Search Results (111)

Background: Artificial intelligence and machine learning are playing crucial roles in digital transformation, aiming to improve the efficiency, effectiveness, equity, and responsiveness of primary health systems and their services. Method: Using synthetic knowledge synthesis and bibliometric and thematic analysis triangulation, we identified the most productive and prolific countries, institutions, funding sponsors, source titles, publications productivity trends, and principal research categories and themes. Results: The United States and the United Kingdom were the most productive countries; Plos One and BJM Open were the most prolific journals; and the National Institutes of Health, USA, and the National Natural Science Foundation of China were the most productive funding sponsors. The publication productivity trend is positive and exponential. The main themes are related to natural language processing in clinical decision-making, primary health care optimization focusing on early diagnosis and screening, improving health-based social determinants, and using chatbots to optimize communications with patients and between health professionals. Conclusions: The use of machine learning in primary health care aims to address the significant global burden of so-called “missed diagnostic opportunities” while minimizing possible adverse effects on patients. Full article

Organic farming relies on sustainable, eco-friendly practices that promote soil health, biodiversity, and climate resilience. Bioresources—derived from plants, animals, and microorganisms—are pivotal in replacing synthetic inputs with natural alternatives. This review presents an integrated analysis of bioresources, highlighting their classification, functionality, and role in organic systems through biofertilizers, biopesticides, organic amendments, and bioenergy. Despite their potential, challenges such as knowledge gaps, limited scalability, and technical constraints hinder their widespread adoption. The review emphasizes the ecological, economic, and social benefits of bioresource integration while identifying critical barriers and proposing strategic directions for research, policy, and practice. By addressing these gaps, bioresources can enhance nutrient cycling, pest management, and soil regeneration, offering a viable path toward sustainable agriculture. This synthesis supports the development of context-specific, circular, and resilient organic farming systems that align with global sustainability goals. Full article

Microplastics (MPs), defined as synthetic polymer particles less than 5 mm in diameter, are widely acknowledged as ubiquitous contaminants in aquatic ecosystems, including freshwater, marine, and polar environments. Global concern with MPs has significantly increased; nevertheless, much of the current knowledge remains fragmented and, at times, limited to specific regions or ecological compartments. This study emphasizes the necessity of a thorough synthesis by critically analyzing global microplastics’ dispersion patterns, ecological consequences, and associated human health concerns. A systematic approach was employed, integrating specific search terms and establishing inclusion and exclusion criteria across various scientific databases to obtain a representative collection of literature. The study covers important topics such as the classification of MPs, their distribution, environmental impacts, and interactions with other pollutants, including heavy metals, pharmaceuticals and endocrine-disrupting chemicals. Particular emphasis is placed on comparing ecosystem-specific vulnerabilities, such as those found in tropical wetlands, marine gyres, and polar systems. The review examines potential human exposure pathways, via contaminated seafood, water, and air, while also compiling new information about cellular and physiological damage, including oxidative stress, inflammation, hormone disruption, and possible genetic effects. This investigation highlights the value of collaborative monitoring, the adoption of biodegradable alternatives, policy development, and interdisciplinary research by integrating knowledge from ecology and public health. The primary objective is to advance ecosystem-specific mitigation techniques and promote evidence-based policy development in addressing this intricate environmental issue. Full article

Silver nanowires (AgNWs) have garnered significant attention in nanotechnology due to their unique mechanical and electrical properties and versatile applications. This review explores the synthesis of AgNWs, with a specific focus on the utilization of millifluidic flow reactors (MFRs) as a promising platform for controlled and efficient production. It begins by elucidating the exceptional characteristics and relevance of AgNWs in various technological domains and then delves into the principles and advantages of MFRs by showcasing their pivotal role in enhancing the precision and scalability of nanowire synthesis. Within this review, an overview of the diverse synthetic methods employed for AgNW production using MFRs is provided. Special attention is given to the intricate parameters and factors influencing synthesis and how MFRs offer superior control over these critical variables. Recent advances in this field are highlighted, revealing innovative strategies and promising developments that have emerged. As with any burgeoning field, challenges are expected, so future directions are explored, offering insights into the current limitations and opportunities for further exploration. In conclusion, this review consolidates the state-of-the-art knowledge in AgNW synthesis and emphasizes the critical role of MFRs in shaping the future of nanomaterial production and nanomanufacturing. Full article

