Exploring Freeze-Drying as a Method for Diagnostic Optimization of Synovial Fluid Spectroscopic Data: An ATR-FTIR Analysis on Primary Osteoarthritic Patients

Nikolaos Pradakis, Konstantinos Marmanis, Theodoros Markopoulos, Michael Maragakis, Maria D. Koffa, Konstantinos E. Tilkeridis
{"title":"Exploring Freeze-Drying as a Method for Diagnostic Optimization of Synovial Fluid Spectroscopic Data: An ATR-FTIR Analysis on Primary Osteoarthritic Patients","authors":"Nikolaos Pradakis, Konstantinos Marmanis, Theodoros Markopoulos, Michael Maragakis, Maria D. Koffa, Konstantinos E. Tilkeridis","doi":"10.25103/jestr.171.04","DOIUrl":null,"url":null,"abstract":"The need for cost-efficient, simple and radiation free technologies in clinical diagnostics has orientated the scientific community in the investigation of techniques such as vibrational spectroscopy. Vibrational spectroscopy due to its simplicity both in sample preparation as well as friendly use combined with the technological evolution in data processing and storage could play a promising role in diagnostics even for multifactorial diseases such as osteoarthritis via biofluid analysis. However, despite the related works aiming in synovial fluid there is not a common line in terms of sample preparation. Hence, it is important to establish an effective sample pretreatment protocol to avoid subjectivity and provide the possibility of reliable comparisons among results. In this work, freeze drying preprocessing technique was compared with natural drying in terms of diagnostic performance in 35 knee synovial fluids aspirated from primary osteoarthritic patients with 2 and 4 Kellgren-Lawrence scores. Principal component analysis combined with mean spectra analysis was implemented for this diagnostic purpose. Results have shown that natural drying technique tends to generate more distinct enhanced interclass variations among synovial dried samples compared to freeze drying, despite the latest potential in other biological samples.","PeriodicalId":15707,"journal":{"name":"Journal of Engineering Science and Technology Review","volume":"85 4","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Engineering Science and Technology Review","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.25103/jestr.171.04","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Engineering","Score":null,"Total":0}
引用次数: 0

Abstract

The need for cost-efficient, simple and radiation free technologies in clinical diagnostics has orientated the scientific community in the investigation of techniques such as vibrational spectroscopy. Vibrational spectroscopy due to its simplicity both in sample preparation as well as friendly use combined with the technological evolution in data processing and storage could play a promising role in diagnostics even for multifactorial diseases such as osteoarthritis via biofluid analysis. However, despite the related works aiming in synovial fluid there is not a common line in terms of sample preparation. Hence, it is important to establish an effective sample pretreatment protocol to avoid subjectivity and provide the possibility of reliable comparisons among results. In this work, freeze drying preprocessing technique was compared with natural drying in terms of diagnostic performance in 35 knee synovial fluids aspirated from primary osteoarthritic patients with 2 and 4 Kellgren-Lawrence scores. Principal component analysis combined with mean spectra analysis was implemented for this diagnostic purpose. Results have shown that natural drying technique tends to generate more distinct enhanced interclass variations among synovial dried samples compared to freeze drying, despite the latest potential in other biological samples.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
探索冷冻干燥作为诊断优化滑膜液光谱数据的方法:对原发性骨关节炎患者的 ATR-FTIR 分析
临床诊断需要成本效益高、操作简单和无辐射的技术,这促使科学界对振动光谱等技术进行研究。振动光谱法由于样品制备简单、使用方便,再加上数据处理和存储技术的发展,在诊断多因素疾病(如通过生物流体分析诊断骨关节炎)方面大有可为。然而,尽管相关研究以滑膜液为目标,但在样品制备方面并没有统一的方法。因此,建立一个有效的样品预处理方案非常重要,以避免主观性,并提供可靠的结果比较的可能性。在这项研究中,我们比较了冷冻干燥预处理技术和自然干燥技术对 35 例凯尔格伦-劳伦斯评分为 2 分和 4 分的原发性骨关节炎患者膝关节滑液的诊断性能。为达到诊断目的,采用了主成分分析与平均光谱分析相结合的方法。结果表明,与冷冻干燥相比,自然干燥技术往往会在滑膜干燥样本中产生更明显的增强类间差异,尽管在其他生物样本中具有最新的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
CiteScore
1.00
自引率
0.00%
发文量
66
审稿时长
24 weeks
期刊介绍: The Journal of Engineering Science and Technology Review (JESTR) is a peer reviewed international journal publishing high quality articles dediicated to all aspects of engineering. The Journal considers only manuscripts that have not been published (or submitted simultaneously), at any language, elsewhere. Contributions are in English. The Journal is published by the Eastern Macedonia and Thrace Institute of Technology (EMaTTech), located in Kavala, Greece. All articles published in JESTR are licensed under a CC BY-NC license. Copyright is by the publisher and the authors.
期刊最新文献
An Overview on Enhancing Materials’ Tribological and Mechanical Characteristics by Using Gas Metal Arc Weld Hardfacing Simulation and Evaluation of GAN-based Implementation of Infrared Texture Generation An Improved Electrostatic Cleaning System for Dust Removal from Photovoltaic Panels Study on Crack Propagation Characteristics of Foam Concrete–Soil Composite with Different Height Ratios under Dynamic and Static Loading Conditions Analysis of the Mechanical Behaviour of Double-wall Steel Boxed Cofferdam Structures
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1