Fabrication of a core-shell nanofibrous wound dressing with an antioxidant effect on skin injury.

Oxidative stress is one of the obstacles preventing wound regeneration, especially for chronic wounds. Herein, designing a wound dressing with an anti-oxidant function holds great appeal for enhancing wound regeneration. In this study, a biocompatible and degradable nanofiber with a core-shell structure was fabricated via coaxial electrospinning, in which polycaprolactone (PCL) was applied as the core structure, while the shell was composed of a mixture of silk fibroin (SF) and tocopherol acetate (TA). The electrospun PST nanofibers were proven to have a network structure with significantly enhanced mechanical properties. The PSTs exhibited a diameter distribution with an average of 321 ± 134 nm, and the water contact angle of their surface is 124 ± 2°. The PSTs also exhibited good tissue compatibility, which can promote the adhesion and proliferation of L929 cells. Besides, the dissolution of silk fibroin encourages the release of TA, which could play a synergistic effect and regulate the oxidative stress effect in the damaged area, for it promotes the adhesion and proliferation of skin fibroblasts (L929), reduces the cytotoxicity of hydrogen peroxide to cells, and lowers the level of reactive oxygen species. The animal experiment indicated that the PSTs would promote the reconstruction of skin. These nanofibers are expected to repair skin ulcers related to diabetes.

Double-network cellulose-based hybrid hydrogels with favourable biocompatibility and antibacterial activity for wound healing.

Bacterial infections are among the leading causes of delayed wound healing. At present, a series of antibacterial materials, such as antibiotics, antimicrobial peptides (AMPs), metals and metal oxides (MMOs), have been used to fabricate antibacterial wound dressings. However, their translational potential is limited owing to their poor biocompatibility. ε-Polylysine (ε-PL) is a natural macromolecule with excellent biocompatibility and broad-spectrum antibacterial activity. Herein, ε-PL was incorporated into a cellulose/γ-polyglutamic acid (γ-PGA) composite hydrogel to form a novel double-network hydrogel termed as CGLH. The elastic modulus of CGLH increased from 0.097 ± 0.015 MPa to 0.441 ± 0.096 MPa, and the equilibrium swelling ratio increased from 382.7 ± 24.3 % to 611.2 ± 8.6 %. Several preclinical models were used to investigate the translational potential of this hydrogel. CGLH exhibited good biocompatibility and antibacterial activity, which promoted the healing of infected and critical-size wounds within 12 days. CGLH had positive effects on collagen synthesis, vascularization and cell proliferation. As a result, this study not only provided an effective alternative for wound healing but also proposed a double-network strategy for creating biocompatible and antibacterial biomaterials.

Polarized Micro-Raman Spectroscopy and 2D Convolutional Neural Network Applied to Structural Analysis and Discrimination of Breast Cancer.

Raman spectroscopy has been efficiently used to recognize breast cancer tissue by detecting the characteristic changes in tissue composition in cancerization. In addition to chemical composition, the change in bio-structure may be easily obtained via polarized micro-Raman spectroscopy, aiding in identifying the cancerization process and diagnosis. In this study, a polarized Raman spectral technique is employed to obtain rich structural features and, combined with deep learning technology, to achieve discrimination of breast cancer tissue. The results reconfirm that the orientation of collagen fibers changes from parallel to vertical during breast cancerization, and there are significant structural differences between cancerous and normal tissues, which is consistent with previous reports. Optical anisotropy of collagen fibers weakens in cancer tissue, which is closely related with the tumor's progression. To distinguish breast cancer tissue, a discrimination model is established based on a two-dimensional convolutional neural network (2D-CNN), where the input is a matrix containing the Raman spectra acquired at a set of linear polarization angles varying from 0° to 360°. As a result, an average discrimination accuracy of 96.01% for test samples is achieved, better than that of the KNN classifier and 1D-CNN that are based on non-polarized Raman spectra. This study implies that polarized Raman spectroscopy combined with 2D-CNN can effectively detect changes in the structure and components of tissues, innovatively improving the identification and automatic diagnosis of breast cancer with label-free probing and analysis.

On-chip monolithic Fourier transform spectrometers assisted by cGAN spectral prediction.

