Search Results (5,620)

Background: Inborn errors of metabolism (IEMs) represent a diverse group of genetic disorders affecting biochemical pathways. Despite advances in diagnostic technologies, comprehensive understanding of their historical evolution, classification systems, and diagnostic approaches remains fragmented. Objectives: This systematic review and meta-analysis aimed to synthesize evidence on the historical development, classification frameworks, and diagnostic modalities for IEMs, diagnostic accuracy, and prevalence estimates, providing a comprehensive resource for clinicians and researchers. Methods: Following PRISMA 2020 guidelines, we conducted a systematic search of seven electronic databases (PubMed/MEDLINE, Embase, Scopus, Web of Science, Google Scholar, SciSpace and ArXiv) from January 2000 to March 2026. Studies addressing historical perspectives, classification systems, or diagnostic approaches for IEMs were included. Two independent reviewers performed screening, data extraction, and quality assessment. Meta-analyses were conducted using random-effects models for diagnostic accuracy and prevalence estimates. Results: From 1342 identified records, 54 studies met the inclusion criteria, encompassing 8,234,567 individuals across 35 countries. Historical analysis revealed 16 major milestones from Garrod’s 1902 “chemical individuality” concept to the current AI-powered diagnostics. Four major classification systems were identified: pathophysiological (intoxication, energy deficiency, complex molecule disorders), biochemical pathway (amino acid, organic acid, urea cycle, carbohydrate, fatty acid oxidation, mitochondrial, peroxisomal, lysosomal disorders), organelle-based, and the integrated Society for the Study of Inborn Errors of Metabolism (SSIEM) nosology. Meta-analysis demonstrated high diagnostic performance of tandem mass spectrometry (MS/MS) with a pooled sensitivity of 99.1% (95% CI: 98.6–99.5) and specificity of 99.8% (95% CI: 99.7–99.9%). The pooled global prevalence of IEMs was 50.9 per 100,000 live births (95% CI 45.2–56.8). Next-generation sequencing achieved a diagnostic yield of 42.8% (95% CI: 38.2–47.5%) in suspected cases. Emerging AI-powered diagnostic tools demonstrated high discrimination performance with area under the curve (AUC) values exceeding 0.95 for specific IEM, though external validation remains limited. Newborn screening expanded from single-disease to comprehensive panels detecting over 50 disorders. Conclusions: This comprehensive review demonstrates that IEMs have evolved from rare curiosities to systematically diagnosable conditions through technological advances. Integration of metabolomics, genomics, proteomics and artificial intelligence promises further diagnostic improvements. Standardized classification systems and evidence-based diagnostic algorithms are essential for optimal patient care. Future directions include artificial intelligence-enhanced diagnostics, expanded screening, and personalized medicine approaches. Full article

The growing global demand for energy and rising greenhouse gas emissions require effective mitigation strategies, including carbon capture and storage (CCS) technologies. This study reviews 16 widely used simulation tools, including Aspen Plus, MATLAB, Fluent, and gPROMS, for steady-state and dynamic modeling of post-combustion, pre-combustion, and oxy-fuel combustion carbon capture processes. The tools are evaluated using five criteria: chemical process simulation capability, dynamic modeling functionality, thermodynamic property management, heat transfer accuracy, and tool integration features. The results reveal distinct strengths across platforms. Aspen Plus and Aspen Plus Dynamics perform strongly in chemical process simulation and thermodynamic property modeling, reflecting their robustness in reaction modeling and property estimation. gPROMS excels in dynamic modeling, demonstrating strong capability for time-dependent and transient process analysis. MATLAB achieves the highest score in tool integration, highlighting its flexibility in coupling with optimization solvers, control systems, and external programming environments. Fluent shows strong performance in heat transfer modeling, particularly for detailed thermal analysis in oxy-fuel combustion systems. Most existing studies focus on individual carbon capture technologies rather than simulation tool capabilities. Following the PRISMA 2020 guidelines, a systematic search of Scopus yielded 53 peer-reviewed papers on CCS simulation, which were analyzed to identify dominant tools and inform the AHP-based evaluation. This work addresses that gap by clarifying tool-specific advantages, supporting informed model selection to improve the efficiency and sustainability of CCS process design. Full article

