Distinguishing IDH mutation status in gliomas using FTIR-ATR spectra of peripheral blood plasma indicating clear traces of protein amyloid aggregation DOI Creative Commons
Saiko Kino, Masayuki Kanamori, Yuji Matsuura

et al.

Research Square (Research Square), Journal Year: 2023, Volume and Issue: unknown

Published: Dec. 13, 2023

Abstract BACKGROUND Glioma is a primary brain tumor, and obtaining an accurate assessment of its molecular profile in minimally invasive manner important determining treatment strategies. Among the abnormalities gliomas, mutations isocitrate dehydrogenase (IDH) gene are particularly strong predictors sensitivity prognosis. In this study, we attempted to non-invasively diagnose glioma development presence IDH using multivariate analysis plasma mid-infrared absorption spectra for comprehensive sensitive view changes blood components associated with disease genetic mutations. These component discussed terms wavenumbers that contribute discrimination. METHODS Plasma samples were collected at our institutes from 84 patients (13 oligodendrogliomas, 17 IDH-mutant astrocytoma, 7 wild-type diffuse glioma, 47 glioblastomas) before commencing their 72 healthy participants. FTIR-ATR obtained each sample, PLS discriminant was performed absorbance wavenumber fingerprint region biomolecules as explanatory variable. This data used distinguishing participants RESULTS The derived classification algorithm distinguished 83% accuracy (area under curve (AUC) receiver operating characteristic (ROC) = 0.908) diagnosed mutation 75% (AUC 0.752 ROC) cross-validation 30% total test data. Presence suggests increase ratio β-sheet structures conformational composition proteins glioma. Furthermore, these more pronounced gliomas. CONCLUSIONS infrared could be gliomas high degree accuracy. spectral shape protein band showed b-sheet significantly higher than participants, aggregation distinct feature

Language: Английский

Machine Learning and Artificial Intelligence Systems Based on the Optical Spectral Analysis in Neuro-Oncology DOI Creative Commons
Tatiana A. Savelieva, I.D. Romanishkin, A. Ospanov

et al.

Photonics, Journal Year: 2025, Volume and Issue: 12(1), P. 37 - 37

Published: Jan. 4, 2025

Decision support systems based on machine learning (ML) techniques are already empowering neuro-oncologists. These provide comprehensive diagnostics, offer a deeper understanding of diseases, predict outcomes, and assist in customizing treatment plans to individual patient needs. Collectively, these elements represent artificial intelligence (AI) neuro-oncology. This paper reviews recent studies which apply algorithms optical spectroscopy data from central nervous system (CNS) tumors, both ex vivo vivo. We first cover general issues such as the physical basis optical-spectral methods used neuro-oncology, basic spectral signal preprocessing, feature extraction, clustering, supervised classification methods. Then, we review more detail methodology results applying ML fluorescence, elastic inelastic scattering, IR spectroscopy.

Language: Английский

Citations

1
Advancing Brain Research through Surface-Enhanced Raman Spectroscopy (SERS): Current Applications and Future Prospects DOI Creative Commons

Suzan Elsheikh,

Nathan Coles,

Ojodomo J. Achadu

et al.

Biosensors, Journal Year: 2024, Volume and Issue: 14(1), P. 33 - 33

Published: Jan. 10, 2024

Surface-enhanced Raman spectroscopy (SERS) has recently emerged as a potent analytical technique with significant potential in the field of brain research. This review explores applications and innovations SERS understanding pathophysiological basis diagnosis disorders. holds advantages over conventional spectroscopy, particularly terms sensitivity stability. The integration label-free presents promising opportunities for rapid, reliable, non-invasive brain-associated diseases, when combined advanced computational methods such machine learning. to deepen our enhancing diagnosis, monitoring, therapeutic interventions. Such advancements could significantly enhance accuracy clinical further brain-related processes diseases. assesses utility diagnosing disorders Alzheimer's Parkinson's stroke, cancer. Recent technological advances instrumentation techniques are discussed, including nanoparticle design, substrate materials, imaging technologies. We also explore prospects emerging trends, offering insights into new technologies, while addressing various challenges limitations associated

Language: Английский

Citations

7
Raman Spectroscopy and AI Applications in Cancer Grading: An Overview DOI Creative Commons
Pietro Manganelli Conforti, Gianmarco Lazzini, Paolo Russo

et al.

