Search Results (1,089)

The integration of HS-SPME-GC/MS and UPLC-MS/MS techniques enabled the profiling of volatile organic compounds (VOCs) and amino acids (AAs) in 18 crabapple flower cultivars, facilitating the development of a novel VOC–AA model. Among the 51 identified VOCs, benzyl alcohol, benzaldehyde, and ethyl benzoate were predominant, categorizing cultivars into fruit-almond, fruit-sweet, and mixed types. The amino acids, namely glutamic acid (Glu), asparagine (Asn), aspartic acid (Asp), serine (Ser), and alanine (Ala) constituted 83.6% of the total AAs identified. Notably, specific amino acids showed positive correlations with key VOCs, suggesting a metabolic regulatory mechanism. The Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) model, when combined with volatile organic compounds (VOCs) and amino acid profiles, enabled more effective aroma type classification, providing a robust foundation for further studies on aroma mechanisms and targeted breeding. Full article

The early detection of liver cirrhosis (LC) is crucial due to its high morbidity and mortality in advanced stages. Reliable, non-invasive diagnostic tools are essential for timely intervention. Exhaled human breath, reflecting metabolic changes, offers significant potential for disease diagnosis. This paper focuses on the emerging role of sensor array-based volatile organic compounds (VOCs) analysis of exhaled breath, particularly using electronic nose (e-nose) technology to differentiate LC patients from healthy controls (HCs). This study included 55 participants: 27 LC patients and 28 HCs. Sensor’s measurement data were analyzed using machine learning techniques, such as principal component analysis (PCA), discriminant function analysis (DFA), and support vector machines (SVMs) that were utilized to uncover meaningful patterns and facilitate accurate classification of sensor-derived information. The diagnostic accuracy was thoroughly assessed through receiver operating characteristic (ROC) curve analysis, with specific emphasis on assessing sensitivity and specificity metrics. The e-nose effectively distinguished LC from HC, with PCA explaining 92.50% variance and SVMs achieving 100% classification accuracy. This study demonstrates the significant potential of e-nose technology towards VOCs analysis in exhaled breath, as a valuable tool for LC diagnosis. It also explores feature extraction methods and suitable algorithms for effectively distinguishing between LC patients and controls. This research provides a foundation for advancing breath-based diagnostic technologies for early detection and monitoring of liver cirrhosis. Full article

Alzheimer’s disease (AD) is a multi-factorial neurodegenerative disease with a complex pathomechanism that can be best treated with multi-target medications. Among the possible molecular targets involved in AD, acetylcholinesterase (AChE) and monoamine oxidase B (MAO-B) are well recognized because they control the neurotransmitters responsible for memory processes. This review discusses the current understanding of AD pathology, recent advances in AD treatment, and recent reports in the field of dual AChE/MAO-B inhibitors for treating AD. We provide a classification of dual inhibitors based on their chemical structure and describe active compounds belonging to, i.a., chalcones, coumarins, chromones, imines, and hydrazones. Special emphasis is given to the computer-aided strategies of dual inhibitors design, their structure–activity relationships, and their interactions with the molecular targets at the molecular level. Full article

Per- and polyfluoroalkyl substances (PFAS) are of concern because of their potential thyroid hormone system disruption by binding to human transthyretin (hTTR). However, the amount of experimental data is scarce. In this work, new classification and regression QSARs were developed to predict the hTTR disruption based on experimental data measured for 134 PFAS. Bootstrapping, randomization procedures, and external validation were used to check for overfitting, to avoid random correlations, and to evaluate the predictivity of the QSARs, respectively. The best QSARs were characterized by good performances (e.g., training and test accuracies in classification of 0.89 and 0.85, respectively; R², Q²_loo, and Q²_F3 in regression of 0.81, 0.77, and 0.82, respectively) and significantly broader domains compared to the few existing similar models. The application of QSARs application to the OECD List of PFAS allowed for the identification of structural categories of major concern, such as per- and polyfluoroalkyl ether-based, perfluoroalkyl carbonyl, and perfluoroalkane sulfonyl compounds. Forty-nine PFAS showed a stronger binding affinity to hTTR than the natural ligand T4. Uncertainty quantification for each model and prediction further enhanced the reliability assessment of predictions. The implementation of the new QSARs in non-commercial software facilitates their application to support future research efforts and regulatory actions. Full article

