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Background: Anomalies of the aortic arch and its branches rarely develop. The surgery for esophageal cancer may be challenging with the presence of these anomalies. This study is aimed to analyze the influence of these variations during the esophagectomy., Methods: A total of 21 patients with aortic arch and brachiocephalic vessel variations were retrospectively identified from 2013 to 2019. Anomalies were distributed: 15 patients with left-sided aortic arch combined with aberrant right subclavian artery (LAA + ARSA), 2 right-sided aortic arch with mirror-image arch branches (RAA + MIAB), and 4 right-sided aortic arch combined with aberrant left subclavian artery (RAA + ALSA). Perioperative characteristics and long-term survival were analyzed., Results: Tumors were mostly located in the upper and middle thorax (42.9% and 47.6%, respectively). Of the 15 patients with LAA + ARSA, 13 underwent McKeown esophagectomy and 2 via transhiatal approach. Left thoracotomy was performed on all 6 patients with RAA. The R0 resection rate was 90.5% (19/21). Recurrent laryngeal nerve (RLN) injury occurred in 2 patients. Two patients died within 30 days postoperatively. Lymph node yield was 23.6 ± 1.2, with a metastasis rate of 38.1% (8/21). The median follow-up time was 18 months. Recurrence occurred in 6 patients (11-35 months) and 4 patients died after recurrence with a median time to death of 21 months (8-47 months)., Conclusion: For resectable esophageal cancer combined with aortic arch and its branches anomalies, satisfactory surgical results can be obtained under careful preoperative evaluation and reasonable surgical approach selection.

Classification of protein sequences into functional and structural families based on machine learning methods is a hot research topic in machine learning and Bioinformatics. In fact, the underlying protein classification problem is a huge multiclass problem. Generally, the multiclass problem can be reduced to a set of binary classification problems. The protein in one class are seen as positive examples while those outside the class are seen as negative examples. However, the class imbalance problem will arise in this case because the number of protein in one class is usually much smaller than that of the protein outside the class. To handle the challenge, we propose a novel framework to classify the protein. We firstly use free scores (FS) to perform feature extraction for protein; then, the inverse random under sampling (IRUS) is used to create a large number of distinct training sets; next, we use a new ensemble approach to combine these distinct training sets with a new fuzzy total margin support vector machine (FTM-SVM) that we have constructed. we call the novel ensemble classifier as ensemble fuzzy total margin support vector machine (EnFTM-SVM). We then give a full description of our method, including the details of its derivation. Finally, experimental results on fourteen benchmark protein data sets indicate that the proposed method outperforms many state-of-the-art protein classifying methods.

Objective: To research the chemical constituents from the aerial parts of Medicago sativa., Methods: Various column chromatographies were emoployed to isolate and purify the constituents. Their structures were elucidated by spectral analysis (IR, UV, MS, 1H-NMR, 13C-NMR) and chemical evidence., Results: Two constituents were obtained and identified as soysaponin I (I), azukisaponin V (II)., Conclusion: These two compounds are isolated from the arial parts of Mesicago sativa for the first time. Moreover,the compounds are found to have hypolipidemic activity.

Objective: To observe the effects of Cinnamyl aldehyde (CA) on NIH3T3 cell cycle and explore the possible mechanism further., Method: Flowcytometry was used for observing cell cycle distribution. Expressions of proliferation cell nuclear antigen (PCNA) and Cyclin D1 protein in NIH3T3 cells were assessed by immunocytochemistry., Result: After culture with CA for 24 hours, the percentage of populations of S phase was enhanced by 3% (P < 0.05) and cell proliferation index (PrI, S + G2/M) was increased by 3.5% (P < 0.01) , but G2/M phase had no obvious changes. The expressions of Cyclin D1 and PCNA proteins were improved markly by CA compared with controlgroup (P < 0.01)., Conclusion: CA could promote more cells in G0/G1 phase into S phase, which may be related to the regulation of the expressions of PCNA and Cyclin D1.