Homeobox A3 (HOXA3), one of HOX transcription factors, regulates gene expression during embryonic development. HOXA3 expression has been reported to be associated with several cancers; however, its role in colon cancer and underlying mechanism are still unclear. The expression of HOXA3 in 232 paired of human colon tumor and adjacent non-tumorous tissues were measured by qPCR. The relationship between HOXA3 expression and clinical outcomes were analyzed by Kaplan-Meier survival curves analysis. Human colon cancer cell lines HT29 and HTC116 were transfected with HOXA3 siRNA, or HOXA3 expressing vector, and then cell proliferation and apoptosis were assessed, respectively. Western blot was performed to detect the activation of EGFR/Ras/Raf/MEK/ERK signaling pathway. Moreover, HOXA3-overexpressing and HOXA3-suppressing HT29 cells were subcutaneous injected into nod mice to confirm the regulation of HOXA3 on EGFR/Ras/Raf/MEK/ERK signaling in regulating tumor growth. HOXA3 was upregulated in colon tumor tissues and cell lines, and upregulated expression of HOXA3 was associated with low survival rate. Knockdown of HOXA3 suppressed cell viability and clone formation, while induced cell apoptosis. HOXA3 knockdown could not induce the increase of cell apoptosis on the condition of EGFR overexpression. In vivo xenograft studies, HOXA3-suppressing cells showed less tumorigenic. Moreover, HOXA3 knockdown suppressed the activation of EGFR/Ras/Raf/MEK/ERK signaling pathway. To conclude, this study indicated that HOXA3 might act as a promoter of human colon cancer formation by regulating EGFR/Ras/Raf/MEK/ERK signaling pathway. HOXA3 might be a potential therapeutic target for the treatment of colon cancer.

MRI is the gold standard for confirming a pelvic lymph node metastasis diagnosis. Traditionally, medical radiologists have analyzed MRI image features of regional lymph nodes to make diagnostic decisions based on their subjective experience; this diagnosis lacks objectivity and accuracy. This study trained a faster region-based convolutional neural network (Faster R-CNN) with 28,080 MRI images of lymph node metastasis, allowing the Faster R-CNN to read those images and to make diagnoses. For clinical verification, 414 cases of rectal cancer at various medical centers were collected, and Faster R-CNN-based diagnoses were compared with radiologist diagnoses using receiver operating characteristic curves (ROC). The area under the Faster R-CNN ROC was 0.912, indicating a more effective and objective diagnosis. The Faster R-CNN diagnosis time was 20 s/case, which was much shorter than the average time (600 s/case) of the radiologist diagnoses. Faster R-CNN enables accurate and efficient diagnosis of lymph node metastases. .

Objective Detecting adverse drug events (ADEs) and medications related information in clinical notes is important for both hospital medical care and medical research. We describe our clinical natural language processing (NLP) system to automatically extract medical concepts and relations related to ADEs and medications from clinical narratives. This work was part of the 2018 National NLP Clinical Challenges Shared Task and Workshop on Adverse Drug Events and Medication Extraction. Materials and Methods The authors developed a hybrid clinical NLP system that employs a knowledge-based general clinical NLP system for medical concepts extraction, and a task-specific deep learning system for relations identification using attention-based bidirectional long short-term memory networks. Results The systems were evaluated as part of the 2018 National NLP Clinical Challenges challenge, and our attention-based bidirectional long short-term memory networks based system obtained an F-measure of 0.9442 for relations identification task, ranking fifth at the challenge, and had <2% difference from the best system. Error analysis was also conducted targeting at figuring out the root causes and possible approaches for improvement. Conclusions We demonstrate the generic approaches and the practice of connecting general purposed clinical NLP system to task-specific requirements with deep learning methods. Our results indicate that a well-designed hybrid NLP system is capable of ADE and medication-related information extraction, which can be used in real-world applications to support ADE-related researches and medical decisions.

Background: Artificial intelligence-assisted image recognition technology is currently able to detect the target area of an image and fetch information to make classifications according to target features. This study aimed to use deep neural networks for computed tomography (CT) diagnosis of perigastric metastatic lymph nodes (PGMLNs) to simulate the recognition of lymph nodes by radiologists, and to acquire more accurate identification results. Methods: A total of 1371 images of suspected lymph node metastasis from enhanced abdominal CT scans were identified and labeled by radiologists and were used with 18,780 original images for faster region-based convolutional neural networks (FR-CNN) deep learning. The identification results of 6000 random CT images from 100 gastric cancer patients by the FR-CNN were compared with results obtained from radiologists in terms of their identification accuracy. Similarly, 1004 CT images with metastatic lymph nodes that had been post-operatively confirmed by pathological examination and 11,340 original images were used in the identification and learning processes described above. The same 6000 gastric cancer CT images were used for the verification, according to which the diagnosis results were analyzed. Results: In the initial group, precision-recall curves were generated based on the precision rates, the recall rates of nodule classes of the training set and the validation set; the mean average precision (mAP) value was 0.5019. To verify the results of the initial learning group, the receiver operating characteristic curves was generated, and the corresponding area under the curve (AUC) value was calculated as 0.8995. After the second phase of precise learning, all the indicators were improved, and the mAP and AUC values were 0.7801 and 0.9541, respectively. Conclusion: Through deep learning, FR-CNN achieved high judgment effectiveness and recognition accuracy for CT diagnosis of PGMLNs. Trial Registration: Chinese Clinical Trial Registry, No. ChiCTR1800016787; http://www.chictr.org.cn/showproj.aspx?proj=28515.

Adders are key building blocks of many error-tolerant applications. Leveraging the application-level error tolerance, a number of approximate adders were proposed recently. Many of them belong to the category of block-based approximate adders. For approximate circuits, besides normal metrics such as area and delay, another important metric is the error measurement. Given the popularity of block-based approximate adders, in this work, we propose an accurate and efficient method to obtain the error statistics of these adders. We first show how to calculate the error rates. Then, we demonstrate an approach to get the exact error distribution, which can be used to calculate other error characteristics, such as mean error distance and mean square error.