Label-Free Infrared Spectroscopic Imaging Reveals Heterogeneity of β-Sheet Aggregates in Alzheimer’s Disease

Author(s):  
Matthew P. Confer ◽  
Brooke M. Holcombe ◽  
Abigail G. Foes ◽  
John M. Holmquist ◽  
Savannah C. Walker ◽  
...  
2010 ◽  
Author(s):  
Rohit Bhargava ◽  
Rohith K. Reddy ◽  
Jason Ip ◽  
Frances N. Pounder ◽  
Matthew V. Schulmerich ◽  
...  

Amyloid ◽  
2017 ◽  
Vol 24 (sup1) ◽  
pp. 163-164
Author(s):  
David Martinez-Marin ◽  
Hari Sreedhar ◽  
Michael J. Walsh ◽  
Maria M. Picken

2019 ◽  
Author(s):  
Rupali Mankar ◽  
Carlos E. Bueso-Ramos ◽  
C. Cameron Yin ◽  
Juliana E. Hidalgo-Lopez ◽  
Sebastian Berisha ◽  
...  

AbstractOsteosclerosis and myefibrosis are complications of myeloproliferative neoplasms. These disorders result in excess growth of trabecular bone and collagen fibers that replace hematopoietic cells, resulting in abnormal bone marrow function. Treatments using imatinib and JAK2 pathway inhibitors can be effective on osteosclerosis and fibrosis, therefore accurate grading is critical for tracking treatment effectiveness. Current grading standards use a four-class system based on analysis of biopsies stained with three histological stains: hematoxylin and eosin (H&E), Masson’s trichrome, and reticulin. However, conventional grading can be subjective and imprecise, impacting the effectiveness of treatment. In this paper, we demonstrate that mid-infrared spectroscopic imaging may serve as a quantitative diagnostic tool for quantitatively tracking disease progression and response to treatment. The proposed approach is label-free and provides automated quantitative analysis of osteosclerosis and collagen fibrosis.


2016 ◽  
Vol 89 (5) ◽  
pp. 1153-1159 ◽  
Author(s):  
Vishal K. Varma ◽  
Andre Kajdacsy-Balla ◽  
Sanjeev K. Akkina ◽  
Suman Setty ◽  
Michael J. Walsh

2008 ◽  
Vol 73A (12) ◽  
pp. 1158-1164 ◽  
Author(s):  
Gerald Steiner ◽  
Saskia Küchler ◽  
Andreas Hermann ◽  
Edmund Koch ◽  
Reiner Salzer ◽  
...  

Author(s):  
Eric Zimmermann ◽  
Sudipta S. Mukherjee ◽  
Kianoush Falahkheirkhah ◽  
Mark C. Gryka ◽  
Andre Kajdacsy-Balla ◽  
...  

Context.— Myocardial fibrosis underpins a number of cardiovascular conditions and is difficult to identify with standard histologic techniques. Challenges include imaging, defining an objective threshold for classifying fibrosis as mild or severe, as well as understanding the molecular basis for these changes. Objective.— To develop a novel, rapid, label-free approach to accurately measure and quantify the extent of fibrosis in cardiac tissue using infrared spectroscopic imaging. Design.— We performed infrared spectroscopic imaging and combined that with advanced machine learning–based algorithms to assess fibrosis in 15 samples from patients belonging to the following 3 classes: (1) nonpathologic (control) donor hearts; (2) patients receiving transplant; and (3) tissue from patients undergoing implantation of ventricular assist device. Results.— Our results show excellent sensitivity and accuracy for detecting myocardial fibrosis as demonstrated by high area under the curve of 0.998 in the receiver-operating characteristic curve measured from infrared imaging. Fibrosis of various morphologic subtypes are then demonstrated with virtually generated picrosirius red images, which show good visual and quantitative agreement (correlation coefficient = 0.92, ρ = 7.76 × 10−15) with stained images of the same sections. Underlying molecular composition of the different subtypes were investigated with infrared spectra showing reproducible differences presumably arising from differences in collagen subtypes and/or crosslinking. Conclusions.— Infrared imaging can be a powerful tool in studying myocardial fibrosis and gleaning insights into the underlying chemical changes that accompany it. Emerging methods suggest that the proposed approach is compatible with conventional optical microscopy and its consistency makes it translatable to the clinical setting for real-time diagnoses as well as for objective and quantitative research.


Author(s):  
Shaiju S. Nazeer ◽  
Hari Sreedhar ◽  
Vishal K. Varma ◽  
David Martinez-Marin ◽  
Christine Massie ◽  
...  

2019 ◽  
Vol 103 (4) ◽  
pp. 698-704
Author(s):  
Imran Uraizee ◽  
Vishal K. Varma ◽  
Hari Sreedhar ◽  
Francesca Gambacorta ◽  
Shaiju S. Nazeer ◽  
...  

2021 ◽  
Vol 13 ◽  
Author(s):  
Kai Liu ◽  
Jiasong Li ◽  
Raksha Raghunathan ◽  
Hong Zhao ◽  
Xuping Li ◽  
...  

As the major neurodegenerative disease of dementia, Alzheimer’s disease (AD) has caused an enormous social and economic burden on society. Currently, AD has neither clear pathogenesis nor effective treatments. Positron emission tomography (PET) and magnetic resonance imaging (MRI) have been verified as potential tools for diagnosing and monitoring Alzheimer’s disease. However, the high costs, low spatial resolution, and long acquisition time limit their broad clinical utilization. The gold standard of AD diagnosis routinely used in research is imaging AD biomarkers with dyes or other reagents, which are unsuitable for in vivo studies owing to their potential toxicity and prolonged and costly process of the U.S. Food and Drug Administration (FDA) approval for human use. Furthermore, these exogenous reagents might bring unwarranted interference to mechanistic studies, causing unreliable results. Several label-free optical imaging techniques, such as infrared spectroscopic imaging (IRSI), Raman spectroscopic imaging (RSI), optical coherence tomography (OCT), autofluorescence imaging (AFI), optical harmonic generation imaging (OHGI), etc., have been developed to circumvent this issue and made it possible to offer an accurate and detailed analysis of AD biomarkers. In this review, we present the emerging label-free optical imaging techniques and their applications in AD, along with their potential and challenges in AD diagnosis.


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