Search Results (2,850)

Historically, the multiple uses of cannabis as a medicine, food, and for recreational purposes as a psychoactive drug span several centuries. The various components of the plant (i.e., seeds, roots, leaves and flowers) have been utilized to alleviate symptoms of inflammation and pain (e.g., osteoarthritis, rheumatoid arthritis), mood disorders such as anxiety, and intestinal problems such as nausea, vomiting, abdominal pain and diarrhea. It has been established that the intestinal microbiota progresses neurological, endocrine, and immunological network effects through the gut–microbiota–brain axis, serving as a bilateral communication pathway between the central and enteric nervous systems. An expanding body of clinical evidence emphasizes that the endocannabinoid system has a fundamental connection in regulating immune responses. This is exemplified by its pivotal role in intestinal metabolic and immunity equilibrium and intestinal barrier integrity. This neuromodulator system responds to internal and external environmental signals while also serving as a homeostatic effector system, participating in a reciprocal association with the intestinal microbiota. We advance an exogenous cannabinoid–intestinal microbiota–endocannabinoid system axis potentiated by the intestinal microbiome and medicinal cannabinoids supporting the mechanism of action of the endocannabinoid system. An integrative medicine model of patient care is advanced that may provide patients with beneficial health outcomes when prescribed medicinal cannabis. Full article

Endophytic microbes in medicinal plants often possess beneficial traits for plant health. This study focuses on the bacterial endophyte strain B.L.Ns.14, isolated from Nigella sativa leaves, which demonstrated multiple plant growth-promoting properties. In vitro tests showed that B.L.Ns.14 supports plant growth, colonization, and tolerance to abiotic stress. The strain also exhibited antifungal activity against phytopathogens such as Rhizoctonia solani, Colletotrichum acutatum, Verticillium dahliae, and Fusarium oxysporum f. sp. radicis-lycopersici. Whole-genome analysis, supported by ANI and dDDH values, identified B.L.Ns.14 as Bacillus halotolerans. Genome mining revealed 128 active carbohydrate enzymes (Cazymes) related to endophytism and biocontrol functions, along with genes involved in phosphate solubilization, siderophore and IAA production, biofilm formation, and motility. Furthermore, genes for osmolyte metabolism, Na+/H+ antiporters, and stress response proteins were also identified. The genome harbors 12 secondary metabolite biosynthetic gene clusters, including those for surfactin, plipastatin mojavensin, rhizocticin A, and bacilysin, known for their antagonistic effects against fungi. Additionally, B.L.Ns.14 promoted Arabidopsis thaliana growth under both normal and saline conditions, and enhanced Solanum lycopersicum growth via seed biopriming and root irrigation. These findings suggest that Bacillus halotolerans B.L.Ns.14 holds potential as a biocontrol and plant productivity agent, warranting further field testing. Full article

The SARS-CoV-2 main protease (Mpro, also known as 3CLpro) is a key target for antiviral therapy due to its critical role in viral replication and maturation. This study investigated the inhibitory effects of Bofutrelvir, Nirmatrelvir, and Selinexor on 3CLpro through molecular docking, molecular dynamics (MD) simulations, and free energy calculations. Nirmatrelvir exhibited the strongest binding affinity across docking tools (AutoDock Vina: −8.3 kcal/mol; DiffDock: −7.75 kcal/mol; DynamicBound: 7.59 to 7.89 kcal/mol), outperforming Selinexor and Bofutrelvir. Triplicate 300 ns MD simulations revealed that the Nirmatrelvir-3CLpro complex displayed high conformational stability, reduced root mean square deviation (RMSD), and a modest decrease in solvent-accessible surface area (SASA), indicating enhanced structural rigidity. Gibbs free energy analysis highlighted greater flexibility in unbound 3CLpro, stabilized by Nirmatrelvir binding, supported by stable hydrogen bonds. MolProphet prediction tools, targeting the Cys145 residue, confirmed that Nirmatrelvir exhibited the strongest binding, forming multiple hydrophobic, hydrogen, and π-stacking interactions with key residues, and had the lowest predicted IC₅₀/EC₅₀ (9.18 × 10⁻⁸ mol/L), indicating its superior potency. Bofutrelvir and Selinexor showed weaker interactions and higher IC₅₀/EC₅₀ values. MM/PBSA analysis calculated a binding free energy of −100.664 ± 0.691 kJ/mol for the Nirmatrelvir-3CLpro complex, further supporting its stability and binding potency. These results underscore Nirmatrelvir’s potential as a promising therapeutic agent for SARS-CoV-2 and provide novel insights into dynamic stabilizing interactions through AI-based docking and long-term MD simulations. Full article

