Computational Assessment of Spectral Heterogeneity within Fresh Glioblastoma Tissue Using Raman Spectroscopy and Machine Learning Algorithms DOI Creative Commons

Karoline Klein,

Gilbert Georg Klamminger, Laurent Mombaerts

и другие.

Research Square (Research Square), Год журнала: 2023, Номер unknown

Опубликована: Сен. 13, 2023

Abstract Understanding and classifying inherent tumor heterogeneity is a multimodal approach, which can be undertaken at the genetic, biochemical, or morphological level, among others. Optical spectral methods such as Raman Spectroscopy aim rapid non-destructive tissue analysis, where each spectrum generated reflects individual molecular composition of an examined spot within (heterogenous) sample. Using combination supervised unsupervised machine learning well solid database spectra native glioblastoma samples, we succeed not only in distinguishing explicit areas - vital necrotic correctly predicted with accuracy 76% but also determining different entities histomorphologically distinct class tissue. Measurements non-pathological, autoptic brain hereby serve healthy control since their respective spectroscopic properties form reproducible cluster The demonstrated decipherment will valuable especially field spectroscopically guided surgery to delineate margins assist resection control.

Язык: Английский

Spectral insights: Navigating the frontiers of biomedical and microbiological exploration with Raman spectroscopy DOI
Elvin S. Allakhverdiev, Bekzhan D. Kossalbayev, А. К. Садвакасова

и другие.

Journal of Photochemistry and Photobiology B Biology, Год журнала: 2024, Номер 252, С. 112870 - 112870

Опубликована: Фев. 15, 2024

Язык: Английский

Процитировано

7
Computational Assessment of Spectral Heterogeneity within Fresh Glioblastoma Tissue Using Raman Spectroscopy and Machine Learning Algorithms DOI Creative Commons

Karoline Klein,

Gilbert Georg Klamminger, Laurent Mombaerts

и другие.

Molecules, Год журнала: 2024, Номер 29(5), С. 979 - 979

Опубликована: Фев. 23, 2024

Understanding and classifying inherent tumor heterogeneity is a multimodal approach, which can be undertaken at the genetic, biochemical, or morphological level, among others. Optical spectral methods such as Raman spectroscopy aim rapid non-destructive tissue analysis, where each spectrum generated reflects individual molecular composition of an examined spot within (heterogenous) sample. Using combination supervised unsupervised machine learning well solid database spectra native glioblastoma samples, we succeed not only in distinguishing explicit areas—vital necrotic correctly predicted with accuracy 76%—but also determining different entities histomorphologically distinct class vital tissue. Measurements non-pathological, autoptic brain hereby serve healthy control since their respective spectroscopic properties form reproducible cluster The demonstrated decipherment will valuable, especially field spectroscopically guided surgery to delineate margins assist resection control.

Язык: Английский

Процитировано

4
Innovation in Non-Invasive Diagnosis and Disease Monitoring for Meningiomas DOI Creative Commons

B. Korte,

Dimitrios Mathios

International Journal of Molecular Sciences, Год журнала: 2024, Номер 25(8), С. 4195 - 4195

Опубликована: Апрель 10, 2024

Meningiomas are tumors of the central nervous system that vary in their presentation, ranging from benign and slow-growing to highly aggressive. The standard method for diagnosing classifying meningiomas involves invasive surgery can fail provide accurate prognostic information. Liquid biopsy methods, which exploit circulating tumor biomarkers such as DNA, extracellular vesicles, micro-RNA, proteins, more, offer a non-invasive dynamic approach classification, prognostication, evaluating treatment response. Currently, clinically approved liquid test does not exist. This review provides discussion current research challenges implementing techniques advancing meningioma patient care.

Язык: Английский

Процитировано

4
Clinical study of the diagnosis of thyroid tumours using Raman spectroscopy DOI

Qingjian He,

Lianjin Qin,

Yongqiang Yao

и другие.

Brazilian Journal of Otorhinolaryngology, Год журнала: 2025, Номер 91(3), С. 101568 - 101568

Опубликована: Фев. 28, 2025

Язык: Английский

Процитировано

0
Advances in imaging techniques for real-time microbial visualization in wastewater treatment reactors: Challenges, applications, and process optimization DOI

Arukula Deepa,

Anthati Mastan,

Buddolla Viswanath

и другие.

