Search Results (497)

Dopamine D2-like receptors, including D2, D3, and D4, are members of the aminergic G protein-coupled receptor (GPCR) family and are targets for neurological disorders. The development of subtype selective ligands is important for enhanced therapeutics and reduced side effects; however, it is challenging to design and develop selective ligands owing to the high degree of sequence homology among D2-like subtypes. To gain insight into the structural basis of subtype selectivity of piperazinylalkyl pyrazole/isoxazole analogs for D2-like dopamine receptors, we carried out 3D quantitative structure–activity relationship (3D-QSAR) and molecular docking studies. The 3D-QSAR models for the D2, D3, and D4 subtypes showed robust correlation coefficients (r²) of 0.960, 0.912, and 0.946, as well as reliable predictive values (Q²) of 0.511, 0.808, and 0.560, respectively. Contour map analysis revealed key structural determinants for ligand activity, highlighting the distinct steric and electrostatic requirements for each subtype. These findings were further rationalized by molecular docking studies, which confirmed that interactions with non-conserved residues modulate binding affinity. Crucially, our analysis identified a critical structural basis for D4 subtype selectivity. This selectivity is attributed to a spatial constraint within the hydrophobic pocket formed by TMs 3, 5, and 6. This constraint restricts the orientation of bulky substituents on the 4-phenylpiperazine moiety. These findings provide actionable structural insights for the rational design of next-generation subtype-selective antagonists for D2-like dopamine receptors. Full article

Various parameters, like blade design, rotational speed, feed speed, tooth geometry, wood moisture content, and wood species, influence the efficiency and quality of sawing processes. Knowing the optimal combination of these factors could lead to lower power consumption and high surface quality during wood processing. Therefore, in this study, we applied a novel method that could be used to optimize the cutting of wood with circular saw blades. The analyzed factors included rotational speed, feed speed, blade type (the number of cutting teeth and blade geometries), and two wood species, such as beech and spruce. The samples were cut longitudinally using two circular saw blades. The power consumption and the roughness of the processed surfaces were experimentally measured using an active/reactive electrical power transducer and a DAQ connected to a computer and a diamond stylus roughness meter, respectively. Once the data were gathered and processed, an artificial neural network modeling technique was involved in designing two models: one model for the cutting power and the other for surface roughness. Both models are characterized by high values of performance indicators. Therefore, the models could be considered a reliable tool that could be used to predict the cutting power and the surface roughness for the cutting of wood with circular saw blades. Next, response surface methodology was used to identify how each factor affects the cutting power and the surface quality, and to find the optimal values for both. The results showed that the most important factor that influences the roughness of the processed surfaces is the feed speed; the second factor is the blade rotation speed; the third factor is the tool type (the number of cutting teeth combined with their geometry). The optimal machining conditions recommended by the optimization algorithm (low power consumption and low roughness) imply minimum feed speed values (3.5 m/min) and medium (4500 rpm for 54-tooth blade) or high (6000 rpm for 24-tooth blade) blade rotation speeds. A further study will be conducted to consider the behavior of wood species during the circular sawing of wood and to clarify the influence of the different constructive parameters of the blades (number of teeth, tooth geometry) on their performance. Full article

Heart failure (HF) is a complex and multifactorial syndrome with high morbidity and mortality rates worldwide. Accumulative evidence suggests that microRNAs (miRNAs) play critical roles in maintaining cardiac homeostasis. The dysregulation of various miRNAs has been reported in different studies on failing human hearts. However, little is known about their circulatory profile. In this study, comprehensive miRNA profiling was performed by next-generation sequencing for plasma samples of 24 HF and 24 age and sex-matched, non-HF patients. A total of 1391 miRNAs were detected, of which 228 miRNAs and 261 miRNAs were commonly expressed in the HF and non-HF groups, respectively. Eight miRNAs (hsa-let-7b-3p, hsa-miR-92b-5p, hsa-miR-145-3p, hsa-miR-206, hsa-miR-664a-5p, hsa-miR-1307-5p, hsa-miR-1908-5p, and hsa-miR-3074-5p) were found to be dysregulated between HF and non-HF patients. The expression of another seven miRNAs (hsa-miR-589-5p, hsa-miR-30b-5p, hsa-miR-654-3p, hsa-miR-1292-5p, hsa-miR-659-5p, hsa-miR-548d-5p, and hsa-miR-7847-3p) was frequently observed in HF patients but not in non-HF cases. Subsequent analyses of target gene prediction and associated pathways revealed the enrichment of pathways related to vascular development, the cell cycle, and transcriptional activity. These data reveal the expression profile and the dysregulation of circulating miRNAs in our patients with HF. Full article