An electronic nose is a device capable of characterizing samples of substances and products by their aroma. The development of such devices relies on a series of non-specific sensors that react to gases and generate different signals, which can be used for compound identification and sample classification. The deployment of such devices often requires the possibility of having remote access over the Internet to manage their operation and to collect the sampled data. In this context, the application of web technologies to the monitoring and supervision of these systems connected to the Internet, which can be considered as an Internet of Things (IoT) device, offers the advantage of not requiring the development of client-side applications. Users can employ a browser to connect to the IoT device and monitor or control its operation. Moreover, web design enables the development of cross-platform web monitoring systems. In addition, the inclusion of the MQTT protocol and the utilization of a virtual private network (VPN) enable a secure transmission and collection of the sampled data. In this work, all these technologies have been applied in the development of a system to manage and collect data to monitor rot in lemons treated with sodium benzoate before harvest. Full article

Technology roadmapping is conducted by systematic mapping of technological evolution through patent analytics to inform innovation strategies. This study proposes an integrated framework combining hierarchical Latent Dirichlet Allocation (LDA) modeling with multiphase technology lifecycle theory, analyzing 113,449 Derwent patent abstracts (2008–2022) across three dimensions: technological novelty, functional applications, and competitive advantages. By segmenting innovation stages via logistic growth curve modeling and optimizing topic extraction through perplexity validation, we constructed dynamic technology roadmaps to decode latent evolutionary patterns in AI-powered programmable manipulators (B25J classification) within an innovation trajectory. Key findings revealed: (1) a progressive transition from electromechanical actuation to sensor-integrated architectures, evidenced by 58% compound annual growth in embedded sensing patents; (2) application expansion from industrial automation (72% early stage patents) to precision medical operations, with surgical robotics growing 34% annually since 2018; and (3) continuous advancements in adaptive control algorithms, showing 2.7× growth in reinforcement learning implementations. The methodology integrates quantitative topic modeling (via pyLDAvis visualization and cosine similarity analysis) with qualitative lifecycle theory, addressing the limitations of conventional technology analysis methods by reconciling semantic granularity with temporal dynamics. The results identify core innovation trajectories—precision control, intelligent detection, and medical robotics—while highlighting emerging opportunities in autonomous navigation and human–robot collaboration. This framework provides empirically grounded strategic intelligence for R&D prioritization, cross-industry investment, and policy formulation in Industry 4.0. Full article

The rising demand for freshwater, driven by population growth, economic development, and climate change, necessitates proactive watershed management. This study focuses on prioritizing the watersheds of the Doon Valley in the Indian Himalayan Region (IHR) using geospatial techniques. It involves a detailed morphometric analysis incorporating hydrological and topographical parameters, ranking the watersheds using the compound factor value (CFV), and prioritizing them based on the given CFV. The Doon Valley watersheds exhibit dendritic to parallel drainage patterns and moderate relief. The study identifies the Suswa watershed as the most susceptible, necessitating urgent conservation attempts to mitigate soil erosion and ensure sustainable land use. In contrast, the Song watershed, characterized by steep slopes and high relief, requires targeted management strategies to control rapid runoff and prevent potential flooding. The Asan watershed, with a medium priority classification, also requires intervention to prevent ecological degradation. Prioritization based on the CFV provides a strategic framework for targeted management, offering valuable insights for policymakers and planners. This research supports sustainable watershed management by guiding effective conservation practices and addressing the specific needs of each watershed. Full article

Background/Objectives: Human carbonic anhydrases (hCAs) are metalloenzymes involved in essential physiological processes, and their selective inhibition holds therapeutic potential across a wide range of disorders. However, the high degree of structural similarity among isoforms poses a significant challenge for the design of selective inhibitors. In this work, we present a machine learning (ML)-based platform for the isoform-specific prediction and profiling of small molecules targeting hCA I, II, IX, and XII. Methods: By integrating four molecular representations with four ML algorithms, we built 64 classification models, each extensively optimized and validated. The best-performing models for each isoform were applied in a virtual screening campaign for ~2 million compounds. Results: Following a multi-step refinement process, 12 candidates were identified, purchased, and experimentally tested. Several compounds showed potent inhibitory activity in the nanomolar to submicromolar range, with selectivity profiles across the isoforms. To gain mechanistic insights, SHAP-based feature importance analysis and molecular docking supported by molecular dynamics simulations were employed, highlighting the structural determinants of the predicted activity. Conclusions: This study demonstrates the effectiveness of integrating ML, cheminformatics, and experimental validation to accelerate the discovery of selective carbonic anhydrase inhibitors and provides a generalizable framework for activity profiling across enzyme isoforms. Full article

