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.

Crystal-Inspired Cellular Metamaterials and Triply Periodic Minimal Surfaces.

Triply periodic minimal surfaces (TPMSs) are found in many natural objects including butterfly wings, sea urchins, and biological membranes. They simultaneously have zero mean curvature at every point and a crystallographic group symmetry. A metamaterial can be created from such periodic surfaces or used as a reinforcement of a composite material. While a TPMS as a mathematical object has been known since 1865, only novel additive manufacturing (AM) technology made it possible to fabricate cellular materials with complex TPMS shapes. Cellular TPMS-based metamaterials have remarkable properties related to wetting/liquid penetration, shock absorption, and the absence of stress concentrators. Recent studies showed that TPMSs are also found in natural crystals when electron surfaces are considered. Artificial crystal-inspired metamaterials mimic such crystals including zeolites and schwarzites. These metamaterials are used for shock, acoustic waves, and vibration absorption, and as structural materials, heat exchangers, and for other applications. The choice of the crystalline cell of a material, as well as its microstructure, plays a decisive role in its properties. The new area of crystal-inspired materials has many common features with traditional biomimetics with models being borrowed from nature and adjusted for engineering applications.

Benchmarking Unsupervised Clustering Algorithms for Atomic Force Microscopy Data on Polyhydroxyalkanoate Films.

Surface of polyhydroxyalkanoate (PHA) films of varying monomer compositions are analyzed using atomic force microscopy (AFM) and unsupervised machine learning (ML) algorithms to investigate and classify films based on global attributes such as the scan size, film thickness, and monomer type. The experiment provides benchmarked results for 12 of the most widely used clustering algorithms via a hybrid investigation approach while highlighting the impact of using the Fourier transform (FT) on high-dimensional vectorized data for classification on various pools of data. Our findings indicate that the use of a one-dimensional (1D) FT of vectorized data produces the most accurate outcome. The experiment also provides insights into case-by-case investigations of algorithm performances and the impact of various data pools. Lastly, we show an early version of our tool aimed at investigating surfaces using ML approaches and discuss the results of our current experiment to configure future improvements.

Selenium catalysis enables negative feedback organic oscillators.

The construction of materials regulated by chemical reaction networks requires regulatory motifs that can be stacked together into systems with desired properties. Multiple autocatalytic reactions producing thiols are known. However, negative feedback loop motifs are unavailable for thiol chemistry. Here, we develop a negative feedback loop based on the selenocarbonates. In this system, thiols induce the release of aromatic selenols that catalyze the oxidation of thiols by organic peroxides. This negative feedback loop has two important features. First, catalytic oxidation of thiols follows Michaelis-Menten-like kinetics, thus increasing nonlinearity for the negative feedback. Second, the strength of the negative feedback can be tuned by varying substituents in selenocarbonates. When combined with the autocatalytic production of thiols in a flow reactor, this negative feedback loop induces sustained oscillations. The availability of this negative feedback motif enables the future construction of oscillatory, homeostatic, adaptive, and other regulatory circuits in life-inspired systems and materials.

Expanding Predictive Capacities in Toxicology: Insights from Hackathon-Enhanced Data and Model Aggregation.

In the realm of predictive toxicology for small molecules, the applicability domain of QSAR models is often limited by the coverage of the chemical space in the training set. Consequently, classical models fail to provide reliable predictions for wide classes of molecules. However, the emergence of innovative data collection methods such as intensive hackathons have promise to quickly expand the available chemical space for model construction. Combined with algorithmic refinement methods, these tools can address the challenges of toxicity prediction, enhancing both the robustness and applicability of the corresponding models. This study aimed to investigate the roles of gradient boosting and strategic data aggregation in enhancing the predictivity ability of models for the toxicity of small organic molecules. We focused on evaluating the impact of incorporating fragment features and expanding the chemical space, facilitated by a comprehensive dataset procured in an open hackathon. We used gradient boosting techniques, accounting for critical features such as the structural fragments or functional groups often associated with manifestations of toxicity.

NeuroClick: software for mimicking click reaction to generate drug-like molecules permeating the blood-brain barrier.

Background: Traditional methods for chemical library generation in virtual screening often impose limitations on the accessible chemical space or produce synthetically irrelevant structures. Incorporating common chemical reactions into generative algorithms could offer significant benefits. Materials & methods: In this study, we developed NeuroClick, a graphical user interface software designed to perform in silico azide-alkyne cycloaddition, a widely utilized synthetic approach in modern medicinal chemistry. Results & conclusion: NeuroClick facilitates the generation and filtering of large combinatorial libraries at a remarkable rate of 10,000 molecules per minute. Moreover, the generated products can be filtered to identify subsets of pharmaceutically relevant compounds based on Lipinski's rule of five and blood-brain barrier permeability prediction. We demonstrate the utility of NeuroClick by generating and filtering several thousand molecules for dopamine D3 receptor ligand screening.