Background: The frequency of propensity score matching (PSM) use in research is exponentially increasing; however, its use in nursing has not yet been explored and is possibly underused. Methods: Synthetic knowledge synthesis has been used on two corpora of publications from the Web of Science bibliographic database for the following purposes: first, to identify the content of the current nursing PSM studies; second, to identify the content of nursing observational, retrospective, or other quasi-experimental studies; and finally, based on the above analyses, to explore new possibilities for further use of PSM in nursing. Findings: The use of PSM in nursing is very sparse, but the number and content of observational, retrospective, and similar nursing research is increasing and becoming more extensive. Ten prolific themes in observational nursing studies were identified. Based on these studies, several influential studies in which PSM has already been successfully used in comparable healthcare topics have been selected as opportunities for extended PSM use in nursing. Conclusions: As shown in the healthcare disciplines, the extended use of PSM in nursing research might make nursing research more consistent, relevant, internally and externally valid, and consequently more useful in clinical practice and research. Full article

This research investigates the effects of steel (ST) and synthetic (SYN) fibers on the workability and mechanical properties of HPFRC. It also analyzes their influence on the material’s microstructural characteristics. ST fibers improve tensile strength, fracture toughness, and post-cracking performance owing to their rigidity, mechanical interlocking, and robust adhesion with the matrix. SYN fibers, conversely, mitigate shrinkage-induced micro-cracking, augment ductility, and enhance concrete performance under dynamic stress while exerting negative effects on workability. Hybrid fiber systems, which include ST and SYN fibers, offer synergistic advantages by enhancing fracture management at various scales and augmenting ductility and energy absorption capability. Scanning electron microscopy (SEM) has been crucial in investigating fiber–matrix interactions, elucidating the effects of ST and SYN fibers on hydration, crack-bridging mechanisms, and interfacial bonding. ST fibers establish thick interfacial zones that facilitate effective stress transfer, whereas SYN fibers reduce micro-crack formation and enhance long-term durability. Nonetheless, research deficiencies persist, encompassing optimal hybrid fiber configurations, the enduring performance of fiber-reinforced concrete (FRC), and sustainable fiber substitutes. Future investigations should examine multi-scale reinforcing techniques, intelligent fibers for structural health assessment, and sustainable fiber alternatives. The standardization of testing methodologies and cost–benefit analyses is essential to promote industrial deployment. This review offers a thorough synthesis of the existing knowledge, emphasizing advancements and potential to enhance HPFRC for high-performance and sustainable construction applications. The findings facilitate the development of new, durable, and resilient fiber-reinforced concrete systems by solving current difficulties. Full article

Rotating machines predominantly operate under healthy conditions, leading to a limited availability of fault data and a significant class imbalance in diagnostic datasets. These challenges hinder the development and deployment of fault diagnosis methods based on deep learning in practice. Considering these issues, a novel hierarchical adaptive wavelet-guided adversarial network with physics-informed regularization (HAWAN-PIR) is proposed. First, a hierarchical wavelet-based imbalance severity score is used to quantify the data imbalance within the datasets. Second, HAWAN-PIR generates synthetic fault data in the time domain via multiscale wavelet decomposition and represents the first attempt to embed physics-informed regularization to incorporate relevant fault knowledge. The quality of the synthetic fault data is then evaluated via a comprehensive multiscale synthesis quality index. Furthermore, a scale-aware dynamic mixing algorithm is proposed to optimally integrate synthetic data with real data. Finally, a one-dimensional convolutional neural network (1-D CNN) is employed for extracting features and classifying faults. The effectiveness of the proposed method is validated through two case studies: motor bearings and planetary gearboxes. The results show that HAWAN-PIR can synthesize high-quality fake data and improve the diagnostic accuracy of the 1-D CNN by 17% for the bearing case and 15% for the gearbox case. Full article

Metallosurfactants combine the unique soft-matter properties of surfactants with magnetic functionalities of metal ions. The inclusion of iron-based species, in particular, can further boost the functionality of the material, owing to iron’s ability to adopt multiple oxidation states and form both high-spin and low-spin complexes. Motivated by this, a series of hybrid inorganic-organic dodecylpyridinium metallosurfactants with iron-containing counterions was developed. It was established that using either divalent or trivalent iron halides in a straightforward synthetic procedure yields C₁₂Py-metallosurfactants with distinct complex counterions: (C₁₂Py)₂[Fe₂X₆O] and (C₁₂Py)[FeX₄] (X = Cl or Br), respectively. A combination of techniques—including conductometry, dynamic and electrophoretic light scattering, single-crystal and thermogravimetric analysis, and magnetic measurements—provided in-depth insights into their solution and solid-state properties. The presence of different iron-based counterions significantly influences the crystal structure (interdigitated vs. non-interdigitated bilayers), magnetic properties (paramagnetic vs. nonmagnetic singlet ground state), and self-assembly (vesicles vs. micelles) of the dodecylpyridinium series. To our knowledge, this is the first report on the synthesis and characterization of hybrid organic-inorganic metallosurfactants containing the μ-oxo-hexahalo-diferrate anion. Full article