In casting processes, mechanical properties such as hardness are highly sensitive to both chemical composition and process parameters, making parameter design a complex and uncertain task during the embodiment stage of engineering design. Conventional trial-and-error-based approaches are often costly, time-consuming, and impractical in industrial environments. To address these challenges, this study proposes an optimized fuzzy artificial neural network (FANN)-based decision-support approach for hardness-oriented parameter design in a casting process. The developed model uses chemical composition variables, including carbon, silicon, manganese, phosphorus, sulfur, chromium, copper, and tin, together with process parameters such as casting temperature and casting time as inputs, while Brinell hardness is considered as the output. A dataset consisting of 170 experimental casting samples was employed; 128 samples were used for model development and hyperparameter selection, and 42 samples were reserved as an independent final test set. The proposed model was implemented as a scaled direct FANN weighted ensemble, in which fuzzified input variables were used to predict standardized continuous hardness values. A total of 300 FANN configurations were evaluated using five-fold cross-validation, and the five best-performing configurations were combined through RMSE-based weighted ensemble averaging. The final model was compared with Random Forest, Linear Regression, Ridge Regression, and SVR-RBF models using MSE, RMSE, MAE, R², MAPE, normalized RMSE, and ±5% prediction success rate. The results showed that the optimized FANN ensemble achieved the lowest mean RMSE in the full-data five-fold cross-validation analysis, slightly outperforming the Random Forest benchmark. In the independent final test set, Random Forest produced the lowest prediction error, whereas the proposed FANN ensemble remained competitive and achieved the same ±5% prediction success rate as Random Forest, Linear Regression, and Ridge Regression. Furthermore, a target-hardness case study demonstrated that the proposed approach could identify candidate casting conditions very close to a desired hardness level, with the nearest prediction reaching 202.985 HB for a target value of 203 HB. These findings indicate that the proposed FANN-based framework can serve not only as a hardness prediction model but also as a practical fuzzy decision-support tool for target-hardness-oriented parameter design in casting processes. Full article

The Irish Association for Cancer Research (IACR) Annual Conference 2025, held in Belfast, showcased cutting-edge developments across the cancer research landscape. This report summarizes key presentations, highlighting innovations in drug delivery, exercise interventions, artificial intelligence and computational biology, cancer stem cell plasticity, and translational research approaches. The meeting emphasised mechanistically informed strategies, multi-modal therapies, and the integration of patient-relevant models to improve therapeutic outcomes. The sessions collectively underscored the importance of combining biological, chemical, and physical approaches, as well as emerging tools in precision oncology, to address therapeutic resistance and enhance patient care. Full article

The commercialization of lithium metal batteries, a key technology for high-density energy storage, is hindered by issues with coulombic efficiency, which dictates battery stability and life. In this paper, we propose a machine learning framework to forecast liquid electrolyte efficiency, where two experimental data sources were combined to create a curated dataset of 283 records. In addition, to assess several ensemble learning algorithms, thirteen chemical descriptors were used, as well as interpretability analysis and Bayesian optimization to guarantee physicochemical consistency. We found that the optimized CatBoost model achieved a coefficient of determination (R2) of 0.61 on the test set and a mean squared error (MSE) of 0.0924, representing a significant improvement in predictive accuracy compared to previous standards. Furthermore, these results demonstrate that regulating oxygen levels in solvent environments is a key component of high-density energy storage. These results can serve as a virtual screening tool in order to discover high-performance electrolytes with the minimum experimental costs. Full article

