Network-based integrated analysis for toxic effects of high-concentration formaldehyde inhalation exposure through the toxicogenomic approach

Deciphering the Electronic Coupling Dynamics of Laser-induced Ru/Cu Electrocatalyst for Dual-Side Hydrogen Production and Formic Acid Co-synthesis via DFT Analysis

Herein, a straightforward approach using pulsed laser technology to synthesize selective hexagonal-close-packed (hcp) Ru nanoparticles attached to Cu nanospheres (Ru/Cu) as bifunctional electrocatalyst for catalyzing the hydrogen evolution reaction (HER) and formaldehyde oxidation reaction (FOR) are reported. Initially, Ru-doped CuO flakes are synthesized using a coprecipitation method followed by transformation into Ru/Cu composites through a strategy involving pulsed laser irradiation in liquid. Specifically, the optimized Ru/Cu-4 composite not only demonstrates a low overpotential of 182 mV at 10 mA·cm-2 for the HER but also an ultralow working potential of 0.078 V (versus reversible hydrogen electrode) for the FOR at the same current density. Remarkably, the FOR∥HER-coupled electrolyzer employing the Ru/Cu-4∥Ru/Cu-4 system achieves H2 production at both electrodes with a cell voltage of 0.42 V at 10 mA·cm-2 while co-synthesizing formic acid. Furthermore, density functional theory analyses elucidate that the superior activity of the Ru/Cu composite originates from optimized adsorption energies of reactive species on the catalyst surfaces during the HER and FOR, facilitated by the synergistic coupling between Ru and Cu. This study presents an alternative strategy for synthesizing highly effective electrocatalytic materials for use in energy-efficient H2 production with the cosynthesis of value-added chemicals suitable for practical applications.

Inhalation Toxicity Screening of Consumer Products Chemicals using OECD Test Guideline Data-based Machine Learning Models

This study aimed to screen the inhalation toxicity of chemicals found in consumer products such as air fresheners, fragrances, and anti-fogging agents submitted to K-REACH using machine learning models. We manually curated inhalation toxicity data based on OECD test guideline 403 (Acute inhalation), 412 (Sub-acute inhalation), and 413 (Sub-chronic inhalation) for 1709 chemicals from the OECD eChemPortal database. Machine learning models were trained using ten algorithms, along with four molecular fingerprints (MACCS, Morgan, Topo, RDKit) and molecular descriptors, achieving F1 scores ranging from 51 % to 91 % in test dataset. Leveraging the high-performing models, we conducted a virtual screening of chemicals, initially applying them to data-rich chemicals generally used in occupational settings to determine the prediction uncertainty. Results showed high sensitivity (75 %) but low specificity (23 %), suggesting that our models can contribute to conservative screening of chemicals. Subsequently, we applied the models to consumer product chemicals, identifying 79 as of high concern. Most of the prioritized chemicals lacked GHS classifications related to inhalation toxicity, even though they were predicted to be used in many consumer products. This study highlights a potential regulatory blind spot concerning the inhalation risk of consumer product chemicals while also indicating the potential of artificial intelligence (AI) models to aid in prioritizing chemicals at the screening level.

Trajectory in biological metal-organic frameworks: Biosensing and sustainable strategies-perspectives and challenges

The ligand attribute of biomolecules to form coordination bonds with metal ions led to the discovery of a novel class of materials called biomolecule-associated metal-organic frameworks (Bio-MOFs). These biomolecules coordinate in multiple ways and provide versatile applications. Far-spread bio-ligands include nucleobases, amino acids, peptides, cyclodextrins, saccharides, porphyrins/metalloporphyrin, proteins, etc. Low-toxicity, self-assembly, stability, designable and selectable porous size, the existence of rigid and flexible forms, bio-compatibility, and synergistic interactions between metal ions have led Bio-MOFs to be commercialized in industries such as sensors, food, pharma, and eco-sensing. The rapid growth and commercialization are stunted by absolute bio-compatibility issues, bulk morphology that makes it rigid to alter shape/porosity, longer reaction times, and inadequate research. This review elucidates the structural vitality, biocompatibility issues, and vital sensing applications, including challenges for incorporating bio-ligands into MOF. Critical innovations in Bio-MOFs' applicative spectrum, including sustainable food packaging, biosensing, insulin and phosphoprotein detection, gas sensing, CO2 capture, pesticide carriers, toxicant adsorptions, etc., have been elucidated. Emphasis is placed on biosensing and biomedical applications with biomimetic catalysis and sensitive sensor designing.

The Mechanism of Mori Folium and Eucommiae Cortex against Cyclophosphamide-Induced Immunosuppression Integrating Network Pharmacology, Molecular Docking, Molecular Dynamics Simulations, and Experimental Validation

It has been reported that Mori Folium (MF) and Eucommiae Cortex (EC) exhibit pharmacological effects in the treatment of immunosuppression. However, the mechanism of MF and EC against immunosuppression remains unclear. This study aims to explore the mechanism of action of MF and EC for the treatment of immunosuppression through network pharmacology, molecular docking, molecular dynamics simulations and animal experiments. As a result, 11 critical components, 9 hub targets, and related signaling pathways in the treatment of immunosuppression were obtained based on network pharmacology. The molecular docking suggested that 11 critical components exhibited great binding affinity to 9 hub targets of immunosuppression. The molecular dynamics simulations results showed that (-)-tabernemontanine-AR, beta-sitosterol-AR and Dehydrodieugenol-HSP90AA1 complexes are stably bound. Additionally, in the animal experiments, the treated group results compared to the control group suggest that MF and EC have a significant effect on the treatment of immunosuppression. Therefore, MF and EC treatment for immunosuppression may take effects in a multi-component, multi-target, and multi-pathway manner. The results herein may provide novel insights into the treatment of immunosuppression in humans.

