Search

This article systematically reviews the pedagogical approaches adopted in high school chemistry education with the aim of highlighting the most effective evidence-based instructional practices reported in the literature. For this, a meta-analysis of 35 pedagogical approaches is analyzed, and their effect sizes (Cohen's d) are compared in terms of students' learning performance. In comparison to the traditional teaching approach, the data confirmed the effectiveness of using various teaching strategies in chemistry education toward student accomplishment. The results indicate that, in general, deploying chemistry education practices in secondary education improves student learning compared to control settings (d = 1.06). Furthermore, this impact is considerably stronger when the problem-based learning approach (d = 3.35) is adopted. Therefore, according to the meta-analytical evaluation, embracing various learning paths through creative teaching promotes academic achievement. In terms of instructional quality, the findings provide a comprehensive picture of recent educational practices in chemistry instruction, which will serve as the foundation for future educational changes. We believe the results of this study will promote a progression of intimation to evolve the current high school chemistry practices and pave the direction for future research in chemistry education.

Methodology of functional low-fat frozen dairy dessert production, FFD: full-fat vanilla frozen dairy dessert with 6% fat to serve as a control, LFD: low-fat vanilla frozen dairy dessert (1% fat), T1: as low-fat frozen dairy dessert (1%) supplemented with 0.5% dried lemongrass (DLG), T2: as low-fat frozen dairy dessert (1%) supplemented with 1% DLG, T3: as low-fat frozen dairy dessert (1%) supplemented with 1.5% DLG.

Abstract Five dimethylamino-based chalcone derivatives (AC) were synthesized and evaluated for their inhibition degree against monoamine oxidase (MAO) enzymes. All AC compounds showed better inhibitory activity against MAO-B than that against MAO-A. AC4 showed the highest inhibitory ability with an IC50 value of 0.020 µM, similar to that of a reference drug safinamide (IC50 = 0.019 µM) against MAO-B, followed by AC1 (IC50 = 0.068 µM) and AC3 (IC50 = 0.083 µM). Substituent -F in ring A (AC4) increased the MAO-B inhibition, followed by -H (AC1), -Br (AC3), and -Cl (AC2). The selectivity index (SI) value of AC4 was high (SI = 82.00) as well as other compounds (44.41 to 98.15). AC4 was found to be a reversible inhibitor as confirmed through analysis using the dialysis method. Interestingly, AC4 was observed to be a noncompetitive MAO-B inhibitor with a rare case and with Ki values of 0.011 ± 0.0036 µM. These experiments confirmed that AC4 is a reversible and potent selective inhibitor of MAO-B. Molecular docking experiments revealed that AC4 showed the highest inhibitory activity with a docking score (-9.510 kcal/mol). A study using molecular dynamics modeling revealed that the protein–ligand complex was more stable. It was observed that AC4 was non-cytotoxic in the study using L929 cell line. In conclusion, compound AC4 shows promise as a MAO-B inhibitor.

In the Haber-Bosch process (HBp), which uses elevated pressure and temperature to produce more concentration of nitrogen and hydrogen gases, 90% of the 175 million metric tons of NH3 generated worldwide in 2016 were manufactured in this process. According to the road plan for sustainable ammonia production sustainably, using water as a reducing agent is the most effective way to fix nitrogen in close-quarters ambiance. A complete explanation of theoretical and practical work on electrocatalytic nitrogen reduction is provided in this article, with special attention paid to the low selectivity of nitrogen reduction to ammonia in comparison to protons to hydrogen. Since they are essential for accurately achieving high nitrogen production and Faradaic efficiency (FE), their information outlines electrocatalysts, electrolyte selection criteria, and managed experiment design. Under diverse conditions, the evolution of theory and experiment is examined. Finally, feedback is given on this field’s present issues and prospects.

This research work aims to synthesize environmentally benign and cost-effective metal nanoparticles. In this current research scenario, the leaf extract of Cedrela toona was used as a reducing agent to biosynthesize silver nanoparticles (AgNPs). The synthesis of AgNPs was confirmed by the color shift of the reaction mixture, i.e., silver nitrate and plant extract, from yellow to dark brown colloidal suspension and was established by UV-visible analysis showing a surface plasmon resonance band at 434 nm. Different experimental factors were optimized for the formation and stability of AgNPs, and the optimum conditions were found to be 1 mM AgNO3 concentration, a 1:9 ratio of extract/precursor, and an incubation temperature of 70°C for 4 h. The Fourier transform infrared spectroscopy spectra indicated the presence of phytochemicals in the leaf extract that played the role of bioreducing agents in forming AgNPs. X-ray diffraction patterns confirmed the presence of AgNPs with a mean size of 25.9 nm. The size distribution and morphology of AgNPs were investigated by scanning electron microscopy, which clearly highlighted spherical nanoparticles with a size distribution of 22–30 nm with a mean average size of 25.5 nm. Moreover, prominent antibacterial activity was found against Enterococcus faecalis (21 ± 0.5 mm), Bacillus subtilis (20 ± 0.9 mm), Pseudomonas aeruginosa (18 ± 0.3 mm), Staphylococcus aureus (16 ± 0.7 mm), Klebsiella pneumoniae (16 ± 0.3 mm), and Escherichia coli (14 ± 0.7 mm). In addition, antioxidant activity was determined by DPPH and ABTS assays. Higher antioxidant activity was reported in AgNPs compared to the plant extract in both DPPH (IC50 = 69.62 µg·ml−1) and ABTS assays (IC50 = 47.90 µg·ml−1). Furthermore, cytotoxic activity was also investigated by the MTT assay against MCF-7 cells, and IC50 was found to be 32.55 ± 0.05 µg·ml−1. The crux of this research is that AgNPs synthesized from the Cedrela toona leaf extract could be employed as antibacterial, antioxidant, and anticancer agents for the treatment of bacterial, free radical-oriented, and cancerous diseases.