Objectives: This study was designed to assess the effectiveness of chitosan as a coupling agent for improving the tensile bond strength of fiber posts. Methods: A total of 91 single-rooted mandibular teeth were root canal-filled. Post spaces were created and categorized into seven groups: Group A (Control), Group B (Silane), Group C (Chitosan), Group D (37% Phosphoric acid + Silane), Group E (37% Phosphoric acid + Chitosan), Group F (10% Hydrogen Peroxide + Silane), and Group G (10% Hydrogen Peroxide + Chitosan). Posts were cemented and tensile bond strength was measured, while the morphological structure of the fiber posts was analyzed using Scanning Electron Microscopy. One-way (ANOVA) and Tukey’s multiple comparison tests were performed at a level of significance of 5%. The percentages of fracture patterns among the groups were compared. Results: 10% Hydrogen peroxide + Chitosan exhibited the significantly highest tensile bond strength (p < 0.001). Adhesive failures were more frequent in Groups A, B, C, and D, whereas cohesive failures within the resin cement were predominant in Groups E, F, and G. Conclusions: The protocol of using 10% hydrogen peroxide followed by a chitosan coupling agent significantly improved tensile bond strengths for glass fiber posts, which highlights the potential of using chitosan as a natural biopolymer and an alternative to synthetic coupling agents to develop more effective bonding strategies for dental restorations. Full article

This article looked at how insert mounting errors affect the cutting tool performance in the face milling of 1.0503 steel. This study was conducted for 490-050Q22-08M inserts mounted in a Sandvik Coromant 490-050Q22-08M CoroMill cutter attached to an AVIA VMC 800 vertical milling center. A 3D geometrical model of the cutter was developed to determine the engagement of the particular inserts in the material removal process at different feeds per tooth. The test results showed that, at feeds ranging from 0.02 mm/tooth to 0.06 mm/tooth, only three out of five inserts took part in the face milling process, while at feeds higher than 0.12 mm/tooth, all the inserts mounted in the cutter body were engaged. The relative displacements in the tool-workpiece system were measured along the axis of rotation of the tool using a Renishaw XL-80 laser interferometer. The vibration signals recorded during cutting confirmed that there was a clear relationship between the number of inserts engaged in the process and the root mean square, the arithmetic mean, and the DC component. Multiple 2D scans of the face milled surface to measure parameters Ra and Rt helped determine the feed range where the cutting process was stable. The conducted studies allowed for the identification of optimal operating ranges for a tool with parameterized errors in the mounting of inserts within the tool body. The influence of these mounting errors, in correlation with the feed per tooth, on the surface roughness of 1.0503 steel was presented and compared with five other materials. Full article

In this study, we develop new efficient parallel techniques for solving both distinct and multiple roots of nonlinear problems at the same time. The parallel techniques represent an innovative contribution to the discipline, with local convergence of the ninth order. Theoretical research shows the rapid convergence and effectiveness of the proposed parallel schemes. To assess the suggested scheme’s stability and consistency, we look at certain biomedical engineering applications, such as osteoporosis in Chinese women, blood rheology, and differential equations. Overall, detailed analyses of convergence behavior, memory utilization, computational time, and percentage computational efficiency show that the novel parallel techniques outperform the traditional methods. The proposed methods would be more suitable for large-scale computational problems in biomedical applications due to their advantages in memory efficiency, CPU time, and error reduction. Full article

