Search Results (33)

Background/Objectives: The resistance mutations EGFR^{L858R/T790M/C797S} in epidermal growth factor receptor (EGFR) are key factors in the reduced efficacy of Osimertinib. Predicting the inhibitory effects of Osimertinib derivatives against these mutations is crucial for the development of more effective inhibitors. This study aims to predict the inhibitory effects of Osimertinib derivatives against EGFR^{L858R/T790M/C797S} mutations. Methods: Six models were established using heuristic method (HM), random forest (RF), gene expression programming (GEP), gradient boosting decision tree (GBDT), polynomial kernel function support vector machine (SVM), and mixed kernel function SVM (MIX-SVM). The descriptors for these models were selected by the heuristic method or XGBoost. Comprehensive learning particle swarm optimizer was adopted to optimize hyperparameters. Additionally, the internal and external validation were performed by leave-one-out cross-validation ($Q_{LOO}^2$), 5-fold cross validation ($Q_{5-fold}^2$) and concordance correlation coefficient (CCC), $Q_{F1}^2$, and $Q_{F2}^2$. The properties of novel EGFR inhibitors were explored through molecular docking analysis. Results: The model established by MIX-SVM whose kernel function is a convex combination of three regular kernel functions is best: $R^2$ and RMSE for training set and test set are 0.9445, 0.1659 and 0.9490, 0.1814, respectively; $Q_{LOO}^2$, $Q_{5-fold}^2$, CCC, $Q_{F1}^2$, and $Q_{F2}^2$ are 0.9107, 0.8621, 0.9835, 0.9689, and 0.9680. Based on these results, the IC₅₀ values of 162 newly designed compounds were predicted using the HM model, and the top four candidates with the most favorable physicochemical properties were subsequently validated through PEA. Conclusions: The MIX-SVM method will provide useful guidance for the design and screening of novel EGFR^{L858R/T790M/C797S} inhibitors. Full article

Lung cancer is one of the most prevalent and deadly forms of cancer, accounting for a significant portion of cancer-related deaths worldwide. It typically originates in the lung tissues, particularly in the cells lining the airways, and early detection is crucial for improving patient survival rates. Computed tomography (CT) imaging has become a standard tool for lung cancer screening, providing detailed insights into lung structures and facilitating the early identification of cancerous nodules. In this study, an improved Faster R-CNN model is employed to detect early-stage lung cancer. To enhance the performance of Faster R-CNN, a novel dual-attention optimized multi-scale CNN (DA OMS-CNN) architecture is used to extract representative features of nodules at different sizes. Additionally, dual-attention RoIPooling (DA-RoIpooling) is applied in the classification stage to increase the model’s sensitivity. In the false-positive reduction stage, a combination of multiple 3D shift window transformers (3D SwinT) is designed to reduce false-positive nodules. The proposed model was evaluated on the LUNA16 and PN9 datasets. The results demonstrate that integrating DA OMS-CNN, DA-RoIPooling, and 3D SwinT into the improved Faster R-CNN framework achieves a sensitivity of 96.93% and a CPM score of 0.911. Comprehensive experiments demonstrate that the proposed approach not only increases the sensitivity of lung cancer detection but also significantly reduces the number of false-positive nodules. Therefore, the proposed method can serve as a valuable reference for clinical applications. Full article

