A therapy that would once have been considered a feat of science fiction has reversed aggressive and incurable blood cancers in some patients, doctors report. The treatment involves precisely editing the DNA in white blood cells to transform them into a cancer-fighting living drug. The first girl to be treated, whose story we reported in 2022, is still free of the disease and now plans to become a cancer scientist. Now eight more children and two adults with T-cell acute lymphoblastic leukaemia have been treated, with almost two thirds (64%) of patients in remission. T-cells are supposed to be the body's guardians - seeking out and destroying threats - but in this form of leukaemia, they grow out of control.

Could humanity finally be gaining the upper hand in our age-old fight against cancer? Recent scientific and medical advances have added several new weapons to our arsenal, including personalised gene therapy, artificial intelligence screening, simple blood tests -- and potentially soon vaccines. Cancer accounted for nearly 10 million deaths -- almost one in six of the global total -- in 2020, according to the World Health Organization. Ahead of World Cancer Day on Saturday, here are some of the promising recent developments in diagnosing and treating the disease. Immunotherapy drugs, which stimulate the immune system to track down and kill cancerous cells, have been one the biggest advances in cancer treatment over the last decade.

The study was groundbreaking, not only for the deep-learning predictability, but also because it allowed researchers to see what the model learned about the immune system. "DeepTCR's predictive power is exciting," said Dr. John-William Sidhom, first author of the study, "but what I found more fascinating is that we were able to view what the model learned about the immune system's response to immunotherapy." He also mentioned the great potential for creating future medications with the information. "We can now exploit that information to develop more robust models, and possibly better treatment approaches, for many diseases, even those outside of oncology." DeepTCR was developed by Dr. Sidhom while he was an M.D./Ph.D student at the Johns Hopkins University School of Medicine.

The study was groundbreaking, not only for the deep-learning predictability, but also because it allowed researchers to see what the model learned about the immune system. "DeepTCR's predictive power is exciting," said Dr. John-William Sidhom, first author of the study, "but what I found more fascinating is that we were able to view what the model learned about the immune system's response to immunotherapy." He also mentioned the great potential for creating future medications with the information. "We can now exploit that information to develop more robust models, and possibly better treatment approaches, for many diseases, even those outside of oncology." DeepTCR was developed by Dr. Sidhom while he was an M.D./Ph.D student at the Johns Hopkins University School of Medicine.

With the cancer vaccine scheduled to be tested in humans at the end of this year, and new AI-driven advanced detection techniques, we're getting closer than ever to winning the war against cancer. We can now predict this most dreaded disease before it occurs, and treat it with new drugs that can target the unique DNA weaknesses of that specific malignancy. Spotting cancer as early as possible is of paramount importance. If a tumor is diagnosed at an early stage, doctors can treat it with a much higher chance of success before it gets too big. The more a malignancy has spread, the lower the patient's chances of surviving.

Libratus, the artificial intelligence (AI) engine designed by Professor Tuomas Sandholm at Carnegie Mellon University (CMU) and his graduate student Noam Brown has made an impression on Jason Les, one of the world's top poker players. Poker News, the poker industry's online news magazine, recently interviewed Les. A couple questions were telling when asked about which is a better name for his firstborn child and which is the more annoying opponent, Claudico or Libratus. For both questions, he responded with Libratus.[1],[2] In January, Les and three others of the world's top four poker champions--Dong Kim, Daniel McAulay, and Jimmy Chou--were challenged to 20 days of No-limit Heads-up Texas Hold'em poker at the Brains versus Artificial Intelligence tournament in Pittsburgh's Rivers Casino.

Technological and scientific breakthroughs are outlining a future that could look vastly different from today, and help cure many of the problems the world is facing--from cures for dreaded diseases to making diagnostics cheaper and more easily accessible--at a much faster pace than ever before. In the case of malaria, for instance, as compared to the traditional treatment via drugs--increased drug resistance puts a brake on the efficacy of most cures--Indian scientists have successfully indicated the treatment can be shifted to using proteins to prevent the pathogen from infecting RBCs; in 2015, 212 million people were affected by malaria, and around half a million died. And in the case of cancer--expected to rise from 14.1 million new cases in 2012 to 21.7 million by 2030--the treatment could soon move away from the traditional chemo- and radio-therapy to engineered immune-cells therapy; this started 5-6 years ago, but advances now are far more rapid than in the past. While it was originally developed by the Children's Hospital of Philadelphia, many private firms and public institutions are trying to perfect this. In this cell therapy, called chimeric antigen receptor (CAR) T-cell therapy, doctors harvest a patient's T-cells which, as part of the immune system, fight against infection.

We propose an artificial immune model for intrusion detection in distributed systems based on a relatively recent theory in immunology called Danger theory. Based on Danger theory, immune response in natural systems is a result of sensing corruption as well as sensing unknown substances. In contrast, traditional self-nonself discrimination theory states that immune response is only initiated by sensing nonself (unknown) patterns. Danger theory solves many problems that could only be partially explained by the traditional model. Although the traditional model is simpler, such problems result in high false positive rates in immune-inspired intrusion detection systems. We believe using danger theory in a multi-agent environment that computationally emulates the behavior of natural immune systems is effective in reducing false positive rates. We first describe a simplified scenario of immune response in natural systems based on danger theory and then, convert it to a computational model as a network protocol. In our protocol, we define several immune signals and model cell signaling via message passing between agents that emulate cells. Most messages include application-specific patterns that must be meaningfully extracted from various system properties. We show how to model these messages in practice by performing a case study on the problem of detecting distributed denial-of-service attacks in wireless sensor networks. We conduct a set of systematic experiments to find a set of performance metrics that can accurately distinguish malicious patterns. The results indicate that the system can be efficiently used to detect malicious patterns with a high level of accuracy.

The human immune system has numerous properties that make it ripe for exploitation in the computational domain, such as robustness and fault tolerance, and many different algorithms, collectively termed Artificial Immune Systems (AIS), have been inspired by it. Two generations of AIS are currently in use, with the first generation relying on simplified immune models and the second generation utilising interdisciplinary collaboration to develop a deeper understanding of the immune system and hence produce more complex models. Both generations of algorithms have been successfully applied to a variety of problems, including anomaly detection, pattern recognition, optimisation and robotics. In this chapter an overview of AIS is presented, its evolution is discussed, and it is shown that the diversification of the field is linked to the diversity of the immune system itself, leading to a number of algorithms as opposed to one archetypal system. Two case studies are also presented to help provide insight into the mechanisms of AIS; these are the idiotypic network approach and the Dendritic Cell Algorithm.

Dendritic cells are antigen presenting cells that provide a vital link between the innate and adaptive immune system. Research into this family of cells has revealed that they perform the role of coordinating T-cell based immune responses, both reactive and for generating tolerance. We have derived an algorithm based on the functionality of these cells, and have used the signals and differentiation pathways to build a control mechanism for an artificial immune system. We present our algorithmic details in addition to some preliminary results, where the algorithm was applied for the purpose of anomaly detection. We hope that this algorithm will eventually become the key component within a large, distributed immune system, based on sound immunological concepts.