AIMC Topic: Antibodies, Viral

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Surface-Enhanced Raman Scattering Combined with Machine Learning for Rapid and Sensitive Detection of Anti-SARS-CoV-2 IgG.

Biosensors
This work reports an efficient method to detect SARS-CoV-2 antibodies in blood samples based on SERS combined with a machine learning tool. For this purpose, gold nanoparticles directly conjugated with spike protein were used in human blood samples t...

Development, study, and comparison of models of cross-immunity to the influenza virus using statistical methods and machine learning.

Voprosy virusologii
INTRODUCTION: The World Health Organization considers the values of antibody titers in the hemagglutination inhibition assay as one of the most important criteria for assessing successful vaccination. Mathematical modeling of cross-immunity allows fo...

A Machine Learning Method for Allocating Scarce COVID-19 Monoclonal Antibodies.

JAMA health forum
IMPORTANCE: During the COVID-19 pandemic, the effective distribution of limited treatments became a crucial policy goal. Yet, limited research exists using electronic health record data and machine learning techniques, such as policy learning trees (...

COVID-19 IgG antibodies detection based on CNN-BiLSTM algorithm combined with fiber-optic dataset.

Journal of virological methods
The urgent need for efficient and accurate automated screening tools for COVID-19 detection has led to research efforts exploring various approaches. In this study, we present pioneering research on COVID-19 detection using a hybrid model that combin...

Machine-learning-assisted high-throughput identification of potent and stable neutralizing antibodies against all four dengue virus serotypes.

Scientific reports
Several computational methods have been developed to identify neutralizing antibodies (NAbs) covering four dengue virus serotypes (DENV-1 to DENV-4); however, limitations of the dataset and the resulting performance remain. Here, we developed a new c...

Developing Deep LSTMs With Later Temporal Attention for Predicting COVID-19 Severity, Clinical Outcome, and Antibody Level by Screening Serological Indicators Over Time.

IEEE journal of biomedical and health informatics
OBJECTIVE: The clinical course of COVID-19, as well as the immunological reaction, is notable for its extreme variability. Identifying the main associated factors might help understand the disease progression and physiological status of COVID-19 pati...

A Paper-Based Multiplexed Serological Test to Monitor Immunity against SARS-COV-2 Using Machine Learning.

ACS nano
The rapid spread of SARS-CoV-2 caused the COVID-19 pandemic and accelerated vaccine development to prevent the spread of the virus and control the disease. Given the sustained high infectivity and evolution of SARS-CoV-2, there is an ongoing interest...

A prospective cohort-based artificial intelligence evaluation system for the protective efficacy and immune response of SARS-CoV-2 inactivated vaccines.

International immunopharmacology
BACKGROUND: Novel coronaviruses constitute a significant health threat, prompting the adoption of vaccination as the primary preventive measure. However, current evaluations of immune response and vaccine efficacy are deemed inadequate.

Predicting humoral responses to primary and booster SARS-CoV-2 mRNA vaccination in people living with HIV: a machine learning approach.

Journal of translational medicine
BACKGROUND: SARS-CoV-2 mRNA vaccines are highly immunogenic in people living with HIV (PLWH) on effective antiretroviral therapy (ART). However, whether viro-immunologic parameters or other factors affect immune responses to vaccination is debated. T...

Humoral Immune Responses after an Omicron-Adapted Booster BNT162b2 Vaccination in Patients with Lymphoid Malignancies.

Viruses
To accommodate waning COVID-19 vaccine immunity to emerging SARS-CoV-2 variants, variant-adapted mRNA vaccines have been introduced. Here, we examine serological responses to the BA.1 and BA.4-5 Omicron variant-adapted BNT162b2 COVID-19 vaccines in p...