In this study, the polar root extract of Cyperus scariosus R.Br. was used for the biogenic synthesis of ZnO NPs. The results of this study show that ZnO NPs have a spherical structure with an average size of 85.4 nm. The synthesized catalysts were tested for their photocatalytic activity by degrading methyl orange and methylene blue under sunlight. Improved degradation efficiencies of 79.44% and 84.92% were achieved within 120 min. ZnO NPs exhibited strong antibacterial activity against both Gram-positive Listeria monocytogenes (18 mm) and Staphylococcus epidermidis (20 mm) and Gram-negative strains of Escherichia coli (16 mm) and Bordetella bronchiseptica (14 mm), as shown by the inhibition zones, which were comparable to the positive control (ceftriaxone) but larger than the plant root extract. ZnO NPs showed high antioxidant activity, as a ferric-reducing antioxidant power assay value of 66.29 µg (AAE µg·mL−1) and a DPPH value of 57.44 µg (AAE µg·mL−1) were obtained at a concentration of 500 µL, which was higher than those of the C. scariosus root extract. Quantification of the total phenolic and flavonoid content yielded values of 57.63 µg (GAE µg·mL−1) and 70.59 µg (QCE µg·mL−1), respectively. At a concentration of 500 μL (1 mg·mL−1), the tested nanoparticles (NPs) showed a greater anti-inflammatory effect (84.12%) compared to the root extract of C. scariosus (34.39%). Overall, our findings highlight the versatile properties of green synthesized ZnO NPs and demonstrate their potential for environmental remediation and antimicrobial formulations, as well as promising candidates for further investigation in biomedical fields such as drug delivery and therapy.

Abstract Adverse Outcome Pathways (AOPs) have been proposed to facilitate mechanistic understanding of interactions of chemicals/materials with biological systems. Each AOP starts with a molecular initiating event (MIE) and possibly ends with adverse outcome(s) (AOs) via a series of key events (KEs). So far, the interaction of engineered nanomaterials (ENMs) with biomolecules, biomembranes, cells, and biological structures, in general, is not yet fully elucidated. There is also a huge lack of information on which AOPs are ENMs-relevant or -specific, despite numerous published data on toxicological endpoints they trigger, such as oxidative stress and inflammation. We propose to integrate related data and knowledge recently collected. Our approach combines the annotation of nanomaterials and their MIEs with ontology annotation to demonstrate how we can then query AOPs and biological pathway information for these materials. We conclude that a FAIR (Findable, Accessible, Interoperable, Reusable) representation of the ENM-MIE knowledge simplifies integration with other knowledge. Scientific contribution This study introduces a new database linking nanomaterial stressors to the first known MIE or KE. Second, it presents a reproducible workflow to analyze and summarize this knowledge. Third, this work extends the use of semantic web technologies to the field of nanoinformatics and nanosafety.

Aloe species are known for the treatment of various conditions including diabetes mellitus, hypocholesteremia, and glycation end products. Nevertheless, the biological activity of Aloe armatissima is yet to be reported. It is a first-time report to evaluate the Aloe armatissima leaves (AAL) extract for its antioxidant, anti-glycation, anti-hyperglycemic, and anti-hyperlipidemic potential. In vitro tests of 1,1-diphenyl-2-picrylhydrazyl for the antioxidant and HSA for the antiglycation activity whereas in vivo models were used to assess the toxicity, antihyperglycemic, and anti-hypercholesteremic effects. The volatile profile was determined via gas chromatography-mass spectrometry. The IC50 values of 116 ± 0.66 (μg/mL) for antioxidant activity and 0.21 ± 0.009 (mg/mL) for antiglycation activity were observed for the AAL extract. The acute toxicity in the animal model revealed a lack of toxicity for the extract. The in vivo models exhibited a dose-dependent hypoglycemic and anti-hyperglycemic effects with significant (P < 0.01) blood glucose levels reduction. Moreover, a profound decrease in serum cholesterol, triglyceride, and LDL along with a significant (P < 0.05) increase in HDL and serum insulin levels was recorded. The statistical analysis demonstrated the values of F (24,125) = 23.95, P = 0.001, effect size = 1.95 (normoglycemic mice), F (24,125) = 143.21, P = 0.001, effect size = 4.79 (glucose loaded mice), and F (24,125) = 82.69, P = 0.001, effect size = 3.6 (diabetic model). GCMS showed the presence of eleven compounds with tetratetracontane (100%), β-sitosterol (27.76), and vitamin E (18.68) in major amounts. The results underscore the extract’s capacity to effectively combat various ailments; however, the active phytochemicals need to be isolated and the pharmacological activities may be established at the molecular level.

Few-shot font generation seeks to create high-quality fonts using minimal reference style images, addressing traditional font design’s labor-intensive and time-consuming nature, particularly for languages with large character sets like Chinese and Korean. Existing methods often require multi-stage training or predefined components, which can be time-consuming and limit generalizability. This paper introduces Patch-Font, a novel single-stage method that overcomes the limitations of prior approaches, such as multi-stage training or reliance on predefined components, by integrating a patch-based attention mechanism and a multitask encoder. Patch-Font jointly captures global style elements (e.g., overall font family characteristics) and local style details (e.g., serifs, stroke shapes), ensuring high fidelity to the target style while maintaining computational efficiency. Our approach incorporates triplet margin loss with hard positive/negative mining to disentangle style from content and a style fidelity loss to enhance local style consistency. Experiments on Korean (printed and handwritten) and Chinese fonts demonstrate that Patch-Font outperforms state-of-the-art methods in style accuracy, perceptual quality, and generation speed while generalizing robustly to unseen characters and font styles. By simplifying the font creation process and delivering high-quality results, Patch-Font represents a significant step forward in making font design more accessible and scalable for diverse languages.

