Search Results (1,793)

Why should a genome be protected? Because it contains our most private data! A genome contains an organism’s set of genetic material (DNA and, in viruses, RNA), and it contains all genes and non-coding sequences. The structure of DNA was described by Watson and Crick in 1953, but the first studies were conducted a century earlier by Miescher, who described the structure and chemical composition of the nucleus. The first action aimed at securing the results of genetic research was the creation of databases for the results obtained using genetic fingerprinting technology. The discovery of the sequencing method and the introduction of the polymerase chain reaction laid the foundations for understanding the genome’s function. Automated DNA sequencing proved to be hundreds of times faster than traditional methods, thus reducing the cost and time of genome analyses. Thousands of genomic data points are stored in private and governmental databases. The security of patients’ genomic data must be ensured by protecting it from unauthorized use while, at the same time, enabling research for the sake of public health. The falling prices of genome sequencing and the increasing availability of commercial sequencing for the public could result in ethical problems and undermine the safety of personal information. Full article

In the context of global carbon neutrality goals, substituting hydrogen for carbon as a reductant represents a critical pathway for mitigating emissions in the iron and steel industry. Hydrogen-based molten reduction technology, characterized by its rapid reaction kinetics and high feedstock flexibility, has emerged as a pivotal direction for the industry’s low-carbon transition. This article systematically reviews research progress on the hydrogen-based reduction of molten iron oxides. The thermodynamic behavior of molten systems is discussed, confirming the feasibility of reducing molten FeO with hydrogen at elevated temperatures. Furthermore, discrepancies and nonlinear characteristics within current mainstream thermodynamic databases regarding the high-temperature molten region are identified. Kinetic studies demonstrate that reduction rates in the molten state significantly exceed those in the solid state. The rate-limiting step is shown to vary with reaction conditions, primarily shifting between interfacial chemical reaction and liquid-phase mass transfer control. Additionally, the influence mechanisms of key parameters—including temperature, reaction time, gas flow rate, gas composition, and slag composition—on the reduction process are comprehensively reviewed. By synthesizing existing methodologies and theoretical advancements, this review aims to provide a theoretical reference for optimizing hydrogen-based molten reduction processes for iron oxides. Full article

Emerging organic pollutants (EOPs) in aquatic environments have attracted increasing attention because many occur at trace levels, undergo transformation during environmental transport, and contribute to poorly resolved mixture risks. Traditional targeted analysis is inherently restricted to predefined compounds, whereas high-resolution mass spectrometry (HRMS)-based full-scan workflows provide broader opportunities for discovering known unknowns and previously unrecognized contaminants. This review critically synthesizes an omics-based analytical framework for aquatic environments, covering sample digitalization, instrumental analysis and acquisition modes, chemical fingerprint/non-target screening, suspect screening, effect-directed analysis, and confidence-based structural identification. Particular emphasis is placed on practical decision points and trade-offs, including dissolved versus particulate-associated analytes, LC-HRMS versus GC-HRMS coverage, hard versus soft ionization, DDA- versus DIA-type acquisition, database dependence, and the persistent difficulty of linking analytical features to toxicological relevance. The review also discusses emerging directions involving artificial intelligence, chemometrics, organometallic contaminants, and microplastic-associated chemicals. By clarifying conceptual boundaries and highlighting current limitations, this article aims to support the development of more critical, transparent, and risk-oriented workflows for the discovery and prioritization of emerging pollutants in aquatic environments. Full article