Background: Confident chemical annotation in nontarget small-molecule mass spectrometry critically depends on the availability of high-quality tandem mass spectral (MS²) reference libraries. While community efforts have driven significant expansion of open-access repositories, technical challenges in assembling standardized, metadata-rich records continue to limit broader participation, underscoring the need for improved computational tools to assist contributors. Methods: To promote the creation and sharing of standardized reference MS² spectral records, we have developed Librarian, a free, open-access web application designed for rapid and scalable assembly of high-resolution MS² libraries. Librarian integrates automated retrieval and harmonization of chemical identifiers and metadata from PubChem, compound mixture design for high-resolution mass spectrometry (HRMS) acquisition, and assembly of curated MS² spectra into repository-ready records compatible with public spectral databases. Results: Through a simple in-browser interface, Librarian offers a flexible end-to-end workflow compatible with popular open-source pre-processing tools to lower technical barriers and facilitate broader community participation in library development. As a demonstration, we used Librarian to create and deposit a spectral library comprising over 1500 new MS² records into MassBank, which was further applied in retrospective analysis of environmental datasets. Conclusions: Librarian streamlines the creation of standardized, metadata-rich and repository-ready MS² reference records. Addressing a key bottleneck in community spectral library development and sharing, Librarian supports the continued growth of open-access resources for metabolomics, exposomics, and environmental mass spectrometry. The Librarian web application is publicly accessible via the SciLifeLab Serve platform. Full article

Salmonella remains one of the most critical zoonotic pathogens in the poultry sector, linked to animal disease, foodborne illness, and the global crisis of antimicrobial resistance (AMR). Poultry acts as a major reservoir, enabling Salmonella transmission from hatchery to retail products through horizontal, vertical, and environmental routes. Despite the use of biosecurity, vaccination, antibiotics, and chemical decontamination, effective and sustainable control across the poultry value chain remains difficult, particularly in the face of rising multidrug-resistant strains and growing consumer concerns over chemical residues. Bacteriophages (phages), viruses that selectively infect and lyse bacteria, have emerged as a promising biological alternative for Salmonella control. Although many studies have reported the effectiveness of phages against bacterial species, including Salmonella, in the poultry industry, reports on their full potential to combat antimicrobial-resistant Salmonella across the entire poultry value chain remain limited. Therefore, this review synthesizes current evidence on the application of phages throughout the poultry value chain, including on-farm interventions, processing plant decontamination, and food packaging and storage. Findings from the reviewed articles indicate over a 90% reduction in Salmonella spp. in poultry farms and post-harvest meat, along with lower mortality in phage-treated groups compared to untreated groups; however, these outcomes depend on several factors (e.g., phage strains, concentrations, application methods, and environmental conditions). Laboratory, pilot, and field studies consistently demonstrate that phage preparations, especially when formulated as cocktails or combined with complementary interventions, can achieve substantial reductions in Salmonella, including antibiotic-resistant serovars, in live birds, eggs, poultry environments, and meat products. Unlike antibiotics and chemical sanitizers, phages act with high specificity, preserving beneficial microbiota and maintaining the sensory and nutritional quality of poultry products. Their safety has been supported by toxicological and genomic assessments, and several phage-based products have obtained regulatory approval, including Generally Recognized as Safe (GRAS) status for food applications in the United States. By integrating efficacy, safety, regulatory, and practical deployment data, this review highlights bacteriophages as a scientifically validated and One Health–aligned tool capable of reducing Salmonella transmission from farm to fork across the poultry value chain, thereby laying the foundation for their future adoption in the poultry industry. Phage-based interventions offer a sustainable pathway to enhance food safety, limit antimicrobial resistance (AMR) dissemination, and strengthen consumer confidence in poultry products. However, the major limitation is the emergence of phage-resistant bacterial strains, as well as the potential involvement of some phages in the transfer of resistance and virulence genes, which could raise public concern. Nevertheless, the use of phage cocktails and whole-genome sequencing, involving tools such as ResFinder and virulence finder, can facilitate the selection of safe phages for application. Full article

