Soft Hydrogel Actuator for Fast Machine-Learning-Assisted Bacteria Detection

Electrochemical platform for detecting Escherichia coli bacteria using machine learning methods

We present an electrochemical platform designed to reduce time of Escherichia coli bacteria detection from 24 to 48-h to 30 min. The presented approach is based on a system which includes gallium-indium (eGaIn) alloy to provide conductivity and a hydrogel system to preserve bacteria and their metabolic species during the analysis. The work is dedicated to accurate and fast detection of Escherichia coli bacteria in different environments with the supply of machine learning methods. Electrochemical data obtained during the analysis is processed via multilayer perceptron model to identify i.e. predict bacterial concentration in the samples. The performed approach provides the effectiveness of bacteria identification in the range of 102-109 colony forming units per ml with the average accuracy of 97%. The proposed bioelectrochemical system combined with machine learning model is prospective for food analysis, agriculture, biomedicine.

Tesla valve-assisted biosensor for dual-mode and dual-target simultaneous determination of foodborne pathogens based on phage/DNAzyme co-modified zeolitic imidazolate framework-encoded probes

Sensitive and accurate detection of multiplex foodborne pathogens is crucial for food safety. In this work, a dual-mode and dual-target biosensor regulated by a Tesla valve was established for simultaneously determining Escherichia coli O157:H7 (E. coli) and Salmonella typhimurium (S. T). Two zeolitic imidazolate framework (ZIF-8) signal probes decorated with electroactive materials (ferrocene or methylene blue), DNAzyme, and different phages were synthesized to specifically recognize the targets and generate fluorescent/electrochemical dual-mode signals. In the presence of bacteria, they were captured and enriched on two individual working electrodes through the modified 4-mercaptophenylboric acid. The encoded signal probes added on different working electrodes could be conjugated with the corresponding target bacteria depending on the specificity of phages. Under the acidic condition, the DNAzyme could catalyze click chemistry for fluorescent signals. Simultaneously, the released ferrocene and methylene blue from ZIF-8 could generate electrochemical signals at different potentials. Benefiting from the flow regulation feature of the Tesla valve, the triggered fluorescent and electrochemical signals in the two individual electrodes would not influence each other, achieving simultaneous dual-mode and dual-target determination of foodborne pathogens. It depicted good linearity ranged 10-10⁷ CFU mL^-1. And the corresponding detection of limits were 5 CFU mL^-1 and 8 CFU mL^-1 for two bacteria, respectively. A low false positive was realized through the dual-mode strategy. The proposed biosensor can not only on-site, specifically, and sensitively determine E. coli and S. T, but also provide the wide prospect in rapid screening of other foodborne pathogens.

Diffusion-Limited Processes in Hydrogels with Chosen Applications from Drug Delivery to Electronic Components

Diffusion is one of the key nature processes which plays an important role in respiration, digestion, and nutrient transport in cells. In this regard, the present article aims to review various diffusion approaches used to fabricate different functional materials based on hydrogels, unique examples of materials that control diffusion. They have found applications in fields such as drug encapsulation and delivery, nutrient delivery in agriculture, developing materials for regenerative medicine, and creating stimuli-responsive materials in soft robotics and microrobotics. In addition, mechanisms of release and drug diffusion kinetics as key tools for material design are discussed.

A New Regularized Spatiotemporal Attention-Based LSTM with Application to Nitrogen Oxides Emission Prediction