Background: Informal electronic waste (e-waste) recycling practices release a complex mixture of pollutants, particularly heavy metals, into the environment. Chronic exposure to these contaminants has been linked to a range of health risks, but the molecular underpinnings remain poorly understood. In this study, we investigated the alterations in metabolic profiles due to e-waste exposure and linked these metabolites to systemic biological effects. Methods: We applied untargeted high-resolution metabolomics using dual-column LC-MS/MS and a multi-step analysis workflow combining MS1 feature detection, MS2 annotation, and chemical ontology classification, to characterize urinary metabolic alterations in 91 e-waste workers and 51 community controls associated with the Agbogbloshie site (Accra, Ghana). The impacts of heavy metal exposure in e-waste workers were assessed by establishing linear regression and four-parameter logistic (4PL) models between heavy metal levels and metabolite concentrations. Results: Significant metal-associated metabolomic changes were identified. Both linear and nonlinear models revealed distinct sets of exposure-responsive compounds, highlighting diverse biological responses. Ontology-informed annotation revealed systemic effects on lipid metabolism, oxidative stress pathways, and xenobiotic biotransformation. This study demonstrates how integrating chemical ontology and nonlinear modeling facilitates exposome interpretation in complex environments and provides a scalable template for environmental biomarker discovery. Conclusions: Integrating dose–response modeling and chemical ontology analysis enables robust interpretation of exposomics datasets when direct compound identification is limited. Our findings indicate that e-waste exposure induces systemic metabolic alterations that can underlie health risks and diseases. Full article

The excessive use of synthetic pesticides has not only resulted in increased resistance among weeds and pests, leading to significant economic loss, but has also raised serious health and environmental concerns. Chalcones and their derivatives, known for their herbicidal, fungicidal, bactericidal, and antiviral properties, are emerging as promising bio-based candidates. These naturally occurring compounds have long been recognized for their beneficial health effects and wide-range applications. However, their limited concentration in plants, along with poor solubility and bioavailability, brings challenges for their development. The aim of this study was to examine the properties of a synthetic substance, pinocembrin dihydrochalcone (3-phenyl-1-(2,4,6-trihydroxyphenyl)-1-propanone), including its soil dissipation and adsorption. Additionally, we evaluated its antioxidant activity through the DPPH assay and FRAP experiments. This analysis aims to provide insights into its potential classification as a low risk pesticide. Full article

Raman spectroscopy has become an indispensable analytical technique in pharmaceutical research, offering non-invasive, rapid, and chemically specific insights into pharmaceutical compounds. In this study, we present a comprehensive benchmark of machine learning models for classifying 32 pharmaceutical compounds based on their Raman spectral signatures. A diverse array of algorithms—including Support Vector Machines (SVMs), Random Forests, k-Nearest Neighbors (k-NN), Gradient Boosting (XGBoost, LightGBM), and 1D Convolutional Neural Networks (CNNs)—were evaluated on a publicly available dataset. The results demonstrate outstanding classification performance across models, with linear SVM achieving the highest accuracy of 99.88%, followed closely by CNN (99.26%). Ensemble methods such as Random Forest and XGBoost also yielded high accuracies above 98.3%. In addition to strong predictive performance, SHAP (SHapley Additive exPlanations) analysis was employed to interpret model decisions. CNN models, in particular, revealed well-localized and chemically meaningful spectral regions critical to classification. This combination of high accuracy and interpretability highlights the promise of explainable AI in pharmaceutical analysis and quality control, offering robust, transparent, and scalable solutions for real-world applications. Full article

Background: Biomimetic strategies have gained increasing attention for their ability to enhance the delivery, stability, and functionality of nutraceuticals by emulating natural biological systems. However, the literature remains fragmented, often focusing on isolated technologies without integrating regulatory, predictive, or translational perspectives. Objective: This review aims to provide a comprehensive and multidisciplinary synthesis of biomimetic and bio-inspired nanocarrier strategies for nutraceutical delivery, while identifying critical gaps in standardization, scalability, and clinical translation. Results: We present a structured classification matrix that maps biomimetic delivery systems by material type, target site, and bioactive compound class. In addition, we analyze predictive design tools (e.g., PBPK modeling and AI-based formulation), regulatory frameworks (e.g., EFSA, FDA, and GSRS), and risk-driven strategies as underexplored levers to accelerate innovation. The review also integrates ethical and environmental considerations, and highlights emerging trends such as multifunctional hybrid systems and green synthesis routes. Conclusions: By bridging scientific, technological, and regulatory domains, this review offers a novel conceptual and translational roadmap to guide the next generation of biomimetic nutraceutical delivery systems. It addresses key bottlenecks and proposes integrative strategies to enhance design precision, safety, and scalability. Full article

