Search Results (848)

Background: Clear aligners have become a common alternative to fixed appliances for tooth movement, and thermoplastic retainers hold the outcome. The prolonged intraoral contact of these devices has made the materials a focus of biocompatibility research. Objectives: This paper aims to summarize laboratory evidence on the biocompatibility of clear aligners and thermoplastic retainers. Materials included thermoformed polyethylene terephthalate glycol-modified (PETG), multilayer polyurethane, and directly printed resins. Primary outcomes were cytotoxicity, endocrine activity, and chemical or particle release. Methods: We systematically searched PubMed, the Cochrane Library, and Google Scholar through 31 May 2025, and we followed the PRISMA 2020 statement (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). We applied predefined eligibility criteria. Two reviewers screened records and extracted data in duplicate, including study design, extraction conditions, surface-area-to-volume ratio (SA/V), cell models, endpoints, and analytical sensitivity as the limit of detection (LOD) and limit of quantification (LOQ). We assessed the risk of bias across seven domains and graded certainty by outcome. The Supplementary File provides full search strategies, data, and the extraction workbook. We did not register a protocol prospectively. Results: Seventeen studies met the inclusion criteria. Materials spanned multilayer polyurethanes (SmartTrack, Clarity), PETG sheets (Essix ACE, Duran), and directly printed resins (Graphy TC-85DAC); a subset tested zinc-oxide (ZnO) nanoparticle coatings. Typical extractions immersed 0.1–1 g of material in cell-culture medium or artificial saliva at 37 °C for 24 h to 30 days. Cell viability usually remained ≥80%. Mild cytotoxicity (about 60–70% viability) appeared with harsher extractions, extended soaks, or an inadequate post-curing of printed parts. The estrogen-sensitive proliferation assay (E-Screen) returned negative results. In saliva-like media, bisphenol A (BPA) and related leachables were undetectable or in the low ng/mL range. In printed resins, urethane dimethacrylate (UDMA) sometimes appeared in water extracts, and amounts varied with curing quality. Evidence for chemical leaching and endocrine outcomes is sparse. We found no eligible in vitro study that quantified particle or microplastic release while also measuring a biological endpoint; we discuss particle findings from mechanical wear simulations only as the external context. Limitations: The evidence base is limited to in vitro studies. Many reports incompletely described extraction ratios and processing parameters. Risk of bias and certainty: Most studies used appropriate cell models and controls, but the reporting of surface-area-to-volume ratios, LOD/LOQ, and detailed post-processing parameters was often incomplete. Sample sizes were small, and dynamic wear or enzymatic conditions were uncommon. The overall risk of bias was moderate, and the certainty of evidence was low to moderate due to heterogeneity and in vitro indirectness. Conclusions: Under standard laboratory conditions, clear aligners and thermoplastic retainers show a favorable biocompatibility profile. For printed resins, outcomes depend mainly on processing quality, especially thorough washing and appropriate light-curing parameters. To improve comparability and support clinical translation, we recommend harmonized test protocols, transparent reporting, interlaboratory ring trials, and targeted clinical biomonitoring. Full article

The ever-increasing costs of in vitro and in vivo testing are compelling scientists to increasingly rely on computational models for predictive characterisation at early stages of drug discovery and development. The complexity of this stage requires high-throughput screening methods that can rapidly generate comprehensive information about new chemical compounds. This review explores innovative approaches assessing pharmacokinetic and pharmacodynamic properties of new chemical entities, with a focus on integrating machine learning as a transformative analytical tool. Machine learning algorithms are highlighted for their capability to train sufficient predictors combining biomimetic chromatography data (a high-throughput alternative for several physicochemical assays) with molecular features and/or molecular fingerprints obtained in silico and in vivo data of known compounds to allow efficient prediction of in vivo data for new chemical entities. By synthesising recent methodological advancements and giving useful practical approaches, the review provides insights into computational strategies that can significantly accelerate compound library screening and drug development processes. Full article

