Association between visceral obesity index and diabetes: a systematic review and meta-analysis.

CONTENT: The correlation between visceral obesity index (VAI) and diabetes and accuracy of early prediction of diabetes are still controversial. OBJECTIVE: This study aims to review the relationship between high level of VAI and diabetes, and early predictive value of diabetes. DATA SOURCES: The databases of PubMed, Cochrane, Embase, and Web of Science were searched until October 17, 2023. STUDY SELECTION: After adjusting for confounding factors, the original study on the association between VAI and diabetes was analyzed. DATA EXTRACTION: We extracted odds ratio (OR) between VAI and diabetes management after controlling for mixed factors, and the sensitivity, specificity and diagnostic four grid table for early prediction of diabetes. DATA SYNTHESIS: 53 studies, comprising 595,946 participants were included. The findings of the meta-analysis elucidated that in cohort studies, a high VAI significantly increased the risk of diabetes mellitus in males (OR = 2.83 (95% CI: 2.30-3.49)) and females (OR = 3.32 (95% CI: 2.48-4.45)). The ROC, sensitivity, and specificity of VAI for early prediction of diabetes in males were 0.64 (95% CI: 0.62-0.66), 0.57 (95% CI: 0.53-0.61), and 0.65 (95% CI: 0.61-0.69), respectively, and 0.67 (95% CI: 0.65-0.69), 0.66 (95% CI: 0.60-0.71), and 0.61 (95% CI: 0.57-0.66) in females, respectively. CONCLUSIONS: VAI is an independent predictor of the risk of diabetes, yet its predictive accuracy remains limited. In future studies, determine whether VAI can be utilized in conjunction with other related indicators to early predict the risk of diabetes, to enhance the accuracy of prediction of the risk of diabetes.

A hybrid electrically-and-piezoelectrically driven micromixer built on paper for microfluids mixing.

This study aims to explore the channel patterns and the characteristic parameters of the zigzag microchannel based on microfluidic paper-based analytical devices (µPADs), in which the mixing efficiency and speed can be greatly enhanced. Better mixing of the solutions was obtained by adding a simple directing electric field to the optimized structure of the zigzag microchannel on paper-based chips instead of the traditional complex devices. A higher mixing efficiency was reached when the direct-current (DC) power supply reached 20 V. Meanwhile, a piezoelectric transducer (PZT) driver was used in the mixing experiment with the paper-based zigzag microchannel. The results show that the mixing efficiency reached a maximum value when the input voltage and frequency were 30 V and 150 Hz, respectively. These paper-based devices meet the requirements of the biochemical analysis field because they are low cost, easy to operate, and have high efficiencies, giving them good prospects for future applications. Graphical Abstract.

An integrated platform for fibrinogen quantification on a microfluidic paper-based analytical device.

Fibrinogen (FIB) plays a key role in blood coagulation and thrombosis and its concentration in blood can directly reflect health conditions, thus efficient detection of FIB would benefit the treatments of certain diseases such as liver and heart diseases. However, there is a lack of sensitive, simple, rapid and cheap FIB detection device currently, in lieu of expensive and sophisticated approaches in laboratories. Here, we propose a novel plasma separation and FIB detection platform based on a microfluidic paper-based analytical device (µPAD). It is the first time that dielectrophoretic (DEP) force is combined with capillary force on paper for plasma separation, and the separation efficiency of plasma reaches about 95%, ensuring reliable downstream FIB detection, for which we also propose a new method called the resistance-fibrinogen detection (RFD) method. It not only avoids the use of large-scale instruments for detection, but also possesses high precision and simplicity. The method is found to be reliable in FIB detection for various concentrations ranging from 127.0 to 508.0 mg dL-1. Moreover, the results obtained from the proposed µPAD show an excellent agreement (R2 = 0.9985) with those obtained from an automatic coagulation analyzer with natural human blood samples. Overall, the proposed platform provides a low-cost and reliable approach for FIB detection, especially for clinical use in resource-limited areas.

