Microfluidic Approaches and Methods Enabling Extracellular Vesicle Isolation for Cancer Diagnostics

Advances in cancer research over the past half-century have clearly determined the molecular origins of the disease. Central to the use of molecular signatures for continued progress, including rapid, reliable, and early diagnosis is the use of biomarkers. Specifically, extracellular vesicles as biomarker cargo holders have generated significant interest. However, the isolation, purification, and subsequent analysis of these extracellular vesicles remain a challenge. Technological advances driven by microfluidics-enabled devices have made the challenges for isolation of extracellular vesicles an emerging area of research with significant possibilities for use in clinical settings enabling point-of-care diagnostics for cancer. In this article, we present a tutorial review of the existing microfluidic technologies for cancer diagnostics with a focus on extracellular vesicle isolation methods.

Optical Hydrogel Detector for pH Measurements

Measuring pH has become a major key for determining health conditions, and food safety. The traditional pH assessment approaches are costly and offer low sensitivity. Here, a novel pH sensor based on a pH-responsive hydrogel has been developed. A Fresnel lens pattern was replicated on the surface of the pH-responsive hydrogel using the replica mould method. The pH sensors were tested in a pH range of 4–7. Introducing various pH solutions to the pH sensor led to volumetric shifts as the hydrogel swelled with pH. Consequently, the dimensions of the replicated Fresnel lens changed, modifying the focal length and the focus efficiency of the optical sensor. As a result, the measured optical power at a fixed distance from the sensor changed with pH. The optical sensor showed the best performance in the acidic region when pH changed from 4.5 to 5.5, in which the recorded power increased by 13%. The sensor exhibited high sensitivity to pH changes with a short respond time in a reversible manner. The developed pH optical sensor may have applications in medical point-of-care diagnostics and wearable continuous pH detection devices.

Convolution Neural Networks for Point-of-Care Diagnostics of Bacterial Infections in Blood

ABSTRACTA droplet of blood, when evaporated on a surface, leaves dried residue—the fractal patterns formed on the dried residues can act as markers for infection present in the blood. Exploiting the unique patterns found in the residues of a naturally dried droplet of blood, we propose a Point-of-Care (POC) diagnostic tool for detecting broad-spectrum of bacterial infections (such as Enterobacter aerogenes, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, and Pseudomonas aeruginosa, Salmonella Typhi) in blood. The diagnosis process we propose is straightforward and can be performed with the following steps: A droplet of blood (healthy or infected) of volume range 0.5 to 2 μl is allowed to dry on a clean glass surface and is imaged using a conventional optical microscope. A computer algorithm based on the framework of convolution neural network (CNN) is used to classify the captured images of dried blood droplets according to the bacterial infection. In total, our multiclass model reports an accuracy of 92% for detecting six bacterial species infections in the blood (with control being the uninfected or healthy blood). The high accuracy of detecting bacteria in the blood reported in this article is commensurate with the standard bacteriological tests. Thus, this article presents a proof-of-concept of a potential futuristic tool for a rapid and low-cost diagnosis of bacterial infection in the blood.

Emerging Biosensing and Transducing Techniques for Potential Applications in Point-of-Care Diagnostics

With the deepening of our understanding in life science, molecular biology, nanotechnology, optics, electrochemistry and other areas, an increasing number of biosensor design strategies have emerged in recent years, capable...

4th Swiss Symposium in Point-of-Care Diagnostics held at the Davos Conference Center on 21 October, 2021

After last year's successful online symposium, the 4th edition of the Swiss Symposium in POC Diagnostics gathered more than 150 participants from medicine, industry and science as well as from different European countries to meet at the Davos Conference Center for an exciting program with 13 expert speakers, a poster session and a product & technology exhibition. The mandatory COVID-certificate to access the event has allowed people to meet (again at last!), network and share their views and success stories in the field of POC Diagnostics that continues to be propelled by digitalization, new technological possibilities, user needs and the COVID-19 pandemic.

Machine Learning-Based HIV Risk Estimation Using Incidence Rate Ratios

HIV/AIDS is an ongoing global pandemic, with an estimated 39 million infected worldwide. Early detection is anticipated to help improve outcomes and prevent further infections. Point-of-care diagnostics make HIV/AIDS diagnoses available both earlier and to a broader population. Wide-spread and automated HIV risk estimation can offer objective guidance. This supports providers in making an informed decision when considering patients with high HIV risk for HIV testing or pre-exposure prophylaxis (PrEP). We propose a novel machine learning method that allows providers to use the data from a patient's previous stays at the clinic to estimate their HIV risk. All features available in the clinical data are considered, making the set of features objective and independent of expert opinions. The proposed method builds on association rules that are derived from the data. The incidence rate ratio (IRR) is determined for each rule. Given a new patient, the mean IRR of all applicable rules is used to estimate their HIV risk. The method was tested and validated on the publicly available clinical database MIMIC-IV, which consists of around 525,000 hospital stays that included a stay at the intensive care unit or emergency department. We evaluated the method using the area under the receiver operating characteristic curve (AUC). The best performance with an AUC of 0.88 was achieved with a model consisting of 53 rules. A threshold value of 0.66 leads to a sensitivity of 98% and a specificity of 53%. The rules were grouped into drug abuse, psychological illnesses (e.g., PTSD), previously known associations (e.g., pulmonary diseases), and new associations (e.g., certain diagnostic procedures). In conclusion, we propose a novel HIV risk estimation method that builds on existing clinical data. It incorporates a wide range of features, leading to a model that is independent of expert opinions. It supports providers in making informed decisions in the point-of-care diagnostics process by estimating a patient's HIV risk.