Recombinantly expressed rhFEB remodeled the skin defect of db/db mice.

Fibronectin (FN) and collagen are vital components of the extracellular matrix (ECM). These proteins are essential for tissue formation and cell alignment during the wound healing stage. In particular, FN interacts with collagens to activate various intracellular signaling pathways to maintain ECM stability. A novel recombinant extra domain-B fibronectin (EDB-FN)-COL3A1 fusion protein (rhFEB) was designed to mimic the ECM to promote chronic and refractory skin ulcer wound healing. rhFEB significantly enhanced cell adhesion and migration, vascular ring formation, and the production of new collagen I (COL1A1) in vitro. rhFEB decreased M1 macrophages and further modulated the wound microenvironment, which was confirmed by the treatment of db/db mice with rhFEB. Accelerated wound healing was shown during the initial stages in rhFEB-treated db/db mice, as was enhanced follicle regeneration, re-epithelialization, collagen deposition, granulation, inflammation, and angiogenesis. The wound chronicity of diabetic foot ulcers (DFUs) remains the main challenge in current and future treatment. rhFEB may be a candidate molecule for regulating M1 macrophages during DFU healing. KEY POINTS: • A recombinant protein EDB-FN-collagen III (rhFEB) was highly expressed in Escherichia coli • rhFEB protein induces COL1A1 secretion in human skin fibroblasts • rhFEB protein accelerates diabetic wound healing.

Recombinant expression a novel fibronectin-collage fusion peptide modulating stem cell stemness via integrin ß3.

Constructing bionic extracellular matrix (ECM) is an attractive proposition for tissue engineering and clinical regeneration therapy involving the stemness of stem cells. Here, a novel recombinant protein fibronectin-collagen peptide (FCP) was designed to modulate the function of ECM expressed by Picha. pastoris strain X33. This FCP promotes cell migration and adhesion and maintains rBMSC stemness by binding integrin ß3. Its effects were blocked by both integrin ß3 siRNA and the integrin ß3 inhibitor Cilengitide. A template-independent ab initio prediction modeling approach is the best approach to construct a stable FCP protein model, which predicts the binding sites between FCP and integrin ß3. FCP may be used in the in vitro culture and clinical regeneration of stem cells that highly express integrin ß3, such as hematopoietic stem cells. The study provides information on the molecular structure of FCP and its bioactivity, which can be used to design new compounds. KEY POINTS: • Design a novel recombinant fibronectin-collagen peptide biomimetic ECM. • FCP promotes cell adhesion, migration, and proliferation. • Predicted and verified FCP structure and affinity with integrin ß3. • FCP binds integrin ß3 to maintain rBMSC stemness.

Another Perspective of Over-Smoothing: Alleviating Semantic Over-Smoothing in Deep GNNs.

Graph neural networks (GNNs) are widely used for analyzing graph-structural data and solving graph-related tasks due to their powerful expressiveness. However, existing off-the-shelf GNN-based models usually consist of no more than three layers. Deeper GNNs usually suffer from severe performance degradation due to several issues including the infamous "over-smoothing" issue, which restricts the further development of GNNs. In this article, we investigate the over-smoothing issue in deep GNNs. We discover that over-smoothing not only results in indistinguishable embeddings of graph nodes, but also alters and even corrupts their semantic structures, dubbed semantic over-smoothing. Existing techniques, e.g., graph normalization, aim at handling the former concern, but neglect the importance of preserving the semantic structures in the spatial domain, which hinders the further improvement of model performance. To alleviate the concern, we propose a cluster-keeping sparse aggregation strategy to preserve the semantic structure of embeddings in deep GNNs (especially for spatial GNNs). Particularly, our strategy heuristically redistributes the extent of aggregations for all the nodes from layers, instead of aggregating them equally, so that it enables aggregate concise yet meaningful information for deep layers. Without any bells and whistles, it can be easily implemented as a plug-and-play structure of GNNs via weighted residual connections. Last, we analyze the over-smoothing issue on the GNNs with weighted residual structures and conduct experiments to demonstrate the performance comparable to the state-of-the-arts.

DWSSA: Alleviating over-smoothness for deep Graph Neural Networks.

