AI Medical Compendium Topic

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Immune System

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Immunomodulation of Zerumbone via Decreasing the Production of Reactive Oxygen Species from Immune Cells.

Pakistan journal of biological sciences : PJBS
BACKGROUND AND OBJECTIVE: Zerumbone has been reported to exert anti-inflammatory, anti-ulcer and anti-hyperglycemic effects but the specific mechanism through which zerumbone exerts its anti-inflammatory action through inhibiting reactive oxygen spec...

Estimating glucose requirements of an activated immune system in growing pigs.

Journal of animal science
Activated immune cells become obligate glucose utilizers, and a large i.v. lipopolysaccharide (LPS) dose causes insulin resistance and severe hypoglycemia. Therefore, study objectives were to quantify the amount of glucose needed to maintain euglycem...

A Novel Hybrid Clonal Selection Algorithm with Combinatorial Recombination and Modified Hypermutation Operators for Global Optimization.

Computational intelligence and neuroscience
Artificial immune system is one of the most recently introduced intelligence methods which was inspired by biological immune system. Most immune system inspired algorithms are based on the clonal selection principle, known as clonal selection algorit...

An immune-inspired swarm aggregation algorithm for self-healing swarm robotic systems.

Bio Systems
Swarm robotics is concerned with the decentralised coordination of multiple robots having only limited communication and interaction abilities. Although fault tolerance and robustness to individual robot failures have often been used to justify the u...

An Extreme Learning Machine Based on Artificial Immune System.

Computational intelligence and neuroscience
Extreme learning machine algorithm proposed in recent years has been widely used in many fields due to its fast training speed and good generalization performance. Unlike the traditional neural network, the ELM algorithm greatly improves the training...

Cellular frustration algorithms for anomaly detection applications.

PloS one
Cellular frustrated models have been developed to describe how the adaptive immune system works. They are composed by independent agents that continuously pair and unpair depending on the information that one sub-set of these agents display. The emer...

Precision immunoprofiling to reveal diagnostic signatures for latent tuberculosis infection and reactivation risk stratification.

Integrative biology : quantitative biosciences from nano to macro
Latent tuberculosis infection (LTBI) is estimated in nearly one quarter of the world's population, and of those immunocompetent and infected ~10% will proceed to active tuberculosis (TB). Current diagnostics cannot definitively identify LTBI and prov...

Reporting and connecting cell type names and gating definitions through ontologies.

BMC bioinformatics
BACKGROUND: Human immunology studies often rely on the isolation and quantification of cell populations from an input sample based on flow cytometry and related techniques. Such techniques classify cells into populations based on the detection of a p...

The FluPRINT dataset, a multidimensional analysis of the influenza vaccine imprint on the immune system.

Scientific data
Machine learning has the potential to identify novel biological factors underlying successful antibody responses to influenza vaccines. The first attempts have revealed a high level of complexity in establishing influenza immunity, and many different...

Ontological model of multi-agent Smart-system for predicting drug properties based on modified algorithms of artificial immune systems.

Theoretical biology & medical modelling
BACKGROUND: Currently, due to the huge progress in the field of information technologies and computer equipment, it is important to use modern approaches of artificial intelligence in order to process extensive chemical information at creating new dr...