Detection and extraction of heavy metal ions using paper-based analytical devices fabricated via atom stamp printing.

As a promising concept, microfluidic paper-based analytical devices (µPADs) have seen rapid development in recent years. In this study, a new method of fabricating µPADs by atom stamp printing (ASP) is proposed and studied. The advantages of this new method compared to other methods include its low cost, ease of operation, high production efficiency, and high resolution (the minimum widths of the hydrophilic channels and hydrophobic barriers are 328 and 312 µm, respectively). As a proof of concept, µPADs fabricated with the ASP method were used to detect different concentrations of Cu2+ via a colorimetric method. Moreover, combined with a distance-based detection method, these devices achieved a Cu2+ detection limit of down to 1 mg/L. In addition, a new paper-based solid-liquid extraction device (PSED) based on a three-dimensional (3D) µPAD with a "3 + 2" structure and a recyclable extraction mode was developed. Specifically, using the characteristics of paper filtration and capillary force, the device completed multiple extraction and filtration steps from traditional solid-liquid extraction processes with high efficiency. The developed PSED platform allows the detection of heavy metal ions much more cheaply and simply and with a faster response time at the point of care, and it has great promise for applications in food safety and environmental pollution in resource-limited areas.

Versatile Microfluidic Mixing Platform for High- and Low-Viscosity Liquids via Acoustic and Chemical Microbubbles.

Microfluidic mixers have been extensively studied due to their wide application in various fields, including clinical diagnosis and chemical research. In this paper, we demonstrate a mixing platform that can be used for low- and high-viscosity liquid mixing by integrating passive (utilizing the special circulating crossflow characteristics of a zigzag microstructure and cavitation surfaces at the zigzag corners) and active (adding an acoustic field to produce oscillating microbubbles) mixing methods. By exploring the relationship between the active and passive mixing methods, it was found that the microbubbles were more likely generated at the corners of the zigzag microchannel and achieved the best mixing efficiency with the acoustically generated microbubbles (compared with the straight channel). In addition, a higher mixing effect was achieved when the microchannel corner angle and frequency were 60° and 75 kHz, respectively. Meanwhile, the device also achieved an excellent mixing effect for high-viscosity fluids, such as glycerol (its viscosity was approximately 1000 times that of deionized (DI) water at 25 °C). The mixing time was less than 1 s, and the mixing efficiency was 0.95 in the experiment. Furthermore, a new microbubble generation method was demonstrated based on chemical reactions. A higher mixing efficiency (0.97) was achieved by combining the chemical and acoustic microbubble methods, which provides a new direction for future applications and is suitable for the needs of lab-on-a-chip (LOC) systems and point-of-care testing (POCT).

Predicting archetypal nanoparticle shapes using a combination of thermodynamic theory and machine learning.

Machine learning is a useful way of identifying representative or pure nanoparticle shapes as part of a larger ensemble, but its predictive capabilities can be limited when a large dataset of candidate structures must already exist. Ideally one would like to use machine learning to define the ideal dataset for future, more computationally intensive, studies before a significant amount of resources are consumed. In this work we combine an established analytical phenomenological model and statistical machine learning to predict the archetypes and prototypes of a diverse ensemble of 2380 platinum nanoparticle morphologies developed with less than twenty input electronic structure simulations. By parameterising a size- and shape-dependent thermodynamic model, probabilities are assigned to seventeen different shapes between three and thirty nanometres, which together with structural features such as nanoparticle diameter, surface area, sphericity and facet configuration form the basis for archetypal analysis and K-means clustering. Using this approach we rapidly identify six "pure" archetypes and twelve "representative" prototypes that can be used in future computational studies of properties such as catalysis.

Correlating anisotropy and disorder with the surface structure of platinum nanoparticles.

