Artificial Intelligence-Enhanced Analysis of Genomic DNA Visualized with Nanoparticle-Tagged Peptides under Electron Microscopy.

DNA visualization has advanced across multiple microscopy platforms, albeit with limited progress in the identification of novel staining agents for electron microscopy (EM), notwithstanding its ability to furnish a broad magnification range and high-resolution details for observing DNA molecules. Herein, a non-toxic, universal, and simple method is proposed that uses gold nanoparticle-tagged peptides to stain all types of naturally occurring DNA molecules, enabling their visualization under EM. This method enhances the current DNA visualization capabilities, allowing for sequence-specific, genomic-scale, and multi-conformational visualization.

Enhancing readmission prediction models by integrating insights from home healthcare notes: Retrospective cohort study.

Background: Hospital readmission is an important indicator of inpatient care quality and a significant driver of increasing medical costs. Therefore, it is important to explore the effects of postdischarge information, particularly from home healthcare notes, on enhancing readmission prediction models. Despite the use of Natural Language Processing (NLP) and machine learning in prediction model development, current studies often overlook insights from home healthcare notes.

Scanning Electron Microscopy Imaging of Large DNA Molecules Using a Metal-Free Electro-Stain Composed of DNA-Binding Proteins and Synthetic Polymers.

This paper presents the first scanning electron microscopy (SEM)-based DNA imaging in biological samples. This novel approach incorporates a metal-free electro-stain reagent, formulated by combining DNA-binding proteins and synthetic polymers to enhance the visibility of 2-nm-thick DNA under SEM. Notably, DNA molecules stain with proteins and polymers appear as dark lines under SEM.

On-chip selection of adenosine aptamer using graphene oxide-coated magnetic nanoparticles.

Systematic evolution of ligands by exponential enrichment (SELEX) is a method that is generally used for developing aptamers, which have arisen the promising alternatives for antibodies. However, conventional SELEX methods have limitations, such as a limited selection of target molecules, time-consuming and complex fabrication processes, and labor-intensive processes, which result in low selection yields. Here, we used (i) graphene oxide (GO)-coated magnetic nanoparticles in the selection process for separation and label-free detection and (ii) a multilayered microfluidic device manufactured using a three-dimensionally printed mold that is equipped with automated control valves to achieve precise fluid flows.

Statistical Characterization of the Morphologies of Nanoparticles through Machine Learning Based Electron Microscopy Image Analysis.

Although transmission electron microscopy (TEM) may be one of the most efficient techniques available for studying the morphological characteristics of nanoparticles, analyzing them quantitatively in a statistical manner is exceedingly difficult. Herein, we report a method for mass-throughput analysis of the morphologies of nanoparticles by applying a genetic algorithm to an image analysis technique. The proposed method enables the analysis of over 150,000 nanoparticles with a high precision of 99.

Multifunctional Heterogeneous Carbon Nanotube Nanocomposites Assembled by DNA-Binding Peptide Anchors.

Although carbon nanotubes (CNTs) are remarkable materials with many exceptional characteristics, their poor chemical functionality limits their potential applications. Herein, a strategy is proposed for functionalizing CNTs, which can be achieved with any functional group (FG) without degrading their intrinsic structure by using a deoxyribonucleic acid (DNA)-binding peptide (DBP) anchor. By employing a DBP tagged with a certain FG, such as thiol, biotin, and carboxyl acid, it is possible to introduce any FG with a controlled density on DNA-wrapped CNTs.