Keplerate {Mo132}-Stearic Acid Conjugates: Supramolecular Synthons for the Design of Dye-Loaded Nanovesicles, Langmuir-Schaefer Films, and Infochemical Applications.

Self-assembly gives rise to the versatile strategies of smart material design but requires precise control on the supramolecular level. Here, inorganic-organic synthons (conjugates) are produced by covalently grafting stearic acid tails to giant polyoxometalate (POM) Keplerate-type {Mo132} through an organosilicon linker (3-aminopropyltrimethoxysilane, APTMS). Using the liposome production approach, the synthons self-assemble to form hollow nanosized vesicles (100-200 nm in diameter), which can be loaded with organic dyes─eriochrome black T (ErChB) and fluorescein (FL)─where the POM layer serves as a membrane with subnanopores for cell-like communication. The dye structure plays an essential role in embedding dyes into the vesicle's shell, which opens the way to control the colloidal stability of the system. The produced vesicles are moved by an electric field and used for the creation of an infochemistry scheme with three types of logic gates (AND, OR, and IMP). To design 2D materials, synthons can form spread films, from simple addition on the water-air interface to lateral compression in the Langmuir bath, and highly ordered structures appear, demonstrating electron diffraction in Langmuir-Schaefer (LS) films. These results show the significant potential of POM-based synthons and nanosized vesicles to supramolecular design the diversity of smart materials.

Ultrasensitive Detection of PSA Using Antibodies in Crowding Polyelectrolyte Multilayers on a Silicon Nanowire Field-Effect Transistor.

Immunosensors based on field-effect transistors with nanowire channels (NWFETs) provide fast and real-time detection of a variety of biomarkers without the need for additional labels. The key feature of the developed immunosensor is the coating of silicon NWs with multilayers of polyelectrolytes (polyethylenimine (PEI) and polystyrene sulfonate (PSS)). By causing a macromolecular crowding effect, it ensures the "soft fixation" of the antibodies into the 3-D matrix of the oppositely charged layers. We investigated the interaction of prostate-specific antigen (PSA), a biomarker of prostate cancer, and antibodies adsorbed in the PEI and PSS matrix. In order to visualize the formation of immune complexes between polyelectrolyte layers using SEM and AFM techniques, we employed a second clone of antibodies labeled with gold nanoparticles. PSA was able to penetrate the matrix and concentrate close to the surface layer, which is crucial for its detection on the nanowires. Additionally, this provides the optimal orientation of the antibodies' active centers for interacting with the antigen and improves their mobility. NWFETs were fabricated from SOI material using high-resolution e-beam lithography, thin film vacuum deposition, and reactive-ion etching processes. The immunosensor was characterized by a high sensitivity to pH (71 mV/pH) and an ultra-low limit of detection (LOD) of 0.04 fg/mL for PSA. The response of the immunosensor takes less than a minute, and the measurement is carried out in real time. This approach seems promising for further investigation of its applicability for early screening of prostate cancer and POC systems.

Analyzing the Effects of Single Nucleotide Polymorphisms on hnRNPA2/B1 Protein Stability and Function: Insights for Anticancer Therapeutic Design.

Heterogeneous nuclear ribonucleoprotein A2/B1 (hnRNPA2/B1) is a pivotal player in m6A recognition, RNA metabolism, and antiviral responses. In the context of cancer, overexpression of hnRNPA2/B1, abnormal RNA levels, and m6A depositions are evident. This study focuses on two significant nonsynonymous single nucleotide polymorphisms (nsSNPs) within hnRNPA2/B1, namely, F66L and E92K. Our structural analyses reveal decreased stability in these mutants, with E92K being predicted to undergo destabilizing post-translational methylation. Furthermore, our extensive analysis of 44,239 tumor samples from the COSMIC database uncovers that amino acid position 92 exhibits the second-highest mutation frequency within hnRNPA2/B1, particularly associated with breast and lung cancers. This experimental data aligns with our theoretical studies, highlighting the substantial impact of the nsSNP at position 92 on hnRNPA2/B1's stability and functionality. Given the critical role of pre-mRNA splicing, transcription, and translation regulation in cellular function, it is important to assess the impact of these nsSNPs on the stability and function of the hnRNPA2/B1 protein to design more efficient anticancer therapeutics.

Machine learning methods for liquid crystal research: phases, textures, defects and physical properties.

Liquid crystal materials, with their unique properties and diverse applications, have long captured the attention of researchers and industries alike. From liquid crystal displays and electro-optical devices to advanced sensors and emerging technologies, the study and application of liquid crystals continue to be of paramount importance in the fields of materials science, chemistry and physics. With the ever-increasing complexity and diversity of liquid crystal materials, researchers face new challenges in understanding their behaviors, properties, and potential applications. On the other hand, machine learning, a rapidly evolving interdisciplinary field at the intersection of computer science and data analysis, has already become a powerful tool for unraveling implicit correlations and predicting new properties of a wide variety of physical and chemical systems and structures. Here we aim to consider how machine learning methods are suitable for solving fundamental problems in the field of liquid crystals and what are the advantages of this artificial intelligence based approach.