Mining activities are characterised by a multiplicity of inherent occupational hazards. Exposure to mineral dust such as silica, asbestos, and coal dust is common in mining, leading to pneumoconiosis. Exposure to respirable silica-containing dust is one of the common respiratory hazards associated with adverse health effects such as silicosis, lung cancer, renal failure, scleroderma, systemic lupus erythematosus (SLE) and chronic obstructive pulmonary disease (COPD), to mention but just a few. In southern Africa, there is a rising epidemic of silicosis, human immunodeficiency virus (HIV) and tuberculosis (TB). Excessive exposure to silica-containing dust exacerbates the TB and silicosis epidemic in mining areas. There is poor control of dust exposure and a lack of occupational hygiene assessments of silica dust in mining in southern Africa. In southern Africa, there remains a persistent knowledge gap regarding the extent of occupational exposures to respirable chemical substances, such as silica dust. Consequently, occupational hygiene air monitoring was conducted in mining companies across four low-income Southern Africa Development Community (SADC) countries, Lesotho, Mozambique, Malawi and Zambia, to provide a baseline exposure dataset. The hazardous nature of work associated with mining activities still persists in these low-income countries, with 53% (n = 72) of quarries and 20% (n = 19) of coal mines having respirable quartz exposures exceeding the reference occupational exposure limit (OEL) of 0.1 milligrams per cubic meter (mg/m³). The highest exposure ranges for quartz were recorded in surface aggregate quarries, with the maximum concentration recorded at 2.739 mg/m³. The highest number of air samples (93%, n = 111), which were in compliance with the OEL of 3 mg/m³ for respirable dust, were recorded in the copper, diamond, ruby, cement quarry and gold mines. This exploratory study confirms the variable extent of mineworker exposure to respirable dust and corresponding quartz fractions emanating from different mining activities. The collected exposure data provides a baseline overview of exposures within the mining industry in the SADC region. It also serves as a vital input for future regional exposure surveillance databases, as well as preliminary data for directing future research towards regional exposure prevention initiatives. Full article

Despite the wide and accepted implementation of contemporary pharmaceutical medicine, the use of medicinal plants still prevails in several regions around the world, including Mexico. According to the World Health Organization (WHO), the use of incorrect species in natural and complementary medicine is a threat to consumer safety. Therefore, there is a need to characterize properly those plant species used in traditional medicine. In this study, a medicinal plant called Calanca, which is traded in the local market of a small community within the State of Puebla (Mexico), was characterized by different approaches. Conventional and molecular taxonomy analyses showed that Calanca belonged to the Asteraceae family, genus Chrysactinia. On one hand, molecular markers (rbcL, matK and ITS) helped to identify Calanca at the species level, being identified as C. mexicana. On the other hand, although not used for molecular taxonomy, additional gene markers were amplified and submitted to the GenBank database to expand the toolkit for C. mexicana identification. In addition, soil taxonomy and quantitative chemical analyses provided insights into the relationship between growing conditions and the chemical compounds produced by C. mexicana. Chemical compounds associated with medicinal properties such as phenolic acids, flavonoids, terpenes, and anthocyanins were identified in C. mexicana extracts. Finally, greenhouse conditions for the cultivation of this species were also investigated. Overall, this comprehensive characterization provides the essential botanical and chemical foundation required for future toxicological and clinical safety assessments, while establishing a robust framework for the long-term conservation of this endangered medicinal resource. Full article

Background/Objectives: Cyclooxygenase-2 (COX-2) is a well-established target in the development of anti-inflammatory drugs due to its central role in mediating inflammation. The identification of novel COX-2 inhibitors remains a key focus in pharmaceutical research. This study aimed to develop a robust and interpretable machine learning framework to predict COX-2 inhibitory activity and support virtual screening efforts. Methods: A curated dataset of 2052 compounds was obtained from the ChEMBL database. Molecular structures were encoded using Morgan fingerprints derived from SMILES representations. Several machine learning algorithms were trained and evaluated, including ensemble-based methods. Model performance was assessed using internal validation and external test sets. Robustness was further evaluated through Y-randomization tests. Model interpretability was investigated using SHAP (SHapley Additive exPlanations) analysis to identify key structural features contributing to activity. Results: Among the evaluated models, ensemble methods demonstrated superior predictive performance, with the Random Forest algorithm providing the most consistent and reliable results across validation and external datasets. Y-randomization confirmed that the model predictions were not due to chance correlations. SHAP analysis revealed that the most influential features corresponded to chemically meaningful substructures aligned with known COX-2 pharmacophore characteristics. The final optimized model was successfully deployed as a publicly accessible web application for real-time prediction using SMILES input. Conclusions: This study demonstrates the effectiveness of explainable machine learning approaches in predicting COX-2 inhibitory activity. The developed framework provides a reliable and interpretable tool for accelerating COX-2 inhibitor discovery and facilitating virtual screening in drug development. Full article