The growing threat of airborne biological agents necessitates rapid, sensitive, and portable detection systems to mitigate risks to public health and national security. We present a comprehensive overview of biosensor technologies developed for airborne biothreat detection, with a focus on aptamer-based electrochemical sensors. These sensors offer key advantages in portability, chemical stability, and adaptability for multiplexed detection in field settings. The urgency for real-time surveillance tools capable of identifying viral, bacterial, and toxin-based agents is discussed, particularly in the context of biodefense. Aerosolized particle capture strategies are reviewed, focusing on microfluidics for micron-sized particles and condensation-based systems for submicron-sized particles, which are preferred for their small-volume operation and seamless integration with biosensors. Key biosensor components are described, including recognition elements—such as aptamers—and transduction mechanisms like electrochemical impedance spectroscopy. EIS is highlighted for its label-free, miniaturizable, and real-time readout capabilities, making it well-suited for portable biosensors. Advances in sensing strategies for both viral and bacterial targets are explored, featuring innovations in nanoporous membrane platforms, nanomaterials, and multiplexed assay formats. Recent developments demonstrate improved sensitivity through nanopore-based signal amplification and enhanced selectivity using engineered aptamer libraries. The review concludes by addressing current limitations, including environmental stability, system integration, and the need for validation with complex real-world samples. Future directions point toward the development of fully integrated, field-deployable biosensing platforms that combine effective aerosol capture with robust and selective biosensing technologies. Full article

Objectives: Female fertility is increasingly threatened by environmental pollutants such as fine particulate matter (PM_2.5 and NO₂), endocrine-disrupting chemicals (BPA, phthalates, PFAS, and PCBs), and microplastics. These exposures are associated with impaired ovarian reserve, reduced implantation rates, and lower assisted reproductive technology (ART) success. Given the rising prevalence of obesity and weight-loss interventions, particularly bariatric surgery, understanding the combined influence of metabolic and environmental factors on reproductive outcomes is of critical importance. This review aimed to synthesize recent evidence on how these exposures interact to affect female fertility. Methods: A narrative review was conducted of studies published between 2019 and 2025 using PubMed, Google Scholar, Web of Science, and Wiley Online Library. The PubMed Boolean search string was “female fertility”, “ovarian function”, “IVF” and “pollution”, “endocrine disruptors”, “air pollutants”, and “microplastics”. Searches were limited to English language publications, with the last search performed on 30 March 2025. Human, animal, and in vitro data were screened separately. Human evidence was prioritized, and confounding factors (age, BMI, and smoking) were considered during interpretation. Results: Environmental pollutants were consistently associated with diminished ovarian reserve, poor oocyte quality, and reduced live birth rates in ART. PFAS exposure correlated with lower fecundability, while PM_2.5 and NO₂ were linked to decreased AMH and AFC levels. Mechanistic animal and in vitro studies support these findings through pathways involving oxidative stress, endocrine disruption, and epigenetic alterations. Rapid metabolic changes, particularly post-bariatric surgery, may transiently increase circulating lipophilic toxicants and reduce antioxidant defenses, amplifying reproductive vulnerability. Conclusions: Environmental exposures, especially PM_2.5, NO₂, PFAS, and microplastics, adversely influence ovarian and embryonic competence. Rapid metabolic transitions may further modulate this susceptibility through pollutant mobilization and micronutrient imbalances. Future interdisciplinary prospective studies integrating exposure monitoring, metabolic profiling, and reproductive endpoints are essential to guide clinical recommendations and precision fertility counseling. Full article

Crown gall disease, caused by soil-borne bacterial pathogens, such as Allorhizobium vitis, poses a significant threat to grapevine cultivation in Japan, particularly under environmental conditions exacerbated by climate change. Effective chemical control options are limited, highlighting the need for sustainable biocontrol strategies. In this study, we screened a library of soil bacteria with known antagonistic activity against major grapevine fungal pathogens and identified Pseudomonas sp. strain YU44 as a broad-spectrum antagonist of crown gall pathogens A. vitis and Rhizobium radiobacter. In vitro assays demonstrated that YU44 inhibits the growth of both pathogens by secreting bioactive compounds. In vivo bioassays confirmed that pretreatment with YU44 significantly suppresses crown gall formation in grapevine and rose seedlings. Additionally, YU44 application to soil near the stem base reduces disease severity in grapevine seedlings, supporting its potential as a practical biocontrol agent. Although complete disease suppression is not achieved, YU44 represents a promising environmentally friendly alternative for integrated disease management because it can complement resistant rootstocks, sanitation practices, and cultivation methods. These findings highlight YU44’s potential as an adaptive management tool for crown gall disease in the face of climate change. Full article