Voronoi Entropy as a Ligand Molecular Descriptor of Protein-Ligand Interactions.

We investigate the correlation between the Voronoi entropy (VE) of ligand molecules and their affinity to receptors to test the hypothesis that less ordered ligands have higher mobility of molecular groups and therefore a higher probability of attaching to receptors. VE of 1144 ligands is calculated using SMILES-based 2D graphs representing the molecular structure. The affinity of the ligands with the SARS-CoV-2 main protease is obtained from the BindingDB Database as half-maximal inhibitory concentration (IC50) data. The VE distribution is close to the Gaussian, 0.4 ≤ Sv ≤ 1.66, and a strong correlation with IC50 is found, IC50 = -275 Sv + 613 nM, indicating the correlation between ligand complexity and affinity. On the contrary, the Shannon entropy (SE) descriptor failed to provide enough evidence to reject the null hypothesis (p-value > 0.05), indicating that the spatial arrangement of atoms is crucial for molecular mobility and binding.

Electrochemical Sensor to Detect Antibiotics in Milk Based on Machine Learning Algorithms.

The present study is dedicated to the problem of electrochemical analysis of multicomponent mixtures, such as milk. A combination of cyclic voltammetry facilities and machine learning techniques made it possible to create a pattern recognition system for the detection of antibiotic residues in skimmed milk. A multielectrode sensor including copper, nickel, and carbon fiber was fabricated for the collection of electrochemical data. Processes occurring at the electrode surface were discussed and simulated with the help of molecular docking and density functional theory modeling. It was assumed that the antibiotic fingerprint reveals a potential drift of electrodes, owing to complexation with metal ions present in milk. The gradient boosting algorithm showed the best efficiency in training the machine learning model. High accuracy was achieved for the recognition of antibiotics in milk. The elaborated method may be incorporated into existing milking systems at dairy farms for monitoring the residue concentrations of antibiotics.

Deposition of Nanostructured Tungsten Oxide Layers by a New Method: Periodic Modulation of the Deposition Angle.

Periodic modulation of the deposition angle (PMDA) is a new method to deposit nanostructured and continuous layers with controllable periodic density fluctuation. The method is used for the magnetron sputtering of a WO3 layer for an electrochromic device (ECD). An experimental study indicates that the electrochromic coloration-bleaching rate nearly doubles and the electrochromic efficiency grows by about 25% in comparison with the traditional method. The ECD efficiency rises with the increasing degree of nanostructure ordering, surface roughness, and homogeneity of the WO3 layer. The method is promising for coating deposition techniques needed to produce versatile devices with specific requirements for ion transport in surface layers, coatings, and interfaces, such as fuel cells, batteries, and supercapacitors.

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.

Universal Method Based on Layer-by-Layer Assembly for Aptamer-Based Sensors for Small-Molecule Detection.

Development of a fast and accurate pesticide analysis system is a challenging task, as a large amount of commonly used pesticide has negative effects on humans' health. Detection of pesticide residues is crucial for food safety management and environmental protection. Aptamers─short single-stranded oligonucleotides (RNA or DNA) selected by aptamer selection method SELEX─can selectively bind to their target pesticide molecules with high affinity. Thus, in the present study, we developed an electrochemical biosensor based on aptamers to detect the commonly used pesticide, glyphosate. Carbon fibers were used as the platform to assemble polyelectrolyte layers with the incorporated aptamers selectively binding with glyphosate molecules for electrochemical detection. The best limit of detection of 0.3 µM was achieved at open-circuit potential measurements, which is comparable to the current need in detection of glyphosate. The developed method can be implemented into existing systems for the determination of pesticides on farms to control residual concentrations of glyphosate in soil and water.

Calix[4]arene-pyrazole conjugates as potential cancer therapeutics.

Tumor selectivity is yet a challenge in chemotherapy-based cancer treatment. A series of calixarenes derivatized at the lower rim with 3-phenyl-1H-pyrazole units with variable upper-rim substituent and conformations of macrocyclic core, alkyl chain length between heterocycle and core, as well as phenolic monomer (5-(4-tert-butylphenyloxy)methoxy-3-phenyl-1H-pyrazole) have been synthesized and characterized in a range of therapeutically relevant cellular models (M-HeLa, MCF7, A-549, PC3, Chang liver, and Wi38) from different target organs/systems. Specific cytotoxicity for M-HeLa cells has been observed in tert-butylcalix[4]arene pyrazoles in 1,3-alternate (compound 7b) and partial cone (compound 7c) conformations with low mutagenicity and haemotoxicity and in vivo toxicity in mice. Compounds 7b,c have induced mitochondrial pathway of apoptosis of M-HeLa cells through caspase-9 activation preceded by the cell cycle arrest at G0/G1 phase. A concomitant overexpression of DNA damage markers in pyrazole-treated M-HeLa cells suggests that calixarene pyrazoles target DNA, which was supported by the presence of interactions between calixarenes and ctDNA at the air-water interface.

