Cancer is the cause of death for one in seven individuals worldwide. It is widely acknowledged that screening and early diagnosis are of vital importance for improving the likelihood of recovery. However, given the costly, time-consuming, and invasive nature of the many methods currently in use, patients often do not take advantage of the services available to them. Consequently, many researchers are exploring the possibility of developing fast, reliable, and non-invasive diagnostic tools that can be used directly or by local physicians at the point-of-care. Herein, we look at the use of established biomarkers in cancer therapy and investigate emerging biomarkers exhibiting future potential. The incorporation of these biomarkers into point-of-care devices could potentially reduce the strain currently experienced by screening programs in hospitals and healthcare systems. Results derived from point-of-care tests should be accurate, sensitive, and generated rapidly to assist in the selection of the best course of treatment for optimal patient care. Essentially, point-of-care diagnostics should enhance the well-being of patients and lead to a reduction in cancer-related deaths.

Pancreatic cancer (Pa) is generally a very aggressive disease, with few effective approaches available for early diagnosis or therapy. These factors, combined with the aggressiveness and chemoresistance of Pa, results in a bleak outcome post-diagnosis. Cancer-related biomarkers have established capabilities for diagnosis, prognosis and screening and can be exploited to aid in earlier less-invasive diagnosis and optimization of targeted therapies. Pa has only one US FDA-approved biomarker, CA19-9, which has significant limitations. Hence, it is vital that novel biomarkers are identified and validated to diagnose, treat, control and monitor Pa. This review focuses on existing and potential Pa-associated markers and discusses how they may be applied in cohort for improved management of Pa.

Harmful algal blooms (HABs) are a major global concern due to their propensity to cause environmental damage, healthcare issues and economic losses. In particular, the presence of toxic phytoplankton is a cause for concern. Current HAB monitoring programs often involve laborious laboratory-based analysis at a high cost and with long turnaround times. The latter also hampers the potential to develop accurate and reliable models that can predict HAB occurrence. However, a promising solution for this issue may be in the form of remotely deployed biosensors, which can rapidly and continuously measure algal and toxin levels at the point-of-need (PON), at a low cost. This review summarises the issues HABs present, how they are difficult to monitor and recently developed biosensors that may improve HAB-monitoring challenges.

Globally, both communicable and non-communicable diseases pose a serious threat to populations in developed as well as developing countries. Access to reliable diagnostic testing along with qualified health practitioners is severely limited in low resource and very remote areas and following natural catastrophes. Areas covered: This paper provides an overview of the challenges involved and suggests strategies to address them. The emergence of more robust, user-friendly, cost-effective and 'sample-to-result' point-of-care (POC) tools, along with the proliferation of mobile technologies, may provide a practical approach in addressing some of the challenges. Expert commentary: The successful implementation of POC testing requires the availability of versatile diagnostic technologies, improved platforms and back-up infrastructure, successful leveraging of human resources through training and, finally, engagement/coordination of associated stakeholders, including public health agencies, diagnostics companies, healthcare practitioners and local rural authorities.

This review is designed to focus on antibodies and the attributes that make them ideal for applications in microfluidics-based diagnostic/separation platforms. The structures of different antibody formats and how they can be engineered to be highly effective in microfluidics-based environments will be highlighted. Suggested novel stratagems on the ideal way in which they can be employed in microfluidics systems, based on an informed knowledge of their structures and properties rather than random choice selection, as is often currently employed, will be provided. Finally, a critical assessment of current shortcomings in the approaches used along with possible ways for their resolution will be given.