Influenza has been a global health concern for the past century. Current seasonal influenza vaccines primarily elicit an antibody response that targets the immunodominant head domain of the viral glycoprotein hemagglutinin (HA), which consistently mutates due to selective pressure. To circumvent this problem, we introduce a “tethered antigenic suppression” strategy to shield the HA head domain and refocus the immune response towards the conserved but immunosubdominant stalk domain of HA. Specifically, we tethered an antibody fragment (Fab) that recognizes the Sb antigenic site in the HA head domain to the HA protein with a linker. We immunized separate groups of female mice with the Fab-tethered HA or regular HA and characterized the elicited antibody response. We demonstrate that shielding the HA head domain with a tethered Fab suppresses the antibody titers towards all five key antigenic sites in the HA head domain while eliciting a robust anti-stalk antibody response. Our work highlights the potential of tethered antigenic suppression as a strategy to refocus the antibody response towards conserved epitopes on protein antigens.

The widespread deployment of deep neural networks (DNNs) in critical real-time applications has spurred significant research into their security and robustness. A key vulnerability identified is that DNN decisions can be maliciously altered by introducing carefully crafted noise into the input data, leading to erroneous predictions. This is known as an adversarial attack. In this paper, we propose a novel detection framework leveraging segmentation masks and image segmentation techniques to identify adversarial attacks on DNNs, particularly in the context of autonomous driving systems. Our defense technique considers two levels of adversarial detection. The first level mainly detects adversarial inputs with large perturbations using the U-net model and one-class support vector machine (SVM). The second level of defense proposes a dynamic segmentation algorithm based on the k-means algorithm and a verifier model that controls the final prediction of the input image. To evaluate our approach, we comprehensively compare our method to the state-of-the-art feature squeeze method under a white-box attack, using eleven distinct adversarial attacks across three benchmark and heterogeneous data sets. The experimental results demonstrate the efficacy of our framework, achieving overall detection rates exceeding 96% across all adversarial techniques and data sets studied. It is worth mentioning that our method enhances the detection rates of FGSM and BIM attacks, reaching average detection rates of 95.65% as opposed to 62.63% in feature squeezing across the three data sets.

Biomass gasification efficiently produces heat, electricity, and power. However, removing harmful contaminants from raw syngas is crucial. Tar production is a challenge due to blockage, plugging, and corrosion. Tar steam reforming (TSR) is the most promising technique, converting high molecular weight hydrocarbons into CO, CO2, H2, and CH4. In this study, a model of biomass gasification using wheat straw as biomass feedstock has been developed using Aspen Plus. The gasification flow sheet encompasses gasification, catalytic filter candle, gas cleaning, impurity removal reactor, separator, and subsequent sorbent reactors. Ni-based catalysts with Ni ratios (5%, 10%, and 15%) are used to simulate TSR reactions. Results show that the 15% Ni-Co-Al2O3 catalyst outperforms the 10% and 5% Ni-Co-Al2O3 catalysts. This study explores the impact of temperature, catalyst loadings, and steam-to-carbon (S/C) ratios on toluene conversion and hydrogen yield in catalytic steam reforming, along with temperature, steam-to-biomass ratios, and equivalence ratio on syngas fraction in gasification. It includes a techno-economic analysis of wheat straw gasification to improve syngas energy through efficient tar conversion.

This research paper focuses on enhancing the surface characteristics of the 316 stainless steel (SS316) alloy, including roughness, microhardness, and corrosion resistance. Where the application of ND-YAG laser technology, a highly relevant and timely area, was investigated deeply. The Q-switching Nd: YAG Laser was used with varying laser energy levels within the context of the laser shock peening (LSP) technique. The corrosion resistance of the 316 ss alloy is evaluated in a corrosive environment of 500 mL of saliva (with a pH of 5.6) through electrochemical corrosion testing. Corrosion rate was determined based on the analysis of polarization curves. The outcomes of this research reveal that as the laser energy was increased, there was a noticeable enhancement in the mechanical properties of the 316 ss alloy’s surface. Importantly, the corrosion rate experiences a significant reduction, decreasing from 4.94 mm/yr to 3.59 mm/yr following laser shock peening (LSP) application.

Automatic and accurate dental arch segmentation is a fundamental task in computer-aided dentistry. Recent trends in digital dentistry are tackling the design of 3D crowns using artificial intelligence, which initially requires a proper semantic segmentation of teeth from intraoral scans (IOS). In practice, most IOS are partial with as few as three teeth on the scanned arch, and some of them might have preparations, missing, or incomplete teeth. Existing deep learning-based methods (e.g., MeshSegNet, DArch) were proposed for dental arch segmentation, but they are not as efficient for partial arches that include imperfections such as missing teeth and preparations. In this work, we present the ArchSeg framework that can leverage various deep learning models for semantic segmentation of perfect and imperfect dental arches. The Point Transformer V2 deep learning model is used as the backbone for the ArchSeg framework. We present experiments to demonstrate the efficiency of the proposed framework to segment arches with various types of imperfections. Using a raw dental arch scan with two labels indicating the range of present teeth in the arch (i.e., the first and the last teeth), our ArchSeg can segment a standalone dental arch or a pair of aligned master/antagonist arches with more available information (i.e., die mesh). Two generic models are trained for lower and upper arches; they achieve dice similarity coefficient scores of 0.936±0.008 and 0.948±0.007, respectively, on test sets composed of challenging imperfect arches. Our work also highlights the impact of appropriate data pre-processing and post-processing on the final segmentation performance. Our ablation study shows that the segmentation performance of the Point Transformer V2 model integrated in our framework is improved compared with the original standalone model.

