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.
Despite the growing popularity of corpus linguistics among researchers in recent decades, a corpus-based approach remains largely unknown to most teachers in primary and secondary schools. Drawing on ...Shulman's concept of pedagogical content knowledge, this study differentiated between two key terms - corpus literacy and corpus-based language pedagogy - and investigated how a group of TESOL teacher trainees developed their corpus literacy and corpus-based language pedagogy in a two-step training scheme. The first step, focussing on using corpus data as a learning tool, was conducted in a physical classroom; the second step, focussing on using corpus data as a teaching tool, took place in a virtual online classroom. A mixed methods design, including surveys, interviews and analyses of lesson plans, was used to collect and analyse the data. The findings revealed that most participants gained a good level of corpus literacy as measured by the self-designed survey. They also obtained a good level of initial competence in using corpus-based language pedagogy, as revealed by the rating of their lesson design and the content analyses of the lessons and interview data. The results support a differentiation between corpus literacy and corpus-based language pedagogy, attesting to the effectiveness of the two-step corpus-based teacher training. The study provides several insights regarding how to scaffold teachers in corpus-based training and teach students with corpus resources to address their vocabulary needs and difficulties. Finally, a few issues are raised regarding what teachers may consider when implementing effective corpus-based teaching in school settings.
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•In situ detection of 20 groups of breast tissues Raman spectroscopy.•Spectral analysis was performed on healthy and cancerous breast tissues.•The 1D-CNN was built to classify two ...types of breast tissues.
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.
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.
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Guided by the conceptual framework for next generation designs for mobile-assisted language learning (MALL) in informal setting, our study investigates how mobile devices impact the learning ...practices and habits amongst adult distant learners of English at a higher education institution in China. Data sources include quantitative data of 148 completed responses to an online survey and qualitative data from follow-up email interviews. The study demonstrates that students' main motivation for engaging in mobile assisted English learning is to fill in the gaps in their daily schedule in order to maximize available time, and the majority use mobile devices to support formal course learning. The skills and knowledge areas practiced with mobile devices are listening, followed by speaking and pronunciation, vocabulary, reading, and translation. Our study also reveals that teachers play a very limited role as perceived by students, while students expect more support from teachers for their out-of-class hour mobile assisted language learning. The research findings bear significant pedagogical implications in terms of integrating MALL into language learning curriculum, learner support and the need for continuous teacher training.
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
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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.
Coaxial electrospinning produced biodegradable nanofibers (PSTs) with a core-shell structure. These PSTs exhibited anti-oxidative properties as well as outstanding biocompatibility and excellent mechanical properties.
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