Background/Objectives: Postpartum depression (PPD) affects approximately 10–20% of women during and after pregnancy, posing significant risks to maternal health, infant development, and family dynamics. Identifying modifiable risk factors is essential for prevention. Emerging evidence suggests that vitamin D, a neuroactive steroid hormone involved in neurotransmitter synthesis, neuroinflammation regulation, and calcium homeostasis, may play a protective role against mood disorders, including PPD. Methods: The search was conducted through a comprehensive search of the PubMed, Scopus, and Web of Science databases using a combination of Medical Subject Headings (MeSH) and free-text terms including “vitamin D”, “25-hydroxyvitamin D”, “deficiency”, “pregnancy”, “postpartum”, “depression”, “antenatal depression”, “maternal mental health”, and “perinatal mood disorders”. Results: Numerous observational studies and systematic review reports around the world reinforce the potential global relevance of vitamin D insufficiency. This study advances personalized and precision medicine approaches by emphasizing the importance of individualized screening for vitamin D deficiency during pregnancy and postpartum, enabling tailored interventions that could mitigate the risk of postpartum depression. Conclusions: In conclusion, while a definitive causal relationship between vitamin D deficiency and perinatal depression remains unproven, screening for vitamin D levels during pregnancy could serve as a low-risk intervention to support maternal mental health. Future research should focus on well designed, large-scale randomized trials and standardization of diagnostic criteria to clarify vitamin D’s role in preventing perinatal depression. Recognizing vitamin D status as a modifiable biomarker allows for targeted nutritional and pharmacological strategies to optimize maternal mental health. Full article

This paper focuses on the use of advanced optimization design strategies to improve the performance and service life of engine pistons, with emphasis on enhancing their stiffness, strength, and dynamic characteristics. As a core component of the engine, the structural design and optimization of the piston are of great significance to its efficiency and reliability. First, a three-dimensional (3D) model of the piston was constructed and imported into ANSYS Workbench for finite element modeling and high-quality meshing. Based on the empirical formula, the actual working environment temperature and heat transfer coefficient of the piston were accurately determined and used as boundary conditions for thermomechanical coupling analysis to accurately simulate the thermal and deformation state under complex working conditions. Dynamic characteristic analysis was used to obtain the displacement–frequency curve, providing key data support for predicting resonance behavior, evaluating structural strength, and optimizing the design. In the optimization stage, five geometric dimensions are selected as design variables. The deformation, mass, temperature, and the first to third natural frequencies are considered as optimization goals. The response surface model is constructed by means of the design of the experiments method, and the fitted model is evaluated in detail. The results show that the models are all significant. The adequacy of the model fitting is verified by the “Residuals vs. Run” plot, and potential data problems are identified. The “Predicted vs. Actual” plot is used to evaluate the fitting accuracy and prediction ability of the model for the experimental data, avoiding over-fitting or under-fitting problems, and guiding the optimization direction. Subsequently, the sensitivity analysis was carried out to reveal the variables that have a significant impact on the objective function, and in-depth analysis was conducted in combination with the response surface. The multi-objective genetic algorithm (MOGA), screening, and response surface methodology (RSM) were, respectively, used to comprehensively optimize the objective function. Through experiments and analysis, the optimal solution of the MOGA algorithm was selected for implementation. After optimization, the piston mass and deformation remained relatively stable, and the working temperature dropped from 312.75 °C to 308.07 °C, which is conducive to extending the component life and improving the thermal efficiency. The first to third natural frequencies increased from 1651.60 Hz to 1671.80 Hz, 1656.70 Hz to 1665.70 Hz, and 1752.90 Hz to 1776.50 Hz, respectively, significantly enhancing the dynamic stability and vibration resistance. This study integrates sensitivity analysis, response surface models, and genetic algorithms to solve multi-objective optimization problems, successfully improving piston performance. Full article

Phenotypic data of cotton can accurately reflect the physiological status of plants and their adaptability to environmental conditions, playing a significant role in the screening of germplasm resources and genetic improvement. Therefore, this study proposes a cotton phenotypic data extraction algorithm that integrates ResDGCNN with an improved region-growing method and constructs a 3D point cloud dataset of cotton covering the entire growth period under real growth conditions. To address the challenge of significant structural variations in cotton organs across different growth stages, we designed an innovative point cloud segmentation algorithm, ResDGCNN, which integrates residual learning with dynamic graph convolution to enhance organ segmentation performance throughout all developmental stages. In addition, to address the challenge of accurately segmenting overlapping regions between different cotton organs, we introduced an optimization strategy that combines point distance mapping with curvature-based normal vectors and developed an improved region-growing algorithm to achieve fine segmentation of multiple cotton organs, including leaves, stems, and flower buds. Experimental data show that, in the task of organ segmentation throughout the entire cotton growth cycle, the ResDGCNN model achieved a segmentation accuracy of 67.55%, with a 4.86% improvement in mIoU compared to the baseline model. In the fine-grained segmentation of overlapping leaves, the model achieved an R² of 0.962 and an RMSE of 2.0. The average relative error in stem length estimation was 0.973, providing a reliable solution for acquiring 3D phenotypic data of cotton. Full article