In today’s healthcare industry, safeguarding patient data is critical due to the increasing digitization of medical records, which makes them vulnerable to cyber threats. Telehealth services, while providing immense benefits in terms of accessibility and efficiency, introduce complex challenges in maintaining data privacy and security. This paper proposes a privacy-preserving framework for secure data sharing within telehealth services, employing blockchain technology and advanced cryptographic techniques. The framework ensures that all patient health data are encrypted using homomorphic encryption before storage on the blockchain, guaranteeing confidentiality and protecting data from unauthorized access. Secure multi-party computation (SMPC) is integrated for encrypted data computations, maintaining data confidentiality even during operations. Smart contracts enforce access control, ensuring that patient preferences and regulatory requirements such as the HIPAA and the GDPR are met. Furthermore, the framework includes auditing and verifying data integrity mechanisms, making it resilient against cyber threats such as impersonation, replay, and Man-In-The-Middle attacks. The analysis demonstrates the framework’s superior performance in addressing these challenges compared to that of existing systems. Future work suggests integrating AI-driven threat detection and quantum-resistant cryptographic techniques to enhance security further and adapt to the evolving telehealth landscape.

Water pollution is a major concern due to its detrimental effects on the environment and public health. The particular danger of inorganic pollutants arises from their persistent toxicity and inability to biodegrade. Recently, electrocoagulation (EC) has been demonstrated as an alternative sustainable approach to purifying wastewater due to the increasingly strict pollution prevention rules. In particular, EC has been used to remove inorganic pollutants, such as Cr, Zn, Pb, or As. EC has emerged as a sustainable tool for resource recovery of some inorganic pollutants such as N and P that, when recovered, have value as plant nutrients and are critical in a circular economy. These recovered materials can be obtained from diverse agricultural drainage water and recycled as fertilizers. In this work, a state-of-the-art technique is reviewed describing the advances in contaminant removal and nutrient recovery using EC through an in-depth discussion of the factors influencing the contaminant removal process, including operating pH, time, power, and concentration. Furthermore, limitations of the EC technology are reviewed, including the high-power consumption, fast deterioration of the sacrificial electrodes, and the types of contaminants that could not be efficiently removed. Finally, new emerging constructs in EC process optimization parameters are presented.

During a crown generation procedure, dental technicians depend on commercial software to generate a margin line to define the design boundary for the crown. The margin line generation remains a non-reproducible, inconsistent, and challenging procedure. In this work, we propose to generate margin line points on prepared teeth meshes using adaptive point learning inspired by the AdaPointTr model. We extracted ground truth margin lines as point clouds from the prepared teeth and crown bottom meshes. The chamfer distance (CD) and infoCD loss functions were used for training a supervised deep learning model that outputs a margin line as a point cloud. To enhance the generation results, the deep learning model was trained based on three different resolutions of the target margin lines, which were used to back-propagate the losses. Five folds were trained and an ensemble model was constructed. The training and test sets contained 913 and 134 samples, respectively, covering all teeth positions. Intraoral scanning was used to collect all samples. Our post-processing involves removing outlier points based on local point density and principal component analysis (PCA) followed by a spline prediction. Comparing our final spline predictions with the ground truth margin line using CD, we achieved a median distance of 0.137 mm. The median Hausdorff distance was 0.242 mm. We also propose a novel confidence metric for uncertainty quantification of generated margin lines during deployment. The metric was defined based on the percentage of removed outliers during the post-processing stage. The proposed end-to-end framework helps dental professionals in generating and evaluating margin lines consistently. The findings underscore the potential of deep learning to revolutionize the detection and extraction of 3D landmarks, offering personalized and robust methods to meet the increasing demands for precision and efficiency in the medical field.

Abstract Despite the vital activity of many compounds, they lack that effectiveness due to their low solubility in water. Unfortunately, for this reason, rutin often leads to low tissue permeability and insufficient bioavailability, which has greatly limited its pharmacological utility. Therefore, the present investigation is designed to overcome this problem by formulating the rotin to rotin nanocrystals (RNCs) with studying their some pharmacological applications in vitro and in silico. RNCs were created via the ultrasonication approach and showed a spherical shape via Transmission electron microscopy with a mean particle size of 27 nm. RNCs reflected inhibitory action against Helicobacter pylori with an inhibition zone (IZ) of 22.67 mm compared to rutin (IZ of 18 mm) and standard control (IZ of 19.5 mm). RNCs exhibited less minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) (7.8 µg/mL) than rutin (62.5 µg/mL). The MBC/MIC index of rutin and RNCs indicated their bactericidal properties. RNCs were more acutely (92.12%) than rutin (85.43%) for inhibition the H. pylori biofilm formation. A promising half maximal inhibitory concentration (IC50) (6.85 µg/mL) was recorded using RNCs for urease inhibition compared to the IC50 value of rutin (97.8 µg/mL). The activity of rutin and RNCs was tested against cancer cells of human colon cancer (HT-29) and normal Vero cells. IC50 values of RNCs were less 168.23 ± 1.15 µg/mL and 297.69 ± 4.23 µg/mL than the IC50 values of rutin 184.96 ± 4.33 µg/mL and 335.31 ± 2.02 µg/mL against HT-29 cells and normal Vero cells, respectively. Different percentages (72.2, 77.3, and 81.9%) of hemolysis inhibition were recorded using RNCs, but 63.6, 68.9, 73.6, and 80.6% were obtained using rutin at 600, 800, and 1000 µg/mL, respectively. Butyrylcholinesterase (BChE) inhibition % was documented at a lower IC50 value for RNCs (12.74 µg/mL) than the IC50 of rutin (18.15 µg/mL). The target molecule underwent molecular docking research against H. pylori [Protein Data Bank (PDB) code: 4HI0], HT-29 cells (PDB code: 2HQ6), and BChE (PDB code: 6EMI) in order to enhance the interactions between rutin and the chosen receptors and to estimate its molecular operating environment (MOE) affinity scoring. Rutin has predicted strong binding interactions and potent activity against the examined proteins 4HI0, 2HQ6, and 6EMI with low binding scores of − 7.47778 kcal/mol, − 7.68511 kcal/mol, and − 9.50333 kcal/mol, respectively.