Accurate classification of defects in the semiconductor manufacturing process is critical for improving yield and ensuring quality. While previous works have mainly focused on improving classification accuracy, we propose a model that can simultaneously assess accuracy, prediction confidence, and interpretability in wafer defect classification. To solve the class imbalance problem, we used a weighted cross-entropy loss function and convolutional neural network–based model to achieve a high accuracy of 97.8% on the test dataset and applied a temperature-scaling technique to enhance confidence. Furthermore, by simultaneously employing local interpretable model-agnostic explanations and gradient-weighted class activation mapping, the rationale for the predictions of the model was visualized, allowing users to understand the decision-making process of the model from various perspectives. This research can provide a direction for the next generation of intelligent quality management systems by enhancing the applicability of the proposed model in actual semiconductor production sites through explainable predictions. Full article

As robotic systems advance in autonomy and sophistication while being used in uncertain environments, the challenge of building reliable and robust electric motors that are embedded into robotic systems has never been a more important engineering problem. Thermal distress caused by extended operation or excessive loading can negatively affect a motor’s performance and efficiency and lead to catastrophic hardware failure. This paper proposes a novel intelligent control framework that includes real-time thermal feedback for hybrid electric motors that are embedded into robotic systems. The framework relies on adaptive control techniques and lightweight machine learning techniques to estimate internal motor temperatures and dynamically change operational parameters. Unlike traditional reactive methods, this framework provides a spacious active/predictive method of heat management, while preserving efficiency and allowing for responsive control. Simulations, experimental validations, and preliminary trials that deployed real robotic systems demonstrated that our framework allows for reductions in peak temperatures by up to 18% and extends motor lifetime by 22%, while retaining control stability and a range of variations in PWM adjustments of ±12% across disparate workloads. These results demonstrate the efficacy of intelligent and thermally aware motor control architectures and processes to improve the reliability of autonomous robotic systems and open the door for next-generation embedded controllers that will allow robotic platforms to self-manage thermal effects in resilient, adaptable robots. Full article

Antibody–drug conjugates (ADCs) are a rapidly advancing class of targeted cancer therapeutics that couple the antigen specificity of monoclonal antibodies (mAbs) with the potent cytotoxicity of small-molecule drugs. In their core design, a tumor-targeting antibody is covalently linked to a cytotoxic payload via a chemical linker, enabling the selective delivery of highly potent agents to malignant cells while sparing normal tissues, thereby improving the therapeutic index. Humanized and fully human immunoglobulin G1(IgG1) antibodies are the most common ADC backbones due to their stability in systemic circulation, robust Fcγ receptor engagement for immune effector functions, and reduced immunogenicity. Antibody selection requires balancing tumor specificity, internalization rate, and binding affinity to avoid barriers to tissue penetration, such as the binding-site barrier effect, while emerging designs exploit tumor-specific antigen variants or unique post-translational modifications to further enhance selectivity. Advances in antibody engineering, linker chemistry, and payload innovation have reinforced the clinical success of ADCs, with more than a dozen agents FDA approved for hematologic malignancies and solid tumors and over 200 in active clinical trials. This review critically examines established and emerging conjugation strategies, including lysine- and cysteine-based chemistries, enzymatic tagging, glycan remodeling, non-canonical amino acid incorporation, and affinity peptide-mediated methods, and discusses how conjugation site, drug-to-antibody ratio (DAR) control, and linker stability influence pharmacokinetics, efficacy, and safety. Innovations in site-specific conjugation have improved ADC homogeneity, stability, and clinical predictability, though challenges in large-scale manufacturing and regulatory harmonization remain. Furthermore, novel ADC architectures such as bispecific ADCs, conditionally active (probody) ADCs, immune-stimulating ADCs, protein-degrader ADCs, and dual-payload designs are being developed to address tumor heterogeneity, drug resistance, and off-target toxicity. By integrating mechanistic insights, preclinical and clinical data, and recent technological advances, this work highlights current progress and future directions for next-generation ADCs aimed at achieving superior efficacy, safety, and patient outcomes, especially in treating refractory cancers. Full article