Background/Objectives: The global rise in antibiotic resistance necessitates the development of novel antimicrobial agents. Antimicrobial peptides (AMPs), key components of innate immunity, are promising candidates. This study aimed to develop novel therapeutic peptides with enhanced properties through the mutagenesis of natural AMPs and high-throughput screening. Methods: We constructed mutant libraries of three broad-spectrum AMPs—melittin, cecropin, and Hm-AMP2—using mutagenesis with partially degenerate oligonucleotides. Libraries were expressed in Escherichia coli, and antimicrobial activity was assessed through bacterial growth kinetics and droplet serial dilution assays. Candidate molecules were identified by DNA sequencing, and the most promising variants were chemically synthesized. Antimicrobial activity was determined by minimal inhibitory concentration (MIC) against E. coli and Bacillus subtilis, while cytotoxicity was evaluated in human Expi293F cells (IC₉₀) viability. The therapeutic index was calculated as the ratio of an AMP’s cytotoxic concentration to its effective antimicrobial concentration. Results: Mutant forms of melittin (MR1P7, MR1P8) showed significantly reduced cytotoxicity while retaining antimicrobial activity. Cecropin mutants exhibited reduced efficacy against E. coli, but variants CR2P2, CR2P7, and CR2P8 gained activity against Gram-positive bacteria. Mutagenesis of Hm-AMP2 generally decreased activity against E. coli, though two variants (A2R1P5 and A2R3P6) showed retained or enhanced efficacy against B. subtilis while maintaining low cytotoxicity. Conclusions: The proposed strategy successfully generated peptides with improved therapeutic profiles, including reduced toxicity or a broader spectrum of antimicrobial activity, despite not improving all parameters. This approach enables the discovery of novel bioactive peptides to combat antibiotic-resistant pathogens. Full article

The human Ccr4–Not complex is a central regulator of post-transcriptional gene regulation, impacting on translation and mRNA degradation. In mRNA degradation, Ccr4–Not participates in the shortening of the mRNA poly(A)-tail via two catalytic subunits. The Caf1 nuclease is encoded by the highly similar paralogues CNOT7 or CNOT8. In addition to its poly(A)-specific ribonuclease activity, this subunit also provides a structural role by binding Ccr4, the second catalytic nuclease subunit encoded by the paralogues CNOT6 or CNOT6L. To facilitate investigations into the roles of the Caf1 subunit, and to complement genetic tools, we set out to identify inhibitors of the enzymatic activity of Caf1/CNOT7. To this end, we screened a library of 10,880 chemically diverse, drug-like compounds using a fluorescence-based biochemical assay. This effort led to the discovery of 15 inhibitors of Caf1/CNOT7 with biochemical IC₅₀ values below 25 μM. Molecular docking was performed to explore potential binding modes of these compounds. The compounds reported here may be useful to differentiate between catalytic and non-catalytic roles of Caf1/CNOT7. In addition, they may be valuable starting points for the development of more potent inhibitors of the Caf1/CNOT7 poly(A)-selective ribonuclease. Full article

Type 2 diabetes mellitus (T2DM) is a growing global health challenge, expected to affect over 600 million people by 2045. The discovery of new antidiabetic agents remains resource-intensive, motivating the use of machine learning (ML) for virtual screening based on molecular structure. In this study, we developed a predictive pipeline integrating two distinct descriptor types: high-dimensional numerical features from the Mordred library (>1800 2D/3D descriptors) and categorical ontological annotations from the ClassyFire and ChEBI systems. These encode hierarchical chemical classifications and functional group labels. The dataset included 45 active compounds and thousands of inactive molecules, depending on the descriptor system. To address class imbalance, we applied SMOTE and created balanced training and test sets while preserving independent validation sets. Thirteen ML models—including regression, SVM, naive Bayes, decision trees, ensemble methods, and others—were trained using stratified 12-fold cross-validation and evaluated across training, test, and validation. Ridge Regression showed the best generalization (MCC = 0.814), with Gradient Boosting following (MCC = 0.570). Feature importance analysis highlighted the complementary nature of the descriptors: Ridge Regression emphasized ClassyFire taxonomies such as CHEMONTID:0000229 and CHEBI:35622, while Mordred-based models (e.g., Random Forest) prioritized structural and electronic features like MAXsssCH and ETA_dEpsilon_D. This study is the first to systematically integrate and compare structural and ontological descriptors for antidiabetic compound prediction. The framework offers a scalable and interpretable approach to virtual screening and can be extended to other therapeutic domains to accelerate early-stage drug discovery. Full article