Due to the competition between numerous physicochemical variables during formation and processing, platinum nanocatalysts typically contain a mixture of shapes, distributions of sizes, and a considerable degree of surface imperfection. Structural imperfection and sample polydispersivity are inevitable at scale, but accepting bulk and surface diversity as legitimate design features provides new opportunities for nanoparticle design. In recent years disorder and anisotropy have been embraced as useful design parameters but predicting the impact of uncontrollable imperfection a priori is challenging. In the present work we have created an ensemble of uniquely imperfect nanoparticles extracted from classical molecular dynamics trajectories and applied statistical filters to restrict the ensemble in ways that reflect common industrial design principles. We find that targeting different sizes and size distributions may be an effective way of promoting or suppressing internal disorder or crystallinity (as required), but the degree of anisotropy of the particle as a whole has a greater impact on the population of different types of surface ordering and active sites. These results indicate that tuning of disordered and anisotropic Pt nanoparticles is possible, but it is not as straightforward as geometrically ideal nanoparticles with a high degree of crystallinity. It is unlikely that a synthesis strategy could eliminate this diversity entirely, or ensure this type of structural complexity does not develop post-synthesis under operational conditions, but it may be possible to bias the formation of specific bulk structures and the surface anisotropy.

Native tissue-based strategies for meniscus repair and regeneration.

Meniscus injuries appear to be becoming increasingly common and pose a challenge for orthopedic surgeons. However, there is no curative approach for dealing with defects in the inner meniscus region due to its avascular nature. Numerous strategies have been applied to regenerate and repair meniscus defects and native tissue-based strategies have received much attention. Native tissue usually has good biocompatibility, excellent mechanical properties and a suitable microenvironment for cellular growth, adhesion, redifferentiation, extracellular matrix deposition and remodeling. Classically, native tissue-based strategies for meniscus repair and regeneration are divided into autogenous and heterogeneous tissue transplantation. Autogenous tissue transplantation is performed more widely than heterogeneous tissue transplantation because there is no immunological rejection and the success rates are higher. This review first discusses the native meniscus structure and function and then focuses on the use of the autogenous tissue for meniscus repair and regeneration. Finally, it summarizes the advantages and disadvantages of heterogeneous tissue transplantation. We hope that this review provides some suggestions for the future design of meniscus repair and regeneration strategies.

Tissue-derived scaffolds and cells for articular cartilage tissue engineering: characteristics, applications and progress.

There are many factors to consider in the field of tissue engineering. For articular cartilage repair, this includes seed cells, scaffolds and chondrotrophic hormones. This review primarily focuses on the seed cells and scaffolds. Extracellular matrix proteins provide a natural scaffold for cell attachment, proliferation and differentiation. The structure and composition of tissue-derived scaffolds and native tissue are almost identical. As such, tissue-derived scaffolds hold great promise for biomedical applications. However, autologous tissue-derived scaffolds also have many drawbacks for transplantation, as harvesting autografts is limited to available donor sites and requires secondary surgery, therefore imparting additional damage to the body. This review summarizes and analyzes various cell sources and tissue-derived scaffolds applied in orthopedic tissue engineering.

Applications of stem cell-derived exosomes in tissue engineering and neurological diseases.

Exosomes are extracellular vesicles with diameters of 30-100 nm that are key for intercellular communication. Almost all types of cell, including dendritic cells, T cells, mast cells, epithelial cells, neuronal cells, adipocytes, mesenchymal stem cells, and platelets, can release exosomes. Exosomes are present in human body fluids, such as urine, amniotic fluid, malignant ascites, synovial fluid, breast milk, cerebrospinal fluid, semen, saliva, and blood. Exosomes have biological functions in immune response, antigen presentation, intercellular communication, and RNA and protein transfer. This review provides a brief overview of the origin, morphological characteristics, enrichment and identification methods, biological functions, and applications in tissue engineering and neurological diseases of exosomes.