Aqueous precipitation and crystallization are central to impurity removal, product formation, and resource recovery in mineral and chemical processing, but robust inline monitoring remains challenging because supersaturation is not measured directly and conductivity signals are affected by temperature, composition drift, bubbles, solids, polarization, and fouling. Electrical conductivity (EC) is attractive as a low-cost, rugged process analytical tool, yet its usefulness depends on mechanistic interpretation: EC reflects charge-carrier concentration and mobility rather than supersaturation itself. This review organizes the literature into a layered framework covering (i) measurement integrity and deployment, (ii) bulk-signal extraction in multiphase media, (iii) estimation of latent variables such as dissolved concentration or supersaturation proxies, and (iv) control readiness based on conductivity-derived targets. Frequency-aware conductivity extraction, event-anchored verification, and observer-based estimation are treated as optional, complementary modules. A Ca-carbonate/CaCO₃ system is used as an illustrative case because its coupling among conductivity, pH/speciation, supersaturation, and precipitation is especially transparent, although the framework is intended for broader processing systems, including complex liquors and slurries. Opportunities are also highlighted for nanomaterials to improve both precipitation control and EC information content. Full article

The Nuri deposit is currently the only Cu-W-Mo deposit in the Gangdese metallogenic belt, Tibet, China, that contains large-scale tonnages for both Cu and WO₃ resources, accompanied by a medium-scale Mo resources. Previous studies have suggested the potential presence of concealed porphyry-type orebodies at depth, yet effective exploration tools for verifying this hypothesis remain lacking. In this study, microscopic identification, electron probe microanalysis (EPMA), and laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) were integrated to investigate the mineral chemical characteristics of chlorite from the Nuri deposit. The aim was to evaluate the effectiveness of chlorite geochemistry as an exploration vector for predicting deep concealed porphyry orebodies and to establish corresponding exploration indicators. Chlorite in the deposit can be genetically classified into metasomatic (Chl-I) and hydrothermal (Chl-II) types. Both types are Mg-rich varieties, indicating formation under conditions of low oxygen fugacity and low pH. With decreasing vertical distance to the orebody and toward the southeast direction of the exploration section, the contents of Ti (10–950 ppm) and V (50–820 ppm), as well as the Ti/Sr, Ti/Mn, Ti/Li, and V/Li ratios, progressively increase. In contrast, the concentrations of Li (36–390 ppm), Mn (1270–6730 ppm), Sr (1–510 ppm), and Zn (110–1100 ppm) systematically decrease. These systematic compositional variations demonstrate that chlorite geochemistry is an effective exploration tool in the Nuri mining area and suggest the presence of a concealed mineralization center or porphyry orebody beneath the interval from ZK4501 to ZK4502. Full article

The demand for sustainable, high-performance biomaterials has driven intense research towards natural polysaccharide hydrogels. Accordingly, this study aimed to synthesize novel starch-based hydrogel materials, considering their inherent hydrogel-forming capabilities together with diverse potential applications (e.g., pharmaceuticals, medicine, and the cleaning application for the artifacts). To obtain hydrogels with enhanced mechanical and physico-chemical properties, starch was combined with other polymeric species (i.e., alginate, polyvinyl alcohol, and polyvinylpyrrolidone), and a gelling process was induced by using calcium cations or borate anions. Two distinct hydrogels (named S-Ca and S-SB, respectively) were prepared and characterized by a range of instrumental and experimental techniques. The assessed properties included water and solvent resistance, equilibrium water content, water-releasing capacity, morphology and microstructural features with their composition by SEM-EDS analysis, and mechanical properties (tensile strength, elasticity, Young’s modulus, and hardness). The results indicated that the investigated hydrogels exhibited suitable properties for a variety of applications, including surface cleaning processes in the field of cultural heritage conservation. For instance, they showed equilibrium water content (between 80 and 90%) comparable with other hydrogels commonly used as cleaning tools (e.g., agar and p(HEMA)/PVP) and quite low water-releasing capacity (between 10 and 17 mgcm⁻²). Moreover, the S-SB hydrogel displayed distinctly better tensile strength and elongation at break than hydrogel prepared in the presence of Ca²⁺ (S-Ca). Notably, S-SB experienced considerable elasticity improvement after freezing–thawing cycles, as indicated by a decrease in tensile strength (from 275 to 102 kPa) and an increase in elongation at break (from 121 to 275%). However, it should be noted that the hydrogel selection depends on the requirements of the target application, as different processes demand materials with distinct characteristics. Hence, both S-Ca and S-SB hydrogels were tested as cleaning tools for the removal of artificially aged acrylic coating (i.e., Paraloid B-72) from the surface of marble and wood specimens, respectively. The tests provided positive results, as aged coating was satisfactorily removed by applying the hydrogels loaded with a nanostructured emulsion (NSE). These novel starch-based hydrogels demonstrate significant potential as high-performance alternatives to conventional hydrogel systems currently used in conservation science as well as in other industrial applications. Full article