Silicon photonic spatial heterodyne Fourier transform spectrometers (SH-FTSs) are attractive with chip-scale monolithic arrays of imbalanced Mach-Zehnder interferometers; however, there exist optical path difference (OPD) errors from the inevitable fabrication imperfection, which will severely distort the retrieved spectra. In this Letter, we propose that a predictive model can be created for rapid and accurate spectral recovery based on the conditional generative adversarial network (cGAN) featuring strong input-on-output supervision, instead of both complicated physical OPD modification and time-consuming iterative spectral calculation. As a demonstration, cGAN spectral prediction was performed for our previously presented dual-polarized SH-FTS with large OPD errors [Opt. Lett.44, 2923 (2019)OPLEDP0146-959210.1364/OL.44.002923]. Due to the strong noise-resistant capability, the cGAN-predicted spectra can stay reliable, even though the signal-to-noise ratio of acquired interferograms dramatically drops from 1000 to 100, implying a lower limit of detection.

Classifying breast cancer tissue by Raman spectroscopy with one-dimensional convolutional neural network.

As the most common cancer in women, breast cancer is becoming lethal worldwide. However, the current breast diagnosis technologies are not enough to meet the requirements in clinic due to some shortages of early-stage insensitiveness, time consumption and relying on the doctor's experience, etc. It's necessary to develop a creative method for the automatical diagnosis of breast cancer. Therefore, Raman spectroscopy and one-dimensional convolutional neural network (1D-CNN) algorithm were combined together for the first time to classify the healthy and cancerous breast tissues in this study. First, a number of Raman spectra were collected from breast samples of 20 patients for spectral analysis. Then, a 1D-CNN model was developed and trained for classification. In addition, the Fisher Discrimination Analysis (FDA) and Support Vector Machine (SVM) classifiers were trained and tested with the same spectral data for comparison. The best classification performance, namely the overall diagnostic accuracy of 92%, the sensitivity of 98% and the specificity of 86%, has been achieved by using 1D-CNN model. This study proves that 1D-CNN combined with Raman spectroscopy can classify breast tissues effectively and automatically and lay the foundation for automated cancer diagnosis in the future.

Mechanistic Evaluation of the Opposite Effects on Initial Burst Induced by Two Similar Hydrophilic Additives From Octreotide Acetate-Loaded PLGA Microspheres.

The purpose of the present study was to make a detailed comparison of 2 similar additives about their opposite effects on the initial burst of octreotide acetate from poly(lactic-co-glycolic acid) microspheres. We focused on identifying the key factor that influenced the initial burst of microspheres induced by small hydrophilic additives. The apparent reason resulting in such differences was different pore closing rates on the surface of these 2 batches. However, the potential mechanism was still unknown. To compare with the single-additive system, these 2 additives were coencapsulated together into the same formulation. Of surprise, the inhibition effect of glucose on burst release somehow disappeared and even turned out to be opposite. This phenomenon greatly reminds us that there must be some interactions between glucose and polymer, which are likely to be disturbed by coencapsulated CaCl2. However, small amount of additive can hardly make any detected difference. Therefore, additive-loaded microspheres without drug were prepared to further investigate the potential factors. Under this condition, differences were found. The key factor for glucose-induced accelerated pore closure and reduction in initial burst was the formation of hydrogen bonds between the glucose molecule and the polymer matrix.

Development and research of a coaxial microwave plasma thruster.

An overview of the research on a coaxial microwave plasma thruster at Northwestern Polytechnic University is presented. Emphasis is put on the development and research on key components of the thruster system, a microthrust balance, plasma plume diagnostics, and a numerical simulation of the plasma flow field inside the thruster cavity. The developed thruster cavity is chosen from a coaxial resonant cavity with concentrated capacitance, which can operate well in atmosphere and vacuum conditions. The development of a microwave source shows that a magnetron powered by a switch power supply has advantages in the power level and efficiency, but a solid state microwave source synthesized from the arsenide field effect transistor is superior in weight and volume. Through elimination of the effect of large gravity and resistance force induced by a gas pipe line and a microwave transmitting line on the microthrust, 15 mN and 340 s in the performance of the microwave plasma thruster at 70 W and with helium gas are measured. Diagnosing experiment shows that the plasma plume density is in the range of (1-7.2)x10(16)m(3). Numerical simulation of the plasma flow field inside the coaxial thruster cavity shows that there is a good match between the microwave power and gas flow rate.