As the energy crisis and environmental pollution continue to intensify, the demand for clean energy has increased. Using two-dimensional materials to catalyze overall water splitting is an important pathway for clean energy production. This study investigated the catalytic hydrogen evolution reaction (HER), oxygen evolution reaction (OER), and oxygen reduction reaction (ORR) of tri-atomic clusters supported on a two-dimensional material, graphenylene (GPN). The structural stability of GPN was thoroughly investigated, and materials were employed as substrates to support a series of 28 distinct trimer clusters composed of 3d, 4d, and 5d transition metals. Ideal combinations of these systems were screened and designed. The loading configurations of TM₃@GPN in two different systems were systematically studied. The stability of the catalyst was assessed by calculating the binding and cohesive energies and by performing molecular dynamics simulations, to confirm the catalyst stability. The optimal bifunctional catalysts for overall water splitting were identified as Au₃@GPN, Pt₃@GPN, and Pd₃@GPN, all of which demonstrated superior overall water splitting performance. As a novel two-dimensional material, biphenylene-based materials, when used to support metal clusters as bifunctional catalysts for water splitting, represent an efficient and innovative approach. Full article

Multifunctional structures with excellent wave-absorbing and load-bearing properties have attracted much attention in recent years. Unlike other wave-absorbing materials, honeycomb wave-absorbing materials have appealing radar absorption and mechanical properties. However, the existing honeycomb wave-absorbing materials have problems such as narrow absorption band and poor compression resistance. In this study, a novel frequency selective surface-enhanced composite honeycomb absorbers (FSS-CHAs) are fabricated by combining a honeycomb structure with wonderful load-bearing capacity and FSS through screen-printing and inlay-locking techniques. After reflectivity measurements, the effective absorption band (RL < −10 dB) of CHA is 6.25–17.47 GHz and a bandwidth of 11.22 GHz, the effective absorption band of the FSS-CHA is 3.96–18 GHz and a bandwidth of 14.04 GHz, 25.13% improvement compared to the CHA, the mechanism of wave absorption is explained using transmission line theory. The simulation results show that the wide bandwidth is due to the different absorption mechanisms of FSS-CHA at low and high frequencies. The compression test shows that the compression strength of FSS-CHA is 17.10 MPa. In addition, FSS-CHA has a low cost of only USD 270.7/m². This study confirms the possibility of combining FSS with radar-absorbing honeycombs, which provides a reference for the design of future broadband wave-absorbing structures, offers a novel approach to integrating FSS with CHA, and aims to optimize their efficacy and utility in stealth technology. Full article

To overcome the limitations of the hybrid tractor bumping tests, which include extended cycle times, high costs, and impracticality for single-part reliability verification, this study focuses on the exhaust system mounting bracket of a hybrid tractor. A novel approach that combines multi-objective particle swarm optimization (MOPSO) and wavelet decomposition algorithms was employed to enhance the reconstruction of shock vibration signals. This approach aims to enable the efficient acquisition of input signals for subsequent shaker table testing. The methodology involves a systematic evaluation of the spectral correlation between the original signal and the reconstructed signal at the stent’s response position, along with signal compression time. These parameters collectively constitute the objective function. The multi-objective particle swarm optimization algorithm is then deployed to explore a range of crucial parameters, including wavelet basic functions, the number of wavelet decomposition layers, and the selection of wavelet components. This exhaustive exploration identifies an optimized signal reconstruction method that accurately represents shock vibration loads. Upon rigorous screening based on our defined objectives, the optimal solution vector was determined, which includes the utilization of the dB10 wavelet basic function, employing a 12-layer wavelet decomposition, and selecting wavelet components a12 and d3~d11. This specific configuration enables the retention of 95% of the damage coefficients while significantly compressing the test time to just 46% of the original signal duration. The implications of our findings are substantial as the reconstructed signal obtained through our optimized approach can be readily applied to shaker excitation. This innovation results in a notable reduction in test cycle time and associated costs, making it particularly valuable for engineering applications, especially in tractor design and testing. Full article