Blast furnace (BF) ironmaking is a complex multiphase process involving gas–solid flow, heat transfer, chemical reactions, burden movement, and phase transformation under high-temperature conditions. Since many internal states of the blast furnace cannot be directly observed during operation, numerical simulation and mathematical modeling have become important tools for understanding furnace behavior and optimizing operational parameters. This paper reviews recent advances in blast furnace numerical simulation and internal state reconstruction methods. Existing approaches, including packed-bed flow models, cohesive zone reconstruction methods, burden distribution models, and temperature field prediction methods, are summarized and discussed. In addition, the evolution of blast furnace mathematical models from early one-dimensional steady-state formulations to modern three-dimensional multifluid and hybrid simulation approaches is reviewed. Recent developments in computational fluid dynamics (CFD), the discrete element method (DEM), digital twin, and data-driven modeling are also discussed. Compared with traditional simplified models, modern multidimensional and hybrid approaches show improved capability in describing asymmetric furnace inner states, multiphase transport behavior, and operational parameter effects under industrial conditions. However, challenges still remain in achieving computational efficiency, parameter calibration, multiphase coupling, and real-time industrial application. Future studies are expected to focus on the integration of mechanism-based simulation and intelligent data-driven methods to improve prediction accuracy, operational adaptability, and intelligent control capability in blast furnace ironmaking. Full article

Chemical hybridization agents (CHAs) enable efficient, large-scale hybrid seed production, yet their mechanisms remain poorly understood. Understanding how CHAs induce male sterility at the metabolic level is important for both basic pollen biology and crop breeding. Here, we performed integrated metabolomic analyses to investigate the metabolic basis of the action of trifluoromethanesulfonamide (TFMSA) across multiple species and tissues. TFMSA treatment induced systemic metabolic reprogramming across species, prominently affecting amino acid metabolism, central carbon metabolism, and one-carbon metabolism. Although individual metabolite responses varied among species, pathway-level analyses consistently revealed coordinated modulation of carbon–nitrogen metabolic networks. In reproductive tissues, TFMSA induced tissue-specific metabolic changes. In cowpea anthers, proline was the only metabolite significantly altered and was strongly depleted, whereas in floral tissues several amino acids, including phenylalanine and tyrosine, were accumulated. Pathway analysis revealed altered amino acid metabolism, suggesting that systemic metabolic responses accompanied the proline reduction in anthers. These findings indicate that TFMSA induces male sterility through coordinated metabolic reprogramming across tissues and species, leading to depletion of key metabolites required for pollen development. This study provides a metabolic framework for understanding CHA-induced male sterility and highlights TFMSA as a powerful tool for probing metabolic regulation of pollen development. Full article

Optimizing harvest time and oil production requires accurate olive fruit quality characterization. Traditional chemical methods are costly and tedious, leading to poor monitoring resolution and reliance on subjective visual assessments. While spectroscopy offers a non-destructive alternative, standard equipment remains complex and prohibitively expensive for smallholder farmers. To address this, we propose a methodology using a custom-made, low-cost multispectral device. Built upon the AS7265x board, the system acquires 18 spectral bands in the visible and near-infrared range (410–940 nm). We used these spectral data to feed artificial neural network (ANN) models for estimating the quality of intact olives. During a two-season field experiment, we monitored ripening to acquire spectral signatures and ground-truth values for oil content per fresh weight (OCFW), oil content per dry matter (OCDM), moisture (M), and titratable acidity (TA). External validation showed high accuracy for OCFW (R²p = 0.86), OCDM (R²p = 0.86), and M (R²p = 0.89), proving the system’s reliability. However, TA estimation showed lower performance (R²p = 0.21), indicating limited spectral correlation. These findings pave the way for affordable, real-time smart farming tools for olive quality monitoring. Full article