Soil electrical resistivity (SER) is widely used as an indirect indicator of soil physical, chemical, and hydrological properties and plays an important role in applications such as grounding system design, geotechnical site characterization, agricultural soil monitoring, and environmental contamination assessment. However, SER is strongly influenced by environmental variables including soil moisture content, temperature, salinity, and soil texture, which makes accurate prediction challenging under heterogeneous field conditions. A systematic review was conducted following the PRISMA 2020 protocol using the Scopus database to identify peer-reviewed studies published between 2018 and 2026 related to predictive models of soil electrical resistivity based on environmental parameters. After applying defined inclusion and exclusion criteria, a set of relevant studies was selected for qualitative and comparative analysis. The reviewed studies consistently identify soil moisture content as the most frequently reported influential factor affecting SER, followed by temperature, salinity, and soil texture. This observation reflects the predominant focus of the analyzed literature within the selected time frame rather than a definitive representation of all controlling physical processes. Similarly, the reviewed literature suggests that empirical and statistical models remain valuable due to their simplicity and interpretability, whereas machine learning approaches such as artificial neural networks, support vector regression, and ensemble methods are often reported to achieve higher predictive accuracy in complex soil environments. The predictive SER modeling represents a rapidly evolving research field, and future work should focus on hybrid physics-informed machine learning models, the development of standardized datasets, and the integration of predictive algorithms with emerging sensing technologies and IoT-based monitoring systems. Full article

Background: The transition from fossil-derived polymer additives to renewable alternatives is essential to mitigate environmental persistence and ensure chemical safety within the plastics industry. This review provides a comprehensive overview of recent developments in bio-based functional additives and their integration into circular economy frameworks. Methods: Following PRISMA guidelines, a systematic literature search was conducted using the Scopus database for studies published between 2023 and 2026. Search terms targeted bio-based plasticizers, flame retardants, antioxidants, and compatibilizers. Studies were screened against predefined inclusion criteria, specifically focusing on experimental validation in polymer matrices, while data mining was employed to map emerging research fronts. Results: From an initial 996 records, 54 studies were selected after removing duplicates and ineligible articles. The findings highlight a paradigm shift from passive physical fillers toward active, multifunctional macromolecular agents. Recent literature demonstrates that targeted molecular interventions, such as phosphorylated lignin and biomimetic structures, can resolve trade-offs between ductility and thermal stability at low loadings (<5 wt%). Synthesis routes, performance outcomes, and end-of-life trajectories for each additive class are summarized. Conclusions: Bio-based additives have evolved from simple substitutes into strategic tools for the molecular programming of sustainable polymers. Although challenges regarding scalability and high-temperature processing persist, their integration into circular economy strategies establishes a clear roadmap for next-generation bioplastics. Full article

The onset of the COVID-19 pandemic prompted the rapid development and deployment of novel strategies and methodologies to manage the dissemination of microorganisms. Understanding the crucial role that contaminated surfaces play in the spread of viruses highlights the importance of having effective cleaning and disinfection protocols in place for inanimate objects. A variety of antimicrobial agents have shown strong effectiveness against the SARS-CoV-2 virus. Various factors can impact on the performance of these agents. As a result, technologies utilizing ozone’s microbicidal effects have been developed or improved for cleaning indoor areas, surfaces, and materials, despite ozone’s diverse uses being known for years. Ozone offers the advantage of adaptability for both gaseous and aqueous use, depending on the nature of the decontaminated surfaces. Moreover, ozone-infused water is ecologically benign, possesses microbial-fighting capabilities, and synergistically reinforces the biocidal action of other chemical disinfectants. This review aims to summarize the efforts dedicated to harnessing gaseous and aqueous ozone as a valuable means to eliminate the SARS-CoV-2 virus from environments, surfaces, clinical equipment, and office supplies. This review sourced evidence-based articles from electronic databases, including MEDLINE (via PubMed), EMBASE, the Cochrane Library (CENTRAL), and preprint repositories. The findings illustrated that ozone could serve as an additional tool for curbing the proliferation of COVID-19 and other viral infections. Additionally, we elucidated the operational attributes of ozone, the variables that influence its disinfection potency, and the mechanisms of its virucidal action. Notably, this review does not encompass the disinfection of the COVID-19 virus in wastewater. Full article

