In recent decades, nanopores have become a promising diagnostic tool. Protein and solid-state nanopores are increasingly used for both RNA/DNA sequencing and small molecule detection. The latter is of great importance, as their detection is difficult or expensive using available methods such as HPLC or LC-MS. DNA aptamers are an excellent detection element for sensitive and specific detection of small molecules. Herein, a method for quantifying small molecules using a ready-to-use sequencing platform is described. Taking ethanolamine as an example, a strand displacement assay is developed in which the target-binding aptamer is displaced from the surface of magnetic particles by ethanolamine. Non-displaced aptamer and thus the ethanolamine concentration are detected by the nanopore system and can be quantified in the micromolar range using our in-house developed analysis software. This method is thus the first to describe a label-free approach for the detection of small molecules in a protein nanopore system.

The exponential growth of Big Data has revolutionized various aspects of our lives, generating vast amounts of data through everyday devices and activities. This article explores the evolving concept of Big Data, its impact on research, industries, and personal well-being. It focuses on the intersection of Big Data and health care, specifically in the field of Health Analytics, which leverages large-scale data analysis to gain insights into patient health, disease trends, and healthcare delivery. The article discusses how Big Data has transformed biomedical research, enabling hypothesis generation and uncovering new pathways that traditional methods may not have revealed. It emphasizes the importance of data quality and the unique challenges posed by heterogeneity when combining data from diverse sources. Furthermore, the article explores the broader implications of Big Data, both positive and negative. It highlights how industries utilize Big Data for product improvement and customer insights, while also raising concerns about privacy, surveillance, and data transparency. In the medical field, Big Data and Health Analytics hold tremendous potential to improve healthcare outcomes, reduce costs, and enable precision medicine. By analyzing vast amounts of health-related data, personalized risk assessments, diagnoses, and interventions can be tailored to individual patients. Additionally, Big Data analysis can identify global trends, at-risk groups, and hidden links to advance research and find potential cures. The article also discusses the concept of P4 medicine (predictive, preventive, personalized, and participatory medicine), which aims to provide tailored health care based on individual characteristics, genetics, lifestyle, and medical history. Finally, the article presents examples of using Big Data and advanced analytics, such as natural language processing, machine learning, and image analysis, to detect disease outbreaks, monitor drug safety, predict depression, and improve clinical imaging. In conclusion, Big Data and Health Analytics offer transformative opportunities for health care, enabling data-driven insights, precision medicine, and improved patient outcomes. However, challenges related to data privacy, security, and skilled data interpretation need to be addressed to fully harness the potential of Big Data in health care.

Next-generation whole-genome sequencing is essential for high-resolution surveillance of bacterial pathogens, for example, during outbreak investigations or for source tracking and escape variant analysis. However, current global sequencing and bioinformatic bottlenecks and a long time to result with standard technologies demand new approaches.

In recent decades, nanopores have become a promising diagnostic tool. Protein and solid-state nanopores are increasingly used for both RNA/DNA sequencing and small molecule detection. The latter is of great importance because small molecules are difficult or expensive to detect using available methods such as HPLC or LC-MS. Moreover, DNA aptamers are an excellent detection element for sensitive and specific detection of small molecules. Here, we describe a method for the quantification of ethanolamine using Oxford Nanopore’s ready-to-use sequencing platform. To this end, we have developed a strand displacement assay using a binding ethanolamine aptamer and magnetic beads. The displaced aptamer can be detected using the MinION® nanopores and analysed/quantified using our in-house developed analysis software.

Everyone has or will have experienced some degree of neck pain. Typically, neck pain is associated with the sensation of tense, tight, or stiff neck muscles. However, it is unclear whether the neck muscles are objectively stiffer with neck pain. This study used 1099 ultrasound elastography images (elastograms) obtained from 38 adult women, 20 with chronic neck pain and 18 asymptomatic. For training machine learning algorithms, 28 numerical characteristics were extracted from both the original and transformed shear wave velocity color-coded images as well as from respective image segments. Overall, a total number of 323 distinct features were generated from the data. A supervised binary classification was performed, using six machine-learning algorithms. The random forest algorithm produced the most accurate model to distinguish the elastograms of women with chronic neck pain from asymptomatic women with an AUC of 0.898. When evaluating features that can be used as biomarkers for muscle dysfunction in neck pain, the region of the deepest neck muscles (M. multifidus) provided the most features to support the correct classification of elastograms. By constructing summary images and associated Hotelling's T-2 maps, we enabled the visualization of group differences and their statistical confirmation.