This review examines the emerging role of machine learning (ML) in pharmaceutical chemistry, with emphasis on molecular design, synthetic feasibility, and structure–property–performance (SPP) relationships. By enabling pre-synthesis prediction of physicochemical properties, reaction pathways, and pharmaceutical performance, ML can reduce empirical trial-and-error experimentation and support more efficient exploration of chemical space. A structured narrative review design with PRISMA-aligned systematic search elements was used to evaluate 101 studies, enabling transparent literature identification, eligibility screening, and thematic synthesis across heterogeneous ML applications in pharmaceutical chemistry. This review examines structure–property relationships (SPRs) and property–performance relationships (PPRs), with emphasis on key pharmaceutical endpoints such as solubility, permeability, stability, dissolution, and bioavailability. An integrated SPP framework is proposed to connect molecular structure, intermediate properties, and final performance outcomes while incorporating retrosynthetic analysis and experimental feedback and closed-loop optimization. Recent frontier developments are also discussed, including molecular foundation models, multimodal language–graph models, diffusion-based molecular generation, E(3)-equivariant models, and MolMIM-like latent-space optimization. This review also covers co-folding and joint ligand–protein modeling, Boltz-2-like affinity prediction, AlphaFold 3-related biomolecular interaction modeling, and absorption, distribution, metabolism, excretion, and toxicity (ADMET) prediction. Key limitations include dataset leakage, benchmark inconsistency, assay variability, conformational and protonation-state effects, reproducibility challenges, regulatory constraints, and the gap between computational prediction and prospective experimental validation. Future progress is expected to depend on hybrid physics–ML models, uncertainty-aware prospective validation, autonomous experimentation, explainable artificial intelligence, and sustainability-aware molecular design. Overall, ML is evolving from a predictive tool into a chemically informed decision-support framework for rational, synthesis-aware, and experimentally validated pharmaceutical development. Full article

Introduction: The temperature of rivers in the Iberian Peninsula has increased due to global warming. In addition, these rivers are polluted by contaminants of emerging concern, such as polycyclic aromatic hydrocarbons (PAHs). Higher temperatures and pollution concurrently impose threats to the Iberian Peninsula’s endemic species, including the brown trout (Salmo trutta), a cold-water species widely used in ecotoxicological studies. Because the liver is the main biotransformation organ, and is particularly sensitive to both chemical and temperature changes, in vitro liver models may represent valuable alternatives for assessing combined stressor effects, complying with the 3Rs principle. Objective: In line with the above, the present study aimed to evaluate the combined effects of a 4 °C temperature increase and the model PAH benzo[k]fluoranthene (B[k]F) on fish liver cells using a primary brown trout hepatocyte culture as a model. Methodology: Primary hepatocytes were seeded in 6-well plates at a density of 1.0 × 10⁶ cells/mL and exposed for 48 h to 1, 10, and 20 µM B[k]F at 18 °C (normothermia) and 22 °C (warming scenario). Cell viability was assessed using trypan blue, alamarBlue, and lactate dehydrogenase (LDH) assays. Cytochrome P450 (CYP)1A was evaluated in terms of its gene expression by RT-qPCR and its protein expression through immunocytochemistry (ICC). The immunostaining was quantified using a score system which considered five intensity staining levels. Results: Exposure to B[k]F and to the higher temperature increased LDH leakage without interaction effects. In contrast, the other viability assays did not show significant differences across conditions. Regarding CYP1A, both gene and protein expression increased with all B[k]F concentrations in relation to the controls, but were not influenced by temperature. Notably, the lowest B[k]F concentration (1 µM) elicited the highest CYP1A gene expression, suggesting a non-monotonic response. Conclusions: Overall, the model was responsive to both temperature (4 °C) increase and to B[k]F, validating its usefulness for assessing liver pollutant effects in the context of global warming. These findings support the application of fish primary hepatocyte models as relevant tools in ecotoxicology under environmentally realistic multi-stressor scenarios. Full article