Accurate prediction of syngas composition is essential for process design, optimization, and scale-up, yet it remains challenging due to interactions among operating conditions, biomass properties, and chemical reactions. This study used a database of 450 experimental observations spanning a wide range of biomass feedstocks and operating conditions to compare the predictive performance of Aspen Plus simulations and Machine Learning models in estimating the concentrations of CO, CO₂, H₂, and CH₄ in syngas. Aspen Plus was used to simulate the 4 stages of the biomass gasification process under different operating conditions, with special focus on the three reactor modules (RPlug, RGibbs, and REquil) modeling the last two stages. In parallel, Machine Learning models using four regression algorithms (XGBoost, Support Vector Machines, Random Forest and Artificial Neural Networks), with different preprocessing and data-splitting strategies, were evaluated for predicting syngas composition. The best Machine Learning models achieved R² values of 0.753 (CO), 0.866 (CO₂), 0.879 (H₂) and 0.734 (CH₄) on the test set. These results outperformed the Aspen Plus approach and highlight the potential of Machine Learning models as complementary or alternative tools for modelling biomass gasification. Shapley Additive Explanation analysis identified the most influential input variables, revealing key roles for the steam-to-biomass ratio and the equivalence ratio in predicting syngas composition. This study demonstrates that existing Aspen Plus simulation models require further development to improve performance metrics across a wide range of biomass feedstocks and operating conditions. Full article

The synergistic effects of soil acidification and heavy metal pollution present major challenges for global agroecosystems. To systematically trace the evolution of research and identify key topics in this field, this study employed CiteSpace to visualize and analyze 691 records from the China National Knowledge Infrastructure (CNKI) and 6747 highly relevant articles or reviews from the Web of Science (WOS) Core Collection database from 2005 to 2025. The results indicate a steady to rapid rise in global publications, with China contributing the largest share, at 2468 publications. This has produced a research cluster centered around the Chinese Academy of Sciences (CAS); however, the centrality of its international cooperation remains limited. Studies in the CNKI database are driven by agricultural needs, focusing on national food security, rice yield stability, improvement of arable land, and heavy metal passivation and remediation, with a concentration on basic agricultural science. By contrast, research in the WOS database emphasizes fundamental mechanisms and interdisciplinary integration, addressing aluminum toxicity, microbial communities, the nitrogen cycle, and global climate change, intersecting fields such as environmental science, soil science, ecology, and microbiology. The evolution of research hotspots shows a clear trajectory: from acidity regulation and chemical speciation analysis of heavy metals (2005–2013), to heavy metal passivation, remediation, and phytoremediation (2014–2018), and then to biochar materials, microbiome analysis, and the synergistic role of carbon sequestration (2019–2025). This study argues that future research should move beyond single remediation measures and adopt integrated strategic management to jointly improve bioremediation efficiency, promote soil carbon sequestration and soil health, and enhance microbial adaptation to global climate change. Full article

Background: Radish seeds are our model on glucosinolates (GSLs), which is a class of secondary metabolites in medicinal plants of the Brassicaceae family. Multilayer perceptron (MLP) network is highly effective in the study of complex plants. This study came up with a smart plan through the Python language. Methods: First, we used the MLP network to pick out GSL precursor ions, running them through a deep learning filter. Next, we set up an automated screening system and looked at how standard chemicals break down. To speed things up, we created a scoring system that flagged promising compounds. After that, we built a tracer molecular network, basically connecting compounds according to how the plant makes them, which helped us label everything accurately. Finally, we brought in a math-based tool that pieces together different chemical parts to predict new GSL structures. Results: With this workflow, we annotated 195 glucosinolate-related compounds in radish seeds. That includes 86 regular GSLs, 34 malonyl products, 40 sinapoyl compounds, and 35 diglycosides. Among them, eight compounds were confirmed by comparison with authentic standards (retention time and MS/MS data), whereas the remaining compounds were tentatively annotated based on accurate mass measurements, diagnostic fragment ions, Tracer Molecular Nnetworking, and literature/database matching. In addition, 36 compounds were considered putatively novel derivatives pending further structural confirmation. Conclusions: This new approach reduces the time spent on determining chemicals in complicated samples. This can be done with other vegetables and medicinal herbs by researchers. It assists us in knowing the chemistry of plants in a deeper manner. Full article