Traditional methods for obtaining soil heavy metal content are expensive, inefficient, and limited in monitoring range. In order to meet the needs of soil environmental quality evaluation and health status assessment, visible near-infrared spectroscopy and XRF spectroscopy for monitoring heavy metal content in soil have attracted much attention, because of their rapid, nondestructive, economical, and environmentally friendly features. The use of either of these spectra alone cannot meet the accuracy requirements of traditional measurements, while the synergistic use of the two spectra can further improve the accuracy of monitoring heavy metal lead content in soil. Therefore, this study applied various spectral transformations and preprocessing to vis-NIR and XRF spectra; used the whale optimization algorithm (WOA) and competitive adaptive re-weighted sampling (CARS) algorithms to identify feature spectra; designed a combination variable model (CVM) based on multi-layer spectral data fusion, which improved the spectral preprocessing and spectral feature screening process to increase the efficiency of spectral fusion; and established a quantitative model for soil Pb concentration using partial least squares regression (PLSR). The estimation performance of three spectral fusion strategies, CVM, outer-product analysis (OPA), and Granger-Ramanathan averaging (GRA), was discussed. The results showed that the accuracy and efficiency of the CARS algorithm in the fused spectra estimation model were superior to those of the WOA algorithm, with an average coefficient of determination (R²) value of 0.9226 and an average root mean square error (RMSE) of 0.1984. The accuracy of the estimation models established, based on the different spectral types, to predict the Pb content of the soil was ranked as follows: the CVM model > the XRF spectral model > the vis-NIR spectral model. Within the CVM fusion strategy, the estimation model based on CARS and PLSR (CARS_D1+D2) performed the best, with R² and RMSE values of 0.9546 and 0.2035, respectively. Among the three spectral fusion strategies, CVM had the highest accuracy, OPA had the smallest errors, and GRA showed a more balanced performance. This study provides technical means for on-site rapid estimation of Pb content based on multi-source spectral fusion and lays the foundation for subsequent research on dynamic, real-time, and large-scale quantitative monitoring of soil heavy metal pollution using high-spectral remote sensing images. Full article

Safinamide is an orally active, selective monoamine oxidase-B inhibitor with dopaminergic and non-dopaminergic properties approved by the European Medicine Agency and US Food and Drug Administration for the treatment of mid- to late-stage fluctuating Parkinson’s disease (PD) used in combination with other PD medications such as levodopa. In this study, an analytical quality by design (AQbD) approach was applied to optimize an LC-MS/MS bioanalytical method to determine safinamide in human plasma. A full 3³ factorial design was used to optimize safinamide separation conditions, with a method first screened and optimized using chromatographic responses, including peak area and retention time. The results showed that temperature had a significant indirect effect on retention time and peak area (p < 0.05), while ammonium acetate concentration had an insignificant indirect impact on peak area or retention time. However, the temperature was significantly agonistic to the effect of buffer concentration (p < 0.05). The resultant optimized chromatography conditions utilized 9.0 mM ammonium acetate buffer and acetonitrile (22.0:78.0) as mobile phases at a column temperature of 23.2 °C. The assay was linear from 0.1–1000 ng/mL, met acceptance criteria for inter- and intra-assay precision and accuracies across three quality controls, and was successfully applied to in vitro microsomal metabolic stability. The UPLC/MS/MS method was found to be adequately sensitive and suitable for routine safinamide pharmacokinetic studies. Full article

