Tubuliform silk is one of the seven different types of spider silks, which is well known for its unique tensile behaviour with Flat Tensile Stress–Strain (FTSS) curve. It is found that anisotropic microstructure of β-sheets is responsible for this property. In recent years, bioinspired approaches to engineer fibres supported by modern manufacturing systems have been attracting considerable interest. The present paper aims to investigate a strategy to biomimic the FTSS behaviour of tubuliform silk in synthetic polymer composite fibres by blending polyurethane (PU) and regenerated silk fibroin (RSF) at different ratios. Wet spinning of composite fibres results in the reconstruction of β-sheets in the synthetic fibre matrix. PU/RSF composite fibre at a ratio of 75/25 produce a tensile curve with FTSS characteristics. Secondary structural changes in RSF and interchain directions of β-sheets within the fibre are studied using Fourier Transform Infra-red (FTIR) spectroscopy and Transmission Electron Microscopy (TEM), respectively. Interestingly, results of TEM patterns confirm transverse anisotropic properties of RSF β-sheets. The composite fibres also display tuneable mechanical properties with respect to RSF contents.

Osteoarthritis (OA) is characterized by degradation of the articular cartilage, synovium inflammation, subchondral bone sclerosis and osteophyte formation. OA is the most common degenerative joint disorder among the elderly population. In particular, currently available therapeutic strategies, such as non‑steroidal anti‑inflammatory drugs (NSAIDs) may cause severe side‑effects. Therefore, novel candidate targets for OA therapy are urgently needed. Oroxylin A (OrA) is a natural mono‑flavonoid that can be extracted from Scutellariae radix. The present study aimed to investigate the potential effects of OrA on interleukin (IL)‑1β‑induced chondrocytes inflammatory reactions. The current study performed quantitative PCR, western blotting and cell immunofluorescence to evaluate the effect of Oroxylin A in chondrocyte inflammation. The results demonstrated that OrA significantly attenuated the upregulation of inducible nitric oxide synthase and cyclooxygenase 2 by IL‑1β at both protein and mRNA levels. IL‑1β‑stimulated upregulation of matrix metalloproteinase (MMP)‑3 and MMP‑13 expression, in addition to disintegrin and metalloproteinase with thrombospondin motifs (ADAMTS)‑4 and ADAMTS‑5 expression, were all inhibited by OrA. Treatment with OrA significantly reversed the degradation of type II collagen and aggrecan by IL‑1β. Mechanistically, OrA suppressed the IL‑1β induced activation of ERK1/2 and PI3K/AKT signaling pathways. In conclusion, these findings suggest that OrA can serve as a potential therapeutic agent for the treatment of OA.

Spider dragline silk's exceptional mechanical performance, coupled with its benign production pathway, have inspired the design of diverse smart materials. Remarkably, relatively little is known about the self‐assembly process of major ampullate spidroin 1 (MaSp1) – the main protein constituent of dragline fiber – during its rapid conversion from soluble precursor protein into hierarchically structured mature silk fibers. Herein, the biomimetic potential of MaSp1 is explored using a new recombinant platform with full domain representation. MaSp1 is found to undergo efficient liquid‐liquid phase separation (LLPS) in response to kosmotropic ions, with notably higher propensity compared to MaSp2, and with MaSp1 exhibiting a more complex solubility profile relative to levels of sodium chloride. To further the understanding of silk spinning, the rapid assembly of mesoscale structures is monitored in real‐time in response to ion and pH gradients, revealing a progression from LLPS droplets toward aligned hierarchical fibers. Furthermore, using this biomimetic system, insoluble macroscopic MaSp1 fibers can be readily generated, wherein the application of mechanical deformation triggers the emergence of β‐sheet secondary structures. The insights gained from this study can have broader application in efforts to mimic the formation of biomaterials with spatially organized features across multiple length scales.

This paper presents a three-dimensional nonlinear biomechanical finite element model to simulate elderly breast deformation under arm abduction. The finite element model was constructed based on complex anatomical structures that consist of the soft tissues including torso, breast, pectoralis major muscle, and rigid bones including humerus, sternum, clavicle and ribs. The elderly breast was defined as material nonlinearity and geometric nonlinearity. The computational model can simulate and illustrate the deformation of soft tissues during arm abduction from 30° to 90°. The finite element model was validated by motion data. The Young’s modulus for the clavicular portion and the sternocostal portion of the pectoralis major muscle was characterized as 0.1 MPa and 0.08 MPa, respectively. Besides this, the finite element model presented here features the musculoskeletal system for breast deformation and reveals the synergistic relationship between the breast and the pectoralis major muscle. A questionnaire was conducted to analyze the importance of purchasing factors and the discomfort positions in a sports bra from the perspective of senior women. Combined with the finite element model results, this study provides a promising basis for the design of sports bras in an ergonomic way.

In the research of breast morphology, numerous breast features are measured, whereas only a few parameters are adopted for classification. Therefore, how to extract the key variables from the multi-dimensional features in a rational way is an issue that is focused upon. This study aimed to reduce the complexity of the dimensionality reduction for further improving the objectivity and interpretability of the selected breast features. Since the random forest (RF) algorithm can quantify the feature importance during training, the method was adopted to determine the optimal breast features for classification and recognition in this paper. Firstly, the anthropometric data of 360 females from northwestern China aged from 19 to 27 years were measured by non-contact three-dimensional body scanning technology and the contact manual measurement method. Then, the k-means clustering was applied to categorize breast shapes, and the RF algorithm was utilized to quantify and rank the importance of 25 breast features. Finally, to verify the availability of the RF algorithm on breast feature selection, the t-distributed stochastic neighbor embedding method was adopted to visualize the distribution of breast shape clusters into two dimensions. Meanwhile, four neural networks were determined to recognize the breast morphology. The results demonstrate that fewer breast features can effectively increase the accuracy of breast shape classification and recognition. The best performance of breast shape classification and recognition is obtained when the number of breast features is 13. In this case, the average Hamming loss of four neural networks is the smallest (0.1136). Interestingly, the bust circumference and the horizontal curve of breasts across the bust points are found to be the most important of the 25 breast features in this paper. The importance of the breast curve features is higher than that of the breast cross-sectional features, while the breast positioning features have the lowest importance. Meanwhile, the RF algorithm is verified to be more effective than traditional dimensionality reduction methods, such as principal component analysis, hierarchical clustering, and recursive feature elimination. The approach developed in this paper can be generalized to the dimensionality reduction of other body morphology.