Automatic and accurate esophageal lesion classification and segmentation is of great significance to clinically estimate the lesion status of esophageal disease and make suitable diagnostic schemes. Due to individual variations and visual similarities of lesions in shapes, colors and textures, current clinical methods remain subject to potential high-risk and time-consumption issues. In this paper, we propose an Esophageal Lesion Network (ELNet) for automatic esophageal lesion classification and segmentation using deep convolutional neural networks (DCNNs). The underlying method automatically integrates dual-view contextual lesion information to extract global features and local features for esophageal lesion classification of four esophageal image types (Normal, Inflammation, Barrett, and Cancer) and proposes lesion-specific segmentation network for automatic esophageal lesion annotation of three esophageal lesion types at pixel level. For established clinical large-scale database of 1051 white-light endoscopic images, ten-fold cross-validation is used in method validation. Experiment results show that the proposed framework achieves classification with sensitivity of 0.9034, specificity of 0.9718 and accuracy of 0.9628, and the segmentation with sensitivity of 0.8018, specificity of 0.9655 and accuracy of 0.9462. All of these indicate that our method enables an efficient, accurate and reliable esophageal lesion diagnosis in clinical.The main contributions of our work can be generalized as follows: 1 For the first time, proposed ELNet enables an automatically and reliably comprehensive esophageal lesions classification of four esophageal lesion types (Normal, Inflammation, Barrett, and Cancer) and lesion-specific segmentation from clinically white-light esophageal images to make suitable and repaid diagnostic schemes for clinicians. 2 A novel Dual-Stream network (DSN) is proposed for esophageal lesion classification. DSN automatically integrates dual-view contextual lesion information using two CNN streams to complementarily extract the global features from the holistic esophageal images and the local features from the lesion patches. 3 Lesion-specific esophageal lesion annotation with Segmentation Network with Classification (SNC) strategy is proposed to automatically annotate three lesion types (Inflammation, Barrett, Cancer) at pixel level to reduce the intra-class differences of esophageal lesions. 4 A clinically large-scale database esophageal database is established for esophageal lesions classification and segmentation. This database includes 1051 white-light esophageal images, which consists of endoscopic images in four different lesion types. Each image in this database has a classification label and its corresponding segmentation annotation.

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Clinical diagnosis of esophageal cancer (EC) at early stage is rather difficult. This study aimed to profile the molecules in serum and tissue and identify potential biomarkers in patients with EC. A total of 64 volunteers were recruited, and 83 samples (24 EC serum samples, 21 serum controls, 19 paired EC tissues, and corresponding tumor-adjacent tissues) were analyzed. The gas chromatography time-of-flight mass spectrometry (GC/TOF-MS) was employed, and principal component analysis was used to reveal the discriminatory metabolites and identify the candidate markers of EC. A total of 41 in serum and 36 identified compounds in tissues were relevant to the malignant prognosis. A marked metabolic reprogramming of EC was observed, including enhanced anaerobic glycolysis and glutaminolysis, inhibited tricarboxylic acid (TCA) cycle, and altered lipid metabolism and amino acid turnover. Based on the potential markers of glucose, glutamic acid, lactic acid, and cholesterol, the receiver operating characteristic (ROC) curves indicated good diagnosis and prognosis of EC. EC patients showed distinct reprogrammed metabolism involved in glycolysis, TCA cycle, glutaminolysis, and fatty acid metabolism. The pivotal molecules in the metabolic pathways were suggested as the potential markers to facilitate the early diagnosis of human EC.

Developing artificial plant root models to mimic water absorption using biomaterial-derived inks for three-dimensional (3D) printing is challenging because of their rheological behavior and biocompatibility. Herein, we developed and optimized a cellulose acetate (CA) ink and its printing parameters for extrusion-based 3D-printing to fabricate an object that mimics the mechanical properties and water absorption ability of plant roots. The composition and printing parameters of the CA ink were correlated to its rheological properties for enabling the uninterrupted printing of structures with layer thickness of 0.1 mm and high shape fidelity. The 3D-printing process produces a highly nanoporous (∼87 nm diameter) material without altering the chemistry of CA. The Young’s modulus (1.43 ± 0.14 GPa) and tensile strength (23.30 ± 2.40 MPa) of 3D-printed CA are comparable to those of real plant roots. Furthermore, its high hydraulic conductivity of 3.9 × 10–4 m s–1 MPa–1 indicates its superior water absorption ability. Thus, the 3D-printed CA material possesses immense potential for application in plant science and bioengineering.

Objective: The present study is designed to evaluate the anti-tumor effects of myrrh on human gastric cancer both in vitro and in vivo. Methods: The gastric cancer cell proliferation was determined by MTT assay. Apoptosis was measured by flow cytometry and Hoechst 33342 staining. Wound healing was performed to evaluate the effects of myrrh on the migration. COX-2, PCNA, Bcl-2, and Bax expressions were detected by Western blot analysis. A xenograft nude mice model of human gastric cancer was established to evaluate the anti-cancer effect of myrrh in vivo. Results: Myrrh significantly inhibited cellular proliferation, migration, and induced apoptosis in vitro as well as inhibited tumor growth in vivo. In addition, myrrh inhibited the expression of PCNA, COX-2, and Bcl-2 as well as increased Bax expression in gastric cancer cells. Conclusion: Myrrh may inhibit the proliferation and migration of gastric cancer cells, as well as induced their apoptosis by down-regulating the expression of COX-2.