The surface zone of articular cartilage is the first area impacted by cartilage defects, commonly resulting in osteoarthritis. Chondrocytes in the surface zone of articular cartilage synthesize and secrete lubricin, a proteoglycan that functions as a lubricant protecting the deeper layers from shear stress. Notably, 3D bioprinting is a tissue engineering technique that uses cells encapsulated in biomaterials to fabricate 3D constructs. Gelatin methacrylate (GelMA) is a frequently used biomaterial for 3D bioprinting cartilage. Oxidized methacrylated alginate (OMA) is a chemically modified alginate designed for its tunable degradation rate and mechanical properties. To determine an optimal combination of GelMA and OMA for lubricin expression, we used our novel high-throughput human articular chondrocyte reporter system. Primary human chondrocytes were transduced with PRG4 (lubricin) promoter-driven Gaussia luciferase, allowing for temporal assessment of lubricin expression. A lubricin expression-driven Design of Experiment screen and subsequent validation identified 14% GelMA/2% OMA for further study. Therefore, DoE optimized 14% GelMA/2% OMA, 14% GelMA control, and 16% GelMA (total solid content control) were 3D bioprinted. The combination of lubricin protein expression and shape retention over the 22 days in culture, successfully determined the 14% GelMA/2%OMA to be the optimal formulation for lubricin secretion. This strategy allows for rapid analysis of the role(s) of biomaterial composition, stiffness or other cell manipulations on lubricin expression by chondrocytes, which may improve therapeutic strategies for cartilage regeneration. Full article

Innovations in biocatalysts provide great prospects for intolerant environments or novel reactions. Due to the limited catalytic capacity and the long-term and labor-intensive characteristics of mining enzymes with the desired functions, de novo enzyme design was developed to obtain industrial application candidates in a rapid and convenient way. Here, based on the catalytic mechanisms and the known structures of proteins, we proposed a computational protein design strategy combining de novo enzyme design and laboratory-directed evolution. Starting with the theozyme constructed using a quantum-mechanical approach, the theoretical enzyme-skeleton combinations were assembled and optimized via the Rosetta “inside-out” protocol. A small number of designed sequences were experimentally screened using SDS-PAGE, mass spectrometry and a qualitative activity assay in which the designed enzyme 1a8uD₁ exhibited a measurable hydrolysis activity of 24.25 ± 0.57 U/g towards p-nitrophenyl octanoate. To improve the activity of the designed enzyme, molecular dynamics simulations and the RosettaDesign application were utilized to further optimize the substrate binding mode and amino acid sequence, thus keeping the residues of theozyme intact. The redesigned lipase 1a8uD₁–M8 displayed enhanced hydrolysis activity towards p-nitrophenyl octanoate—3.34 times higher than that of 1a8uD₁. Meanwhile, the natural skeleton protein (PDB entry 1a8u) did not display any hydrolysis activity, confirming that the hydrolysis abilities of the designed 1a8uD₁ and the redesigned 1a8uD₁–M8 were devised from scratch. More importantly, the designed 1a8uD₁–M8 was also able to hydrolyze the natural middle-chained substrate (glycerol trioctanoate), for which the activity was 27.67 ± 0.69 U/g. This study indicates that the strategy employed here has great potential to generate novel enzymes exhibiting the desired reactions. Full article

