Search Results (78,019)

Background and Objectives: This article investigates the transformative impact of 3D and bio 3D printing technologies in assisted reproductive technology (ART), offering a comprehensive review of their applications in improving reproductive outcomes. Materials and Methods: Following PRISMA guidelines, we conducted a thorough literature search focusing on the intersection of ART and additive manufacturing, resulting in the inclusion of 48 research papers. Results: The study highlights bio 3D printing’s potential in revolutionizing female infertility treatments, especially in follicle complex culture and ovary printing. We explore the use of decellularized extracellular matrix (dECM) as bioink, demonstrating its efficacy in replicating the ovarian microenvironment for in vitro maturation of primordial oocytes. Furthermore, advancements in endometrial cavity interventions are discussed, including the application of sustained-release systems for growth factors and stem cell integration for endometrial regeneration, showing promise in addressing conditions like Asherman’s syndrome and thin endometrium. We also examine the role of conventional 3D printing in reproductive medicine, including its use in educational simulators, personalized IVF instruments, and microfluidic platforms, enhancing training and precision in reproductive procedures. Conclusions: Our review underscores both 3D printing technologies’ contribution to the dynamic landscape of reproductive medicine. They offer innovative solutions for individualized patient care, augmenting success rates in fertility treatments. This research not only presents current achievements but also anticipates future advancements in these domains, promising to expand the horizons for individuals and families seeking assistance in their reproductive journeys. Full article

Androstenedione (AD) is an important intermediate for the production of steroidal drugs. The process of transforming phytosterols into AD by Mycolicibacterium is mainly the degradation process of the phytosterol side chain, and the excessive accumulation of propionyl-CoA produced by Mycobacterium will produce toxic effects, which seriously restricts the transformation performance of strains. In this study, Mycolicibacterium sp. LZ2 (Msp) was used as the research object to study the transcription factor PccD of the TetR family, which has the role of propionyl-CoA metabolism regulation. By constructing overexpression and deletion strains of pccD, it was confirmed that pccD had an inhibitory effect on the transcription of propionyl-CoA carboxylase genes (pccA and pccB). Electrophoretic Mobility Shift Assay (EMSA) and DNase I footprint analysis demonstrated that PccD is directly involved in the transcriptional regulation of pccA and pccB and is a negative transcriptional regulator of the pcc operon. In the study of phytosterol transformation, the growth rate and bacterial viability of Msp-ΔpccD were higher than Msp, but the growth of Msp-pccD was inhibited. As a result of testing of intracellular propionyl-CoA levels and AD production yields, it was found that lower propionyl-CoA levels and higher AD production yields were observed in Msp-ΔpccD. The results expand the cognition of propionyl-CoA metabolism regulation and provide a theoretical basis and reference for the rational transformation of phytosterol transformation strains and secondary metabolite synthesis strains with propionyl-CoA as a substrate, which has important research significance. Full article

Two novel membranes based on collagen and two polyphenols, taxifolin pentaglutarate (TfG5) and a conjugate of taxifolin with glyoxylic acid (DfTf), were prepared. Fourier transform infrared spectroscopy examination confirmed the preservation of the triple helical structure of collagen. A scanning electron microscopy study showed that both materials had a porous structure. The incorporation of DfTf into the freeze-dried collagen matrix increased the aggregation of collagen fibers to a higher extent than the incorporation of TfG5, resulting in a more compact structure of the material containing DfTf. It was found that NIH/3T3 mouse fibroblasts were attached to, and relatively evenly spread out on, the surface of both newly obtained membranes. In addition, it was shown that the membranes enhanced skin wound healing in rats with a chemical burn induced by acetic acid. The treatment with the materials led to a faster reepithelization and granulation tissue formation compared with the use of other agents (collagen without polyphenols and buffer saline). It was also found that, in the wound tissue, the level of thiobarbituric acid reactive substances (TBARS) was significantly higher and the level of low-molecular-weight SH-containing compounds (RSH) was significantly lower than those in healthy skin, indicating a rise in oxidative stress at the site of injury. The treatment with collagen membranes containing polyphenols significantly decreased the TBARS level and increased the RSH level, suggesting the antioxidant/anti-inflammatory effect of the materials. The membrane containing TfG5 was more effective than other ones (the collagen membrane containing DfTf and collagen without polyphenols). On the whole, the data obtained indicate that collagen materials containing DfTf and TfG5 have potential as powerful therapeutic agents for the treatment of burn wounds. Full article

