Ready-to-use nanopore platform for label-free small molecule quantification: Ethanolamine as first example.

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.

Real-Time Nanopore Q20+ Sequencing Enables Extremely Fast and Accurate Core Genome MLST Typing and Democratizes Access to High-Resolution Bacterial Pathogen Surveillance.

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 this study, we investigated whether novel nanopore Q20+ long-read chemistry enables standardized and easily accessible high-resolution typing combined with core genome multilocus sequence typing (cgMLST). We set high requirements for discriminatory power by using the slowly evolving bacterium Bordetella pertussis as a model pathogen. Our results show that the increased raw read accuracy enables the description of epidemiological scenarios and phylogenetic linkages at the level of gold-standard short reads. The same was true for our variant analysis of vaccine antigens, resistance genes, and virulence factors, demonstrating that nanopore sequencing is a legitimate competitor in the area of next-generation sequencing (NGS)-based high-resolution bacterial typing. Furthermore, we evaluated the parameters for the fastest possible analysis of the data. By combining the optimized processing pipeline with real-time basecalling, we established a workflow that allows for highly accurate and extremely fast high-resolution typing of bacterial pathogens while sequencing is still in progress. Along with advantages such as low costs and portability, the approach suggested here might democratize modern bacterial typing, enabling more efficient infection control globally.

Specification of Neck Muscle Dysfunction through Digital Image Analysis Using Machine Learning.

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 T2 maps, we enabled the visualization of group differences and their statistical confirmation.