The primary objective of this study is to develop a machine learning-based predictive model using corrosion rate data for magnesium alloys compiled from the literature. Corrosion rates measured under different deformation rates and heat treatment parameters were analyzed using artificial intelligence algorithms. Variables such as chemical composition, heat treatment temperature and time, deformation state, pH, test method, and test duration were used as inputs in the dataset. Various regression algorithms were compared with the PyCaret AutoML library, and the models with the highest accuracy scores were analyzed with Gradient Extra Trees and AdaBoost regression methods. The findings of this study demonstrate that modelling corrosion behaviour by integrating chemical composition with experimental conditions and processing parameters substantially enhances predictive accuracy. The regression models, developed using the PyCaret library, achieved high accuracy scores, producing corrosion rate predictions that are remarkably consistent with experimental values reported in the literature. Detailed tables and figures confirm that the most influential factors governing corrosion were successfully identified, providing valuable insights into the underlying mechanisms. These results highlight the potential of AI-assisted decision systems as powerful tools for material selection and experimental design, and, when supported by larger databases, for predicting the corrosion life of magnesium alloys and guiding the development of new alloys. Full article

In this study, six club raspberry varieties were examined for their aromatic profiles and sensory qualities, and statistical approaches were used to determine how aroma components affect consumer impressions. Analysis of the aroma’s chemical composition was performed utilizing headspace SPME and GC-MS. MS-DIAL -v5.5.250627 software was used to identify components from commercial libraries, after 10 repetitions for each variety, followed by manual verification. A sensory evaluation of fresh fruits, with 55 volunteers, was statistically analyzed and linked to chemical composition using multivariate analysis and the OPLS-HDA classification method, which was employed for the first time. Tula Magic was scored the highest in the sensory evaluation compared to Adelita, Himbo Top, Glen Dee, San Rafael, and Cascade Harvest. 2-Heptanol (fresh, lemongrass-like, herbal, floral, fruity, green), heptanal (fresh, aldehydic, fatty, green, herbal), and 2-methyl-6-hepten-1-ol (oily-green, herbaceous-citrusy) separated Tula Magic from the other varieties assessed. The same components were recognized in OPLS as positive contributors to the flavor score, while terpenoids like trans-β-ionone, α-ionone, and α,β-dihydro-β-ionone, as well as 2-heptanone, scored slightly lower. This suggests that a fine balance between the individual components is key to the overall aroma sensation. Full article

Wetting behavior of molten metals on solid substrates is a critical phenomenon influencing numerous industrial applications, including welding, anti-corrosion coatings, and metal additive manufacturing (AM). In particular, molten metal jetting (MMJ), an emerging AM technology, requires that the molten metal remain pinned at the nozzle exit. Thus, each new metal requires a specific nozzle material to ensure consistent droplet ejection and deposition, making it important to rapidly identify the appropriate wetting combinations. However, traditional measurements of wetting angles require expensive equipment and only allow one combination of materials to be investigated at a time which can be time consuming. This work introduces a rapid screening method based on sessile droplet experiments to evaluate wetting profiles across multiple metal–substrate combinations simultaneously. This study investigates the wetting interactions of molten Al alloy (Al4008), Cu, and Sn on various ceramic and metal substrates to identify optimal material combinations for MMJ nozzle designs. Results demonstrate that Al4008 achieves wetting on ceramic substrates such as AlN, TiO₂, and SiC, with varying mechanisms including chemical reactions and weak surface interactions. Additionally, theoretical predictions regarding miscibility gaps and melting point differences were verified for Cu and Sn on refractory metals like Mo and W. Findings from this study contribute to the establishment of a materials compatibility library, enabling the selection of wetting/non-wetting combinations for stable MMJ operation. This resource not only advances MMJ technologies but also provides valuable insights for broader applications such as welding, coating, and printed electronics. Full article

Expanding the chemical space of DNA-encoded libraries (DELs) is desirable for identifying novel bioactive compounds and enhancing hit quality in affinity-based screening. In this study, we designed and synthesized a new on-DNA diazide platform (DAP) molecule that incorporates both aromatic and aliphatic azido groups within a single scaffold. These orthogonal azides exhibit distinct reactivity profiles, enabling a stepwise warhead construction strategy through chemoselective transformations. This approach facilitates greater structural diversity and efficient incorporation of diverse building blocks. A virtual DEL was generated based on this DAP scaffold, and its chemical space was compared with that of bioactive compounds in the ChEMBL database. The analysis revealed that this virtual library occupied a distinct and previously unexplored region of chemical space, highlighting the potential of this DAP-based strategy for discovering structurally novel DEL members with biological relevance. Full article

