Table3_Multi-Similarities Bilinear Matrix Factorization-Based Method for Predicting Human Microbe–Disease Associations.XLSX

Accumulating studies have shown that microbes are closely related to human diseases. In this paper, a novel method called MSBMFHMDA was designed to predict potential microbe–disease associations by adopting multi-similarities bilinear matrix factorization. In MSBMFHMDA, a microbe multiple similarities matrix was constructed first based on the Gaussian interaction profile kernel similarity and cosine similarity for microbes. Then, we use the Gaussian interaction profile kernel similarity, cosine similarity, and symptom similarity for diseases to compose the disease multiple similarities matrix. Finally, we integrate these two similarity matrices and the microbe-disease association matrix into our model to predict potential associations. The results indicate that our method can achieve reliable AUCs of 0.9186 and 0.9043 ± 0.0048 in the framework of leave-one-out cross validation (LOOCV) and fivefold cross validation, respectively. What is more, experimental results indicated that there are 10, 10, and 8 out of the top 10 related microbes for asthma, inflammatory bowel disease, and type 2 diabetes mellitus, respectively, which were confirmed by experiments and literatures. Therefore, our model has favorable performance in predicting potential microbe–disease associations.</p

Thoracoscopic right upper lobectomy in a patient with displaced posterior segmental bronchus and vascular abnormalities: a case report

Displaced posterior segmental bronchus (B2) accompanied by anomalous pulmonary vessels is a very rare condition. There is a risk of unexpected injuries to bronchi and blood vessels when patients with such anomalies undergo surgery for lung cancer, especially thoracoscopic surgery. We reported a case of thoracoscopic right upper lobectomy in a patient with a displaced B2 and pulmonary vascular variation. A 74-year-old woman was admitted to our hospital with a 2.2 cm × 2.1 cm nodule in the right lung. Three-dimensional computed tomography (3D-CT) revealed the combined apical/anterior segmental branch (B1 + 3) taken off the beginning of the right main bronchus (RMB), at the level of the carina. The displaced B2 taken off the end of the RMB. The anomalous central vein (CV), which passed between B2 and B1 + 3, ran dorsal to the main pulmonary artery (MPA) and directly into the left atrium. The patient consequently underwent uniportal thoracoscopic right upper lobectomy and mediastinal lymph node dissection. The intraoperative findings were completely consistent with 3D-CT. This paper reports a case of a displaced B2 combined with right upper pulmonary vessels malformation. Under the guidance of 3D-CT, the right upper lobectomy was successfully completed by single hole thoracoscopic surgery.</p

sj-tif-2-ajr-10.1177_19458924221100960 - Supplemental material for CircKIAA0368 Promotes Proliferation, Migration, and Invasion by Upregulating HOXA10 in Nasopharyngeal Carcinoma

Supplemental material, sj-tif-2-ajr-10.1177_19458924221100960 for CircKIAA0368 Promotes Proliferation, Migration, and Invasion by Upregulating HOXA10 in Nasopharyngeal Carcinoma by Zhiping Chen, Qiaoying Gong, Daojing Li and Juying Zhou in American Journal of Rhinology & Allergy</p

Table2_Multi-Similarities Bilinear Matrix Factorization-Based Method for Predicting Human Microbe–Disease Associations.XLSX

Accumulating studies have shown that microbes are closely related to human diseases. In this paper, a novel method called MSBMFHMDA was designed to predict potential microbe–disease associations by adopting multi-similarities bilinear matrix factorization. In MSBMFHMDA, a microbe multiple similarities matrix was constructed first based on the Gaussian interaction profile kernel similarity and cosine similarity for microbes. Then, we use the Gaussian interaction profile kernel similarity, cosine similarity, and symptom similarity for diseases to compose the disease multiple similarities matrix. Finally, we integrate these two similarity matrices and the microbe-disease association matrix into our model to predict potential associations. The results indicate that our method can achieve reliable AUCs of 0.9186 and 0.9043 ± 0.0048 in the framework of leave-one-out cross validation (LOOCV) and fivefold cross validation, respectively. What is more, experimental results indicated that there are 10, 10, and 8 out of the top 10 related microbes for asthma, inflammatory bowel disease, and type 2 diabetes mellitus, respectively, which were confirmed by experiments and literatures. Therefore, our model has favorable performance in predicting potential microbe–disease associations.</p

Table5_Multi-Similarities Bilinear Matrix Factorization-Based Method for Predicting Human Microbe–Disease Associations.XLSX

Accumulating studies have shown that microbes are closely related to human diseases. In this paper, a novel method called MSBMFHMDA was designed to predict potential microbe–disease associations by adopting multi-similarities bilinear matrix factorization. In MSBMFHMDA, a microbe multiple similarities matrix was constructed first based on the Gaussian interaction profile kernel similarity and cosine similarity for microbes. Then, we use the Gaussian interaction profile kernel similarity, cosine similarity, and symptom similarity for diseases to compose the disease multiple similarities matrix. Finally, we integrate these two similarity matrices and the microbe-disease association matrix into our model to predict potential associations. The results indicate that our method can achieve reliable AUCs of 0.9186 and 0.9043 ± 0.0048 in the framework of leave-one-out cross validation (LOOCV) and fivefold cross validation, respectively. What is more, experimental results indicated that there are 10, 10, and 8 out of the top 10 related microbes for asthma, inflammatory bowel disease, and type 2 diabetes mellitus, respectively, which were confirmed by experiments and literatures. Therefore, our model has favorable performance in predicting potential microbe–disease associations.</p

sj-tif-1-ajr-10.1177_19458924221100960 - Supplemental material for CircKIAA0368 Promotes Proliferation, Migration, and Invasion by Upregulating HOXA10 in Nasopharyngeal Carcinoma