TrAC Trends in Analytical Chemistry, Год журнала: 2025, Номер 188, С. 118227 - 118227

Опубликована: Март 5, 2025

Язык: Английский

Процитировано

0
Unveiling brain disorders using liquid biopsy and Raman spectroscopy DOI

Jeewan C. Ranasinghe,

Ziyang Wang, Shengxi Huang

и другие.

Nanoscale, Год журнала: 2024, Номер 16(25), С. 11879 - 11913

Опубликована: Янв. 1, 2024

Brain disorders, including neurodegenerative diseases (NDs) and traumatic brain injury (TBI), present significant challenges in early diagnosis intervention. Conventional imaging modalities, while valuable, lack the molecular specificity necessary for precise disease characterization. Compared to study of conventional tissues, liquid biopsy, which focuses on blood, tear, saliva, cerebrospinal fluid (CSF), also unveils a myriad underlying processes, providing abundant predictive clinical information. In addition, biopsy is minimally- non-invasive, highly repeatable, offering potential continuous monitoring. Raman spectroscopy (RS), with its ability provide rich information cost-effectiveness, holds great transformative advancements detection understanding biochemical changes associated NDs TBI. Recent developments enhancement technologies advanced data analysis methods have enhanced applicability RS probing intricate signatures within biological fluids, new insights into pathology. This review explores growing role as promising emerging tool particularly through biopsy. It discusses current landscape future prospects highlighting non-invasive molecularly specific diagnostic tool.

Язык: Английский

Процитировано

2
Neurooncology: 2023 update. DOI
Michel Mittelbronn

PubMed, Год журнала: 2023, Номер 4

Опубликована: Янв. 1, 2023

This article presents some of the author's neuropathological highlights in field on neuro-oncology research encountered 2022. Major advances were made development more precise, faster, easier, less invasive and unbiased diagnostic tools ranging from immunohistochemical prediction 1p/19q loss diffuse glioma, methylation analyses CSF samples, molecular profiling for CNS lymphoma, proteomic recurrent glioblastoma, integrated diagnostics better stratification meningioma, intraoperative making use Raman effect or analysis, to finally, assessment histological slides by means machine learning tumor features. In addition, as discovery a new entity may also be highlight neuropathology community, newly described high-grade glioma with pleomorphic pseudopapillary features (HPAP) has been selected this article. Regarding innovative treatment approaches, drug screening platform brain metastasis is presented. Although speed precision steadily increasing, clinical prognosis patients malignant tumors affecting nervous system remains largely unchanged over last decade, therefore future neuro-oncological focus should put how amazing developments presented can sustainably applied positively impact patient prognosis.

Язык: Английский

Процитировано

1
Perspective: Raman spectroscopy for detection and management of diseases affecting the nervous system DOI Creative Commons
John L. Robertson,

Amr Sayed Issa,

Ryan S. Senger

и другие.

Frontiers in Veterinary Science, Год журнала: 2024, Номер 11

Опубликована: Окт. 21, 2024

Raman spectroscopy (RS) is used increasingly for disease detection, including diseases of the nervous system (CNS). This Perspective presents RS basics and how it has been applied to detection. Research that focused on using a novel Raman-based technology—Rametrix ® Molecular Urinalysis (RMU)—for systemic detection presented, demonstrated by an example RS/RMU technology could be management CNS in companion animals.

Язык: Английский

Процитировано

0
Computational Assessment of Spectral Heterogeneity within Fresh Glioblastoma Tissue Using Raman Spectroscopy and Machine Learning Algorithms DOI Creative Commons

Karoline Klein,

Gilbert Georg Klamminger, Laurent Mombaerts

и другие.

Research Square (Research Square), Год журнала: 2023, Номер unknown

Опубликована: Сен. 13, 2023

Abstract Understanding and classifying inherent tumor heterogeneity is a multimodal approach, which can be undertaken at the genetic, biochemical, or morphological level, among others. Optical spectral methods such as Raman Spectroscopy aim rapid non-destructive tissue analysis, where each spectrum generated reflects individual molecular composition of an examined spot within (heterogenous) sample. Using combination supervised unsupervised machine learning well solid database spectra native glioblastoma samples, we succeed not only in distinguishing explicit areas - vital necrotic correctly predicted with accuracy 76% but also determining different entities histomorphologically distinct class tissue. Measurements non-pathological, autoptic brain hereby serve healthy control since their respective spectroscopic properties form reproducible cluster The demonstrated decipherment will valuable especially field spectroscopically guided surgery to delineate margins assist resection control.

Язык: Английский

Процитировано

0