Although the rapid development of Artificial Intelligence (AI) in recent years has brought increasing academic attention to the intelligent transformation of physical education, the core knowledge structure of this field, such as its primary research topics, has yet to be systematically explored. The LDA (latent Dirichlet allocation) topic model can identify latent themes in large-scale textual data, helping researchers extract key research directions and development trends from extensive literature. This study is based on data from the Web of Science Core Collection and employs a systematic literature screening process, utilizing the LDA topic model for in-depth analysis of relevant literature to reveal the current status and trends of AI technology in physical education. The findings indicate that AI applications in this field primarily focus on three areas: “AI and data-driven optimization of physical education and training”, “computer vision and AI-based movement behavior recognition and training optimization”, and “AI and virtual technology-driven innovation and assessment in physical education”. An in-depth analysis of existing research shows that the intelligentization of physical education, particularly in school and athletic training contexts, not only promotes sustainable development in the field but also significantly enhances teaching quality and safety, allowing educators to utilize data more precisely to optimize teaching strategies. However, current research remains relatively broad and lacks more precise and robust data support. Therefore, this study critically examines the limitations of current research in the field and proposes key research directions for further advancing the intelligent transformation of physical education, providing a solid theoretical framework and guidance for future research. Full article

Background/Objectives: The escalating challenge of antimicrobial resistance (AMR) necessitates the development of targeted antibiotic delivery platforms, minimising systemic administration. Polymer-based drug delivery emerges as a promising solution, ensuring sustained release and prolonged efficacy of bioactive compounds, ensuring long-term efficacy. Methods: This study focuses on encapsulating rifampicin (RIF), a key antibiotic for orthopaedic and wound-related infections, within Poly(d,l-lactide-co-glycolide) (PLGA), a biodegradable polymer, through solvent casting, to formulate a PLGA-RIF composite membrane. Comprehensive characterisation, employing Fourier-transformed infrared spectroscopy (FT-IR), scanning electron microscopy (SEM), thermal analysis and X-ray Diffraction (XRD), confirmed the integrity of both the starting and produced materials. UV-Vis spectroscopy revealed a controlled drug release profile over 21 days in various media, with the chosen media influencing the drug release, notably the tryptic soya broth (TSB) caused the highest release. The quantitative assessment of the antimicrobial efficacy of the developed PLGA-RIF composite was conducted by measuring the size of the inhibition zones against both Gram-negative and Gram-positive bacteria. Results: The results confirmed the composite’s potential as a robust antibacterial biomaterial, demonstrating a rapid and effective antibacterial response. Cytocompatibility tests incorporated human fibroblast and osteoblast-like cell lines and demonstrated that the RIF:PLGA (1:8) formulation maintained eukaryotic cell viability, indicating the composite’s potential for targeted medical applications in combating bacterial infections with minimal systemic impact. Conclusions: This study presents the significance of investigating drug release within appropriate and relevant physiological media. A key novelty of this work therefore lies in the exploration of drug release dynamics across different media, allowing for a comprehensive understanding of how varying physiological conditions may influence drug release and its effect on biological responses. Full article