Supplemental material, sj-tif-1-ajr-10.1177_19458924221100960 for CircKIAA0368 Promotes Proliferation, Migration, and Invasion by Upregulating HOXA10 in Nasopharyngeal Carcinoma by Zhiping Chen, Qiaoying Gong, Daojing Li and Juying Zhou in American Journal of Rhinology & Allergy</p

Table4_Multi-Similarities Bilinear Matrix Factorization-Based Method for Predicting Human Microbe–Disease Associations.XLSX

Accumulating studies have shown that microbes are closely related to human diseases. In this paper, a novel method called MSBMFHMDA was designed to predict potential microbe–disease associations by adopting multi-similarities bilinear matrix factorization. In MSBMFHMDA, a microbe multiple similarities matrix was constructed first based on the Gaussian interaction profile kernel similarity and cosine similarity for microbes. Then, we use the Gaussian interaction profile kernel similarity, cosine similarity, and symptom similarity for diseases to compose the disease multiple similarities matrix. Finally, we integrate these two similarity matrices and the microbe-disease association matrix into our model to predict potential associations. The results indicate that our method can achieve reliable AUCs of 0.9186 and 0.9043 ± 0.0048 in the framework of leave-one-out cross validation (LOOCV) and fivefold cross validation, respectively. What is more, experimental results indicated that there are 10, 10, and 8 out of the top 10 related microbes for asthma, inflammatory bowel disease, and type 2 diabetes mellitus, respectively, which were confirmed by experiments and literatures. Therefore, our model has favorable performance in predicting potential microbe–disease associations.</p

sj-tif-3-ajr-10.1177_19458924221100960 - Supplemental material for CircKIAA0368 Promotes Proliferation, Migration, and Invasion by Upregulating HOXA10 in Nasopharyngeal Carcinoma

Supplemental material, sj-tif-3-ajr-10.1177_19458924221100960 for CircKIAA0368 Promotes Proliferation, Migration, and Invasion by Upregulating HOXA10 in Nasopharyngeal Carcinoma by Zhiping Chen, Qiaoying Gong, Daojing Li and Juying Zhou in American Journal of Rhinology & Allergy</p

Table1_Multi-Similarities Bilinear Matrix Factorization-Based Method for Predicting Human Microbe–Disease Associations.XLSX

Accumulating studies have shown that microbes are closely related to human diseases. In this paper, a novel method called MSBMFHMDA was designed to predict potential microbe–disease associations by adopting multi-similarities bilinear matrix factorization. In MSBMFHMDA, a microbe multiple similarities matrix was constructed first based on the Gaussian interaction profile kernel similarity and cosine similarity for microbes. Then, we use the Gaussian interaction profile kernel similarity, cosine similarity, and symptom similarity for diseases to compose the disease multiple similarities matrix. Finally, we integrate these two similarity matrices and the microbe-disease association matrix into our model to predict potential associations. The results indicate that our method can achieve reliable AUCs of 0.9186 and 0.9043 ± 0.0048 in the framework of leave-one-out cross validation (LOOCV) and fivefold cross validation, respectively. What is more, experimental results indicated that there are 10, 10, and 8 out of the top 10 related microbes for asthma, inflammatory bowel disease, and type 2 diabetes mellitus, respectively, which were confirmed by experiments and literatures. Therefore, our model has favorable performance in predicting potential microbe–disease associations.</p

Table6_Multi-Similarities Bilinear Matrix Factorization-Based Method for Predicting Human Microbe–Disease Associations.XLSX

Accumulating studies have shown that microbes are closely related to human diseases. In this paper, a novel method called MSBMFHMDA was designed to predict potential microbe–disease associations by adopting multi-similarities bilinear matrix factorization. In MSBMFHMDA, a microbe multiple similarities matrix was constructed first based on the Gaussian interaction profile kernel similarity and cosine similarity for microbes. Then, we use the Gaussian interaction profile kernel similarity, cosine similarity, and symptom similarity for diseases to compose the disease multiple similarities matrix. Finally, we integrate these two similarity matrices and the microbe-disease association matrix into our model to predict potential associations. The results indicate that our method can achieve reliable AUCs of 0.9186 and 0.9043 ± 0.0048 in the framework of leave-one-out cross validation (LOOCV) and fivefold cross validation, respectively. What is more, experimental results indicated that there are 10, 10, and 8 out of the top 10 related microbes for asthma, inflammatory bowel disease, and type 2 diabetes mellitus, respectively, which were confirmed by experiments and literatures. Therefore, our model has favorable performance in predicting potential microbe–disease associations.</p