Multiple myeloma (MM) is characterized by the uncontrolled proliferation of monoclonal plasma cells and accounts for approximately 10% of all hematologic malignancies. The clinical outcomes of MM can exhibit considerable variability. Variability in both the genetic and epigenetic characteristics of MM undeniably contributes to tumor dynamics. The aim of the present study was to identify biomarkers with the potential to improve the accuracy of prognosis assessment in MM. Initially, miRNA sequencing was conducted on bone marrow (BM) samples from patients with MM. Subsequently, the expression levels of 27 microRNAs (miRNA) and the gene expression levels of ASF1B, CD82B, CRISP3, FN1, MEF2B, PD-L1, PPARγ, TERT, TIMP1, TOP2A, and TP53 were evaluated via real-time reverse transcription polymerase chain reaction in BM samples from patients with MM exhibiting favorable and unfavorable prognoses. Additionally, the analysis involved the bone marrow samples from patients undergoing examinations for non-cancerous blood diseases (NCBD). The findings indicate a statistically significant increase in the expression levels of miRNA-124, -138, -10a, -126, -143, -146b, -20a, -21, -29b, and let-7a and a decrease in the expression level of miRNA-96 in the MM group compared with NCBD (p < 0.05). No statistically significant differences were detected in the expression levels of the selected miRNAs between the unfavorable and favorable prognoses in MM groups. The expression levels of ASF1B, CD82B, and CRISP3 were significantly decreased, while those of FN1, MEF2B, PDL1, PPARγ, and TERT were significantly increased in the MM group compared to the NCBD group (p < 0.05). The MM group with a favorable prognosis demonstrated a statistically significant decline in TIMP1 expression and a significant increase in CD82B and CRISP3 expression compared to the MM group with an unfavorable prognosis (p < 0.05). From an empirical point of view, we have established that the complex biomarker encompassing the CRISP3/TIMP1 expression ratio holds promise as a prognostic marker in MM. From a fundamental point of view, we have demonstrated that the development of MM is rooted in a cascade of complex molecular pathways, demonstrating the interplay of genetic and epigenetic factors. Full article

Soil contamination by trace metal elements, such as aluminum and barium, presents specific environmental risks, particularly to plant health and agricultural productivity. Excessive accumulation of these toxic elements in plant tissues can alter redox equilibrium and affect homeostasis. This study sought to examine the physiological reactions of Abelmoschus esculentus (L.) under aluminum- and barium-induced stress. The plants were exposed to multiple concentrations of Al or Ba (0, 100, 200, 400 and 600 µM) for 45 days; then, the accumulation potential of Al and Ba, oxidative damage, and antioxidative metabolism were assessed. Key findings showed a proportional distribution of the Al and Ba in roots and aerial parts of the plants, with lower accumulation in the fruits. The occurrence of oxidative damage and the involvement of antioxidant enzymes were demonstrated by increased amounts of malondialdehyde and H₂O₂, enhanced activity of superoxide dismutase, and decreased catalase activity. The study also highlighted that GSH played a primary role in Al detoxification in the roots and fruits, while phytochelatins were more active in Ba-treated plants, particularly in roots and shoots, facilitating Ba sequestration. Full article

The accurate estimation of Digital Elevation Models (DEMs) derived from satellite data is critical for numerous environmental applications. This study evaluates the accuracy and reliability of two satellite-derived elevation models, the ALOS World 3D and SRTM DEMs, specifically for their application in hydrological modeling. A comparative analysis with Terrestrial Laser Scanning (TLS) measurements assessed the agreement between these datasets. Multiple linear regression models were utilized to evaluate the relationships between the datasets and provide detailed insights into their accuracy and biases. The results indicate significant correlations between satellite DEMs and TLS measurements, with adjusted R-square values of 0.8478 for ALOS and 0.955 for the SRTM. To quantify the average difference, root mean square error (RMSE) values were calculated as 10.43 m for ALOS and 5.65 m for the SRTM. Additionally, slope and aspect analyses were performed to highlight terrain characteristics across the DEMs. Slope analysis showed a statistically significant negative correlation between SRTM and TLS slopes (R² = 0.16, p < 4.47 × 10⁻¹⁰ indicating a weak relationship, while no significant correlation was observed between ALOS and TLS slopes. Aspect analysis showed significant positive correlations for both ALOS and the SRTM with TLS aspect, capturing 30.21% of the variance. These findings demonstrate the accuracy of satellite-derived elevation models in representing terrain features relative to high-resolution terrestrial data. Full article

