Narrow Results

The following topics are under this section:

• ASIA-PACIFIC — Shedding Light on COVID-19 Vaccine Development
• ASIA-PACIFIC — Community-led Interventions for Improving Blood Pressure Control
• ASIA-PACIFIC — Importance of Redox Homeostasis in Potential Treatment of Disease
• ASIA-PACIFIC — Novel Processor Architecture to Solve Complex Mathematical Problems
• ASIA-PACIFIC — Uncovering Mechanisms of the Human Biological Clock
• ASIA-PACIFIC — Discovery of Healthy Stomach Stem Cells Reveals Cancer Origins
• ASIA-PACIFIC — Cataloguing Cancer “fingerprints” to Map Cancer Genomes
• ASIA-PACIFIC — Creating a Circular Economy using Greenhouse Gases
• REST OF THE WORLD — Guide for Liquid Biopsy in Individual Glioblastoma Patients
• REST OF THE WORLD — Multi-purpose use of Breast and Kidney Cancer Drugs
• REST OF THE WORLD — The Two Faces of Immune Cells
• REST OF THE WORLD — Fine Particle Air Pollution Linked to Development of Kidney Disease

In artificial immune system, many algorithms based on negative selection methods have been proposed to achieve satisfying classification performances. However, there are still many problems required to be solved, such as parameters sensibility and computational complexity. In this paper, a novel classification algorithm based on T-cells maturation algorithm was proposed for anomaly detection. Data set from UC Irvine Machine Learning Repository was used for 10-fold cross-validation, and simulation results confirmed its similar performances with AIRS. Compared with other classification algorithms based on negative selection methods, the proposed algorithm has no parameters and lower complexity, and can achieve satisfying classification results.

In the past decade, several major breakthroughs and groundbreaking discoveries have been reported from medical science researchers involved in the areas of hematology and oncology, due to improved understanding of the human immune system. Novel amongst these discoveries is the biopharmaceuticals such as monoclonal antibodies and immune checkpoint regulators that have revolutionized our options of therapeutic interventions for patients with diseases once considered untreatable. Autologous/allogeneic/mesenchymal stem cells are collected by Apheresis machines and are used in regenerative medicine by physicians to treat previously considered incurable diseases. Eventually, it has all progressed to the reprogramming of somatic cells by genetic engineering techniques. Chimeric Antigen Receptor (CAR) T-cells are reprogrammed cells with the capacity to kill malignant cells via binding of their new, synthetic and specific receptors to their targets on tumor cells. Some of the CAR T-cell preparations are currently approved by the Food and Drug Agency for the treatment of refractory acute lymphocytic leukemia and diffuse large B cell lymphoma. Their potentials are under investigation in treating chronic lymphocytic leukemia, acute myeloid leukemia, multiple myeloma, and other lymphomas. After constructing a CAR gene and transferring it into patients’ own T-cells’ DNA via a vector, genetically modified CAR T-cells are created. Currently, CAR T-cells are more popular and effective than dendritic cell-based tumor vaccines. Development and usage of CAR T-cells in clinical studies have also been reviewed.

This chapter is on artificial immune system (AIS) which is a biological metaphor for the human immune system, just like a neural network is based on the way the human brain functions. We describe some basic concepts of the AIS algorithm and the negative selection algorithm which is one of the approaches used to implement the AIS. We apply the algorithm to the missing data problem.