Background: The circadian rhythm regulates important functions in the body, such as metabolism, the cell cycle, DNA repair, and immune balance. Disruption of this rhythm can contribute to the development of cancer. Circadian rhythm genes (CRGs) are attracting attention for their connection to various cancers. However, their roles in LUAD are not yet well understood. Additionally, our knowledge of how they function at both the bulk tissue and single-cell levels is limited. This gap hinders a complete understanding of how CRGs impact the development and outcomes of LUAD. Methods: We selected 554 CRGs from public databases. We then obtained transcriptome data from TCGA and GEO. A total of 101 machine learning algorithm combinations were tested using 10 algorithms and 10-fold cross-validation. The best-performing model was based on Stepwise Cox regression and SuperPC. This model was validated with additional datasets. We also examined the relationships between CRGs, immune features, tumor mutation burden (TMB), and the response to immunotherapy. Drug sensitivity was also assessed. Single-cell data identified the cell types with active CRGs. Next, we performed qRT-PCR and other basic experiments to validate the expression of ARNTL2 in LUAD tissues and cell lines. The results indicated that ARNTL2 may play a key role in lung adenocarcinoma. Results: The CRG-based model clearly distinguished LUAD patients based on their risk. High-risk patients exhibited low immune activity, high TMB, and poor predicted responses to immunotherapy. Single-cell data revealed strong CRG signals in epithelial and fibroblast cells. These cell groups also displayed different communication patterns. Laboratory experiments showed that ARNTL2 was highly expressed in LUAD. It promoted cell growth, movement, and invasion. This suggests that ARNTL2 may play a role in promoting cancer. Conclusions: This study developed a machine learning model based on CRGs. It can predict survival and immune status in LUAD patients. The research also identified ARNTL2 as a key gene that may contribute to cancer progression. These findings highlight the significance of the circadian rhythm in LUAD and provide new perspectives for diagnosis and treatment. Full article

The rapid development of wireless communication technology is leading to increasingly scarce spectrum resources, making efficient utilization a critical challenge. This paper proposes a Convolutional Neural Network–Long Short-Term Memory-Integrated Gradient-Weighted Class Activation Mapping (GC-CNN-LSTM) model, aimed at enhancing the accuracy of long-term spectrum prediction across multiple frequency bands and improving model interpretability. First, we achieve multi-frequency long-term spectrum prediction using a CNN-LSTM and compare its performance against models including LSTM, GRU, CNN, Transformer, and CNN-LSTM-Attention. Next, we use an improved Grad-CAM method to explain the model and obtain global heatmaps in the time–frequency domain. Finally, based on these interpretable results, we optimize the input data by selecting high-importance frequency points and removing low-importance time segments, thereby enhancing prediction accuracy. The simulation results show that the Grad-CAM-based approach achieves good interpretability, reducing RMSE and MAPE by 6.22% and 4.25%, respectively, compared to CNN-LSTM, while a similar optimization using SHapley Additive exPlanations (SHAP) achieves reductions of 0.86% and 3.55%. Full article

Background: Skin aging is mainly associated with oxidative stress and enzymatic degradation of collagen and elastin by protease activity. Peptides have antioxidant capacity and inhibitory effects on protease enzymes. Objective: The purpose of this study was to obtain peptides with in vitro anti-aging activity from the enzymatic hydrolysis of bovine casein with actinidin, a protease extracted from the green kiwi fruit (Actinidia deliciosa) Methodology: The enzyme actinidin was extracted from the pulp of the kiwi fruit, purified by ion exchange chromatography and characterized by polyacrylamide electrophoresis (SDS-PAGE). Subsequently, the extracted enzyme was used to hydrolyze commercial bovine casein at 37 °C for 30 min, precipitating the peptide fraction with trichloroacetic acid (TCA), and centrifuged. To determine the anti-aging potential of the peptides in vitro, antioxidant activity was evaluated using the ABTS (2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid)) radical. Additionally, the inhibitory capacity of the peptides against collagenase and elastase enzymes was also studied. To complement the in vitro results, the enzymatic hydrolysis of casein with actinidin was simulated. The binding energy (ΔG) of each of the hydrolysates with the collagenase and elastase enzymes was calculated using molecular docking to predict the peptide sequences with the highest probability of interaction. Results: Actinidin was extracted and purified exhibiting a molecular weight close to 27 kDa. The enzyme hydrolyzed the substrate by 91.6%, and the resulting hydrolysates showed moderate in vitro anti-aging activity: antioxidant (17.5%), anticollagenase (18.55%), and antielastase (28.6%). In silico results revealed 66 peptide sequences of which 30.3% consisted of 4–8 amino acids, a suitable size to facilitate interaction with structural targets. The sequences with the highest affinity were FALPQYLK and VIPYVRYL for collagenase and elastase, respectively. Conclusions: Despite the modest inhibition values, the use of a fruit-derived enzyme and a food-grade substrate is in line with current trends in sustainable and natural cosmetics. These findings highlight the great potential for laying the groundwork for future research into actinidin-derived peptides as multifunctional and eco-conscious ingredients for the development of next-generation anti-aging formulations. Full article