A 7075 aluminum alloy high-lock nut developed multiple cracks after 3 years of exposure to atmospheric conditions. To identify the root cause of the cracking, a comprehensive analysis was conducted, including chemical composition, macro- and micro-fracture analyses, microstructural analysis, mechanical performance verification, and residual stress testing. The results indicated that stress corrosion was the cause of the fractures. After assembly, the crimping part of the high-lock nuts exhibited significant residual tensile stress and stress concentration, which led to stress corrosion in the industrial atmospheric environment. A comparison of the residual tensile stress in high-lock nuts with wall thicknesses of 0.75 mm, 1.00 mm, and 1.25 mm revealed that the residual tensile stress at the crimping part decreased as the wall thickness increased. Additionally, stress corrosion testing demonstrated that high-lock nuts with a wall thickness of 1.25 mm did not undergo stress corrosion within 30 days. Full article

The impact of adding silver nanoparticles (AgNPs) to bioceramic (BC) sealer on their ability to penetrate dentinal tubules is still unknown. Thus, this confocal laser scanning microscopic (CLSM) study aimed to assess the extent of dentinal tubule penetration of BC sealer (TotalFill^® Hiflow BC Sealer™, FKG, Switzerland) with and without AgNPs using the single-cone (SC) technique and the continuous-wave condensation (CWC) technique. AgNPs alone as well as in a mixture with the BC sealer were characterized using scanning electron microscopy and transmission electron microscopy. Single-rooted extracted human teeth (N = 100) were selected and prepared, and then divided into four groups (n = 25). Group 1 (BC/SC): BC sealer obturated with the SC technique. Group 2 (BC+AgNPs/SC): BC sealer with AgNPs obturated with the SC technique. Group 3 (BC/CWC): BC Sealer obturated with the CWC technique. Group 4 (BC+AgNPs/CWC): BC Sealer with AgNPs obturated with the CWC technique. After 2 weeks, roots were horizontally sectioned to obtain 1 mm thick dentin slices that were evaluated with CLSM. Sealer dentinal tubule penetration area and the maximum depth of penetration were measured. Data were analyzed with one-way ANOVA and the Tukey multiple comparison tests (p ≤ 0.05). The characterization process demonstrated a spherical-shaped nanoparticles without obvious agglomeration. The results showed that Group 2 (BC+AgNPs/SC) significantly demonstrated the highest mean tubular penetration depth, while group 3 (BC/CWC) had the lowest mean depth. Group 2 (BC+AgNPs/SC) exhibited the significantly highest mean value for the total area of penetration. However, groups 1 (BC/SC) and 3 (BC/CWC) exhibited the lowest mean value of total penetration area, with no statistically significant difference. The integration of AgNPs with BC sealer markedly enhanced penetration into dentinal tubules. The SC technique demonstrated superior penetration relative to the CWC technique. Full article

The accurate monitoring of the internal hotspot temperature in transformers is crucial for ensuring the stability of power grid operations. Traditional methods typically measure only the surface temperature of transformers, whereas this study proposes a non-invasive thermal inversion algorithm based on a wireless mesh network that effectively predicts the internal hotspot temperature. An electromagnetic-thermal-fluid coupling simulation model was developed to simulate the temperature distribution in transformers under various operating conditions. Subsequently, this study employed a Support Vector Regression (SVR) algorithm to train the sample dataset, optimizing the SVR model using a grid search and cross-validation to enhance the predictive accuracy. After training, the model estimates the hotspot temperature based on surface measurements obtained through a non-contact infrared sensor network. The wireless mesh network, based on the Wi-Fi protocol, provides robust and real-time monitoring even in harsh environments, with data transmitted to a central root node via multiple sensor nodes. The experimental results demonstrate that this method is highly accurate, with predicted temperatures closely matching the results from traditional measurement techniques. This method enhances transformer condition monitoring, helping to extend the transformer lifespan and improve power grid stability. Full article