In this work, a greener approach for the extraction of Greek propolis using ultrasound-assisted extraction method in combination with Natural Deep Eutectic Solvents (NADES) is presented. Propolis is a natural material of outmost interest as it possesses various biological and pharmacological activities and is therefore used for the manufacturing of extracts useful to various fields, such as pharmaceutics, cosmetics etc. Herein, five NADES were task-specifically selected as appropriate extraction solvents since they provide important assets to the final NADES-extracts, comparing to the conventionally used organic solvents. The screening study of the prepared solvents indicated the NADES L-proline/D,L-Lactic acid as the most effective medium for the raw propolis extraction due to the extract’s high total phenolic content as well as its’ significantly higher antioxidant activity. Then, the extraction using the selected NADES, was optimized by performing Experimental Design to study the effect of extraction time, propolis-to-solvent ratio and the %NADES content in the NADES-water system. All the extracts were characterized regarding their antioxidant activity and total phenolic content. The optimum NADES-extract as well as an extract derived by extraction using a conventional hydroethanolic solution were further characterized by performing LC/MS/MS analysis. The results showed that the NADES-extracts composition was similar or superior to the hydroethanolic extracts regarding the presence of valuable phytochemicals such as apigenin, naringenin etc. A disadvantage that is usually mentioned in the literature regarding the extractions using NADES is that the extracted bioactive compounds cannot be easily separated from the NADES in order to obtain dry extracts. However, this drawback can be converted to an asset as the task-specifically designed NADES that are used in this study add value to the end product and the optimum as-obtained NADES-extract has been successfully incorporated in a cosmetic cream formulation. In this work, The antioxidant activity and organoleptic characteristics of the cream formulation were also determined. Full article

In this paper, we present a wound-dressing-based antenna fabricated via screen-printed and inkjet-printed technologies. To inkjet print a conductive film on wound dressing, it must be screen-printed, UV-curable-pasted, and hard-baked to provide appropriate surface wettability. Two passes were UV-curable-pasted and hard-baked at 100 °C for 2 h on the wound dressing to obtain 65° WCA for silver printing. The silver film was printed onto the wound dressing at room-tempature with 23 μm droplet spacing for three passes, then sintered at 120 °C for 1 h. By optimizing the inkjet printing conditions by modifying the surface morphologies and electrical properties, three-pass printed silver films with 3.15 μm thickness and 1.05 × 10⁷ S/m conductivity were obtained. The insertion losses at the resonant frequency (17 and 8.85 GHz) were −2.9 and −2.1 dB for the 5000 and 10,000 μm microstrip transmission lines, respectively. The material properties of wound dressing with the relative permittivity and loss-tangent of 3.15–3.25 and 0.04–0.05, respectively, were determined by two transmission line methods and used for antenna design. A quasi-Yagi antenna was designed and implemented on the wound-dressing with an antenna bandwidth of 3.2–4.6 GHz, maximal gain of 0.67 dBi, and 42% radiation efficiency. The bending effects parallel and perpendicular to the dipole direction of three fixtures were also examined. The gain decreased from 0.67 to −1.22 dBi and −0.44 dBi for a flat to curvature radius of 5 cm fixture after parallel and perpendicular bending, respectively. Although the maximal gain was reduced with the bending radius, the directivity of the radiation pattern remained unchanged. The feasibility of a wound-dressing antenna demonstrates that inkjet-printed technology enables fast fabrication with low cost and environmental friendliness. Additionally, inkjet-printed technology can be combined with sensing technology to realize remote medical monitoring, such as with smart bandages, for assessment of chronic wound status or basic physical conditions. Full article

Trash racks are usually composed of an array of bars installed in a hydropower scheme to safeguard the turbines by collecting water-borne detritus. However, current design approaches for the design of trash racks focus on structural criteria. A little attention renders the proper evaluation of hydraulic criteria, which causes a significant hydraulic head loss in low head hydropower schemes with an integral intake. This study investigates the head loss through trash racks by employing computational fluid dynamics (CFD) for several design combinations. A three-dimensional model of trash racks using fractional area/volume obstacle representation (FAVOR) method in FLOW-3D is set up to define the effects of the meshing on the geometry and several simulations are carried out considering various approach velocities and different bar spacings, inclination angles, and blockage ratios. The results indicate that head loss increases with an increase in approach velocity, the inclination angle of the rack with channel bed, and blockage ratio. It is noticed that a clear spacing between vertical bars greater than or equal to 0.075 m has a minimum head loss before it becomes significantly high for lower spacing. In addition, the head loss coefficient increases for screen angles greater than 60°, which can be considered as an optimal parameter for design purpose. Full article