Materials science and technology, with the advent of nanotechnology, has brought about innumerable nanomaterials and multi-functional materials, with intriguing yet profound properties, into the scientific realm. Even a minor functionalization of a nanomaterial brings about vast changes in its properties that could be potentially utilized in various applications, particularly for biological applications, as one of the primary needs at present is for point-of-care devices that can provide swifter, accurate, reliable, and reproducible results for the detection of various physiological conditions, or as elements that could increase the resolution of current bio-imaging procedures. In this regard, iron oxide nanoparticles, a major class of metal oxide nanoparticles, have been sweepingly synthesized, characterized, and studied for their essential properties; there are 14 polymorphs that have been reported so far in the literature. With such a background, this review’s primary focus is the discussion of the different synthesis methods along with their structural, optical, magnetic, rheological and phase transformation properties. Subsequently, the review has been extrapolated to summarize the effective use of these nanoparticles as contrast agents in bio-imaging, therapeutic agents making use of its immune-toxicity and subsequent usage in hyperthermia for the treatment of cancer, electron transfer agents in copious electrochemical based enzymatic or non-enzymatic biosensors and bactericidal coatings over biomaterials to reduce the biofilm formation significantly.

Early detection of cancers and their precise subtyping are essential to patient stratification and effective cancer management. Data-driven identification of expression biomarkers coupled with microfluidics-based detection shows promise to revolutionize cancer diagnosis and prognosis. MicroRNAs play key roles in cancers and afford detection in tissue and liquid biopsies. In this review, we focus on the microfluidics-based detection of miRNA biomarkers in AI-based models for early-stage cancer subtyping and prognosis. We describe various subclasses of miRNA biomarkers that could be useful in machine-based predictive modeling of cancer staging and progression. Strategies for optimizing the feature space of miRNA biomarkers are necessary to obtain a robust signature panel. This is followed by a discussion of the issues in model construction and validation towards producing Software-as-Medical-Devices (SaMDs). Microfluidic devices could facilitate the multiplexed detection of miRNA biomarker panels, and an overview of the different strategies for designing such microfluidic systems is presented here, with an outline of the detection principles used and the corresponding performance measures. Microfluidics-based profiling of miRNAs coupled with SaMD represent high-performance point-of-care solutions that would aid clinical decision-making and pave the way for accessible precision personalized medicine.

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