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 54816 - 54852

Published: Jan. 1, 2024

Raman spectroscopy (RS) is a label-free molecular vibrational technique that able to identify the fingerprint of various samples making use inelastic scattering monochromatic light. Because its advantages non-destructive and accurate detection, RS finding more for benign malignant tissues, tumor differentiation, subtype classification, section pathology diagnosis, operating either in vivo or vitro . However, high specificity comes at cost. The acquisition rate low, depth information cannot be directly accessed, sampling area limited. Such limitations can contained if data pre- post-processing methods are combined with current Artificial Intelligence (AI), essentially, Machine Learning (ML) Deep (DL). latter modifying approach cancer diagnosis currently used automate many analyses, it has emerged as promising option improving healthcare accuracy patient outcomes by abiliting prediction diseases tools. In very broad context, applications in oncology include risk assessment, early prognosis estimation, treatment selection based on deep knowledge. application autonomous datasets generated analysis tissues could make rapid stand-alone help pathologists diagnose accuracy. This review describes milestones achieved applying AI-based algorithms analysis, grouped according seven major types cancers (Pancreatic, Breast, Skin, Brain, Prostate, Ovarian Oral cavity). Additionally, provides theoretical foundation tackle both present forthcoming challenges this domain. By exploring achievements discussing relative methodologies, offers recapitulative insights recent ongoing efforts position effective screening tool pathologists. Accordingly, we aim encourage future research endeavors facilitate realization full potential AI grading.

Language: Английский

Citations

6
Uncovering potential diagnostic and pathophysiological roles of α‐synuclein and DJ‐1 in melanoma DOI Creative Commons
Agathe Quesnel,

Leya Danielle Martin,

Chaimaa Tarzi

et al.

Cancer Medicine, Journal Year: 2024, Volume and Issue: 13(1)

Published: Jan. 1, 2024

Melanoma, the most lethal skin cancer type, occurs more frequently in Parkinson's disease (PD), and PD is frequent melanoma patients, suggesting mechanisms overlap. α-synuclein, a protein that accumulates brain, oncogene DJ-1, which associated with autosomal recessive forms, are both elevated cells. Whether this indicates progression or constitutes protective response remains unclear. We hereby investigated molecular through α-synuclein DJ-1 interact, novel biomarkers targets melanoma.

Language: Английский

Citations

4
Molecular Insights into α-Synuclein Fibrillation: A Raman Spectroscopy and Machine Learning Approach DOI Creative Commons

Nathan Coles,

Suzan Elsheikh,

Agathe Quesnel

et al.

ACS Chemical Neuroscience, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 28, 2025

The aggregation of α-synuclein is crucial to the development Lewy body diseases, including Parkinson's disease and dementia with bodies. pathway typically involves a defined sequence nucleation, elongation, secondary exhibiting prion-like spreading. This study employed Raman spectroscopy machine learning analysis, alongside complementary techniques, characterize biomolecular changes during fibrillation purified recombinant wild-type protein. Monomeric was produced, purified, subjected 7-day assay generate preformed fibrils. Stages were analyzed using spectroscopy, confirmed through negative staining transmission electron microscopy, mass spectrometry, light scattering analyses. A pipeline incorporating principal component analysis uniform manifold approximation projection used analyze spectral data identify significant peaks, resulting in differentiation between sample groups. Notable shifts found various stages aggregation. Early (D1) included increases α-helical structures (1303, 1330 cm–1) β-sheet formation (1045 cm–1), reductions COO– CH2 bond regions (1406, 1445 cm–1). By D4, these structural persist additional features. At D7, decrease H-bonding (1625 tyrosine ring breathing (830 indicates further stabilization, suggesting shift from initial helical stabilized β-sheets aggregated Additionally, alterations peaks related tyrosine, alanine, proline, glutamic acid identified, emphasizing role amino acids intramolecular interactions transition conformational states fibrillation. approach offers insight into aggregation, enhancing understanding its pathophysiology potential diagnostic relevance.

Language: Английский

Citations

0
Raman Spectroscopy in the Diagnosis of Brain Gliomas: A Literature Review DOI Open Access
E. V. Stupak,

Vadim Glotov,

Arsen S Askandaryan

et al.

Cureus, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 17, 2025

Raman spectroscopy (RS) is increasingly applied in medical fields to distinguish neoplastic from normal tissues, with recent advancements enabling its use neurosurgery. This review explores RS as a diagnostic and surgical aid for brain gliomas, detailing various modalities applications. Through comprehensive search databases including PubMed, Google Scholar, eLibrary, over 300 references were screened, resulting 74 articles that met inclusion criteria. Key findings reveal RS's potential neuro-oncology examining native biopsy specimens, frozen paraffin-embedded body fluids, well performing intraoperative assessments. offers promise identifying differentiating them healthy tissue, establishing precise tumor boundaries during resection.