Robinia pseudoacacia L., commonly known as black locust, is a nitrogen-fixing species characterized by multiple uses. Among these uses, black locust is of special interest to beekeepers due to its abundant flowering and delicious honey. Given the great importance of honey production in Italy, beekeepers are looking for genotypes that have a delayed flowering time. As a consequence, the aim of the present study was to develop a complete protocol of micropropagation for genotypes, which have been selected in the Veneto region due to their delayed flowering, i.e., about 3 months, in comparison with the normal flowering time (from late April to early June). The subsequent steps of the micropropagation protocol (explant decontamination, shoot induction, proliferation, and rooting) were investigated and optimized. The most effective decontamination treatment of explants (axillary buds from shoots developed in a greenhouse) was obtained using 50 mg/L AgNO₃ for 20 min. This method resulted in the highest survival and regeneration rates for the explants (90%), although contamination was slightly higher than when using HgCl₂ and NaOCl. The best medium for shoot establishment was MS with 1 mg/L of mT, which achieved 100% regeneration of the explants. In comparison with BA, mT at 1 mg/L was shown to be the best stimulator of shoot proliferation, especially in combination with 0.7 mg/L GA₃ (Proliferation Rate, 4.7). An intermediate 2 h treatment with AgNO₃, in combination with mT, was shown to be beneficial in improving the shoot proliferation and quality in the subsequent subculture in a gelled medium. As for shoot rooting, the shoots that were pre-treated in NH₄NO₃-free and mT-free MS medium gave the highest ex vitro rooting percentage in a cell tray (80%) and the highest number of roots per shoot (3.6). This optimized protocol opens the door to the massive micropropagation of valuable genotypes of black locust selected for delayed flowering. This is an outcome of extraordinary importance for beekeepers. Full article

To obtain an effective bacterial biocontrol strain against the fungal pathogen Ganoderma pseudoferreum, causing rubber tree red root rot disease, healthy rubber tree tissue from Baisha County, Hainan Province, was selected as the isolation source, and bacterial strains with strong antifungal effects against G. pseudoferreum were screened. The strain was identified by molecular biology, in vitro root segment tests, pot growth promotion tests, and genome detection. The strain was further evaluated by biological function tests, genome annotation analysis, and plant defense-related enzyme activity detection. The results show that strain LSR7 had good antagonistic effects against G. pseudoferreum, and the inhibition rate reached 88.49%. The strain LSR7 was identified as Bacillus velezensis by genome sequencing. In a greenhouse environment, LSR7 prevents and treats red root rot disease in rubber trees and promotes the growth of rubber tree seedlings. LSR7 secreted cell wall hydrolases (protease, glucanase, and cellulase), amylases, and siderophores. LSR7 also formed biofilms, facilitating plant colonization. Genome prediction showed that LSR7 secreted multiple antifungal lipopeptides. LSR7 enhanced rubber tree resistance to G. pseudoferreum by increasing the activity of defense enzymes. Bacillus velezensis LSR7 has biocontrol potential and is a candidate strain for controlling red root rot disease in rubber trees. Full article

In recent years, recommender systems have become a crucial tool, assisting users in discovering and engaging with valuable information and services. Multi-criteria recommender systems have demonstrated significant value in assisting users to identify the most relevant items by considering various aspects of user experiences. Deep learning (DL) models demonstrated outstanding performance across different domains: computer vision, natural language processing, image analysis, pattern recognition, and recommender systems. In this study, we introduce a deep learning model using VAE to improve multi-criteria recommendation systems. Specifically, we propose a variational autoencoder-based model for multi-criteria recommendation systems (VAE-MCRS). The VAE-MCRS model is sequentially trained across multiple criteria to uncover patterns that allow for better representation of user–item interactions. The VAE-MCRS model utilizes the latent features generated by the VAE in conjunction with user–item interactions to enhance recommendation accuracy and predict ratings for unrated items. Experiments carried out using the Yahoo! Movies multi-criteria dataset demonstrate that the proposed model surpasses other state-of-the-art recommendation algorithms, achieving a Mean Absolute Error (MAE) of 0.6038 and a Root Mean Squared Error (RMSE) of 0.7085, demonstrating its superior performance in providing more precise recommendations for multi-criteria recommendation tasks. Full article