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Peptide Fragments

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Aggregation-induced electrochemiluminescence enhancement of Ag-MOG for amyloid β 42 sensing.

Analytica chimica acta
This study aimed to introduce an immunosensor for measuring amyloid β 42 (Aβ) levels by aggregation-induced enhanced electrochemiluminescence (ECL). Metal-organic gels (MOGs) are novel soft materials with advantages such as high gel stability, good l...

A Study on Machine Learning Models in Detecting Cognitive Impairments in Alzheimer's Patients Using Cerebrospinal Fluid Biomarkers.

American journal of Alzheimer's disease and other dementias
Several research studies have demonstrated the potential use of cerebrospinal fluid biomarkers such as amyloid beta 1-42, T-tau, and P-tau, in early diagnosis of Alzheimer's disease stages. The levels of these biomarkers in conjunction with the demen...

Systematic Assessment of Deep Learning-Based Predictors of Fragmentation Intensity Profiles.

Journal of proteome research
In recent years, several deep learning-based methods have been proposed for predicting peptide fragment intensities. This study aims to provide a comprehensive assessment of six such methods, namely Prosit, DeepMass:Prism, pDeep3, AlphaPeptDeep, Pros...

Multimodal deep learning models utilizing chest X-ray and electronic health record data for predictive screening of acute heart failure in emergency department.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Ambiguity in diagnosing acute heart failure (AHF) leads to inappropriate treatment and potential side effects of rescue medications. To address this issue, this study aimed to use multimodality deep learning models combinin...

A two-stage ensemble learning based prediction and grading model for PD-1/PD-L1 inhibitor-related cardiac adverse events: A multicenter retrospective study.

Computer methods and programs in biomedicine
BACKGROUND: Immune-related cardiac adverse events (ircAEs) caused by programmed cell death protein-1 (PD-1) and programmed death-ligand-1 (PD-L1) inhibitors can lead to fulminant and even fatal consequences. This study aims to develop a prediction an...

Machine learning-based prediction of elevated N terminal pro brain natriuretic peptide among US general population.

ESC heart failure
AIMS: Natriuretic peptide-based pre-heart failure screening has been proposed in recent guidelines. However, an effective strategy to identify screening targets from the general population, more than half of which are at risk for heart failure or pre...

Predicting conversion in cognitively normal and mild cognitive impairment individuals with machine learning: Is the CSF status still relevant?

Alzheimer's & dementia : the journal of the Alzheimer's Association
INTRODUCTION: Machine learning (ML) helps diagnose the mild cognitive impairment-Alzheimer's disease (MCI-AD) spectrum. However, ML is fed with data unavailable in standard clinical practice. Thus, we tested a novel multi-step ML approach to predict ...

Sampling Conformational Ensembles of Highly Dynamic Proteins via Generative Deep Learning.

Journal of chemical information and modeling
Proteins are inherently dynamic, and their conformational ensembles play a crucial role in biological function. Large-scale motions may govern the protein structure-function relationship, and numerous transient but stable conformations of intrinsical...

Machine learning integration of multimodal data identifies key features of circulating NT-proBNP in people without cardiovascular diseases.

Scientific reports
N-Terminal Pro-Brain Natriuretic Peptide (NT-proBNP) is important for diagnosing and predicting heart failure or many other diseases. However, few studies have comprehensively assessed the factors correlated with NT-proBNP levels in people with cardi...