The integration of deep learning (DL) and nanozyme-based biosensing has emerged as a transformative strategy for next-generation medical diagnostics. This review explores how DL architectures enhance nanozyme design, functional optimization, and predictive modeling by elucidating catalytic mechanisms such as dual-atom active sites and substrate-surface interactions. Key applications include disease biomarker detection, medical imaging enhancement, and point-of-care diagnostics aligned with the ASSURED criteria. In clinical contexts, advances such as wearable biosensors and smart diagnostic platforms leverage DL for real-time signal processing, pattern recognition, and adaptive decision-making. Despite significant progress, challenges remain—particularly the need for standardized biomedical datasets, improved model robustness across diverse populations, and the clinical translation of artificial intelligence (AI)-enhanced nanozyme systems. Future directions include integration with the Internet of Medical Things, personalized medicine frameworks, and sustainable sensor development. The convergence of nanozymes and DL offers unprecedented opportunities to advance intelligent biosensing and reshape precision diagnostics in healthcare. Full article

Background/Objectives: The rise in antibiotic resistance presents a serious and urgent global health challenge, emphasizing the need to develop new therapeutic compounds. This study focuses on the design and evaluation of a novel series of hybrid molecules that combine the 2-quinolone and 1,2,3-triazole pharmacophores, both recognized for their broad-spectrum antimicrobial properties. Methods: A library of 29 candidate molecules was first designed using in silico techniques, including QSAR modeling, ADMET prediction, molecular docking, and molecular dynamics simulations, to optimize antibacterial activity and drug-like properties. The most promising compounds were then synthesized and characterized by ¹H and ¹³C NMR APT, mass spectrometry (MS), Fourier-transform infrared (FT-IR) spectroscopy, and UV-Vis spectroscopy. Results: Antibacterial evaluation revealed potent activity against both Gram-positive and Gram-negative bacterial strains, with minimum inhibitory concentration (MIC) values ranging from 0.019 to 1.25 mg/mL. Conclusions: These findings demonstrate the strong potential of 2-quinolone–triazole hybrids as effective antibacterial agents and provide a solid foundation for the development of next-generation antibiotics to combat the growing threat of bacterial resistance. Full article

Compounds, which rely on metabolism to exhibit toxicity, pose a challenge for next-generation risk assessment (NGRA). Since many of the currently available non-animal new approach methods (NAMs) lack metabolic activity, their use may lead to an underestimation of the true hazard to humans (false negative predictions). We explored here strategies to deal with metabolite-mediated toxicity in assays for developmental neurotoxicity. First, we present an overview of substances that may serve as potential positive controls for metabolite-related neurotoxicity. Then, we demonstrate, using the MitoMet (UKN4b) assay, which assesses the adverse effects of chemicals on neurites of human neurons, that some metabolites have a higher toxic potency than their parent compound. Next, we designed a strategy to integrate elements of xenobiotic metabolism into assays used for (developmental) neurotoxicity testing. In the first step of this approach, hepatic post-mitochondrial fractions (S9) were used to generate metabolite mixtures (“metabolisation module”). In the second step, these were applied to a NAM (exemplified by the UKN4b assay) to identify metabolite-mediated toxicity. We demonstrate the applicability and transferability of these approaches to other assays, by an exemplary study on the basis of the cMINC (UKN2) assay, another NAM of the developmental neurotoxicity in vitro battery. Based on the experience gained from these experiments, we discuss key issues to be addressed if this approach is to be used more broadly for NAM in the NGRA context. Full article