This study examines the effect of gallium doping on the phase transformation, transmission, and hardness of commercial multispectral-grade ZnS specimens exposed to Ga₂S₃ vapor. Using secondary ion mass spectrometry, we show that Ga diffusion extends into the subsurface down to several tens of microns. X-ray diffraction patterns reveal minimal to no precipitation of wurtzite, resulting in limited infrared transmission loss after treatment. We report a monotonic increase in Vickers surface microhardness with increasing Ga concentration, reaching values more than double those of untreated windows. Future work will focus on optimizing this process and evaluating its effectiveness in enhancing the durability of ZnS windows under harsh environmental conditions. Full article

Gliomas are the most common primary brain tumors, representing approximately 28% of all central nervous system tumors. These tumors are characterized by rapid progression and show a median survival of approximately 18 months. The therapeutic options consist of surgical resection followed by radiotherapy and chemotherapy. Despite the multidisciplinary approach and the biomolecular role of targeted therapies, the median progression-free survival is approximately 6–8 months. The incomplete tumor compliance with treatment is due to several factors such as the presence of the blood–brain barrier, the numerous pathways involved in tumor transformation, and the presence of intra-tumoral mutations. Among these, the interaction between the mutations of genes involved in tumor bio-energetic metabolism and the functional response of the tumor has become the protagonist of numerous studies. In this scenario, the main role is played by mitochondria, cellular organelles delimited by a double membrane and containing their own DNA (mtDNA), which participates in numerous cellular processes such as the regulation of cellular metabolism, cellular proliferation, and apoptosis and is also the main source of cellular energy production. Therefore, it is understood that the mitochondrion, specifically its functional alteration, is a leading figure in tumor transformation, including brain tumors. The acquisition of mutations in the mitochondrial DNA of tumor cells and the subsequent identification of the so-called mitochondria-related genes (MRGs), both functional (mutation of Complex I) and structural (mutations of Complex III/IV), have been seen to play an important role in metabolic reprogramming with increased proliferation, resistance to apoptosis, and the progression of tumorigenesis. This demonstrates that these mitochondrial alterations could have a role not only in the intrinsic tumor biology but also in the extrinsic one associated with the therapeutic response. We aim to summarize the main mitochondrial dysfunction interactions present in gliomas and how they might impact prognosis. Full article

In this research, we introduce a novel method leveraging the Transformer architecture to generate high-fidelity precipitation model outputs. This technique emulates the statistical characteristics of high-resolution datasets while substantially lowering computational expenses. The core concept involves utilizing a blend of coarse and fine-grained simulated precipitation data, encompassing diverse spatial resolutions and geospatial distributions, to instruct Transformer in the transformation process. We have crafted an innovative ST-Transformer encoder component that dynamically concentrates on various regions, allocating heightened focus to critical spatial zones or sectors. The module is capable of studying dependencies between different locations in the input sequence and modeling at different scales, which allows it to fully capture spatiotemporal correlations in meteorological element data, which is also not available in other downscaling methods. This tailored module is instrumental in enhancing the model’s ability to generate outcomes that are not only more realistic but also more consistent with physical laws. It adeptly mirrors the temporal and spatial distribution in precipitation data and adeptly represents extreme weather events, such as heavy and enduring storms. The efficacy and superiority of our proposed approach are substantiated through a comparative analysis with several cutting-edge forecasting techniques. This evaluation is conducted on two distinct datasets, each derived from simulations run by regional climate models over a period of 4 months. The datasets vary in their spatial resolutions, with one featuring a 50 km resolution and the other a 12 km resolution, both sourced from the Weather Research and Forecasting (WRF) Model. Full article