Language: Английский

Citations

0
Advancing clinical diagnostics: the potential of analytical Raman spectroscopy in oncology, dermatology, and diabetes DOI
Rabia’tul A’dawiah, Alex Jie Yap,

Poongkulali Rajarahm

et al.

Applied Spectroscopy Reviews, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 56

Published: April 4, 2025

Language: Английский

Citations

0
Precise Identification of Glioblastoma Micro‐Infiltration at Cellular Resolution by Raman Spectroscopy DOI Creative Commons

Li-jun Zhu,

Jianrui Li,

Jing Pan

et al.

Advanced Science, Journal Year: 2024, Volume and Issue: unknown

Published: July 31, 2024

Precise identification of glioblastoma (GBM) microinfiltration, which is essential for achieving complete resection, remains an enormous challenge in clinical practice. Here, the study demonstrates that Raman spectroscopy effectively identifies GBM microinfiltration with cellular resolution specimens. The spectral differences between infiltrative lesions and normal brain tissues are attributed to phospholipids, nucleic acids, amino unsaturated fatty acids. These biochemical metabolites identified by further confirmed spatial metabolomics. Based on differential spectra, imaging resolves important morphological information relevant a label-free manner. area under receiver operating characteristic curve (AUC) combined machine learning detecting exceeds 95%. Most importantly, cancer cell threshold as low 3 human cells per 0.01 mm

Language: Английский

Citations

3
Raman and autofluorescence spectroscopy for in situ identification of neoplastic tissue during surgical treatment of brain tumors DOI Creative Commons
Ortrud Uckermann,

Jonathan Ziegler,

Matthias Meinhardt

et al.

Journal of Neuro-Oncology, Journal Year: 2024, Volume and Issue: 170(3), P. 543 - 553

Published: Aug. 28, 2024

Abstract Purpose Raman spectroscopy (RS) is a promising method for brain tumor detection. Near-infrared autofluorescence (AF) acquired during RS provides additional useful information identification and was investigated in comparison with delineating tumors situ. Methods spectra were together AF situ within the solid at border routine surgeries (218 spectra; glioma WHO II-III, n = 6; GBM, 10; metastases, meningioma, 3). Tissue classification trained on ex vivo data (375 glioma/GBM patients, 20; 11; 13; epileptic hippocampi, 4). Results Both showed that intensity lower than regions normal tissue. Moreover, positive correlation observed between of band corresponding to lipids 1437 cm − 1 , while negative found protein 1260 . The datasets matched surgeon’s evaluation tissue type, correct rates 0.83 0.84, respectively. Similar achieved histopathology biopsies resected selected measurement positions (AF: 0.80, RS: 0.83). Conclusions Spectroscopy successfully integrated into existing neurosurgical workflows, spectroscopic could be classified based data. confirmed its ability detect tumors, emerged as competitive intraoperative delineation.

Language: Английский

Citations

3
Distinguishing IDH mutation status in gliomas using FTIR-ATR spectra of peripheral blood plasma indicating clear traces of protein amyloid aggregation DOI Creative Commons
Saiko Kino, Masayuki Kanamori, Yoshiteru Shimoda

et al.

BMC Cancer, Journal Year: 2024, Volume and Issue: 24(1)

Published: Feb. 16, 2024

Abstract Background Glioma is a primary brain tumor and the assessment of its molecular profile in minimally invasive manner important determining treatment strategies. Among abnormalities gliomas, mutations isocitrate dehydrogenase (IDH) gene are strong predictors sensitivity prognosis. In this study, we attempted to non-invasively diagnose glioma development presence IDH using multivariate analysis plasma mid-infrared absorption spectra for comprehensive sensitive view changes blood components associated with disease genetic mutations. These component discussed terms wavenumbers that contribute differentiation. Methods Plasma samples were collected at our institutes from 84 patients (13 oligodendrogliomas, 17 IDH-mutant astrocytoma, 7 wild-type diffuse glioma, 47 glioblastomas) before initiation 72 healthy participants. FTIR-ATR obtained each sample, PLS discriminant was performed absorbance wavenumber fingerprint region biomolecules as explanatory variable. This data used distinguish participants Results The derived classification algorithm distinguished 83% accuracy (area under curve (AUC) receiver operating characteristic (ROC) = 0.908) diagnosed mutation 75% (AUC 0.752 ROC) cross-validation 30% total test data. suggest an increase ratio β-sheet structures conformational composition proteins glioma. Furthermore, these more pronounced gliomas. Conclusions infrared could be gliomas high degree accuracy. spectral shape protein band showed significantly higher than participants, aggregation distinct feature

Language: Английский

Citations

2