The insulation layer of flame-retardant cables plays a critical role in mitigating fire hazards by influencing toxic gas emissions and the accuracy of fire modeling. This study systematically explores the pyrolysis kinetics and volatile gas evolution of flame-retardant polyvinyl chloride (PVC) and polyethylene (PE) insulation materials using advanced TG-FTIR-GC/MS techniques. Distinct pyrolysis stages were identified through thermogravimetric analysis (TGA) at heating rates of 10–40 K/min, while the KAS model-free method and Málek fitting function quantified activation energies and reaction mechanisms. Results revealed that flame-retardant PVC undergoes two major stages: (1) dehydrochlorination, characterized by the rapid release of HCl and low activation energy, and (2) main-chain scission, producing aromatic compounds that contribute to fire toxicity. In contrast, flame-retardant PE demonstrates a more stable pyrolysis process dominated by random chain scission and the formation of a dense char layer, significantly enhancing its flame-retardant performance. FTIR and GC/MS analyses further highlighted distinct gas evolution behaviors: PVC primarily generates HCl and aromatic hydrocarbons, whereas PE releases olefins and alkanes with significantly lower toxicity. Additionally, the application of a classification and regression tree (CART) model accurately predicted mass loss behavior under various heating rates, achieving exceptional fitting accuracy (R² > 0.98). This study provides critical insights into the pyrolysis mechanisms of flame-retardant cable insulation and offers a robust data framework for optimizing fire modeling and improving material design. Full article

The authentication of high-value spices such as paprika and cinnamon is critical due to increasing food fraud. This study explored the potential of a multi-analytical approach, combined with chemometric tools, to differentiate 45 paprika and 46 cinnamon samples from the Slovenian market based on their geographic origin, production methods, and possible adulteration. The applied techniques included stable isotope ratio analysis (δ¹³C, δ¹⁵N, δ³⁴S), multi-elemental profiling, FTIR, and antioxidant compound analysis. Distinct isotopic and elemental markers (e.g., δ¹³C, δ³⁴S, Rb, Cs, V, Fe, Al) contributed to classification by geographic origin, with preliminary classification accuracies of 90% for paprika (Hungary, Serbia, Spain) and 89% for cinnamon (Sri Lanka, Madagascar, Indonesia). Organic paprika samples showed higher values of δ¹⁵N, δ³⁴S, and Zn, whereas conventional ones had more Na, Al, V, and Cr. For cinnamon, a 95% discrimination accuracy was achieved between production practice using δ³⁴S and Ba, as well as As, Rb, Na, δ¹³C, S, Mg, Fe, V, Al, and Cu. FTIR differentiated Ceylon from cassia cinnamon and suggested possible paprika adulteration, as indicated by spectral features consistent with oleoresin removal or azo dye addition, although further verification is required. Antioxidant profiling supported quality assessment, although the high antioxidant activity in cassia cinnamon may reflect non-phenolic contributors. Overall, the results demonstrate the promising potential of the applied analytical techniques to support spice authentication. However, further studies on larger, more balanced datasets are essential to validate and generalize these findings. Full article

Chronobiotics represent a pharmacologically diverse group of substances, encompassing both experimental compounds and those utilized in clinical practice, which possess the capacity to modulate the parameters of circadian rhythms. These substances influence fluctuations in various physiological and biochemical processes, including the expression of core “clock” genes in model organisms and cell cultures, as well as the expression of clock-controlled genes. Despite their chemical heterogeneity, chronobiotics share the common ability to alter circadian dynamics. The concept of chronobiotic drugs has been recognized for over five decades, dating back to the discovery and detailed clinical characterization of the hormone melatonin. However, the field remains fragmented, lacking a unified classification system for these pharmacological agents. The current categorizations include natural chrononutrients, synthetic targeted circadian rhythm modulators, hypnotics, and chronobiotic hormones, yet no comprehensive repository of knowledge on chronobiotics exists. Addressing this gap, the development of the world’s first curated and continuously updated database of chronobiotic drugs—circadian rhythm modulators—accessible via the global Internet, represents a critical and timely objective for the fields of chronobiology, chronomedicine, and pharmacoinformatics/bioinformatics. The primary objective of this study is to construct a relational database, ChronobioticsDB, utilizing the Django framework and PostGreSQL as the database management system. The database will be accessible through a dedicated web interface and will be filled in with data on chronobiotics extracted and manually annotated from PubMed, Google Scholar, Scopus, and Web of Science articles. Each entry in the database will comprise a detailed compound card, featuring links to primary data sources, a molecular structure image, the compound’s chemical formula in machine-readable SMILES format, and its name according to IUPAC nomenclature. To enhance the depth and accuracy of the information, the database will be synchronized with external repositories such as ChemSpider, DrugBank, Chembl, ChEBI, Engage, UniProt, and PubChem. This integration will ensure the inclusion of up-to-date and comprehensive data on each chronobiotic. Furthermore, the biological and pharmacological relevance of the database will be augmented through synchronization with additional resources, including the FDA. In cases of overlapping data, compound cards will highlight the unique properties of each chronobiotic, thereby providing a robust and multifaceted resource for researchers and practitioners in the field. Full article