The integration of blockchain technology into supply chain management (SCM) has emerged as a revolutionary force transforming traditional business operations. This study uses bibliometric analysis on 1069 articles from the Scopus database, using text mining and Python to uncover predominant themes and research trends at the intersection of blockchain and SCM. The key findings revealed three main thematic groups: ‘blockchain to improve transparency and traceability in SCM’ (supported by 323 articles), ‘impact of blockchain on supply chain efficiency and cost reduction’ (295 articles), and ‘blockchain-enabled supply chain resilience’ (191 articles). Furthermore, text mining highlighted prominent themes such as ‘decentralized supply chain networks’ (204 articles), ‘smart contracts for automated processes in SCM’ (234 articles), and ‘blockchain for sustainable supply chain practices’ (227 articles). The inclusion of sustainability themes reflects the growing importance of environmentally conscious strategies within supply chains, driven by the capacity of blockchain to reduce waste, and promote resource efficiency. The study identifies critical literature gaps, advocating for further exploration of the socio-economic impacts of blockchain on SCM. The topic extraction suggests new directions for SCM theory, while the role of blockchain in fostering sustainable and ethical supply chains is underscored. Practically, blockchain and IoT emerge as pivotal in the advancement of SCM, with text mining offering industry foresight and emphasizing blockchain-driven resilient strategies. Limitations include reliance on a single database and the recommendation that future studies incorporate diverse sources and qualitative insights. The findings provide a roadmap for academics and practitioners, highlighting potential avenues in SCM, especially in the context of sustainable and ethical practices. Full article

This paper presents a comparative study focused on a modal parameters estimation of specimens manufactured by the FDM technique using a fixed embedded vibrometer based on the laser Doppler principle and roving hammer-impact method. Part of this paper is devoted to testing a fixed circular plate with a honeycomb infill pattern while varying the number of excitation points (DOFs), the number of analysis lines of fast Fourier transformation (FFT), and the locations or numbers of reference degrees of freedom (REFs). Although these parameters did not significantly affect the values found for the natural frequencies of the structure, there were changes in the estimates of the mode shapes (affected by the low number of DOFs), in the height and sharpness of the peaks of the CMIF functions (caused by the increased number of FFT lines), and in the number of identified modes (influenced by the chosen location(s) of REFs), respectively. Subsequently, the authors compared the results of experimental modal analyses carried out under the same conditions on three circular plates with honeycomb, star, and concentric infill patterns made of PLA. The results confirm that specimens with honeycomb or star infill patterns have a higher stiffness than those with concentric infill patterns. The low values of the damping ratios obtained for each structure indicate a strong response to excitation at or near their natural frequencies. Full article

Activins and inhibins, members of the transforming growth factor β (TGFβ) superfamily, were initially recognized for their opposing effects on the secretion of follicle-stimulating hormone. Subsequent research has demonstrated their broader biological roles across various tissue types. Primarily, activins and inhibins function through the classical TGFβ SMAD signaling pathway, but studies suggest that they also act through other pathways, with their specific signaling being complex and context-dependent. Recent research has identified significant roles for activins and inhibins in the cardiovascular system. Their actions in other systems and their signaling pathways show strong correlations with the development and progression of cardiovascular diseases, indicating potential broader roles in the cardiovascular system. This review summarizes the progress in research on the biological functions and mechanisms of activins and inhibins and their signaling pathways in cardiovascular diseases, offering new insights for the prevention and treatment of cardiovascular diseases. Full article

Background: Transformational leadership fosters trusting relationships; new visions; and personal, professional, and cultural growth. Effective leaders support their team’s motivational growth and organizational goals. This study aims to underscore the importance of transformational leadership and its various dimensions, focusing on its impact on job satisfaction and personal mastery among nursing leaders in healthcare organizations. Method: A cross-sectional design with convenience sampling was used. The evaluation tools included the Multifactor Leadership Questionnaire (MLQ-6S), the Satisfaction of Employees in Health Care (SEHC) questionnaire, and the Personal Mastery Scale (PMS). Results: The findings indicate that job satisfaction is influenced by transformational leadership, emphasizing the importance of tailored leadership development strategies within healthcare organizations. The laissez-faire leadership style was the only one showing no correlation with nurses’ job satisfaction. Other leadership styles showed significant positive or negative correlations with the analyzed variables. Conclusions: Transformational leaders are essential for fostering trust and enhancing job satisfaction in healthcare settings. Positive leadership styles contribute to higher levels of job satisfaction and personal mastery among nursing leaders. Conversely, laissez-faire and autocratic leadership styles can negatively impact performance and staff satisfaction. These findings highlight the critical role of leaders in creating positive work environments and supporting employee development and well-being in healthcare. Full article

