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Helical architectures were designed for the additive manufacturing of highly flexible polycaprolactone (PCL) scaffolds for engineering soft tissues, which commonly require high flexibility and predominantly function under large deformation conditions. It was found that the design parameters like revolution and radius of helical architectures highly affected the filament fusion or bonding during the fabrication process. The bonding-induced interlaced helical architectures resulted in a more uniform deformation pattern and a lower modulus. μCT-based finite element method was established to predict the large deformation responses of the scaffolds with helical architectures and patient-specific scaffold potentially for breast reconstruction, which showed well agreement with the experimental results. The fabricated scaffolds exhibited good shape recovery capability even under cyclical compression at a strain of about 20% for 10000 times. This exploration offers a promising way to predict the mechanical responses of flexible scaffolds with complex helical architectures under large deformation conditions.

Objective To compare genomic copy number variations (CNVs) among different subtypes of breast cancer and analyze specific CNVs in each subtype. Methods AIMS software was used for genotype breast cancer (BasL, Her2, LumA and LumB), and GISTIC2.0 software was used to analyze genome-wide CNVs in tumor tissues from TCGA. We collected and analyzed the information and samples of 324 cases of invasive breast cancer admitted to Jiangmen Central Hospital(JMCH). Fluorescence quantitative PCR was used to detect the CNV of ERBB2, TFDP1, MIR148B, CCND1, MDM2 and MIR139 genes in the tumor tissues, to verify TCGA analysis. Results 13q34 was specifically amplified and 12q13.13 was specifically deleted in BasL-type breast cancer. The 17q12 was specifically amplified in Her2 type. The 12q15 was specifically amplified and 11q13.4 was specifically deleted in LumB type, but LumA type had no specific amplification or deletion of chromosome region. The proportion of TFDP1 amplification or MIR148B deletion in BasL type were 57.8% (TCGA) and 71.4% (JMCH), which were significantly higher than other subtypes (P < 0.001). The proportion of ERBB2 amplification of Her2 type were 55.2% (TCGA) and 86.7% (JMCH), which were significantly higher than other subtypes (P < 0.001). The proportion of LumB type with CCND1 or MDM2 amplification or MIR139 deletion were 47.6% (TCGA) and 61.8% (JMCH), which were significantly higher than other subtypes (P < 0.05). Conclusion At the CNV level of breast cancer, Her2 type is characterized by the amplification of ERBB2, BasL type is characterized by the amplification of TFDP1 or the deletion of MIR148B, LumB type is characterized by the amplification of CCND1 or MDM2 or the deletion of MIR139, but LumA type lacks specific CNV characteristics.

Biodegradable scaffolds are considered as the key component of tissue engineering which serve as temporary structural supports for tissue regeneration. The mechanical/biological properties of artif...

3D printed external airway splints derived from degradable biopolymers provide a promising way to treat tracheomalacia (TM). However, most of the splints cannot fully reproduce the lumen shapes and the mechanical properties of native tracheas. In this study, biodegradable external airway splints with native-like lumen geometry and mechanical properties were designed and fabricated. Matchable mechanical properties were realized by the combination of different pore shapes and sizes of the splints. A reliable method for the fabrication of the porous polycaprolactone (PCL) splints was demonstrated via selective laser sintering (SLS). Compression results revealed that the SLS-fabricated PCL splints with the diamond pore shape and an inscribed circle diameter of 3 mm possessed similar mechanical properties to those of native tracheas, before and after implantation in dogs with TM for 12 weeks. These SLS-fabricated PCL biodegradable bionic external airway splints might be good candidate for clinical application in future.

Background: In this case report, we aimed to describe the multimodal imaging characteristics and the successful treatment of idiopathic retinal vasculitis, aneurysms, and neuroretinitis (IRVAN) syndrome in a 39-year-old man. Case presentation: His both eyes were diagnosed with IRVAN syndrome via multimodal imaging, including fundus color photograph, multicolor imaging, infrared ray, fundus autofluorescence, fundus fluorescence angiography and optical coherence tomography angiography. Both eyes were treated with vitrectomy and laser photocoagulation. The treatment was effective. Eighteen months after discharge, the patient had visual acuity of 20/20 in both eyes. Conclusions: This case report demonstrates that vitrectomy and retinal laser photocoagulation can be successful in treating a patient with IRVAN syndrome in both eyes.

Electrohydrodynamic (EHD) printing has attracted extensive attention in tissue engineering due to its unique capacity to mimic the fibrillar organizations of the native extracellular matrix. However, it is still a challenge to incorporate functional bionanomaterials into viscous polymer melts for melt-based EHD printing. Here a melt-based EHD printing process was developed to fabricate polycaprolactone (PCL)/multi-walled carbon nanotubes (MWCNTs) microfibrous scaffolds. The addition of MWCNT in PCL had little effect on the stability of melt-based EHD printing and simultaneously reduced the impedance of the resultant fibrous scaffolds. EHD-printed microfibrous PCL/MWCNTs composite scaffolds possessed similar mechanical properties to that of pure PCL scaffolds and showed good cytocompatibility for cellular spreading and proliferation in vitro. The presented strategy exhibits great promise to uniformly incorporate functional bionanomaterials into EHD-printed microfibrous architectures for specific biological functions.

Additive manufacturing (AM) techniques are widely used to fabricate tissue-engineered scaffolds due to their unique capability in constructing controllable macro/microarchitectures. However, it is still a huge challenge to produce flexible structures with mechanical properties comparable to those of native soft tissues by utilizing AM techniques and clinically-available biodegradable polymers. Here, a novel strategy was developed to design and fabricate flexible polycaprolactone scaffolds with highly-tunable mechanical properties by using sinusoidal filament networks rather than conventional linear filament networks. Selective laser sintering was employed to fabricate morphologically-controlled non-orthogonal scaffolds without the need for additional support structures. Uniaxial compression testing revealed that the elastic modulus of the resultant PCL scaffolds with sinusoidal filament networks can be widely tuned in the range of 1944.0 ± 228.7 kPa to 27.3 ± 12.0 kPa by adjusting the filament period and amplitude-to-period ratio. During periodical compression, the scaffolds experienced a rapid strain-induced softening process within the first 10 cycles of compression. A balanced stage with similar compression responses reached and stably maintained even when the scaffolds were further compressed to 200 times. The presented method might provide a promising approach to design and fabricate various flexible scaffolds with tissue-specific geometry and mechanical properties for soft tissue engineering. Keywords: Polycaprolactone (PCL), Flexible scaffolds, Selective laser sintering (SLS), Tunable mechanical properties, Additive manufacturing (AM)

Wind power fluctuation is a major concern of large scale wind power grid integration. To test methods proposed for wind power grid integration, a large amount of wind data with time series are necessary and will be helpful to improve the methods. Meanwhile, due to the short operation history of most wind farms as well as limitations of data collections, the data obtained from wind farms could not satisfy the needs of data analysis. Consequently, synthetic generation of wind data series could be one of the effective solutions for this issue. In this paper, a method is presented for generating wind data series using Markov chain. Due to the high order Markov chain, the possibility matrix designed for a wind farm could cost a lot of memory, which is a problem with current computer technologies. Dynamic list will be introduced in this paper to reduce the memory required. Communication errors are un-avoidable on long way signal transmission between the control centre and wind farms. Missing of data always happens in the historical wind data series. Using these data to generate wind data series may result in some mistakes when searching related elements in the probability matrix. An adaptive method will be applied in this paper to solve the problem. The proposed method will be verified using a set of one-year historical data. The results show that the method could generate wind data series in an effective way.