Gene expression is a complex process regulated at multiple levels in eukaryotic cells. Translation frequently represents a pivotal step in the control of gene expression. Among the stages of translation, initiation is particularly important, as it governs ribosome recruitment and the efficiency of protein synthesis. The 5′ untranslated region (5′ UTR) of mRNA plays a key role in this process, often exhibiting a complicated and structured landscape. Numerous eukaryotic mRNAs possess long 5′ UTRs that contain diverse regulatory elements, including RNA secondary structures, specific nucleotide motifs, and chemical modifications. These structural features can independently modulate translation through their intrinsic properties or by serving as platforms for trans-acting factors such as RNA-binding proteins. The dynamic nature of 5′ UTR elements allows cells to fine-tune translation in response to environmental and cellular signals. Understanding these mechanisms is not only fundamental to molecular biology but also holds significant biomedical potential. Insights into 5′ UTR-mediated regulation could drive advancements in synthetic biology and mRNA-based targeted therapies. This review outlines the current knowledge of the structural elements of the 5′ UTR, the interplay between them, and their combined functional impact on translation. Full article

Traditional object detectors require extensive instance-level annotations for training. Conversely, few-shot object detectors, which are generally fine-tuned using limited data from unknown classes, tend to show biases toward base categories and are susceptible to variations within these unknown samples. To mitigate these challenges, we introduce a Two-Stage Fine-Tuning Approach (TFA) named Extreme R-CNN, designed to operate effectively with extremely limited original samples through the integration of sample synthesis and knowledge distillation. Our approach involves synthesizing new training examples via instance clipping and employing various data-augmentation techniques. We enhance the Faster R-CNN architecture by decoupling the regression and classification components of the Region of Interest (RoI), allowing synthetic samples to train the classification head independently of the object-localization process. Comprehensive evaluations on the Microsoft COCO and PASCAL VOC datasets demonstrate significant improvements over baseline methods. Specifically, on the PASCAL VOC dataset, the average precision for novel categories is enhanced by up to 15 percent, while on the more complex Microsoft COCO benchmark it is enhanced by up to 6.1 percent. Remarkably, in the 1-shot scenario, the AP50 of our model exceeds that of the baseline model in the 10-shot setting within the PASCAL VOC dataset, confirming the efficacy of our proposed method. Full article

Cannabinoids represent a highly researched group of plant-derived ingredients. The substantial investment of funds from state and commercial sources has facilitated a significant increase in knowledge about these ingredients. Cannabinoids can be classified into three principal categories: plant-derived phytocannabinoids, synthetic cannabinoids and endogenous cannabinoids, along with the enzymes responsible for their synthesis and degradation. All of these compounds interact biologically with type 1 (CB1) and/or type 2 (CB2) cannabinoid receptors. A substantial body of evidence from in vitro and in vivo studies has demonstrated that cannabinoids and inhibitors of endocannabinoid degradation possess anti-inflammatory, antioxidant, antitumour and antifibrotic properties with beneficial effects. This review, which spans the period from 1940 to 2024, offers an overview of the potential therapeutic applications of natural and synthetic cannabinoids. The development of these substances is essential for the global market of do-it-yourself drugs to fully exploit the promising therapeutic properties of cannabinoids. Full article

Mechanical signals from the extracellular matrix are crucial in guiding cellular behavior. Two-dimensional hydrogel substrates for cell cultures serve as exceptional tools for mechanobiology studies because they mimic the biomechanical and adhesive characteristics of natural environments. However, the interdisciplinary knowledge required to synthetize and manipulate these biomaterials typically restricts their widespread use in biological laboratories, which may not have the material science expertise or specialized instrumentation. To address this, we propose a scalable method that requires minimal setup to produce 2D hydrogel substrates with independent modulation of the rigidity and adhesiveness within the range typical of natural tissues. In this method, norbornene-terminated 8-arm polyethylene glycol is stoichiometrically functionalized with RGD peptides and crosslinked with a di-cysteine terminated peptide via a thiol–ene click reaction. Since the synthesis process significantly influences the final properties of the hydrogels, we provide a detailed description of the chemical procedure to ensure reproducibility and high throughput results. We demonstrate examples of cell mechanosignaling by monitoring the activation state of the mechanoeffector proteins YAP/TAZ. This method effectively dissects the influence of biophysical and adhesive cues on cell behavior. We believe that our procedure will be easily adopted by other cell biology laboratories, improving its accessibility and practical application. Full article