Graph Neural Networks (GNNs) have demonstrated great potential in achieving outstanding performance in various graph-related tasks, e.g., graph classification and link prediction. However, most of them suffer from the following issue: shallow networks capture very limited knowledge. Prior works design deep GNNs with more layers to solve the issue, which however introduces a new challenge, i.e., the infamous over-smoothness. Graph representation over emphasizes node features but only considers the static graph structure with a uniform weight are the key reasons for the over-smoothness issue. To alleviate the issue, this paper proposes a Dynamic Weighting Strategy (DWS) for addressing over-smoothness. We first employ Fuzzy C-Means (FCM) to cluster all nodes into several groups and get each node's fuzzy assignment, based on which a novel metric function is devised for dynamically adjusting the aggregation weights. This dynamic weighting strategy not only enables the intra-cluster interactions, but also inter-cluster aggregations, which well addresses undifferentiated aggregation caused by uniform weights. Based on DWS, we further design a Structure Augmentation (SA) step for addressing the issue of underutilizing the graph structure, where some potentially meaningful connections (i.e., edges) are added to the original graph structure via a parallelable KNN algorithm. In general, the optimized Dynamic Weighting Strategy with Structure Augmentation (DWSSA) alleviates over-smoothness by reducing noisy aggregations and utilizing topological knowledge. Extensive experiments on eleven homophilous or heterophilous graph benchmarks demonstrate the effectiveness of our proposed method DWSSA in alleviating over-smoothness and enhancing deep GNNs performance.

Vehicle target detection method based on improved YOLO V3 network model.

For the problem of insufficient small target detection ability of the existing network model, a vehicle target detection method based on the improved YOLO V3 network model is proposed in the article. The improvement of the algorithm model can effectively improve the detection ability of small target vehicles in aerial photography. The optimization and adjustment of the anchor box and the improvement of the network residual module have improved the small target detection effect of the algorithm. Furthermore, the introduction of the rectangular prediction frame with orientation angles into the model of this article can improve the vehicle positioning efficiency of the algorithm, greatly reduce the problem of wrong detection and missed detection of vehicles in the model, and provide ideas for solving related problems. Experiments show that the accuracy rate of the improved algorithm model is 89.3%. Compared to the YOLO V3 algorithm, it is improved by 15.9%. The recall rate is improved by 16%, and the F1 value is also improved by 15.9%, which greatly increased the detection efficiency of aerial vehicles.

Investigation on sexual function in young breast cancer patients during endocrine therapy: a latent class analysis.

Backgrounds: The aim of this study was to investigate the sexual function status of young breast cancer patients during endocrine therapy, identify potential categories of sexual function status, and analyze the factors affecting the potential categories of sexual function status during endocrine therapy. Methods: A cross-sectional survey was conducted on 189 young breast cancer patients who underwent postoperative adjuvant endocrine therapy in Shanghai Ruijin Hospital. The latent class analysis was used to identify potential categories of patient sexual function characteristics with respect to the FSFI sex health measures. Logistic regression analysis was used to analyze the influencing factors for the high risk latent class groups. A nomogram prognostic model were then established to identify high risk patients for female sexual dysfunction (FSD), and C-index was used to determine the prognostic accuracy. Results: Patients were divided into a "high dysfunction-low satisfaction" group and a "low dysfunction-high satisfaction" group depending on the latent class analysis, accounting for 69.3% and 30.7%, respectively. Patients who received aromatase inhibitors (AI) combined with ovarian function suppression (OFS) treatment (p = 0.027), had poor body-image after surgery (p = 0.007), beared heavy medical economy burden(p < 0.001), and had a delayed recovery of sexual function after surgery (p = 0.001) were more likely to be classified into the "high dysfunction-low satisfaction" group, and then conducted into the nomogram. The C-index value of the nomogram for predicting FSD was 0.782. Conclusion: The study revealed the heterogeneity of sexual function status among young breast cancer patients during endocrine therapy, which may help identify high-risk patients and provide early intervention.

Temperature-Controlled Expression of a Recombinant Human-like Collagen I Peptide in Escherichia coli.

Collagen is the functional protein of the skin, tendons, ligaments, cartilage, bone, and connective tissue. Due to its extraordinary properties, collagen has a wide range of applications in biomedicine, tissue engineering, food, and cosmetics. In this study, we designed a functional fragment of human type I collagen (rhLCOL-I) and expressed it in Escherichia coli (E. coli) BL21(DE3) PlysS containing a thermal-induced plasmid, pBV-rhLCOL-I. The results indicated that the optimal expression level of the rhLCOL-I reached 36.3% of the total protein at 42 °C, and expressed in soluble form. In a 7 L fermentation, the yield of purified rhLCOL-I was 1.88 g/L. Interestingly, the plasmid, pBV220-rhLCOL-I, was excellently stable during the fermentation process, even in the absence of antibiotics. Functional analyses indicated that rhLCOL-I had the capacity to promote skin cell migration and adhesion in vitro and in vivo. Taken together, we developed a high-level and low-cost approach to produce collagen fragments suitable for medical applications in E. coli.