The acceleration of industrialization and urbanization have led to the increasingly serious problem of waste gas pollution. Pollutants such as sulfur dioxide (SO₂), nitrogen oxides (NOx), volatile organic compounds (VOCs), ammonia (NH₃), formaldehyde (HCHO), and hydrogen sulfide (H₂S) emitted from industrial production, transportation, and agricultural activities have posed a major threat to the ecological environment and public health. Although traditional physical and chemical treatment methods can partially reduce the concentration of pollutants, they face three core bottlenecks of high cost, high energy consumption, and secondary pollution, and it is urgent to develop sustainable alternative technologies. In this context, probiotic waste gas treatment technology has become an emerging research hotspot due to its environmental friendliness, low energy consumption characteristics, and resource conversion potential. Based on the databases of PubMed, Web of Science Core Collection, Scopus, Embase, and Cochrane Library, this paper systematically searched the literature published from 2014 to 2024 according to the predetermined inclusion and exclusion criteria (such as research topic relevance, experimental data integrity, language in English, etc.). A total of 71 high-quality studies were selected from more than 600 studies for review. By integrating three perspectives (basic theory perspective, environmental application perspective, and waste gas treatment facility perspective), the metabolic mechanism, functional strain characteristics, engineering application status, and cost-effectiveness of probiotics in waste gas bioconversion were systematically analyzed. The main conclusions include the following: probiotics achieve efficient degradation and recycling of waste gas pollutants through specific enzyme catalysis, and compound flora and intelligent regulation can significantly improve the stability and adaptability of the system. This technology has shown good environmental and economic benefits in multi-industry waste gas treatment, but it still faces challenges such as complex waste gas adaptability and long-term operational stability. This review aims to provide useful theoretical support for the optimization and large-scale application of probiotic waste gas treatment technology, promote the transformation of waste gas treatment from ‘end treatment’ to ‘green transformation’, and ultimately serve the realization of sustainable development goals. Full article

Introduction: Tumor necrosis factor-α (TNF-α) is a key regulator of inflammatory responses, and its biological activity is dependent on proteolytic processing by the tumor necrosis factor-α-converting enzyme (TACE), also known as ADAM17. Aberrant TACE activity has been associated with various inflammatory and immune-mediated diseases, positioning it as a compelling target for therapeutic intervention. Methods: While our previous study explored TACE inhibition via repositioned FDA-approved drugs, the present study aims to examine previously untested chemical scaffolds from the Enamine compound library, seeking first-in-class TACE inhibitors. We employed an integrated in silico workflow that combined ligand-based virtual screening using a graph convolutional network (GCN) model trained on known TACE inhibitors with structure-based methodologies, including molecular docking, molecular dynamics (MD) simulations, and binding free energy calculations. Results: Several enamine-derived compounds demonstrated strong predicted inhibitory potential, favorable docking scores, and stable interactions with the TACE active site. Among them, Z1459964184, Z2242870510, and Z1450394746 emerged as lead candidates based on their highly stable 300 ns RMSD and robust hydrogen bonding profile as compared to the reference compound BMS-561392. Conclusions: This study highlights the utilization of deep learning-driven screening combined with extended 300 ns molecular simulations to identify novel small-molecule scaffolds for TACE inhibition and supports further exploration of these hits as potential anti-inflammatory therapeutics. Full article

We describe the first solid-phase synthesis of thiazolo [4,5-d] [1,2,3] triazin-4(3H)-one derivatives using Merrifield resin. The modular sequence involves Thorpe–Ziegler cyclization, sulfone oxidation, and disulfonate nucleophilic substitution, with each step monitored by real-time ATR-FTIR spectroscopy. Conducted under mild conditions with broad functional group tolerance, the protocol delivered a library of 40 compounds in average stepwise yields of 68–97%, requiring only simple resin washing for purification. This study demonstrates a solid-phase route to thiazolotriazinones and illustrates its applicability in heterocyclic library construction and SAR studies. Full article