Background: The IoT and big data are newer technologies that can provide substantial support for healthcare systems, helping them overcome their shortcomings. The aim of this paper was to analyze the relevant literature descriptively, thematically, and chronologically from an interdisciplinary perspective in a holistic way to identify the most prolific research entities and themes. Methods: Synthetic knowledge synthesis qualitatively and quantitatively analyzes the production of literature through a combination of descriptive bibliometrics, bibliometric mapping, and content analysis. For this analysis, the Scopus bibliometric database was used. Results: In the Scopus database, 2272 publications were found; these were published between 1985 and 10 June 2024. The first article in this field was published in 1985. Until 2012, the production of such literature was steadily increasing; after that, exponential growth began, peaking in 2023. The most productive countries were the United States, India, China, the United Kingdom, South Korea, Germany, and Italy. The content analysis resulted in eight themes (four from the perspective of computer science and four from the perspective of medicine) and 21 thematic concepts (8 from the perspective of computer science and 13 from the perspective of medicine). Conclusions: The results show that the IoT and big data have become key technologies employed in preventive healthcare. The study outcomes might represent a starting point for the further development of research that combines the multidisciplinary aspects of healthcare. Full article

Cell-free protein synthesis (CFPS) has emerged as a powerful tool for protein production, with applications ranging from basic research to biotechnology and pharmaceutical development. However, enhancing the efficiency of CFPS systems remains a crucial challenge for realizing their full potential. Computational strategies offer promising avenues for optimizing CFPS efficiency by providing insights into complex biological processes and enabling rational design approaches. This review provides a comprehensive overview of the computational approaches aimed at enhancing CFPS efficiency. The introduction outlines the significance of CFPS and the role of computational methods in addressing efficiency limitations. It discusses mathematical modeling and simulation-based approaches for predicting protein synthesis kinetics and optimizing CFPS reactions. The review also delves into the design of DNA templates, including codon optimization strategies and mRNA secondary structure prediction tools, to improve protein synthesis efficiency. Furthermore, it explores computational techniques for engineering cell-free transcription and translation machinery, such as the rational design of expression systems and the predictive modeling of ribosome dynamics. The predictive modeling of metabolic pathways and the energy utilization in CFPS systems is also discussed, highlighting metabolic flux analysis and resource allocation strategies. Machine learning and artificial intelligence approaches are being increasingly employed for CFPS optimization, including neural network models, deep learning algorithms, and reinforcement learning for adaptive control. This review presents case studies showcasing successful CFPS optimization using computational methods and discusses applications in synthetic biology, biotechnology, and pharmaceuticals. The challenges and limitations of current computational approaches are addressed, along with future perspectives and emerging trends, such as the integration of multi-omics data and advances in high-throughput screening. The conclusion summarizes key findings, discusses implications for future research directions and applications, and emphasizes opportunities for interdisciplinary collaboration. This review offers valuable insights and prospects regarding computational strategies to enhance CFPS efficiency. It serves as a comprehensive resource, consolidating current knowledge in the field and guiding further advancements. Full article

Marine natural products (MNPs) continue to be tested primarily in cellular toxicity assays, both mammalian and microbial, despite most being inactive at concentrations relevant to drug discovery. These MNPs become missed opportunities and represent a wasteful use of precious bioresources. The use of cheminformatics aligned with published bioactivity data can provide insights to direct the choice of bioassays for the evaluation of new MNPs. Cheminformatics analysis of MNPs found in MarinLit (n = 39,730) up to the end of 2023 highlighted indol-3-yl-glyoxylamides (IGAs, n = 24) as a group of MNPs with no reported bioactivities. However, a recent review of synthetic IGAs highlighted these scaffolds as privileged structures with several compounds under clinical evaluation. Herein, we report the synthesis of a library of 32 MNP-inspired brominated IGAs (25–56) using a simple one-pot, multistep method affording access to these diverse chemical scaffolds. Directed by a meta-analysis of the biological activities reported for marine indole alkaloids (MIAs) and synthetic IGAs, the brominated IGAs 25–56 were examined for their potential bioactivities against the Parkinson’s Disease amyloid protein alpha synuclein (α-syn), antiplasmodial activities against chloroquine-resistant (3D7) and sensitive (Dd2) parasite strains of Plasmodium falciparum, and inhibition of mammalian (chymotrypsin and elastase) and viral (SARS-CoV-2 3CL^pro) proteases. All of the synthetic IGAs tested exhibited binding affinity to the amyloid protein α-syn, while some showed inhibitory activities against P. falciparum, and the proteases, SARS-CoV-2 3CL^pro, and chymotrypsin. The cellular safety of the IGAs was examined against cancerous and non-cancerous human cell lines, with all of the compounds tested inactive, thereby validating cheminformatics and meta-analyses results. The findings presented herein expand our knowledge of marine IGA bioactive chemical space and advocate expanding the scope of biological assays routinely used to investigate NP bioactivities, specifically those more suitable for non-toxic compounds. By integrating cheminformatics tools and functional assays into NP biological testing workflows, we can aim to enhance the potential of NPs and their scaffolds for future drug discovery and development. Full article