The data collected from complex process industries are usually time series with considerable nonlinearities and dynamics, as well as excessive redundancy. Moreover, there are temporal and spatial correlations between input variables and key performance variables. These characteristics bring great difficulties to data-driven modeling of the key performance variables. To overcome the problems, a new regularized spatiotemporal attention (STA)-based long short-term memory (LSTM) was developed. First, a standard LSTM network with an STA module was trained to capture the dynamic relationship between input and target variables. Second, the least absolute shrinkage and selection operator was introduced to optimize the STA module. Third, the hyperparameter representing the regularization strength of the algorithm was determined using a moving window cross-validation strategy. Finally, the proposed algorithm was compared to other state-of-the-art algorithms using artificial data, and then it was used to predict the nitrogen oxide emissions of a selective catalytic reduction denitration system. Simulation results showed that the proposed algorithm achieved more accurate predictions than the other algorithms. Furthermore, the statistics and analysis of the importance of the variables are consistent with known chemical-reaction mechanisms and observations of field experts. Thus, the proposed method can provide technical support for the predictive control and optimization of such systems.

Ultrasensitive Hybridization Chain Reaction-Assisted Multisite Exonuclease III Amplification Strategy Combined with a Direct Quantitative Fluorescence Lateral Flow Technique for Multiple Bacterial 16S rRNA Detection

Accurate and in-time detection of bacteria conduces to preventing their rapid spread around the environment, while a nucleic acid test (NAT) is a powerful tool for early diagnosis of pathogens. Herein, we propose a hybridization chain reaction (HCR)-mediated multisite exonuclease III (Exo-III) amplification strategy (HCR/Exo-III amplifier) to achieve the one-pot and ultrasensitive isothermal amplification of bacterial 16S rRNA and a portable fluorescence detection device (PFD) to directly read signals in a lateral flow assay (LFA). In detail, the target-initiated HCR products present multiple binding sites for triggering the Exo-III amplifier that produces numerous target amplicons. Following that, the target amplicons travel up on the strip and bridge between the DNA-CdTe/CdS probes and the capture DNA to form a positive fluorescence line. After that, the strip is inserted into the PFD to accomplish the fluorescence signal reading. The constructed HCR/Exo-III amplifier-based PFD-LFA implemented the simultaneous and specific detection of three bacteria with a detection limit of a few tenths of fM for synthetic 16S rRNA fragments and dozens of CFU/mL for Staphylococcus aureus, Listeria monocytogenes, and Salmonella typhimurium in pure cultures. The sensing platform features isothermal amplification, convenient operation, and good economy, displaying great potential for on-site testing toward multiple nucleic acid analytes.

Uricase Crowding via Polyelectrolyte Layers Coacervation for Carbon Fiber-Based Electrochemical Detection of Uric Acid

Urate oxidase (UOx) surrounded by synthetic macromolecules, such as polyethyleneimine (PEI), poly(allylamine hydrochloride) (PAH), and poly(sodium 4-styrenesulfonate) (PSS) is a convenient model of redox-active biomacromolecules in a crowded environment and could display high enzymatic activity towards uric acid, an important marker of COVID-19 patients. In this work, the carbon fiber electrode was modified with Prussian blue (PB) redox mediator, UOx layer, and a layer-by-layer assembled polyelectrolyte film, which forms a complex coacervate consisting of a weakly charged polyelectrolyte (PEI or PAH) and a highly charged one (PSS). The film deposition process was controlled by cyclic voltammetry and scanning electron microscopy coupled with energy-dispersive X-ray analysis (at the stage of PB deposition) and through quartz crystal microbalance technique (at latter stages) revealed uniform distribution of the polyelectrolyte layers. Variation of the polyelectrolyte film composition derived the following statements. (1) There is a linear correlation between electrochemical signal and concentration of uric acid in the range of 10^-4-10^-6 M. (2) An increase in the number of polyelectrolyte layers provides more reproducible values for uric acid concentration in real urine samples of SARS-CoV-2 patients measured by electrochemical enzyme assay, which are comparable to those of spectrophotometric assay. (3) The PAH/UOx/PSS/(PAH/PSS)₂-coated carbon fiber electrode displays the highest sensitivity towards uric acid. (4) There is a high enzyme activity of UOx immobilized into the hydrogel nanolayer (values of the Michaelis-Menten constant are up to 2 μM) and, consequently, high affinity to uric acid.