Computational analysis of microRNA profiles and their target genes suggests significant involvement in breast cancer antiestrogen resistance

Motivation: Recent evidence shows significant involvement of microRNAs (miRNAs) in the initiation and progression of numerous cancers; however, the role of these in tumor drug resistance remains unknown

BHLHE40 confers a pro-survival and pro-metastatic phenotype to breast cancer cells by modulating HBEGF secretion

Abstract Background Metastasis is responsible for a significant number of breast cancer-related deaths. Hypoxia, a primary driving force of cancer metastasis, induces the expression of BHLHE40, a transcription regulator. This study aimed to elucidate the function of BHLHE40 in the metastatic process of breast cancer cells. Methods To define the role of BHLHE40 in breast cancer, BHLHE40 expression was knocked down by a lentiviral construct expressing a short hairpin RNA against BHLHE40 or knocked out by the CRISPR/Cas9 editing system. Orthotopic xenograft and experimental metastasis (tail vein injection) mouse models were used to analyze the role of BHLHE40 in lung metastasis of breast cancer. Global gene expression analysis and public database mining were performed to identify signaling pathways regulated by BHLHE40 in breast cancer. The action mechanism of BHLHE40 was examined by chromatin immunoprecipitation (ChIP), co-immunoprecipitation (CoIP), exosome analysis, and cell-based assays for metastatic potential. Results BHLHE40 knockdown significantly reduced primary tumor growth and lung metastasis in orthotopic xenograft and experimental metastasis models of breast cancer. Gene expression analysis implicated a role of BHLHE40 in transcriptional activation of heparin-binding epidermal growth factor (HBEGF). ChIP and CoIP assays revealed that BHLHE40 induces HBEGF transcription by blocking DNA binding of histone deacetylases (HDAC)1 and HDAC2. Cell-based assays showed that HBEGF is secreted through exosomes and acts to promote cell survival and migration. Public databases provided evidence linking high expression of BHLHE40 and HBEGF to poor prognosis of triple-negative breast cancer. Conclusion This study reveals a novel role of BHLHE40 in promoting tumor cell survival and migration by regulating HBEGF secretion

A fully-automatic semi-supervised deep learning model for difficult airway assessment

Background: Difficult airway conditions represent a substantial challenge for clinicians. Predicting such conditions is essential for subsequent treatment planning, but the reported diagnostic accuracies are still quite low. To overcome these challenges, we developed a rapid, non-invasive, cost-effective, and highly-accurate deep-learning approach to identify difficult airway conditions through photographic image analysis. Methods: For each of 1000 patients scheduled for elective surgery under general anesthesia, images were captured from 9 specific and different viewpoints. The collected image set was divided into training and testing subsets in the ratio of 8:2. We used a semi-supervised deep-learning method to train and test an AI model for difficult airway prediction. Results: We trained our semi-supervised deep-learning model using only 30% of the labeled training samples (with the remaining 70% used without labels). We evaluated the model performance using metrics of accuracy, sensitivity, specificity, F1-score, and the area under the ROC curve (AUC). The numerical values of these four metrics were found to be 90.00%, 89.58%, 90.13%, 81.13%, and 0.9435, respectively. For a fully-supervised learning scheme (with 100% of the labeled training samples used for model training), the corresponding values were 90.50%, 91.67%, 90.13%, 82.25%, and 0.9457, respectively. When three professional anesthesiologists conducted comprehensive evaluation, the corresponding results were 91.00%, 91.67%, 90.79%, 83.26%, and 0.9497, respectively. It can be seen that the semi-supervised deep learning model trained by us with only 30% labeled samples can achieve a comparable effect with the fully supervised learning model, but the sample labeling cost is smaller. Our method can achieve a good balance between performance and cost. At the same time, the results of the semi-supervised model trained with only 30% labeled samples were very close to the performance of human experts. Conclusions: To the best of our knowledge, our study is the first one to apply a semi-supervised deep-learning method in order to identify the difficulties of both mask ventilation and intubation. Our AI-based image analysis system can be used as an effective tool to identify patients with difficult airway conditions. Clinical trial registration: ChiCTR2100049879 (URL: http://www.chictr.org.cn)