Converting waste fish bones into bioactive materials presents an innovative and eco-friendly approach to materials science. Fish bones, often discarded as waste in the seafood industry, are rich in calcium (Ca) and phosphorous (P), making them ideal precursors for Calcium phosphate (CaP) materials. In this study, different kinds of CaP materials were successfully extracted from diverse fish bone types like Carp fish (CF), Atlantic bonito (AB) and Gilt-head bream (GB) using a heat treatment method. The extracted white powders were characterized using X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), Fourier transform infrared (FTIR), Transmission electron microscopy (TEM), and Inductively coupled plasma mass spectrometry (ICP-MS) techniques and the outcomes compared the most popular phase of CaP material like β -tricalcium phosphate (β-TCP) and Hydroxyapatite (HA) which they are synthesized using microwave refluxing equipment. The XRD pattern of the CF sample closely corresponded with the HA phase, while the AB and GB samples aligned with the biphasic calcium phosphate (HA/β-TCP) phase. The FTIR spectra analysis identified the presence of phosphate, hydroxyl, and carbonate groups. The XPS spectra determined the Ca/P ratio to range between 1.32 and 1.57. In vitro degradation studies were performed in phosphate-buffered saline (PBS) at 37 °C over 1, 3, 7, and 14 days. The ion release profiles of Ca2+ and P5+ were monitored, revealing the most significant degradation rate occurred between 7 and 14 days. The highest leaching levels of Ca ions were observed in the AB fish bones, with concentrations reaching approximately 87.35 mg/L after 14 days of immersion. Based on the results, it is concluded that the biphasic calcium phosphate derived from AB, along with its Ca, P, and other minerals content, exhibits a higher degradation rate than other samples. This indicates its potential as a promising bioactive material suitable for use as a bone tissue substitute.

Abstract In this study, a polar extract of Aconitum lycoctonum L. was used for the synthesis of silver nanoparticles (AgNPs), followed by their characterization using different techniques and evaluation of their potential as antioxidants, amylase inhibitors, anti-inflammatory and antibacterial agents. The formation of AgNPs was detected by a color change, from transparent to dark brown, within 15 min and a surface resonance peak at 460 nm in the UV–visible spectrum. The FTIR spectra confirmed the involvement of various biomolecules in the synthesis of AgNPs. The average diameter of these spherical AgNPs was 67 nm, as shown by the scanning electron micrograph. The inhibition zones showed that the synthesized nanoparticles inhibited the growth of Gram-positive and negative bacteria. FRAP and DPPH assays were used to demonstrate the antioxidant potential of AgNPs. The highest value of FRAP (50.47% AAE/mL) was detected at a concentration of 90 ppm and a DPPH scavenging activity of 69.63% GAE was detected at a concentration of 20 µg/mL of the synthesized AgNPs. 500 µg/mL of the synthesized AgNPs were quite efficient in causing 91.78% denaturation of ovalbumin. The AgNPs mediated by A. lycoctonum also showed an inhibitory effect on α-amylase. Therefore, AgNPs synthesized from A. lycoctonum may serve as potential candidates for antibacterial, antioxidant, anti-inflammatory, and antidiabetic agents.

Abstract A novel adsorbent was prepared using a backbone comprising chemically hybridized dialdehyde cellulose (DAC) with chitosan via Schiff base reaction, followed by graft copolymerization of acrylic acid. Fourier transform infrared spectroscopy (FTIR) confirmed the hybridization while scanning electron microscopy (SEM) revealed intensive covering of chitosan onto the surface of DAC. At the same time, energy dispersive X-ray (EDX) proved the emergence of nitrogen derived from chitosan. The X-ray diffraction (XRD) indicated that the crystallinity of the backbone and graft copolymer structures was neither affected post the hybridization nor the grafting polymerization. The adsorbent showed high swelling capacity (872%) and highly efficient removal and selectivity of Ni2+ in the presence of other disturbing ions such as Pb2+ or Cu2+. The kinetic study found that the second-order kinetic model could better describe the adsorption process of (Cu2+, Ni2+) on the graft copolymer. In contrast, the first-order kinetic model prevails for the binary mixture (Pb2+, Ni2+). Moreover, the correlation coefficient values for the adsorption process of these binary elements using Langmuir and Freundlich isotherms confirmed that the developed grafted DAC/chitosan exhibits a good fit with both isotherm models, which indicates its broadened and complicated structure. Furthermore, the grafted DAC/chitosan exhibited high efficient regeneration and high adsorption capacity for Pb2+, Cu2+ and Ni2+.