Machine Learning for Silver Nanoparticle Electron Transfer Property Prediction.

Nanoparticles exhibit diverse structural and morphological features that are often interconnected, making the correlation of structure/property relationships challenging. In this study a multi-structure/single-property relationship of silver nanoparticles is developed for the energy of Fermi level, which can be tuned to improve the transfer of electrons in a variety of applications. By combining different machine learning analytical algorithms, including k-mean, logistic regression, and random forest with electronic structure simulations, we find that the degree of twinning (characterized by the fraction of hexagonal closed packed atoms) and the population of the {111} facet (characterized by a surface coordination number of nine) are strongly correlated to the Fermi energy of silver nanoparticles. A concise three layer artificial neural network together with principal component analysis is built to predict this property, with reduced geometrical, structural, and topological features, making the method ideal for efficient and accurate high-throughput screening of large-scale virtual nanoparticle libraries and the creation of single-structure/single-property, multi-structure/single-property, and single-structure/multi-property relationships in the near future.

The impact of size and shape distributions on the electron charge transfer properties of silver nanoparticles.

Many applications of silver nanoparticles are moderated by the electron charge transfer properties, such as the ionization potential, electron affinity and Fermi energy, which may be tuned by controlling the size and shape of individual particles. However, since producing samples of silver nanoparticles that are perfectly monodispersed in terms of both size and shape can be prohibitive, it is important to understand how these properties are impacted by polydispersivity, and ideally be able to predict the tolerance for variation of different geometric features. In this study, we use straightforward statistical methods, together with electronic structure simulations, to predict the electron charge transfer properties of different types of ensembles of silver nanoparticles and how restricting the structural diversity in different ways can improve or retard performance. In agreement with previous reports, we confirm that restricting the shape distribution will tune the charge transfer properties toward specific reactions, but by including the quality factors for each case we go beyond this assessment and show how targeting specific classes of morphologies and restricting the distribution of size can impact sensitivity.

Impact of speciation on the electron charge transfer properties of nanodiamond drug carriers.

Unpassivated diamond nanoparticles (bucky-diamonds) exhibit a unique surface reconstruction involving graphitization of certain crystal facets, giving rise to hybrid core-shell particles containing both aromatic and aliphatic carbon. Considerable effort is directed toward eliminating the aromatic shell, but persistent graphitization of subsequent subsurface-layers makes perdurable purification a challenge. In this study we use some simple statistical methods, in combination with electronic structure simulations, to predict the impact of different fractions of aromatic and aliphatic carbon on the charge transfer properties of the ensembles of bucky-diamonds. By predicting quality factors for a variety of cases, we find that perfect purification is not necessary to preserve selectivity, and there is a clear motivation for purifying samples to improve the sensitivity of charge transfer reactions. This may prove useful in designing drug delivery systems where the release of (selected) drugs needs to be sensitive to specific conditions at the point of delivery.

Statistics, damned statistics and nanoscience - using data science to meet the challenge of nanomaterial complexity.

For many years dealing with the complexity of nanoscale materials, the polydispersivity of individual samples, and the persistent imperfection of individual nanostructures has been secondary to our search for novel properties and promising applications. For our science to translate into technology, however, we will inevitably need to deal with the issue of structural diversity and integrate this feature into the next generation of more realistic structure/property predictions. This is challenging in the field of nanoscience where atomic level precision is typically inaccessible (experimentally), but properties can depend on structural variations at the atomic scale. Fortunately there exists a range of reliable statistical methods that are entirely applicable to nanoscale materials; ideal for navigating and analysing enormous amount of information required to accurately describe realistic samples. Combined with advances in automation and information technology the field of data science can assist us in dealing with our big data, characterising our uncertainties, and more rapidly identifying useful structure/property relationships. Taking greater advantage of data-driven methods involves thinking differently about our research, but applied appropriately these methods can accelerate the discovery of nanomaterials that are optimised to make the transition from science to technology.