Phellodendri amurensis Cortex is the dried bark of the cork tree (Phellodendron amurense Rupr.) from the Rutaceae family, and possesses traditional efficacy in clearing heat, drying dampness, purging fire, relieving steaming sensations, detoxifying, and healing sores. Clinically, it is commonly used for treating symptoms such as damp-heat diarrhea and dysentery, jaundice with reddish urine, leukorrhea with vaginal itching, painful and difficult urination due to heat strangury, flaccidity and weakness of the lower limbs, bone-steaming and consumptive fever, night sweats and seminal emission, sores, ulcers, swellings, and toxins, eczema, damp sores, and urinary tract infections. Modern pharmacological studies have further revealed its diverse bioactivities, including antioxidant, antibacterial, anti-inflammatory, immunosuppressive, and anticancer effects. To provide an updated and comprehensive review of the research into Phellodendri amurensis Cortex, this study conducted a thorough literature search and analysis based on databases such as SciFinder, Web of Science, and China National Knowledge Infrastructure. The review integrates information on the plant’s botanical characteristics, geographical distribution, traditional applications, chemical components, quality control methods, and pharmacological effects to present a current and holistic overview of its research status. To date, approximately 170 compounds have been isolated and identified from Phellodendri amurensis Cortex, primarily including alkaloids, phenolics, terpenoids, sterols, lignans, flavonoids, and others. Among these, alkaloids exhibit significant antioxidant and anti-inflammatory activities and demonstrate potential pharmacological value in antibacterial, anticancer, hypoglycemic, and multi-organ protective effects. Although substantial foundational research exists, the mechanisms of action and quality control of Phellodendri amurensis Cortex require further in-depth exploration. Future efforts should focus on clarifying its pharmacodynamic material basis, uncovering new targets and pathways, and improving analytical methods for component analysis and quality control to advance the scientific development and rational utilization of this medicinal material. Full article

The evaluation of the safety of chemical substances requires the identification of a safe dose, which has no adverse effects on humans. This is obtained through animal studies, with exposure prolonged for months. Repeated-dose toxicity is a term in toxicology and pharmacology referring to the highest tested dose of a substance, so-called No Observed Adverse Effect Level (NOAEL). Experimental data on NOAEL taken from the literature and the OpenFoodTox database (total n = 848). To speed up the processing of the enormous number of substances we are exposed to, in silico models are an attractive solution. Monte Carlo technique, incorporating the Las Vegas algorithm, was applied to develop models for repeated-dose toxicity in rats. Optimal descriptors were calculated using correlation weights for attributes of the Simplified Molecular Input Line Entry System (SMILES). Computational experiments were conducted 5 times, with splits obtained using the Las Vegas algorithm. Good predictive potential was observed for these models, with an average determination coefficient on the validation set of 0.77 ± 0.04. Full article

Per- and polyfluoroalkyl substances (PFASs) are persistent endocrine-disrupting chemicals implicated in reproductive dysfunction. Epidemiologic evidence examining their association with endometriosis remains inconsistent. Thus, we conducted a PRISMA-compliant systematic review and meta-analysis using PubMed, Embase, EBSCO Host, and Google Scholar databases. RStudio software was used for all analyses. Random-effects or fixed-effects model was applied to estimate pooled odds ratios (ORs) and standardized mean difference (SMD) in PFAS levels between endometriosis patients and controls. Heterogeneity was assessed using I² statistics. Publication bias was evaluated using funnel plots, and Egger’s and Begg’s tests. Twelve studies met the inclusion criteria for the systematic review and eleven were included in the quantitative synthesis. Overall, PFSAs (OR: 1.50; 95% CI: 1.12–2.00) and PFCAs (OR: 1.46; 95% CI: 1.12–1.90) were significantly associated with increased odds of endometriosis, particularly PFOS and PFOA. However, analyses of pooled SMD did not demonstrate consistent concentration differences between endometriosis cases and controls. Heterogeneity was moderate to high for most compounds. Funnel plot symmetry and Egger’s and Begg’s tests suggest no publication bias. Exposure to PFASs, particularly PFOS and PFOA, may be associated with increased odds of endometriosis. Further prospective studies incorporating mixture modeling and emerging PFASs are warranted. Full article