Abstract Wheat is considered as the most important cereal grain globally. It has a vast economic importance as it is used in producing bread, pastries, and household flour and serving as food for livestock among other uses. Different biological activities of wheat were correlated with the presence of polyphenols due to their antioxidant activities and other preventative capabilities. Wheat can also be used as an antidiabetic, anti-inflammatory, anticancer, antimicrobial, and antiaging agent. Omics has established itself during the past 20 years as a crucial tool for comprehending the internal systems of various plant systems including wheat using LC–MS, GC–MS, and UV spectrophotometry as analytical techniques. The current review represents in depth search regarding wheat cultivation, botanical description, economic significance, quantitative phytochemical characterization, and biological importance. Additionally, a critical assessment of the cited omics research on wheat was conducted with an emphasis on the analytical instrument, methods of analysis and results interpretation.

Self-doped CuS nanoparticles (NPs) were successfully synthesized via microwave-assisted polyol process to act as co-catalysts to TiO2 nanofiber (NF)-based photoanodes to achieve higher photocurrents on visible light-assisted water electrolysis. The strategy adopted to perform the copper cation sulfidation in polyol allowed us to overcome the challenges associated with the copper cation reactivity and particle size control. The impregnation of the CuS NPs on TiO2 NFs synthesized via hydrothermal corrosion of a metallic Ti support resulted in composites with increased visible and near-infrared light absorption compared to the pristine support. This allows an improved overall efficiency of water oxidation (and consequently hydrogen generation at the Pt counter electrode) in passive electrolyte (pH = 7) even at 0 V bias. These low-cost and easy-to-achieve composite materials represent a promising alternative to those involving highly toxic co-catalysts.

Resin bond grinding wheels are the most common grinding tools in the industry. Until now, all research on the additive manufacturing of resin bond grinding wheels has focused on commercially available acrylate resin. However, using a phenol-based bond to print resin-bond grinding wheels has been challenging for researchers and industries. In this study, a photo-curable phenol resin bond grinding wheel was introduced for the first time, offering advantages such as lower cost, high thermal resistance, and good mechanical properties. To enhance the grinding performance of the printed wheels, various additives, such as copper, glass fiber, and carbon fiber, were incorporated into the composition. Different on-machine and out-of-machine measurements, such as force, tool wear, dimensional accuracy, and optical microscopy measurements, were conducted to investigate the grinding performance of the printed wheels. The results demonstrate that printed grinding wheels have strong potential in grinding applications, which was more prominent for the bond reinforced by glass fibers, providing improved mechanical properties (up to 50%), wear resistance (up to 75%), and higher dimensional accuracy (up to 11%).

Ephedra alata leaf extracts have therapeutic properties and contain various natural compounds known as phytochemicals. This study assessed the phytochemical content and antioxidant effects of a Ephedra alata leaf extract, as well as zinc oxide (ZnO) nanoparticle production. The extract contained phenolic acids, including vanillic acid, chlorogenic acid, gallic acid, p-coumaric acid, vanillin and rutin. Its total phenolic content and total flavonoid content were 48.7 ± 0.9 mg.g-1 and 1.7 ± 0.4 mg.g-1, respectively. The extract displayed a DPPH inhibition rate of 70.5%, total antioxidant activity of 49.5 ± 3.4 mg.g-1, and significant antimicrobial activity toward Gram-positive and negative bacteria. The synthesized ZnO nanoparticles had spherical shape, crystallite size of 25 nm, particle size between 5 and 30 nm, and bandgap energy of 3.3 eV. In specific conditions (90 min contact time, pH 7, and 25°C), these nanoparticles efficiently photodegraded 87% of methylene blue, suggesting potential applications for sustainable water treatment and pollution control.

Cancer is an elaborate sequence of disease grades that include uncontrolled cell growth and division, invasion, and metastasis. Overexpression of the epidermal growth factor receptor (EGFR) causes an abnormal signal transduction and is directly linked to cancer development. Most EGFR tyrosine kinase inhibitors (TKIs) are ATP-competitive inhibitors that frequently cause EGFR mutations or chemoresistance. Therefore, targeting the EGFR TK allosteric site has become a highly sought after cancer treatment strategy. Ten new derivatives of 4-(tert-butyl)-3-methoxybenzoic acid containing carbothioamide (compounds 3a-e), triazole (compounds 4a-d) or oxadiazole (compound 5) moieties were designed as EGFR TK allosteric site inhibitors as deduced in silico. The structures of these derivatives were characterized by chemical spectroscopic methods (ATR-FTIR, 1HNMR, 13CNMR and HRESI-MS). According to the molecular docking studies, compounds 3e and 3d showed the highest docking scores (ΔG), which was confirmed by molecular dynamic (MD) simulation studies. The synthesized derivatives, specifically compounds 3d and 3e, exhibited favorable pharmacokinetic profile. In vitro, the newly synthesized derivatives were evaluated for their cytotoxicity against A549 (lung adenocarcinoma), HepG2 (hepatocellular carcinoma), and HCT-116 (colorectal) cancer cell lines via the MTT assay, flow cytometry, RT-PCR, immunoblotting, and kinase inhibition assay. The cytotoxicity results showed that compound 3d was cytotoxic to the three tested cell lines, achieving the lowest IC50 concentration against A549 cancer cells. Compound 3d targeting EGFR tyrosine kinase caused cell cycle arrest at the G2/M phase and induction of the ER-mediated apoptosis pathway. In silico and in vitro antitumor activity findings of compound 3d demonstrated that it is a promising EGFR tyrosine kinase allosteric site inhibitor.