Using novel click chemistry algorithm to design D3R inhibitors as blood-brain barrier permeants.

Dopamine receptor D3 (D3R) has gained attention as a promising therapeutic target for neurological disorders. In this study, an innovative in silico click reaction strategy was employed to identify potential D3R binders. The ligand template, 1-phenyl-4-[4-(1H-1,2,3-triazol-5-yl)butyl]piperazine, with substitution at the 1,2,3-triazole ring, served as the starting point. Generated compounds underwent filtration based on their brain-to-blood concentration ratio (logBB), leading to the identification of 1-{4-[1-(decahydronaphthalen-1-yl)-1H-1,2,3-triazol-5-yl]butyl}-4-phenylpiperazine as the most promising candidate, displaying superior D3R affinity and blood-brain barrier (BBB) permeability compared to the reference ligand, eticlopride. Molecular dynamics simulations further supported these findings. This study presents a novel hit for designing D3R ligands and establishes a workflow utilizing in silico click chemistry to screen compounds with BBB permeability. The proposed click reaction-based algorithm holds significant potential as a valuable tool in the development of effective antipsychotic compounds.

Memristive Effect in Ti3 C2 Tx (MXene) Polyelectrolyte Multilayers.

The emerging novel class of two-dimensional materials - MХenes - have attracted significant research attention. However, there are only few reports on using the most prominent member of the MXene family, Ti3 C2 Tx , as an active material for memristive devices within a polyelectrolyte matrix and its deposition on inert electrodes like ITO and Pt. In this study, we systematically investigate Ti3 C2 Tx MXenes synthesized with two classical delamination agents, such as lithium chloride and tetramethylammonium hydroxide, to identify the most suitable candidate for memristive device applications. The characteristics of memristors based on the hybrid structures consisting of MXene-polyelectrolyte multilayers, specifically polyethyleneimine (PEI) and poly(sodium 4-styrenesulfonate) (PSS) are explored. The PEI(MXene)/PSS memristor exhibits a voltage threshold (VSET/RESET ) range of 1.5-2.0 V, enabling the transition from a high-resistive state (HRS) to a low-resistive state (LRS), along with a significant current switching ratio of approximately two orders of magnitude. The observed VSET/RESET difference of approximately 4 V is further supported by density functional theory (DFT) calculated redox potential. These findings underscore the potential of polyelectrolyte-based memristors, such as the in PEI-Ti3 C2 Tx -PSS system, in facilitating the development of highly functional, self-assembled memristive devices with diverse applications.

Folding-unfolding asymmetry and a RetroFold computational algorithm.

We treat protein folding as molecular self-assembly, while unfolding is viewed as disassembly. Fracture is typically a much faster process than self-assembly. Self-assembly is often an exponentially decaying process, since energy relaxes due to dissipation, while fracture is a constant-rate process as the driving force is opposed by damping. Protein folding takes two orders of magnitude longer than unfolding. We suggest a mathematical transformation of variables, which makes it possible to view self-assembly as time-reversed disassembly, thus folding can be studied as reversed unfolding. We investigate the molecular dynamics modelling of folding and unfolding of the short Trp-cage protein. Folding time constitutes about 800 ns, while unfolding (denaturation) takes only about 5.0 ns and, therefore, fewer computational resources are needed for its simulation. This RetroFold approach can be used for the design of a novel computation algorithm, which, while approximate, is less time-consuming than traditional folding algorithms.

Melamine Barbiturate as a Light-Induced Nanostructured Supramolecular Material for a Bioinspired Oxygen and Organic Radical Trap and Stabilization.

Use of coantioxidant systems is a prospective way to increase the effectiveness of antioxidant species in tissue repair and regeneration. In this paper, we introduce a novel scheme of a reactive oxygen species (ROS) trap and neutralization during self-assembly of supramolecular melamine barbiturate material. The performed reaction chain mimics the biological process of ROS generation in key stages and enables one to obtain stable hydroperoxyl and organic radicals in a melamine barbiturate structure. Melamine barbiturate also neutralizes hydroxyl radicals, and the effectiveness of the radical trap is controlled with ROS scavenger incorporation. The number of radicals dramatically increases during light-inducing and depends on pH. The proposed scheme of the ROS trap and neutralization opens a way to the use of supramolecular assemblies as a component of coantioxidant systems and a source of organic radicals.