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The advancement of biomedicine in a socioeconomically sustainable manner while achieving efficient patient-care is imperative to the health and well-being of society. Magnetic systems consisting of iron based nanosized components have gained prominence among researchers in a multitude of biomedical applications. This review focuses on recent trends in the areas of diagnostic imaging and drug delivery that have benefited from iron-incorporated nanosystems, especially in cancer treatment, diagnosis and wound care applications. Discussion on imaging will emphasise on developments in MRI technology and hyperthermia based diagnosis, while advanced material synthesis and targeted, triggered transport will be the focus for drug delivery. Insights onto the challenges in transforming these technologies into day-to-day applications will also be explored with perceptions onto potential for patient-centred healthcare. 

Abstract: Mitochondria are important intracellular organelles because of their key roles in cellular metabolism,proliferation, and programmed cell death. The differences in the structure and function of themitochondria of healthy and cancerous cells have made mitochondria an interesting target for drug delivery.Mitochondrial targeting is an emerging field as the targeted delivery of cytotoxic payloads andantioxidants to the mitochondrial DNA is capable of overcoming multidrug resistance. Mitochondrialtargeting is preferred over nuclear targeting because it can take advantage of the distorted metabolismin cancer. The negative membrane potential of the inner and outer mitochondrial membranes, as well astheir lipophilicity, are known to be the features that drive the entry of compatible targeting moiety,along with anticancer drug conjugates, towards mitochondria. The design of such drug nanocarrier conjugatesis challenging because they need not only to target the specific tumor/cancer site but have toovercome multiple barriers as well, such as the cell membrane and mitochondrial membrane. This reviewfocuses on the use of peptide-based nanocarriers (organic nanostructures such as liposomes, inorganic,carbon-based, and polymers) for mitochondrial targeting of the tumor/cancer. Both invitro and in vivo key results are reported. 

Cyber-Physical-Human Systems (CPHS) interconnect humans, physical plants and cyber infrastructure across space and time. Industrial processes, electromechanical systems operations and medical diagnosis are some examples where one can see the intersection of humans, physical and cyber components. Emergence of Artificial Intelligence (AI) based computational models, controllers and decision support engines have improved the efficiency and cost effectiveness of such systems and processes. These CPHS typically involve a collaborative decision environment, comprising of AI-based models and human experts. Active Learning (AL) is a category of AI algorithms which aims to learn an efficient decision model by combining domain expertise of the human expert and computational capabilities of the AI model. Given the indispensable role of humans and lack of understanding about human behavior in collaborative decision environments, modeling and prediction of behavioral biases is a critical need. This paper, for the first time, introduces different behavioral biases within an AL context and investigates their impacts on the performance of AL strategies. The modelling of behavioral biases is demonstrated using experiments conducted on a real-world pancreatic cancer dataset. It is observed that classification accuracy of the decision model reduces by at least 20% in case of all the behavioral biases.