Raman Spectroscopy for Extracellular Vesicle Study

The aim of this research project is the characterization of extracellular vesicles (EVs) using vibrational spectroscopy to study the contents and cellular origin of different EVs subtypes. Almost every cell releases tiny particles into their extracellular environment: the particles are known as EVs. The particles have a spherical shape, and their size ranges from 30 nm to 1 µm. The particles transport biomolecules, such as protein, RNA, and DNA. Since the EVs originate from cells, the contents of EVs are dependent on their cellular origin. Therefore, certain EVs include information related to diseases such as cancer, allergies, cardiovascular and autoimmune diseases, and investigating EVs’ cellular origin/cargo is useful as diagnosis and for monitoring the prognosis of therapyOf the various vibrational spectroscopic techniques, Raman spectroscopy was used for this study, which is a nondestructive and non-labeling technique. As the term ‘vibrational spectroscopy’ implies, Raman spectroscopy provides molecular vibration information. Analyzing molecular vibrations not only reveals the chemical composition of the specimen but also allows for a quantitative study, simple comparison between samples, and detection of specific molecules in samples. Raman spectroscopy has proven to be a useful tool for many different applications: material science, biomedical science, and real-life applications such as forensics. Although Raman spectroscopy is a powerful and straight forward technique, the ability of a conventional Raman microscope is limited by the diffraction limit. In a free-space optical system, the diffraction limit sets a lower limit on the total probed volume and, therefore, a limit on the surface-to-volume ratio when studying nanoparticles.Original promotion date was April 16th, 2020 (COVID-19)<br/

When Can AI Reduce Individuals’ Anchoring Bias and Enhance Decision Accuracy? Evidence from Multiple Longitudinal Experiments

This study aimed to identify and explain the mechanism underlying decision-making behaviors adaptive to AI advice. We develop a new theoretical framework by drawing on the anchoring effect and the literature on experiential learning. We focus on two factors: (1) the difference between individuals’ initial estimates and AI advice and (2) the existence of a second anchor (i.e., previous-year credit scores). We conducted two longitudinal experiments in the corporate credit rating context, where correct answers exist stochastically. We found that individuals exhibit some paradoxical behaviors. With greater differences and no second anchor, individuals are more likely to make adjustment efforts, but their initial estimates remain strong anchors. Yet, in multiple-anchor contexts individuals tend to diminish dependence on their initial estimates. We also found that the accuracy of individuals was dependent on their debiasing efforts

Tethering Cells via Enzymatic Oxidative Crosslinking Enables Mechanotransduction in Non-Cell-Adhesive Materials

Cell–matrix interactions govern cell behavior and tissue function by facilitating transduction of biomechanical cues. Engineered tissues often incorporate these interactions by employing cell-adhesive materials. However, using constitutively active cell-adhesive materials impedes control over cell fate and elicits inflammatory responses upon implantation. Here, an alternative cell–material interaction strategy that provides mechanotransducive properties via discrete inducible on-cell crosslinking (DOCKING) of materials, including those that are inherently non-cell-adhesive, is introduced. Specifically, tyramine-functionalized materials are tethered to tyrosines that are naturally present in extracellular protein domains via enzyme-mediated oxidative crosslinking. Temporal control over the stiffness of on-cell tethered 3D microniches reveals that DOCKING uniquely enables lineage programming of stem cells by targeting adhesome-related mechanotransduction pathways acting independently of cell volume changes and spreading. In short, DOCKING represents a bioinspired and cytocompatible cell-tethering strategy that offers new routes to study and engineer cell–material interactions, thereby advancing applications ranging from drug delivery, to cell-based therapy, and cultured meat

APOE Promoter Polymorphism-219T/G is an Effect Modifier of the Influence of APOE ε4 on Alzheimer's Disease Risk in a Multiracial Sample

Variants in the APOE gene region may explain ethnic differences in the association of Alzheimer's disease (AD) with ε4. Ethnic differences in allele frequencies for three APOE region SNPs (single nucleotide polymorphisms) were identified and tested for association in 19,398 East Asians (EastA), including Koreans and Japanese, 15,836 European ancestry (EuroA) individuals, and 4985 African Americans, and with brain imaging measures of cortical atrophy in sub-samples of Koreans and EuroAs. Among ε4/ε4 individuals, AD risk increased substantially in a dose-dependent manner with the number of APOE promoter SNP rs405509 T alleles in EastAs (TT: OR (odds ratio) = 27.02, p = 8.80 × 10-94; GT: OR = 15.87, p = 2.62 × 10-9) and EuroAs (TT: OR = 18.13, p = 2.69 × 10-108; GT: OR = 12.63, p = 3.44 × 10-64), and rs405509-T homozygotes had a younger onset and more severe cortical atrophy than those with G-allele. Functional experiments using APOE promoter fragments demonstrated that TT lowered APOE expression in human brain and serum. The modifying effect of rs405509 genotype explained much of the ethnic variability in the AD/ε4 association, and increasing APOE expression might lower AD risk among ε4 homozygotes

Design and baseline characteristics of the finerenone in reducing cardiovascular mortality and morbidity in diabetic kidney disease trial

Background: Among people with diabetes, those with kidney disease have exceptionally high rates of cardiovascular (CV) morbidity and mortality and progression of their underlying kidney disease. Finerenone is a novel, nonsteroidal, selective mineralocorticoid receptor antagonist that has shown to reduce albuminuria in type 2 diabetes (T2D) patients with chronic kidney disease (CKD) while revealing only a low risk of hyperkalemia. However, the effect of finerenone on CV and renal outcomes has not yet been investigated in long-term trials. Patients and Methods: The Finerenone in Reducing CV Mortality and Morbidity in Diabetic Kidney Disease (FIGARO-DKD) trial aims to assess the efficacy and safety of finerenone compared to placebo at reducing clinically important CV and renal outcomes in T2D patients with CKD. FIGARO-DKD is a randomized, double-blind, placebo-controlled, parallel-group, event-driven trial running in 47 countries with an expected duration of approximately 6 years. FIGARO-DKD randomized 7,437 patients with an estimated glomerular filtration rate >= 25 mL/min/1.73 m(2) and albuminuria (urinary albumin-to-creatinine ratio >= 30 to <= 5,000 mg/g). The study has at least 90% power to detect a 20% reduction in the risk of the primary outcome (overall two-sided significance level alpha = 0.05), the composite of time to first occurrence of CV death, nonfatal myocardial infarction, nonfatal stroke, or hospitalization for heart failure. Conclusions: FIGARO-DKD will determine whether an optimally treated cohort of T2D patients with CKD at high risk of CV and renal events will experience cardiorenal benefits with the addition of finerenone to their treatment regimen. Trial Registration: EudraCT number: 2015-000950-39; ClinicalTrials.gov identifier: NCT02545049

Classifying Raman Spectra of Extracellular Vesicles using a Convolutional Neural Network

We demonstrate a machine learning technique for data classification. In particular, we have classified Raman spectral data obtained from extracellular vesicles using a convolutional neural network (CNN). In this research, 300 spectra from four types of EVs were divided into a training- (60%), validation- (20%) and testing-dataset (20%). Training was performed with the training set and the model is validated with validation set. After the training process, the predictive ability was evaluated with testing set which was not involved in any way during the learning process. We show CNN trained on raw Raman spectra and CNN trained on baseline-corrected Raman data. Classified testing datasets show an accuracy in excess of 90%