Effect of high pressure homogenization treatment on the rheological properties of citrus peel fiber/corn oil emulsion.

BACKGROUND: Citrus fiber is a main component in the peel of citrus and contains natural dietary fiber. It is often used as a functional additive to improve the texture or nutritional property of food. It is also widely used to reduce the content of absorbable fat in sausages and other meat products, and to improve food stability as an emulsifier. In this research, the dynamic rheological properties (linear and non-linear) of citrus peel fiber/corn oil (CF/CO) emulsion system under high pressure homogenization (HPH) treatment was investigated. RESULT: Rheological results illustrated HPH treatment significantly increased the apparent viscosity of the emulsion, reduced the activation energy of the emulsion and distinctly improved the viscoelasticity of the emulsion. Meanwhile, HPH treatment increased the linear viscoelastic region of the sample, and the behavior of the emulsion converted from strain thinning (without HPH treatment) to weak strain overshoot (with HPH treatment). Lissajous curves indicated the viscosity of the sample increased first and then decreased with strain increasing and the third harmonic contributed much more to the first harmonic compared with the fifth harmonic. Chebyshev stress decomposition revealed that, as strain increased, the samples with HPH treatment showed internal-cycle strain hardening behavior first, then turned to internal-cycle softening behavior. CONCLUSION: HPH treatment can significantly improve the processing performance of CF/CO emulsion as well as the stability against large periodic oscillations in food processing. © 2020 Society of Chemical Industry.

Hypoxia-Responsive Polymeric Nanoprodrugs for Combo Photodynamic and Chemotherapy.

Hypoxia in most solid tumors is a major challenge for photodynamic therapy (PDT), and the combination of hypoxia-activated chemotherapy and PDT is a promising approach for enhanced anticancer activity. Herein, we designed hypoxia-responsive polymeric nanoprodrug PNPs to co-deliver photosensitizer 5,10,5,20-tetrakis(4-aminophenyl)-porphine (TAPP) and chlorambucil (CB) to improve the overall therapeutic efficacy. Upon laser irradiation, the central TAPP converted oxygen to produce single oxygen (1O2) for PDT and induced PDT-reduced hypoxia environment, which accelerated the release of activated CB for synergetic cancer cell killing. Consequently, these hypoxia-responsive polymeric nanoprodrugs with a considerable drug-loading content and synergistic therapeutic effect of PDT-CT had great potential for tumor therapy.

Data-Driven Based Characterization of Anisotropic Mechanical Properties for Cancellous Bone From Low-Resolution CT Images.

OBJECTIVES: Exploring the anisotropic mechanical behavior of cancellous bone is crucial for in-vivo bone biomechanical analysis. However, it is challenging to characterize anisotropic mechanical behaviors under low-resolution (LR) clinical CT images due to a lack of microstructural information. The data-driven method proposed in this article accurately characterizes the anisotropic mechanical properties of cancellous bone from LR clinical CT images. METHODS: The trabecular bone cubes of sheep are used to obtain a high-resolution (HR) micro-CT and an LR clinical CT image dataset. First, an auto-encoder model is trained using HR image data. Microstructural features are extracted by the encoder. A fast super-resolution (FSR) model is trained to map LR bone cubes to the features extracted from corresponding HR samples. The pretrained FSR model is used to convert LR clinical CT images to encoded microstructural features. The features are later used to predict target histomorphological parameters, anisotropic elastic tensors, and fabric tensors based on a fully connected neural network. RESULTS: The data-driven model accurately predicts the elastic tensor and fabric tensor of trabecular bones with LR CT images with 0.6 mm/pixel spatial resolution. It was verified that LR clinical CT images could generate microstructural information using a generative deep-learning model and an up-sampling operation. SIGNIFICANCE: This study proves that clinical medical images of cancellous bone can be used for analysis of complex mechanical properties using a data-driven method, which is useful for real-time bone defect diagnosis and personalized bone prosthesis design in clinical application.

Multiscale engineered artificial compact bone via bidirectional freeze-driven lamellated organization of mineralized collagen microfibrils.

Bone, renowned for its elegant hierarchical structure and unique mechanical properties, serves as a constant source of inspiration for the development of synthetic materials. However, achieving accurate replication of bone features in artificial materials with remarkable structural and mechanical similarity remains a significant challenge. In this study, we employed a cascade of continuous fabrication processes, including biomimetic mineralization of collagen, bidirectional freeze-casting, and pressure-driven fusion, to successfully fabricate a macroscopic bulk material known as artificial compact bone (ACB). The ACB material closely replicates the composition, hierarchical structures, and mechanical properties of natural bone. It demonstrates a lamellated alignment of mineralized collagen (MC) microfibrils, similar to those found in natural bone. Moreover, the ACB exhibits a similar high mineral content (70.9 %) and density (2.2 g/cm3) as natural cortical bone, leading to exceptional mechanical properties such as high stiffness, hardness, and flexural strength that are comparable to those of natural bone. Importantly, the ACB also demonstrates excellent mechanical properties in wet, outstanding biocompatibility, and osteogenic properties in vivo, rendering it suitable for a broad spectrum of biomedical applications, including orthopedic, stomatological, and craniofacial surgeries.

A comprehensive bioinformatics analysis identifies mitophagy biomarkers and potential Molecular mechanisms in hypertensive nephropathy.