Abstract Protein mutations, especially those which occur in the binding site, play an important role in inter-individual drug response and may alter binding affinity and thus impact the drug’s efficacy and side effects. Unfortunately, large-scale experimental screening of ligand-binding against protein variants is still time-consuming and expensive. Alternatively, in silico approaches can play a role in guiding those experiments. Methods ranging from computationally cheaper machine learning (ML) to the more expensive molecular dynamics have been applied to accurately predict the mutation effects. However, these effects have been mostly studied on limited and small datasets, while ideally a large dataset of binding affinity changes due to binding site mutations is needed. In this work, we used the PSnpBind database with six hundred thousand docking experiments to train a machine learning model predicting protein-ligand binding affinity for both wild-type proteins and their variants with a single-point mutation in the binding site. A numerical representation of the protein, binding site, mutation, and ligand information was encoded using 256 features, half of them were manually selected based on domain knowledge. A machine learning approach composed of two regression models is proposed, the first predicting wild-type protein-ligand binding affinity while the second predicting the mutated protein-ligand binding affinity. The best performing models reported an RMSE value within 0.5 $$-$$ - 0.6 kcal/mol-1 on an independent test set with an R2 value of 0.87 $$-$$ - 0.90. We report an improvement in the prediction performance compared to several reported models developed for protein-ligand binding affinity prediction. The obtained models can be used as a complementary method in early-stage drug discovery. They can be applied to rapidly obtain a better overview of the ligand binding affinity changes across protein variants carried by people in the population and narrow down the search space where more time-demanding methods can be used to identify potential leads that achieve a better affinity for all protein variants.

Laser-Assisted Chemical Bath Synthesis (LACBS) was used to fabricate pure and magnesium-doped zinc oxide nanoparticles. Analysis of these nanoparticles' structural, morphological, optical, and antimicrobial characteristics was conducted. This analysis spanned across varying concentrations of magnesium-doped zinc oxide from 1 % to 3 %. XRD confirmed the nanoparticles' crystalline nature, revealing the hexagonal wurtzite phase. SEM analysis showcased their nanometric domain existence and hexagonal crystalline morphology, transforming from nanorods to nanotubes. Optical analysis showed band gap energy decrease from 3.27 to 2.85 eV correlating with the magnesium doping concentration increase. Optical absorption displayed a distinctive redshift for the nanoparticles as magnesium concentration increased from 1 % to 3 %. Photocatalytic assessments highlighted the superior degradation ability of 3 % Mg-doped nanoparticles, showing a 98.04 % degradation rate against methylene orange dye under blue light exposure. Antimicrobial activity tests against various pathogens showed that Mg ions' incorporation significantly enhanced antimicrobial performance, demonstrating the effectiveness of the LACBS method.

This paper presents a novel application of machine learning models to clarify the intricate behaviors of expansive soils, focusing on the impact of sand content, saturation level, and dry density. Departing from conventional methods, this research utilizes a data-centric approach, employing a suite of sophisticated machine learning models to predict soil properties with remarkable precision. The inclusion of a 30% sand mixture is identified as a critical threshold for optimizing soil strength and stiffness, a finding that underscores the transformative potential of sand amendment in soil engineering. In a significant advancement, the study benchmarks the predictive power of several models including extreme gradient boosting (XGBoost), gradient boosting regression (GBR), random forest regression (RFR), decision tree regression (DTR), support vector regression (SVR), symbolic regression (SR), and artificial neural networks (ANNs and proposed ANN-GMDH). Symbolic regression equations have been developed to predict the elasticity modulus and unconfined compressive strength of the investigated expansive soil. Despite the complex behaviors of expansive soil, the trained models allow for optimally predicting the values of unconfined compressive parameters. As a result, this paper provides for the first time a reliable and simply applicable approach for estimating the unconfined compressive parameters of expansive soils. The proposed ANN-GMDH model emerges as the pre-eminent model, demonstrating exceptional accuracy with the best metrics. These results not only highlight the ANN’s superior performance but also mark this study as a groundbreaking endeavor in the application of machine learning to soil behavior prediction, setting a new benchmark in the field.

In this paper, experimental tensile tests for pre-cracked high Carbon steel ‘C90’ specimens were performed for quasi-static and dynamic loading. High loading velocity affects the crack patterns by preventing deflection. On the other hand, an efficient numerical tool based on the phase field model was developed and validated to predict brittle fracture trajectories. A staggered numerical scheme was adopted to solve the displacement and damage fields separately. Implementation efficiency in initiating and propagating cracks, even from an undamaged microstructure, was proved. The effect of the critical fracture energy density Gc on the crack path was tested; with smaller Gc, the crack patterns become more complex. In addition, the impact of loading velocities was examined, and earlier and faster crack formation and greater crack branching is observed with higher impact velocity. In this study, bidimensional plane stress cases were treated. The phase field model with hybrid formulation was able to predict crack pattern and especially crack arrest and branching found in the literature. The developed model accurately determined the transition zone of the crack path topology that has been observed experimentally.

As the construction industry is facing the challenge of meeting the ever-increasing demand for environmentally friendly and durable concrete, the role of viscosity-modifying admixtures (VMAs) has become increasingly essential to improve the rheological properties, stability, and mechanical properties of concrete. Additionally, natural polymers are ever evolving, offering multiple opportunities for innovative applications and sustainable solutions. This comprehensive review delves into the historical context and classifications of VMAs, accentuating their impact in enhancing the rheological properties, stability, and mechanical properties of concrete. Emphasis is placed on the environmental impact of synthetic VMAs, promoting the exploration of sustainable alternatives derived from plant-based biopolymers. Indeed, biopolymers, such as cellulose, starch, alginate, pectin, and carrageenan are considered in this paper, focusing on understanding their efficacy in improving concrete properties while enhancing the environmental sustainability within the concrete.