State of the Art of Genomic Technology in Toxicology: A Review

The rapid growth of genomics techniques has revolutionized and impacted, greatly and positively, the knowledge of toxicology, ushering it into a "new era": the era of genomic technology (GT). This great advance permits us to analyze the whole genome, to know the gene response to toxicants and environmental stressors, and to determine the specific profiles of gene expression, among many other approaches. The aim of this work was to compile and narrate the recent research on GT during the last 2 years (2020-2022). A literature search was managed using the PubMed and Medscape interfaces on the Medline database. Relevant articles published in peer-reviewed journals were retrieved and their main results and conclusions are mentioned briefly. It is quite important to form a multidisciplinary taskforce on GT with the aim of designing and implementing a comprehensive, collaborative, and a strategic work plan, prioritizing and assessing the most relevant diseases, so as to decrease human morbimortality due to exposure to environmental chemicals and stressors.

Integrative Data Mining Approach: Case Study with Adverse Outcome Pathway Network Leading to Pulmonary Fibrosis

An adverse outcome pathway (AOP) framework can be applied as an efficient tool for the rapid screening of environmental chemicals. For the development of an AOP, a database mining approach can support an expert derivation approach by gathering a wider range of evidence than in a literature review. In this study, data from various databases were integrated and analyzed to supplement the AOP leading to pulmonary fibrosis by analyzing additional evidence using a data mining approach and establishing an application domain for chemicals. First, we collected chemicals, genes, and phenotypes that were studied and related to pulmonary fibrosis through the Comparative Toxicogenomics Database (CTD). CGPD-tetramers constructed by linking each related chemical, gene, phenotype, and disease can provide the basic components for the assembly of putative AOPs. Next, an AOP network was established by connecting eight existing AOPs for pulmonary fibrosis developed by expert derivation from the AOP Wiki. Finally, the pulmonary fibrosis AOP network was proposed by integrating the AOP network from AOP Wiki and the CGPD-tetramers from the CTD. To prioritize potential chemical stressors in the AOP network, 61 chemicals were ranked using the relevance of the chemical to the AOP and chemical exposure information from the CompTox Chemicals Dashboard. The approach proposed in this study can guide the utilization of available evidence from various databases as well as the literature in constructing AOP networks related to specific diseases.

Design and assembly of biodegradable capsules based on alginate hydrogel composite for the encapsulation of blue dye

The encapsulation of bluing agents in biodegradable polymeric capsules is an emerging option in laundry detergents sector to substitute formaldehyde-based polymers, because they are non-biodegradable, carcinogenic and toxic. In this work, we present for the first time the successful encapsulation of a blue dye in biodegradable capsules which shell was formed by an alginate hydrogel and a polyethylene glycol network. Different types of capsules were synthesized (addition or not of the diacrylate monomer) and irradiation of the crosslinking solution at different times. Furthermore, a deep characterization of each type of capsules was performed (chemical and morphological characterization, assessment of their mechanical and thermal properties, evaluation of their biodegradability), noting that the incorporation of the diacrylate monomer (PEGDMA) and the two different irradiation times selected substantially affected the final properties of the capsules. The obtained results will serve to comprehend how the dye can be released from the capsules.

A bioinformatics framework to identify the biomarkers and potential drugs for the treatment of colorectal cancer

Colorectal cancer (CRC), a common malignant tumor, is one of the main causes of death in cancer patients in the world. Therefore, it is critical to understand the molecular mechanism of CRC and identify its diagnostic and prognostic biomarkers. The purpose of this study is to reveal the genes involved in the development of CRC and to predict drug candidates that may help treat CRC through bioinformatics analyses. Two independent CRC gene expression datasets including The Cancer Genome Atlas (TCGA) database and GSE104836 were used in this study. Differentially expressed genes (DEGs) were analyzed separately on the two datasets, and intersected for further analyses. 249 drug candidates for CRC were identified according to the intersected DEGs and the Crowd Extracted Expression of Differential Signatures (CREEDS) database. In addition, hub genes were analyzed using Cytoscape according to the DEGs, and survival analysis results showed that one of the hub genes, TIMP1 was related to the prognosis of CRC patients. Thus, we further focused on drugs that could reverse the expression level of TIMP1. Eight potential drugs with documentary evidence and two new drugs that could reverse the expression of TIMP1 were found among the 249 drugs. In conclusion, we successfully identified potential biomarkers for CRC and achieved drug repurposing using bioinformatics methods. Further exploration is needed to understand the molecular mechanisms of these identified genes and drugs/small molecules in the occurrence, development and treatment of CRC.