AI Article Synopsis
- Deep UV resonance Raman spectroscopy is an effective method for analyzing protein fibrils, overcoming challenges like low solubility and noncrystalline arrangements.
- This technique allows for selective enhancement of various chromophores in protein fibrils, providing insights into their structure and formation mechanisms.
- Advanced methods such as hydrogen-deuterium exchange and chemometrics can further detail the fibril core structure and protein interactions during fibril formation.
Article Abstract
Deep UV resonance Raman spectroscopy is a powerful technique for probing the structure and formation mechanism of protein fibrils, which are traditionally difficult to study with other techniques owing to their low solubility and noncrystalline arrangement. Utilizing a tunable deep UV Raman system allows for selective enhancement of different chromophores in protein fibrils, which provides detailed information on different aspects of the fibrils' structure and formation. Additional information can be extracted with the use of advanced data treatment such as chemometrics and 2D correlation spectroscopy. In this chapter we give an overview of several techniques for utilizing deep UV resonance Raman spectroscopy to study the structure and mechanism of formation of protein fibrils. Clever use of hydrogen-deuterium exchange can elucidate the structure of the fibril core. Selective enhancement of aromatic amino acid side chains provides information about the local environment and protein tertiary structure. The mechanism of protein fibril formation can be investigated with kinetic experiments and advanced chemometrics.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1007/978-1-4939-2978-8_6 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
The Role of Artificial Intelligence in Predicting Optic Neuritis Subtypes From Ocular Fundus Photographs.
J Neuroophthalmol
December 2024
Division of Ophthalmology (EB-S, AS, AA-A, AS-B, DW, SS, FC), Department of Surgery, University of Calgary, Calgary, Canada; Department of Biomedical Engineering (CN), University of Calgary, Calgary, Canada; Departments of Neurology (LBDL) and Ophthalmology (LBDL), University of Michigan, Ann Arbor, Michigan; and Department of Clinical Neurosciences (SS, FC), University of Calgary, Calgary, Canada.
Background: Optic neuritis (ON) is a complex clinical syndrome that has diverse etiologies and treatments based on its subtypes. Notably, ON associated with multiple sclerosis (MS ON) has a good prognosis for recovery irrespective of treatment, whereas ON associated with other conditions including neuromyelitis optica spectrum disorders or myelin oligodendrocyte glycoprotein antibody-associated disease is often associated with less favorable outcomes. Delay in treatment of these non-MS ON subtypes can lead to irreversible vision loss.
View Article and Find Full Text PDFImproved deep canonical correlation fusion approach for detection of early mild cognitive impairment.
Med Biol Eng Comput
January 2025
Non-Invasive Imaging and Diagnostic Laboratory, Department of Applied Mechanics and Biomedical Engineering, Indian Institute of Technology Madras, Chennai, India.
Detection of early mild cognitive impairment (EMCI) is clinically challenging as it involves subtle alterations in multiple brain sub-anatomic regions. Among different brain regions, the corpus callosum and lateral ventricles are primarily affected due to EMCI. In this study, an improved deep canonical correlation analysis (CCA) based framework is proposed to fuse magnetic resonance (MR) image features from lateral ventricular and corpus callosal structures for the detection of EMCI condition.
View Article and Find Full Text PDFDeep Learning to Simulate Contrast-Enhanced MRI for Evaluating Suspected Prostate Cancer.
Radiology
January 2025
From the Department of Radiology, Shenzhen Nanshan People's Hospital, Shenzhen University, Taoyuan Rd No. 89, Nanshan District, Shenzhen 518000, Guangdong, China (H.H., Z.D., Y.Q.); Medical AI Laboratory and Guangdong Key Laboratory of Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, China (J.M., R.L., B.H.); Department of Medical Imaging, People's Hospital of Longhua, Shenzhen, Guangdong, China (X.P., Y.Z.); and Department of Radiology, Shenzhen People's Hospital, Shenzhen, Guangdong, China (D.Z., G.H.).
Background Multiparametric MRI, including contrast-enhanced sequences, is recommended for evaluating suspected prostate cancer, but concerns have been raised regarding potential contrast agent accumulation and toxicity. Purpose To evaluate the feasibility of generating simulated contrast-enhanced MRI from noncontrast MRI sequences using deep learning and to explore their potential value for assessing clinically significant prostate cancer using Prostate Imaging Reporting and Data System (PI-RADS) version 2.1.
View Article and Find Full Text PDFA semi-supervised deep neuro-fuzzy iterative learning system for automatic segmentation of hippocampus brain MRI.
Math Biosci Eng
December 2024
Department of Electronics and Communication Engineering, Akshaya College of Engineering and Technology, Coimbatore, Tamil Nadu, India.
The hippocampus is a small, yet intricate seahorse-shaped tiny structure located deep within the brain's medial temporal lobe. It is a crucial component of the limbic system, which is responsible for regulating emotions, memory, and spatial navigation. This research focuses on automatic hippocampus segmentation from Magnetic Resonance (MR) images of a human head with high accuracy and fewer false positive and false negative rates.
View Article and Find Full Text PDFResearch on bearing fault diagnosis based on a multimodal method.
Math Biosci Eng
December 2024
School of Information Engineering, Nantong Institute of Technology, Nantong 226002, Jiangsu, China.
As an essential component of mechanical systems, bearing fault diagnosis is crucial to ensure the safe operation of the equipment. However, vibration data from bearings often exhibit non-stationary and nonlinear features, which complicates fault diagnosis. To address this challenge, this paper introduces a novel multi-scale time-frequency and statistical features fusion model (MTSF-FM).
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!