Protein–RNA interactions are essential to many cellular functions, and missense mutations in RNA-binding proteins can disrupt these interactions, often leading to disease. To address this, we developed PRITrans, a specialized computational method aimed at predicting the effects of missense mutations on protein–RNA interactions, which is vital for understanding disease mechanisms and advancing molecular biology research. PRITrans is a novel deep learning model designed to predict the effects of missense mutations on protein–RNA interactions, which employs a Transformer architecture enhanced with multiscale convolution modules for comprehensive feature extraction. Its primary innovation lies in integrating protein language model embeddings with a deep feature fusion strategy, effectively handling high-dimensional feature representations. By utilizing multi-layer self-attention mechanisms, PRITrans captures nuanced, high-level sequence information, while multiscale convolutions extract features across various depths, thereby enhancing predictive accuracy. Consequently, this architecture enables significant improvements in ΔΔG prediction compared to traditional approaches. We validated PRITrans using three different cross-validation strategies on two newly reconstructed mutation datasets, S315 and S630 (containing 315 forward and 315 reverse mutations). The results consistently demonstrated PRITrans’s strong performance on both datasets. PRITrans demonstrated strong predictive capability, achieving a Pearson correlation coefficient of 0.741 and a root mean square error (RMSE) of 1.168 kcal/mol on the S630 dataset. Moreover, its robust performance extended to independent test sets, achieving a Pearson correlation of 0.699 and an RMSE of 1.592 kcal/mol. These results underscore PRITrans’s potential as a powerful tool for protein-RNA interaction studies. Moreover, when tested against existing prediction methods on an independent dataset, PRITrans showed improved predictive accuracy and robustness. Full article

The present work proposes a method for the synthesis of a nanoparticle with a superparamagnetic Fe₃O₄ core coated with SiO₂-NH₂ by ultrasound-assisted coprecipitation. Additionally, the nanoparticle is functionalized with a microinflammation biomarker peptide, and its effects on the viability of monkey kidney endothelial cells and the Vero cell line were evaluated. The main physicochemical properties of the nanoparticles were characterized by X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), X-ray Photoemission Spectroscopy (XPS), a vibrating sample magnetometer (VSM), a field emission scanning electron, Scanning Electron Microscopy (SEM), and High-Resolution Transmission Electron Microscopy (HR-TEM). The results showed that the nanoparticles are spherical, with sizes smaller than 10 nm, with high thermal stability and superparamagnetic properties. They also demonstrated cell viability rates exceeding 85% through Magnetic Resonance Imaging (MRI). The results indicate the potential of these nanoparticles to be used as a contrast agent in magnetic resonance to detect mild brain lesions. Full article

This paper proposes a new approach to pixel-level fusion using the opposite frequency from the discrete wavelet transform with Gaussian or Difference of Gaussian. The low-frequency from discrete wavelet transform sub-band was fused with the Difference of Gaussian, while the high-frequency sub-bands were fused with Gaussian. The final fusion was reconstructed using an inverse discrete wavelet transform into one enhanced reconstructed image. These enhanced images were utilized to improve recognition performance in the face recognition system. The proposed method was tested against benchmark face datasets such as The Database of Faces (AT&T), the Extended Yale B Face Dataset, the BeautyREC Face Dataset, and the FEI Face Dataset. The results showed that our proposed method was robust and accurate against challenges such as lighting conditions, facial expressions, head pose, 180-degree rotation of the face profile, dark images, acquisition with time gap, and conditions where the person uses attributes such as glasses. The proposed method is comparable to state-of-the-art methods and generates high recognition performance (more than 99% accuracy). Full article