MV-mimicking micelles loaded with PEG-serine-ACP nanoparticles to achieve biomimetic intra/extra fibrillar mineralization of collagen in vitro

Since their discovery, matrix vesicles (MVs) containing minerals have received considerable attention for their role in the mineralization of bone, dentin and calcified cartilage. Additionally, MVs' association with collagen fibrils, which serve as the scaffold for calcification in the organic matrix, has been repeatedly highlighted. The primary purpose of the present study was to establish a MVs–mimicking model (PEG-S-ACP/micelle) in vitro for studying the exact mechanism of MVs-mediated extra/intra fibrillar mineralization of collagen in vivo. In this study, high-concentration serine was used to stabilize the amorphous calcium phosphate (S-ACP), which was subsequently mixed with polyethylene glycol (PEG) to form PEG-S-ACP nanoparticles. The nanoparticles were loaded in the polysorbate 80 micelle through a micelle self-assembly process in an aqueous environment. This MVs–mimicking model is referred to as the PEG-S-ACP/micelle model. By adjusting the pH and surface tension of the PEG-S-ACP/micelle, two forms of minerals (crystalline mineral nodules and ACP nanoparticles) were released to achieve the extrafibrillar and intrafibrillar mineralization, respectively. This in vitro mineralization process reproduced the mineral nodules mediating in vivo extrafibrillar mineralization and provided key insights into a possible mechanism of biomineralization by which in vivo intrafibrillar mineralization could be induced by ACP nanoparticles released from MVs. Also, the PEG-S-ACP/micelle model provides a promising methodology to prepare mineralized collagen scaffolds for repairing bone defects in bone tissue engineering.     

Single-cell landscape of the ecosystem in early-relapse hepatocellular carcinoma

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Involvement of Kv1.3 and p38 MAPK signaling in HIV-1 glycoprotein 120-induced microglia neurotoxicity

Inflammatory responses mediated by activated microglia play a pivotal role in the pathogenesis of human immunodeficiency virus type 1 (HIV-1)-associated neurocognitive disorders. Studies on identification of specific targets to control microglia activation and resultant neurotoxic activity are imperative. Increasing evidence indicate that voltage-gated K⁺ (K_v) channels are involved in the regulation of microglia functionality. In this study, we investigated K_v1.3 channels in the regulation of neurotoxic activity mediated by HIV-1 glycoprotein 120 (gp120)-stimulated rat microglia. Our results showed treatment of microglia with gp120 increased the expression levels of K_v1.3 mRNA and protein. In parallel, whole-cell patch-clamp studies revealed that gp120 enhanced microglia K_v1.3 current, which was blocked by margatoxin, a K_v1.3 blocker. The association of gp120 enhancement of K_v1.3 current with microglia neurotoxicity was demonstrated by experimental results that blocking microglia K_v1.3 attenuated gp120-associated microglia production of neurotoxins and neurotoxicity. Knockdown of K_v1.3 gene by transfection of microglia with K_v1.3-siRNA abrogated gp120-associated microglia neurotoxic activity. Further investigation unraveled an involvement of p38 MAPK in gp120 enhancement of microglia K_v1.3 expression and resultant neurotoxic activity. These results suggest not only a role K_v1.3 may have in gp120-associated microglia neurotoxic activity, but also a potential target for the development of therapeutic strategies.     

Isoliquiritigenin prevents hyperglycemia-induced renal injuries by inhibiting inflammation and oxidative stress via SIRT1-dependent mechanism

Diabetic nephropathy (DN) as a global health concern is closely related to inflammation and oxidation. Isoliquiritigenin (ISL), a natural flavonoid compound, has been demonstrated to inhibit inflammation in macrophages. Herein, we investigated the effect of ISL in protecting against the injury in STZ-induced type 1 DN and in high glucose-induced NRK-52E cells. In this study, it was revealed that the administration of ISL not only ameliorated renal fibrosis and apoptosis, but also induced the deterioration of renal function in diabetic mice. Mediated by MAPKs and Nrf-2 signaling pathways, respectively, upstream inflammatory response and oxidative stress were neutralized by ISL in vitro and in vivo. Moreover, as further revealed by the results of molecular docking, sirtuin 1 (SIRT1) binds to ISL directly, and the involvement of SIRT1 in ISL-mediated renoprotective effects was confirmed by studies using in vitro models of SIRT1 overexpression and knockdown. In summary, by reducing inflammation and oxidative stress, ISL has a significant pharmacological effect on the deterioration of DN. The benefits of ISL are associated with the direct binding to SIRT1, the inhibition of MAPK activation, and the induction of Nrf-2 signaling, suggesting the potential of ISL for DN treatment.Subject terms: Pharmacology, Molecular biology     

A study on giant panda recognition based on images of a large proportion of captive pandas

1. As a highly endangered species, the giant panda (panda) has attracted significant attention in the past decades. Considerable efforts have been put on panda conservation and reproduction, offering the promising outcome of maintaining the population size of pandas. To evaluate the effectiveness of conservation and management strategies, recognizing individual pandas is critical. However, it remains a challenging task because the existing methods, such as traditional tracking method, discrimination method based on footprint identification, and molecular biology method, are invasive, inaccurate, expensive, or challenging to perform. The advances of imaging technologies have led to the wide applications of digital images and videos in panda conservation and management, which makes it possible for individual panda recognition in a noninvasive manner by using image‐based panda face recognition method.
2. In recent years, deep learning has achieved great success in the field of computer vision and pattern recognition. For panda face recognition, a fully automatic deep learning algorithm which consists of a sequence of deep neural networks (DNNs) used for panda face detection, segmentation, alignment, and identity prediction is developed in this study. To develop and evaluate the algorithm, the largest panda image dataset containing 6,441 images from 218 different pandas, which is 39.78% of captive pandas in the world, is established.
3. The algorithm achieved 96.27% accuracy in panda recognition and 100% accuracy in detection.
4. This study shows that panda faces can be used for panda recognition. It enables the use of the cameras installed in their habitat for monitoring their population and behavior. This noninvasive approach is much more cost‐effective than the approaches used in the previous panda surveys.