and three Glioblastoma samples). Results prove that tumour brain samples can be discriminated well from the healthy tissues by using only three main principal components, with 96% of accuracy. The largest influence onto the calculated separation is attributed to the spectral regions corresponding in SERS spectra to vibrations of the L-Tryptophan (1450, 1278 cm−1), protein (1300 cm−1), phenylalanine and Amide-I (1005, 1654 cm−1). Therefore, the presented method may open the way for the probable application as a very fast diagnosis tool alternative for conventionally used histopathology or even more as an intraoperative diagnostic tool during brain tumour surgery.
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
大幅提高文件上传限制,最高150M (2024-4-1)

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
小马甲应助Darming采纳,获得10
1秒前
乐乐应助那奇泡芙采纳,获得10
2秒前
3秒前
科研通AI2S应助JW.Huang采纳,获得10
5秒前
7秒前
MIA发布了新的文献求助10
8秒前
豆的的的的豆完成签到,获得积分10
11秒前
可爱的函函应助一颗梧桐采纳,获得10
13秒前
99完成签到,获得积分10
15秒前
丘比特应助MIA采纳,获得10
16秒前
Huang37完成签到 ,获得积分10
17秒前
JamesPei应助你好好好好好采纳,获得10
19秒前
寂寞的雁蓉完成签到,获得积分20
20秒前
小菜完成签到 ,获得积分10
20秒前
领导范儿应助Meihi_Uesugi采纳,获得10
23秒前
28秒前
34秒前
seine发布了新的文献求助10
35秒前
王瑜发布了新的文献求助20
36秒前
43秒前
魁梧的勒发布了新的文献求助40
45秒前
科研码头完成签到 ,获得积分10
46秒前
墨羽翔天完成签到,获得积分10
46秒前
48秒前
小二郎应助纯真玉兰采纳,获得10
49秒前
在水一方应助两飞飞采纳,获得10
51秒前
tori发布了新的文献求助10
53秒前
乐乐应助飘飘采纳,获得10
55秒前
赘婿应助一颗梧桐采纳,获得10
1分钟前
大模型应助科研通管家采纳,获得10
1分钟前
所所应助科研通管家采纳,获得10
1分钟前
NexusExplorer应助科研通管家采纳,获得10
1分钟前
LY0430完成签到 ,获得积分10
1分钟前
英姑应助tori采纳,获得10
1分钟前
Akim应助wangyun采纳,获得10
1分钟前
seine完成签到,获得积分10
1分钟前
1分钟前
1分钟前
阿良完成签到 ,获得积分10
1分钟前
cc发布了新的文献求助10
1分钟前
高分求助中
Thermodynamic data for steelmaking 3000
Teaching Social and Emotional Learning in Physical Education 900
Cardiology: Board and Certification Review 400
[Lambert-Eaton syndrome without calcium channel autoantibodies] 340
Transformerboard III 300
Towards Net Zero Carbon Initiatives A Life Cycle Assessment Perspective 200
Erbium(III) Triflate: A Valuable Catalyst for the Rearrangement of Epoxides to Aldehydes and Ketones 200
热门求助领域 (近24小时)
化学 材料科学 医学 生物 有机化学 工程类 生物化学 纳米技术 物理 内科学 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 电极 光电子学 量子力学
热门帖子
关注 科研通微信公众号,转发送积分 2360617
求助须知:如何正确求助?哪些是违规求助? 2068119
关于积分的说明 5165792
捐赠科研通 1796269
什么是DOI,文献DOI怎么找? 897312
版权声明 557665
科研通“疑难数据库(出版商)”最低求助积分说明 478963