Mitophagy, the selective removal of damaged mitochondria, plays a critical role in kidney diseases, but its involvement in hypertensive nephropathy (HTN) is not well understood. To address this gap, we investigated mitophagy-related genes in HTN, identifying potential biomarkers for diagnosis and treatment. Transcriptome datasets from the Gene Expression Omnibus database were analyzed, resulting in the identification of seven mitophagy related differentially expressed genes (MR-DEGs), namely PINK1, ULK1, SQSTM1, ATG5, ATG12, MFN2, and UBA52. Further, we explored the correlation between MR-DEGs, immune cells, and inflammatory factors. The identified genes demonstrated a strong correlation with Mast cells, T-cells, TGFß3, IL13, and CSF3. Machine learning techniques were employed to screen important genes, construct diagnostic models, and evaluate their accuracy. Consensus clustering divided the HTN patients into two mitophagy subgroups, with Subgroup 2 showing higher levels of immune cell infiltration and inflammatory factors. The functions of their proteins primarily involve complement, coagulation, lipids, and vascular smooth muscle contraction. Single-cell RNA sequencing revealed that mitophagy was most significant in proximal tubule cells (PTC) in HTN patients. Pseudotime analysis of PTC confirmed the expression changes observed in the transcriptome. Intercellular communication analysis suggested that mitophagy might regulate PTC's participation in intercellular crosstalk. Notably, specific transcription factors such as HNF4A, PPARA, and STAT3 showed strong correlations with mitophagy-related genes in PTC, indicating their potential role in modulating PTC function and influencing the onset and progression of HTN. This study offers a comprehensive analysis of mitophagy in HTN, enhancing our understanding of the pathogenesis, diagnosis, and treatment of HTN.Communicated by Ramaswamy H. Sarma.

A Bayesian method with nonlinear noise model to calibrate constitutive parameters of soft tissue.

The measured mechanical responses of soft tissue exhibit large variability and errors, especially for the softest brain tissue, while calibrating its constitutive parameters in a deterministic way remains a common practice. Here we implement a Bayesian method considering the nonlinear noise model to calibrate constitutive parameters of brain tissue. A probability model is first developed based on the measured experimental data, likelihood function, and prior function, from which the posterior distributions of model parameters are formulated. The likelihood function considers the nonlinear behaviors of the constitutive response and noise distribution of the experimentally measured data. Meanwhile, the prior predictive distribution is computed to check the probability model preliminarily. Secondly, the Markov Chain Monte Carlo (MCMC) method is used to compute the posterior distributions of model parameters, enabling assessment of parameter uncertainty, correlation, and model calibration error. Finally, the posterior predictive distributions of the overall response, constitutive response, and noise response are computed to validate the probabilistic model, all of which are consistent with the corresponding data. Furthermore, the effect of the prior distribution, experimental data, and noise model on posterior distribution is studied. Our study provides a general approach to calibrating constitutive parameters of soft tissue despite errors and large variability in experimental data.

Experimentally characterizing the spatially varying anisotropic mechanical property of cancellous bone via a Bayesian calibration method.

Traditional experimental tests for characterizing bone's mechanical properties usually hypothesize a uniaxial stress condition without quantitatively evaluating the influence of spatially varying principal material orientations, which cannot accurately predict the mechanical properties distribution of bones in vivo environment. In this study, a Bayesian calibrating procedure was developed using quantified multiaxial stress to investigate cancellous bone's local anisotropic elastic performance around joints as the spatial variation of main bearing orientations. First, the bone cube specimens from the distal femur of sheep are prepared using traditional anatomical axes. The multiaxial stress state of each bone specimen is calibrated using the actual principal material orientations derived from fabric tensor at different anatomical locations. Based on the calibrated multiaxial stress state, the process of identifying mechanical properties is described as an inverse problem. Then, a Bayesian calibration procedure based on a surrogate constitutive model was developed via multiaxial stress correction to identify the anisotropic material parameters. Finally, a comparison between the experiment and simulation results is discussed by applying the optimal model parameters obtained from the Bayesian probability distribution. Compared to traditional uniaxial methods, our results prove that the calibration based on the spatial variation of the main bearing orientations can significantly improve the accuracy of characterizing regional anisotropic mechanical responses. Moreover, we determine that the actual mechanical property distribution is influenced by complicated mechanical stimulation. This study provides a novel method to evaluate the spatially varying mechanical properties of bone tissues enduring complex mechanical loading accurately and effectively. It is expected to provide more realistic mechanical design targets in vivo for a personalized artificial bone prosthesis in clinical treatment.

Transboundary Water Cooperation in the Post-Cold War Era: Spatial Patterns and the Role of Proximity.

Transboundary water cooperation (TWC) is an important theme of international cooperation. We conducted macro-level research on TWC from the perspective of inter-country relations and constructed a theoretical framework in which multidimensional proximity influences the formation of global TWC. We explained how multidimensional proximity and the constituent elements comprehensively influence the cooperative willingness and ability of actors, which directly drive the generation of global TWC. During the empirical research phase, we constructed the TWC frequency and intensity networks based on historical TWC events data from 1992 to 2013. By using social network analysis and QAP regression analysis, the spatial structure and proximity effect of water cooperation linkages are examined. It can be found that: (1) the reconstruction of territorial space on the eve of the end of the Cold War led to the peak of water cooperation events in 1992. The overall scale of events in the Post-Cold War era was relatively high and fluctuated steadily. (2) Water cooperation linkages have distinct spatial heterogeneity and are concentrated in the Eurasian and the African continents. Water cooperation is sensitive to geographical distance, and high-intensity water cooperation linkages exist in only a few areas. (3) China, Egypt, Germany, the United States, and Russia have prominent positions in the network. The United States, Japan, and other extra-regional powers actively participated in TWC in the Eastern Hemisphere. (4) The regression results show that geographical, economic, organizational, and colonial proximity significantly affect the intensity of water cooperation among countries.