High-entropy alloy (HEA) catalysts have attracted significant attention from researchers. In many cases, HEAs exhibit high activity and selectivity for catalytic reactions due to four “core effects”: high entropy effect, lattice distortion effect, slow diffusion effect, and mixing effect. However, a systematic summary of HEA catalyst design and understanding is lacking. In this review, the reasons for the outstanding performance of HEA catalysts are first discussed from multiple perspectives, such as excellent mechanical properties, ultra-high-performance stability, and the potential for compositional optimization. Furthermore, to deepen our understanding of HEA catalysts, the rational design of HEA catalysts is introduced, covering design principles, element selection, and the use of algorithms for prediction. Next, several common preparation methods for HEAs are introduced, including chemical co-reduction, solution combustion, mechanical alloying, and sol–gel methods. Finally, the research progress of HEA catalysts in hydrogen evolution reactions, oxygen evolution reactions, and oxygen reduction reactions is presented. Unlike existing reviews, this work establishes a unified framework connecting HEA fundamentals (entropy effects), computational design, scalable synthesis, and application-specific performance, while identifying underexplored pathways like lattice-oxygen-mediated mechanisms (LOM) for future research. Full article

Brain tumors elicit complex neuropsychiatric disturbances that frequently occur prior to radiological detection and hinder differentiation from major psychiatric disorders. These syndromes stem from tumor-dependent metabolic reprogramming, neuroimmune activation, neurotransmitter dysregulation, and large-scale circuit disruption. Dinucleotide hypermethylation (e.g., IDH-mutant gliomas), through the accumulation of 2-hydroxyglutarate (2-HG), execute broad DNA and histone hypermethylation, hypermethylating serotonergic and glutamatergic pathways, and contributing to a treatment-resistant cognitive-affective syndrome. High-grade gliomas promote glutamate excitotoxicity via system Xc⁻ transporter upregulation that contributes to cognitive and affective instability. Cytokine cascades induced by tumors (e.g., IL-6, TNF-α, IFN-γ) lead to the breakdown of the blood–brain barrier (BBB), which is thought to amplify neuroinflammatory processes similar to those seen in schizophrenia spectrum disorders and autoimmune encephalopathies. Frontal gliomas present with apathy and disinhibition, and temporal tumors lead to hallucinations, emotional lability, and episodic memory dysfunction. Tumor-associated neuropsychiatric dysfunction, despite increasing recognition, is underdiagnosed and commonly misdiagnosed. This paper seeks to consolidate the mechanistic understanding of these syndromes, drawing on perspectives from neuroimaging, molecular oncology, neuroimmunology, and computational psychiatry. Novel approaches, including lesion-network mapping, exosomal biomarkers or AI-based predictive modeling, have projected early detection and precision-targeted interventions. In the context of the limitations of conventional psychotropic treatments, mechanistically informed therapies, including neuromodulation, neuroimmune-based interventions, and metabolic reprogramming, are essential to improving psychiatric and oncological outcomes. Paraneoplastic neuropsychiatric syndromes are not due to a secondary effect, rather, they are manifestations integral to the biology of a tumor, so they require a new paradigm in both diagnosis and treatment. And defining their molecular and circuit-level underpinnings will propel the next frontier of precision psychiatry in neuro-oncology, cementing the understanding that psychiatric dysfunction is a core influencer of survival, resilience, and quality of life. Full article

Antimicrobial peptides (AMPs) are the promising candidates for the development of next-generation antimicrobials for agriculture and medicine; however, their large-scale production is costly. The γ-core motif peptides, functionally significant fragments of AMPs responsible for the antimicrobial activity, provide a more economical and feasible approach for the commercial development of novel antimicrobials. In the present work, we undertook a comprehensive study of antimicrobial properties of several γ-core peptides derived from defensins and snakins of Filipendula ulmaria, a medicinal plant known for its valuable pharmacological properties. The γ-core peptides were produced by solid-phase synthesis and purified by RP-HPLC. Their physicochemical properties underlying biological activity were predicted. All the peptides ranging in size from 14 to 18 amino acid residues were positively charged. All peptides except one were predicted to be α-helical and antimicrobial. The synthetic peptides were in vitro tested against a wide panel of plant and human fungal and bacterial pathogens. A short overview of the pathogens used in antimicrobial assays with a special emphasis on their economic, social, and medicinal impacts is provided. As a result of our work, we identified the peptides with pronounced activity in low-micromolar range against particular pathogens that can serve as prototypes for the development of novel biopesticides and antimicrobials for medicine. We also revealed synergism of action between particular γ-core peptide pairs and demonstrated that interference with membrane permeabilization contributes to the peptides’ mode of action. The results obtained broaden our understanding of plant AMPs, the key players in plant immunity, and provide novel highly efficient peptides with high potential in practical applications. Full article