Preparation and characterization of bionic bone structure chitosan/hydroxyapatite scaffold for bone tissue engineering.

Three-dimensional oriented chitosan (CS)/hydroxyapatite (HA) scaffolds were prepared via in situ precipitation method in this research. Scanning electron microscopy (SEM) images indicated that the scaffolds with acicular nano-HA had the spoke-like, multilayer and porous structure. The SEM of osteoblasts which were polygonal or spindle-shaped on the composite scaffolds after seven-day cell culture showed that the cells grew, adhered, and spread well. The results of X-ray powder diffractometer and Fourier transform infrared spectrometer showed that the mineral particles deposited in the scaffold had phase structure similar to natural bone and confirmed that particles were exactly HA. In vitro biocompatibility evaluation indicated the composite scaffolds showed a higher degree of proliferation of MC3T3-E1 cell compared with the pure CS scaffolds and the CS/HA10 scaffold was the highest one. The CS/HA scaffold also had a higher ratio of adhesion and alkaline phosphate activity value of osteoblasts compared with the pure CS scaffold, and the ratio increased with the increase of HA content. The ALP activity value of composite scaffolds was at least six times of the pure CS scaffolds. The results suggested that the composite scaffolds possessed good biocompatibility. The compressive strength of CS/HA15 increased by 33.07% compared with the pure CS scaffold. This novel porous scaffold with three-dimensional oriented structure might have a potential application in bone tissue engineering.

The preparation and characterization of chitosan rods modified with Fe3+ by a chelation mechanism.

Chitosan composite rods (CS-Fe(3+)) were prepared via an in situ precipitation method. The relationships among the preparation, structures, and properties of the CS-Fe(3+) composite rods have been investigated. The results of Fourier-transform infrared spectroscopy (FTIR) and core electron X-ray photoelectron spectroscopy (XPS) indicate that the CS and Fe(3+) are coordinated via a chelation mechanism. The content of Fe(3+) in the complex was determined by atomic absorption spectrometry (AAS) and elemental analysis (EA), the results of which suggested that the content of Fe(3+) in the complex can be controlled by the concentration of the ferric salts during coordination. The changes in thermal stability and crystallization properties were measured by thermogravimetric analysis (TGA) and X-ray diffraction (XRD) patterns, respectively. Scanning electron microscopy (SEM) was used to observe the morphological change of the CS-Fe(3+) complex rod. After coordination with Fe(3+), the CS rod had a denser, layered structure. However, the layered structure cannot remain intact when the ratios of -NH(2)/Fe(3+) are 100/15 and 100/20. Moreover, its thermal stability decreased, and its bending strength was improved significantly (from 86 MPa to more than 210 MPa), despite the remarkable decrease in the degree of crystallinity.

Impacts of Filtration on Contrast-Detail Detectability of an X-ray Imaging System.

The purpose of this study is to investigate the impacts of added filtration on the contrast-detail detectability of a digital X-ray imaging system for small animal studies. A digital X-ray imaging system specifically designed for small animal studies was used. This system is equipped with a micro X-ray source with a tungsten target and a beryllium window filtration and a CCD-based digital detector. Molybdenum filters of 0 mm, 0.02 mm, and 0.05 mm in thickness were added. The corresponding X-ray spectra and contrast-detail detectabilities were measured using two phantoms of different thicknesses simulating breast tissue under different exposures. The added Mo filters reduced the low-energy as well as the high-energy photons, hence providing a narrowband for imaging quality improvement. In the experiments with a 1.15 cm phantom, the optimal image detectability was observed using 22 kVp and the 0.05 mm Mo filter. With the 2.15 cm phantom, the best detectability was obtained with 22 kVp and the 0.02 mm Mo filter. Our experiments showed that appropriate filtrations could reduce certain low- and high-energy components of X-ray spectra which have limited contributions to image contrast. At the same time, such filtration could improve the contrast-detail detectability, particularly at relatively low kVp and high filtration. Therefore, optimal image quality can be obtained with the same absorbed radiation dose by the subjects when appropriate filtration is used.