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Electrocatalytic Ethylene Glycol to Long-Chain C<sub>3+</sub> α-Hydroxycarboxylic Acids via Cross-Coupling with Primary Alcohols
Evidence of a Three-State Mechanism in DNA Hairpin Folding
Combination of skin sympathetic nerve activity and urine biomarkers in improving diagnostic accuracy for urge urinary incontinence
Effect of electrical grade glass fibres and silver nanoparticles on the mechanical properties of provisional PMMA material
Abstract The use of provisional crowns and bridges rendered the necessary care for the prepared teeth. The protection of the prepared tooth is one of the most important factors in the long-term success of fixed dental prosthesis. Temporary crowns and bridges for longer periods of use are most often made of acrylic material. Unfortunately, it does not have the appropriate mechanical properties or resistance to microbial colonization Therefore, the purpose is to modify it by adding glass fibers (2% w/w) and silver nanoparticles (0.5% w/w). A total of 160 samples were prepared which were segregated into 4 groups based on the test performed. Each group had 4 subgroups which consisted of samples containing silver nanoparticles (0.5% w/w) and E-glass fiber (2% w/w) mixed with PMMA in their respective concentrations. The samples were then tested for surface roughness, micro-hardness, flexural strength and SEM Analysis. The results of the study showed that there were no effect on the surface roughness values after incorporating silver nanoparticles and E-glass fibers. However, samples containing silver nanoparticles and E-Glass fibre individually had higher values of microhardness and flexural strength than those who had both together. The SEM images showed clumping of silver nanoparticles non-uniform orientation of E-glass fibers. Thus, it can be concluded that silver nanoparticles and E-glass fibre when added separately to PMMA, enhance its mechanical properties. However, better methods of mixing PMMA with silver particles and glass fibers is needed to attain a uniform distribution. Further, the orientation of E-glass fibers could also have an effect on the flexural properties of PMMA.
Estimation of risk perception of mine workers in underground metalliferous mines using multivariate structural equation modelling
A lightweight encryption algorithm for resource-constrained IoT devices using quantum and chaotic techniques with metaheuristic optimization
Discovery of an ApoE4-targeted small-molecule SirT1 enhancer for the treatment of Alzheimer’s disease
Abstract Decreased expression of sirtuin 1 (SirT1) has been implicated in Alzheimer’s disease (AD), and as we previously reported, is related to transcriptional repression by the major risk factor for sporadic AD, apolipoprotein E4 (ApoE4). Herein we describe the discovery of an orally brain-permeable small-molecule, DDL-218, that enhanced SirT1 in ApoE4-expressing neuronal cells and a murine AD model. DDL-218 increased the transcription factor NFYb resulting in upregulation of PRMT5. Mechanistic and modeling studies show that binding of ApoE4 to the SirT1 gene promoter can be displaced by PRMT5 leading to increased SirT1 transcription. DDL-218 treatment elicited improvement in memory in the AD model, suggesting that DDL-218 enhancement of neurotrophic SirT1 in the brain has potential to modulate neuronal activity that may clinically provide an improvement in cognitive function and complement the current anti-Aβ antibody monotherapy. Our findings support further development of DDL-218 as a novel ApoE4-targeted therapeutic candidate for AD.
Intelligent identification method of origin for Alismatis Rhizoma based on image and machine learning
Abstract Alismatis Rhizoma (AR) is widely utilized as a natural medicine across many Asian countries. However, in China, due to its complex origins, AR quality varies, which can affect clinical efficacy. Therefore, there is a need for a method that is both fast and objective to determine the source of AR. In this study, a total of 400 samples of two species and four geographic origins from AR were imaged and processed. From these images, 17 features were extracted, including three shape (S), two color (C), and 12 texture features (T), resulting in a total of 6800 data points. Four commonly used classification models Random Forest (RF), Extreme Learning Machine (ELM), Back Propagation (BP) neural network, and Support Vector Machines (SVM) were tested to find the optimal combination of AR fusion features and classification models. The S + T-RF combinations achieved the best results, with 99.17% accuracy in two species identification and 96.67% accuracy in four geographic origin identification on test sets. These results suggest that image processing combined with the RF model can quickly and effectively identify the complex origins of AR and can provide a reference for the origins identification of other natural medicines.
Investigating the protective effect of hydroxylated fullerenes on cognitive function in rats with temporal lobe epilepsy
Quantitative comparison of the performance of acoustic, optical and pressure sensors for pulse wave analysis
A hybrid variational autoencoder and WGAN with gradient penalty for tertiary protein structure generation
Abstract Elucidating the tertiary structure of proteins is important for understanding their functions and interactions. While deep neural networks have advanced the prediction of a protein’s native structure from its amino acid sequence, the focus on a single-structure view limits understanding of the dynamic nature of protein molecules. Acquiring a multi-structure view of protein molecules remains a broader challenge in computational structural biology. Alternative representations, such as distance matrices, offer a compact and effective way to explore and generate realistic tertiary protein structures. This paper presents TP-VWGAN, a hybrid model to improve the realism of generating distance matrix representations of tertiary protein structures. The model integrates the probabilistic representation learning of the Variational Autoencoder (VAE) with the realistic data generation strength of the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP). The main modification of TP-VWGAN is incorporating residual blocks into its VAE architecture to improve its performance. The experimental results show that TP-VWGAN with and without residual blocks outperforms existing methods in generating realistic protein structures, but incorporating residual blocks enhances its ability to capture key structural features. Comparisons also demonstrate that the more accurately a model learns symmetry features in the generated distance matrices, the better it captures key structural features, as demonstrated through benchmarking against existing methods. This work moves us closer to more advanced deep generative models that can explore a broader range of protein structures and be applied to drug design and protein engineering. The code and data are available at https://github.com/aalaa-sehsah/tp-vwgan.
A highly sensitive Anti-Müllerian hormone test as a promising tool for follicle growth prediction in primary ovarian insufficiency patients
Abstract Primary ovarian insufficiency (POI) patients often require prolonged stimulation for follicular growth. Anti-Müllerian hormone (AMH), produced by granulosa cells of early-stage follicles, is a potential a biomarker for predicting follicular development in POI patients undergoing ovarian stimulation. This retrospective study analyzed 165 patients undergoing 504 long controlled ovarian stimulation cycles. AMH levels were measured three weeks after stimulation initiation using a highly sensitive assay to guide decisions on extending stimulation beyond four weeks. Follicular development occurred in 9.7% of cycles among 41 patients, who had shorter amenorrhea durations and lower baseline follicle-stimulating hormone levels. Three-week AMH levels showed superior predictive ability for follicular development (area under the curve: 0.957; optimal threshold: 2.45 pg/ml) and were negatively correlated with time to follicular detection (R = − 0.326, P < 0.05). However, AMH levels did not significantly affect the precise time required for follicular development or show significant differences in oocyte yield or embryo quality. The study concludes that three-week AMH levels can predict follicular growth in POI patients. These findings suggest that a highly sensitive AMH assay could be a valuable tool for guiding ovarian stimulation in POI patients, potentially improving treatment outcomes.