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.

The obesity-asthma phenotype is characterized by increased asthma severity and decreased glucocorticoid responsiveness. To date, the mechanism underlying the association between obesity and asthma remain to be fully elucidated. The present study investigated the correlation between oxidative stress and the nuclear factor (NF)-κB pathway in obese asthmatic mice. The animals were divided into the following groups: Control (n=8), comprising C57BL/6J mice without exposure to a high-fat diet; non-obese asthma group (n=8), comprising mice of a normal weight subjected to the induction of asthma; obese control group (n=8), comprising C57BL/6J mice subjected to a high-fat diet; and obese asthmatic group (n=8), comprising obese mice subject to the induction of asthma. The levels of the malondialdehyde (MDA) oxidant and glutathione (GSH) antioxidant in the lungs and bronchoalveolar lavage fluid (BALF) were measured using ELISA. The expression levels of inhibitory κB kinase-β (IKK-β) and the inhibitor of κBα (IκB-α) in the pulmonary tissues was determined using western blot analysis. An electrophoretic mobility shift assay was performed to determine the transcription activity of NF-κB. The levels of MDA in the BALF and lung tissues increased significantly in the two asthmatic groups, compared with the control groups (P<0.01). The asthmatic mice showed significantly lower concentrations of GSH in the BALF and lung tissues, compared with the control groups (P<0.01). In the asthmatic animals, the expression of IκB kinase (IKK)-β and activation of NF-κB were upregulated in the pulmonary tissues, compared with those in the control groups (P<0.01). The expression of IKK-β and transcriptional activity of NF-κB were significantly higher the in obese asthmatic mice, compared with the non-obese asthmatic mice (P<0.01). On examining the expression levels of IκB-α in the pulmonary tissues, a significant reduction was found in the asthmatic animals, compared with the controls (P<0.01). In addition, the level of IκB-α was significantly lower in the obese asthmatics, compared with the non-obese asthmatics (P<0.01). MDA was positively correlated with NF-κB in the obese asthmatic group (R=0.83; P<0.05) and non-obese asthmatic group (R=0.82; P<0.05). Oxidative stress was upregulated in the pulmonary tissues of the asthmatic mice. This upregulation was more marked in the obese asthmatic mice, and was positively correlated with activation of the NF-κB signaling pathway in the pulmonary tissues. The results in the present study indicated that higher oxidative stress and activation of the NF-κB signaling pathway were observed in the lung tissues of the obese asthmatics. Furthermore, a positive correlation was identified between oxidative stress and NF-κB.

Background: An artificial intelligence system of Faster Region-based Convolutional Neural Network (Faster R-CNN) is newly developed for the diagnosis of metastatic lymph node (LN) in rectal cancer patients. The primary objective of this study was to comprehensively verify its accuracy in clinical use. Methods: Four hundred fourteen patients with rectal cancer discharged between January 2013 and March 2015 were collected from 6 clinical centers, and the magnetic resonance imaging data for pelvic metastatic LNs of each patient was identified by Faster R-CNN. Faster R-CNN based diagnoses were compared with radiologist based diagnoses and pathologist based diagnoses for methodological verification, using correlation analyses and consistency check. For clinical verification, the patients were retrospectively followed up by telephone for 36 months, with post-operative recurrence of rectal cancer as a clinical outcome; recurrence-free survivals of the patients were compared among different diagnostic groups, by methods of Kaplan-Meier and Cox hazards regression model. Results: Significant correlations were observed between any 2 factors among the numbers of metastatic LNs separately diagnosed by radiologists, Faster R-CNN and pathologists, as evidenced by r radiologist-Faster R-CNN of 0.912, r Pathologist-radiologist of 0.134, and r Pathologist-Faster R-CNN of 0.448 respectively. The value of kappa coefficient in N staging between Faster R-CNN and pathologists was 0.573, and this value between radiologists and pathologists was 0.473. The 3 groups of Faster R-CNN, radiologists and pathologists showed no significant differences in the recurrence-free survival time for stage N0 and N1 patients, but significant differences were found for stage N2 patients. Conclusion: Faster R-CNN surpasses radiologists in the evaluation of pelvic metastatic LNs of rectal cancer, but is not on par with pathologists. Trial Registration: www.chictr.org.cn (No. ChiCTR-DDD-17013842)

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BackgroundRecent investigations suggested that the trend of childhood asthma has been stabilizing or even reversing in some countries. The observation provides contrast to our experience. Thus, the study aimed to investigate the prevalence and clinical features of asthma in children aged 0–14 years in Qingdao China, determine the changes of childhood asthma in China, and discover evidence that can allow better diagnosis and treatment of childhood asthma.MethodsA cluster sampling method was used. We randomly extracted the investigation clusters from schools, kindergartens, and communities in Qingdao. Subsequently, we interviewed the members of the clusters using a questionnaire from the International Study of Asthma and Allergies in Childhood (ISAAC) to find children with asthmatic symptoms. After determination by the doctors, more details on the asthmatic children were obtained by asking questions from the National Epidemiology Study of Asthma and Allergies in China questionnaire to obtain more details. We intended to survey 10,800 children. However, the actual number of children was 10,082.ResultsThe prevalence of asthma in Qingdao children aged 0–14 years was 3.69%. The prevalence among male children was higher than in female (χ2 = 24.53,P < 0.01). Among the asthmatic children, 68.0% had their first attack when they were less than three years old. Moreover, 71.2% once suffered respiratory tract infections. For 95.7% of asthmatic children, the asthma attack was first manifested as cough. Asthmatic children who used inhaled corticosteroids (ICS) only accounted for 46%.ConclusionsThe prevalence of asthma in children aged 0–14 years in Qingdao China increased significantly based on data obtained ten years ago (2000). Respiratory tract infections were the most important precursors of asthma attack. The attack was most commonly manifested as cough. The treatment, especially the use of ICS, was more rational. However, a certain difference was found, which has yet to be contrasted with the Global Initiative for Asthma (GINA) project.