Delayed aortoesophageal and tracheoesophageal fistulas secondary to foreign body ingestion: a case report.

Ingestion of a foreign body (FB) is a common condition with a few potentially life-threatening complications, including esophageal perforation (EP), aortoesophageal fistula (AEF), mediastinal infection, and tracheoesophageal fistula (TEF). In this case, a patient who accidentally ingested a duck bone gradually experienced all of the above complications. To resolve the symptom of difficulty swallowing, the patient underwent emergency treatment for removal of the esophageal FB via endoscopic surgery. Under endoscopy, esophageal mucosal injuries were present, but no other abnormalities, such as active bleeding, were observed. However, the patient returned to our hospital a week later with symptoms of vomiting and black stool and received the diagnosis of EP, AEF and mediastinal infection. Two days later, he vomited 1,000-2,000 mL of blood after experiencing sudden severe chest pain. Then, thoracic endovascular aortic repair (TEVAR) and mediastinal drainage with video-assisted thoracoscopic surgery (VATS) were performed under emergency general anesthesia. Additionally, the patient underwent esophageal stent implantation when TEF was confirmed by tracheal computed tomography (CT). The patient was treated with anti-infective therapy throughout the treatment process. Finally, he recovered and was able to tolerate a liquid diet. Comprehensive evaluation and multidisciplinary cooperation are all very important for the treatment of esophageal foreign bodies and complications.

Optimization of Tumor Disease Monitoring in Medical Big Data Environment Based on High-Order Simulated Annealing Neural Network Algorithm.

With the development of medical informatization, the data related to medical field are growing at an amazing speed, and medical big data appears. The mining and analysis of these data plays an important role in the prediction, monitoring, diagnosis, and treatment of tumor diseases. Therefore, this paper proposes a clustering algorithm of the high-order simulated annealing neural network algorithm and uses this algorithm to extract tumor disease-related big data, constructs training set according to the relevant information mined, designs a kind of dimension reduction model, aiming at the problem of excessive and wrong diagnosis and treatment in the diagnosis and treatment module of tumor disease monitoring mode, and establishes the corresponding control mechanism, so as to optimize the tumor disease monitoring mode. The results show that the clustering accuracy of the high-order simulated annealing neural network algorithm on different data sets (iris, wine, and Pima India diabetes) is 97.33%, 82.11%, and 70.56% and the execution time is 0.75 s, 0.562 s, and 1.092 s, which are better than those of the fast k-medoids algorithm and improved k-medoids clustering algorithm. To sum up, the high-order simulated annealing neural network algorithm can achieve good clustering effect in medical big data mining. The establishment of model M1 can reduce the probability of excessive and wrong medical treatment and improve the effectiveness of diagnosis and treatment module monitoring in tumor disease monitoring mode.

Bone morphogenetic protein-9 promotes the proliferation of non-small cell lung cancer cells by activating PI3K/Akt and Smad1/5 pathways.

Non-small-cell lung carcinoma (NSCLC) is any type of epithelial lung cancer other than small cell lung carcinoma (SCLC), which accounts for about 85% of all lung cancers. Bone morphogenetic protein (BMP)-9 in humans is encoded by the growth differentiation factor 2 gene, which belongs to the transforming growth factor-beta superfamily. In the present study, we explored the role of BMP-9 in A549 and NCI-H1650 cell proliferation and its possible molecular mechanisms. 25 NSCLC patients were recruited to evaluate mRNA expression of BMP-9 to determine its clinicopathologic significance. We found that recombinant protein BMP-9 and overexpression of BMP-9 promoted A549 and NCI-H1650 cell proliferation in vitro, which was abolished by phosphatidylinositol 3-kinase (PI3K) inhibitor (LY294002). Western blot results revealed that BMP-9 significantly activated the PI3K/Akt and Smad1/5 pathway signaling. In vivo, BMP-9 promoted tumor growth and PI3K/Akt and Smad1/5 signaling pathways in an A549 or NCI-H1650 cell line-derived xenograft model. Knockdown BMP-9 or BMP-9 receptor ALK1 inhibited A549 cell growth in vitro and in vivo, which was associated with regulating the PI3K/Akt and Smad1/5 signaling pathways. These results demonstrated that BMP-9 promoted A549 and NCI-H1650 cell proliferation via PI3K/Akt and Smad1/5 signaling pathways.

Identification of a novel glycolysis-related gene signature for predicting metastasis and survival in patients with lung adenocarcinoma.

BACKGROUND: Lung cancer (LC) is one of the most lethal and most prevalent malignant tumors, and its incidence and mortality are increasing annually. Lung adenocarcinoma (LUAD) is the most common pathological type of lung cancer. Several biomarkers have been confirmed by data excavation to be related to metastasis, prognosis and survival. However, the moderate predictive effect of a single gene biomarker is not sufficient. Thus, we aimed to identify new gene signatures to better predict the possibility of LUAD. METHODS: Using an mRNA-mining approach, we performed mRNA expression profiling in large LUAD cohorts (n = 522) from The Cancer Genome Atlas (TCGA) database. Gene Set Enrichment Analysis (GSEA) was performed, and connections between genes and glycolysis were found in the Cox proportional regression model. RESULTS: We confirmed a set of nine genes (HMMR, B4GALT1, SLC16A3, ANGPTL4, EXT1, GPC1, RBCK1, SOD1, and AGRN) that were significantly associated with metastasis and overall survival (OS) in the test series. Based on this nine-gene signature, the patients in the test series could be divided into high-risk and low-risk groups. Additionally, multivariate Cox regression analysis revealed that the prognostic power of the nine-gene signature is independent of clinical factors. CONCLUSION: Our study reveals a connection between the nine-gene signature and glycolysis. This research also provides novel insights into the mechanisms underlying glycolysis and offers a novel biomarker of a poor prognosis and metastasis for LUAD patients.

Long non-coding RNA NEAT1 can predict various malignant tumour lympha node metastasis: a meta-analysis.

It has been found in several studies and research that the long non-coding RNA (Inc RNA) NEAT1 plays an important role in the development and succession in various malignant tumour. The development and metastasis of tumour mainly happen through lymph node. The purpose of this research is to explore the value of lymph node metastasis (LNM) in cancer. We have collected all the concerned studies about NEAT1 and researched the relationship between NEAT1 and lymph node metastasis. We have searched the studies by seeking database PubMed, Web of Science, and China National Knowledge Infrastructure (up to 10 January 2019), as well as a total of 821 patients from eight studies topics and made accordingly meta-analysis. By analyzing the data, we have found that the result is the high expression of NEAT1 associate with the metastasis of lymph node in different malignant tumours. The high level of NEAT1 expression can predict the metastasis of lymph node (OR = 3.36, 95% CI = 1.66-6.77, p = .000) and it is a molecular marker of positive lymph node for treatment.

Design of Polarization Imaging Detection System for Lung Cancer Cells Based on Microfluidic Chip.

Lung cancer has become an important killer that endangers human health. In order to detect lung cancer cells at an early stage, prevent the proliferation of cancer cells, timely identify and treat cancer cells, a polarization imaging detection system for lung cancer cells based on microfluidic microarray is proposed. The system utilizes the different polarization characteristics of lung cancer cells to detect the polarization of lung cancer cells. Polarization imaging analyzes the pathological changes of biological tissues with imaging polarization degree and polarization difference images, so as to detect the differences between normal areas and pathological areas, and thus detect the differences between normal cells and cancer cells and carry out early treatment and prevention. Therefore, this system design has important research significance in medicine.