Ultrasound is a highly adaptable medical imaging modality that offers several applications and a wide range of uses, both for diagnostic and therapeutic purposes. The principles of sound wave propagation and reflection enable ultrasound imaging to function as a highly secure modality. This technique facilitates the production of real-time visual representations, thereby assisting in the evaluation of various medical conditions such as cardiac, gynecologic, and abdominal diseases, among others. The ultrasound modality encompasses a diverse range of modes and mechanisms that serve to enhance the methodology of pathology and physiology assessment. Doppler imaging and US elastography, in particular, are two such techniques that contribute to this expansion. Elastography-based imaging methods have attracted significant interest in recent years for the non-invasive evaluation of tissue mechanical characteristics. These techniques utilize the changes in soft tissue elasticity in various diseases to generate both qualitative and quantitative data for diagnostic purposes. Specialized imaging techniques collect data by identifying tissue stiffness under mechanical forces such as compression or shear waves. However, in this review paper, we provide a comprehensive examination of the fundamental concepts, underlying physics, and limitations associated with ultrasound elastography. Additionally, we present a concise overview of its present-day clinical utilization and ongoing advancements across many clinical domains.

Ultrasound technique is one of the green technologies that is being utilized widely for varying food processes. Our aim in this study was to carry out ultrasonication, pasteurization and chemical preservation (Potassium metabisulfite) techniques on a carrot-orange juice blend. Additionally, the effect of these treatments on the storage period of about 21 days was also determined. The study displayed an array of results under the effect of different treatments. Throughout the storage period of 21 days’ significant results were presented by the carrot juice blend subjected to the ultrasound technique (25 min) giving the highest values for total phenolic content (25.56 ± 1.29 mg GAE/100 mL), total antioxidant activity (573.48 ± 2.29 mg Trolox /100 mL), DPPH (32.32 ± 1.83 %) and reducing power (45.45 ± 1.92 mg AAE/100 mL) with least deterioration, followed by the blends treated with potassium metabisulfite (KMS) and pasteurization. The physicochemical analysis showed a non-significant effect of treatments on pH and total soluble solids (oBrix) of carrot-orange juice blends whereas, the changes in color parameters L*, a* and b* were noted to show changes in treated blends. Similarly, the results for the GC–MS quantification of volatile compounds displayed the highest concentrations in the ultrasonicated blends as compared to other techniques. The peak quantity was obtained for the hexanal (9903.43 ± 7.61 μg.kg−1) followed by 3-Methylbutanal (2638.7 ± 5.44 μg.kg−1), terpinolene (2337.16 ± 5.28 μg.kg−1), elemicin (2198.28 ± 5.28 μg.kg−1), myristicin (1936.62 ± 6.72 μg.kg−1). The use of sonication can effectively enhance the nutritional qualities of juice, as perceived by consumers.

Vascular calcification is calcium deposition occurring in the wall of blood vessels, leading to mechanical stress and rupture due to a loss of elasticity and the hardening of the vessel wall. The role of the Transient Receptor Channel Vanilloid 4 (TRPV4), a Ca2+-permeable cation channel, in the progression of vascular calcification is poorly explored. In this study, we investigated the role of TRPV4 in vascular calcification and the development of abdominal aortic aneurysm (AAA). Experimental mice were randomly divided into four groups: wild-type (WT) sham operated group, WT CaCl2-induced aortic injury, TRPV4-KO sham operated group, and TRPV4-KO CaCl2-induced aortic injury. The TRPV4-knockout (TRPV4-KO) mice and wild-type (WT) mice were subjected to the CaCl2-induced abdominal aortic injury. In histopathological analysis, the aorta of the TRPV4-KO mice showed extensive calcification in the tunica media with a significant increase in the outer diameter (p < 0.0001), luminal area (p < 0.05), and internal circumference (p < 0.05) after CaCl2 injury when compared to WT mice. Additionally, the tunica media of the TRPV4-KO mice aorta showed extensive damage with apparent elongation and disruption of the elastic lamella. These results indicate a protective function of TRPV4 against vascular calcification and the progression of AAA after CaCl2 injury.

An investigation was conducted to examine the photothermal and thermomechanical effects of short-pulse laser irradiation on normal tissues. This study analyzed the impact of short-pulse laser radiation on the heat-affected region within tissues, taking into consideration a set of laser variables, namely wavelength, intensity, beam size, and exposure time. The beam size ranged between 0.5 and 3 mm, and the intensity of the laser radiation ranged from 1 to 5 W/mm2 at wavelengths of 532 and 800 nm. A three-layered, three-dimensional model was implemented and studied in a polar coordinate system (r = 10 mm, z = 12 mm) in COMSOL Multiphysics (version 5.4, COMSOL Inc., Stockholm, Sweden) to perform numerical simulations. The Pennes bioheat transfer model, Beer-Lambert, and Hooke’s law are integrated to simulate the coupled biophysics problem. Temperature and stress distributions resulting from laser radiation were produced and analyzed. The accuracy of the developed model was qualitatively verified by comparing temperature and mechanical variations following the variations of laser parameters with relevant studies. The results of Box-Behnken analysis showed that beam size (S) had no significant impact on the response variables, with p-values exceeding 0.05. Temperature (Tmax) demonstrates sensitivity to both beam intensity (I) and exposure time (T), jointly contributing to 89.6% of the observed variation. Conversely, while beam size (S) has no significant effect on stress value (Smax), wavelength (W), beam intensity (I), and exposure time (T) collectively account for 71.6% of the observed variation in Smax. It is recommended to use this model to obtain the optimal values of the laser treatment corresponding to tissue with specified dimensions and properties.