Flavonoid extracts of Citrus aurantium L. var. amara Engl. Promote browning of white adipose tissue in high-fat diet-induced mice.

ETHNOPHARMACOLOGICAL RELEVANCE: Obesity has become a public burden worldwide due to its booming incidence and various complications, and browning of white adipose tissue (WAT) is recognized as a hopeful strategy to combat it. Blossom of Citrus aurantium L. var. amara Engl. (CAVA) is a popular folk medicine and dietary supplement used for relieving dyspepsia, which is recorded in the Chinese Materia Medica. Our previous study showed that blossom of CAVA had anti-obesity potential, while its role in browning of WAT was still unclear. AIM OF THE STUDY: This study aimed to characterize the constituents in flavonoids from blossom of CAVA (CAVAF) and to clarify the anti-obesity capacities especially the effects on browning of WAT. MATERIALS AND METHODS: Gradient ethanol eluents from blossom of CAVA were obtained by AB-8 macroporous resin. 3T3-L1 cells and pancreatic lipase inhibition assay were employed to investigate the potential anti-obesity effects in vitro. HPLC and UPLC/MS assays were performed to characterize the chemical profiles of different eluents. Network pharmacology and molecular docking assays were used to reveal potential anti-obesity targets. Furthermore, high-fat diet (HFD)-induced mice were constructed to explore the anti-obesity actions and mechanisms in vivo. RESULTS: 30% ethanol eluents with high flavonoid content and great inhibition on proliferation of 3T3-L1 preadipocytes and pancreatic lipase activity were regarded as CAVAF. 19 compounds were identified in CAVAF. Network pharmacology analysis demonstrated that AMPK and PPARα were potential targets for CAVAF in alleviating obesity. Animal studies demonstrated that CAVAF intervention significantly decreased the body weight, WAT weight, serum TG, TC and LDL-C levels in HFD-fed obese mice. HFD-induced insulin resistance and morphological changes in WAT and brown adipose tissue were also markedly attenuated by CAVAF treatment. CAVAF supplementation potently inhibited iWAT inflammation by regulating IL-6, IL-1ß, TNF-α and IL-10 mRNA expression in iWAT of mice. Furthermore, the gene expression levels of thermogenic markers including Cyto C, ATP synthesis, Cidea, Cox8b and especially UCP1 in iWAT of mice were significantly up-regulated by CAVAF administration. CAVAF intervention also markedly increased the expression levels of PRDM16, PGC-1α, SIRT1, AMPK-α1, PPARα and PPARγ mRNA in iWAT of mice. CONCLUSION: CAVAF treatment significantly promoted browning of WAT in HFD-fed mice. These results suggested that flavonoid extracts from blossom of CAVA were probably promising candidates for the treatment of obesity.

Mapping variation of extracellular matrix in human keloid scar by label-free multiphoton imaging and machine learning.

Significance: Rapid diagnosis and analysis of human keloid scar tissues in an automated manner are essential for understanding pathogenesis and formulating treatment solutions. Aim: Our aim is to resolve the features of the extracellular matrix in human keloid scar tissues automatically for accurate diagnosis with the aid of machine learning. Approach: Multiphoton microscopy was utilized to acquire images of collagen and elastin fibers. Morphological features, histogram, and gray-level co-occurrence matrix-based texture features were obtained to produce a total of 28 features. The minimum redundancy maximum relevancy feature selection approach was implemented to rank these features and establish feature subsets, each of which was employed to build a machine learning model through the tree-based pipeline optimization tool (TPOT). Results: The feature importance ranking was obtained, and 28 feature subsets were acquired by incremental feature selection. The subset with the top 23 features was identified as the most accurate. Then stochastic gradient descent classifier optimized by the TPOT was generated with an accuracy of 96.15% in classifying normal, scar, and adjacent tissues. The area under curve of the classification results (scar versus normal and adjacent, normal versus scar and adjacent, and adjacent versus normal and scar) was 1.0, 1.0, and 0.99, respectively. Conclusions: The proposed approach has great potential for future dermatological clinical diagnosis and analysis and holds promise for the development of computer-aided systems to assist dermatologists in diagnosis and treatment.

Mapping the 3D remodeling of the extracellular matrix in human hypertrophic scar by multi-parametric multiphoton imaging using endogenous contrast.

The hypertrophic scar is an aberrant form of wound healing process, whose clinical efficacy is limited by a lack of understanding of its pathophysiology. Remodeling of collagen and elastin fibers in the extracellular matrix (ECM) is closely associated with scar progression. Herein, we perform label-free multiphoton microscopy (MPM) of both fiber components from human skin specimens and propose a multi-fiber metrics (MFM) analysis model for mapping the structural remodeling of the ECM in hypertrophic scars in a highly-sensitive, three-dimensional (3D) manner. We find that both fiber components become wavier and more disorganized in scar tissues, while content accumulation is observed from elastin fibers only. The 3D MFM analysis can effectively distinguish normal and scar tissues with better than 95% in accuracy and 0.999 in the area under the curve value of the receiver operating characteristic curve. Further, unique organizational features with orderly alignment of both fibers are observed in scar-normal adjacent regions, and an optimized combination of features from 3D MFM analysis enables successful identification of all the boundaries. This imaging and analysis system uncovers the 3D architecture of the ECM in hypertrophic scars and exhibits great translational potential for evaluating scars in vivo and identifying individualized treatment targets.

Highly accurate, automated quantification of 2D/3D orientation for cerebrovasculature using window optimizing method.

Significance: Deep-imaging of cerebral vessels and accurate organizational characterization are vital to understanding the relationship between tissue structure and function. Aim: We aim at large-depth imaging of the mouse brain vessels based on aggregation-induced emission luminogens (AIEgens), and we create a new algorithm to characterize the spatial orientation adaptively with superior accuracy. Approach: Assisted by AIEgens with near-infrared-II excitation, three-photon fluorescence (3PF) images of large-depth cerebral blood vessels are captured. A window optimizing (WO) method is developed for highly accurate, automated 2D/3D orientation determination. The application of this system is demonstrated by establishing the orientational architecture of mouse cerebrovasculature down to the millimeter-level depth. Results: The WO method is proved to have significantly higher accuracy in both 2D and 3D cases than the method with a fixed window size. Depth- and diameter-dependent orientation information is acquired based on in vivo 3PF imaging and the WO analysis of cerebral vessel images with a penetration depth of 800 µm in mice. Conclusions: We built an imaging and analysis system for cerebrovasculature that is conducive to applications in neuroscience and clinical fields.

Identification of endoplasmic reticulum formation mechanism by multi-parametric, quantitative super-resolution imaging.

The endoplasmic reticulum (ER) is a highly dynamic membrane-bound organelle in eukaryotic cells which spreads throughout the whole cell and contacts and interacts with almost all organelles, yet quantitative approaches to assess ER reorganization are lacking. Herein we propose a multi-parametric, quantitative method combining pixel-wise orientation and waviness features and apply it to the time-dependent images of co-labeled ER and microtubule (MT) from U2OS cells acquired from two-dimensional structured illumination microscopy (2D SIM). Analysis results demonstrate that these morphological features are sensitive to ER reshaping and a combined use of them is a potential biomarker for ER formation. A new, to the best of our knowledge, mechanism of MT-associated ER formation, termed hooking, is identified based on distinct organizational alterations caused by interaction between ER and MT which are different from those of the other three mechanisms already known, validated by 100% discrimination accuracy in classifying four MT-associated ER formation mechanisms.