Objective:To explore the behavior and influencing factors of mobile health (m-Health) information searching among patients with cancer, aiming to provide evidence for the provision of medical health information.Methods:A cross-sectional survey was conducted.A total of 535 patients with cancer were recruited from a cancer hospital in Zhejiang Province from September to December 2017.Measurement tools included the demographic information questionnaire, mobile health information search behavior questionnaire, mobile health information search environment questionnaire, cancer needs questionnaires-short form and ehealth literacy scale.SPSS 26.0 was used for descriptive statistical analysis, one-way analysis of variance, Pearson correlation analysis and multiple linear regression analysis.Results:The total score of mobile health information search behavior of cancer patients was (60.84±9.60), and 66.5% of participants reported that they "never" or "occasionally" searched health information via mobile.The total score of information needs was (80.99±27.86), electronic health literacy was (26.54±7.85), mobile health information search environment was (8.00±2.86). m-Health information search behavior was positively correlated with information needs ( r=0.251, P<0.01), ehealth literacy ( r=0.538, P<0.01), and m-Health information search environment ( r=0.267, P<0.01). The stepwise regression analysis revealed that the place of residence, working status, income level, ehealth literacy, mobile health information search environment and information needs were statistically significant associated with the m-Health information searching behavior among cancer patients, which accounted for 39.3% of the total variance ( F=12.151, P<0.01). Compared with patients living in the central cities, those living in the small and medium-sized cities( β=0.092, P=0.031) had higher score in m-Health information behavior.Compared with patients working on normal schedule, those took sick days ( β=0.156, P=0.017) and working fewer hours ( β=0.138, P=0.002) had higher score m-Health information behavior.Compared with patients with monthly income of 1 000-3 000 yuan ( β=-0.194, P=0.002), those with monthly income less than 1 000 yuan had higher score in m-Health information behavior.The ehealth literacy ( β=0.425, P=0.000), mobile health information search environment ( β=0.179, P=0.000) and information needs ( β=0.091, P=0.027) were positive influencing factors of m-Health information search behavior. Conclusion:Patients with cancer did not report high m-Health information search behavior.Place of residence, working status, income level, ehealth literacy, m-Health information search environment and information demand were the influencing factors of m-Health information search behavior among patients with cancer.

Objective To identify the risk factors of early post-surgical recurrence of hepatocellular carcinoma (HCC) within 2 years.Methods This retrospective study included 178 consecutive patients with HCC who underwent curative resection between January 2009 to December 2012 at Zhongshan Hospital,Fudan University.There were 151 males and 27 females,with a mean age of (58±11) years.The CT features including rim enhancement,satellite nodule,two-trait predictor of venous invasion (TTPVI),and nonsmooth tumor margins were reviewed.After hospital discharge,the patients were followed-up regularly for at least 2 years to detect tumor recurrence.The primary end point was recurrence of HCC.Results On univariate analyses AFP ≥ 200 μg/L,rim enhancement,TTPVI,non-smooth tumor margins and largest diameter ＞5 cm were correlated with early post-surgical recurrence of HCC.On multivariate analyses,AFP≥200 pg/L (HR=2.144,95％CI:1.350～ 3.406),rim enhancement (HR =2.196,95％ CI:1.345 ～ 3.587),TTPVI (HR=1.735,95％CI:1.086～2.772),and non-smooth tumor margins (HR=2.065,95％CI:1.242～3.432) were independent risk factors of early post-surgical recurrence of HCC.Conclusion AFP≥200 μg/L,rim enhancement,TTPVI,and non-smooth tumor margins were independent risk factors of early post-surgical recurrence of HCC.

In order to improve the level of epidemic prevention and control, and strengthen the observation and monitoring of potential patients with viral pneumonia in isolated state, a medical intelligent monitoring collar based on DA14699 Bluetooth low-power chip was proposed. DA14699 chip is used as the main controller in the design scheme, and the temperature, cough and location information of potential patients are recorded and analyzed by its high-efficiency wireless multi-core processing ability. The LIS3DH three-axis acceleration sensor is used to judge the cough symptoms. The MLX90640 infrared sensor is used to continuously measure the body temperature. The L218 four frequency GSM / GPRS positioning module is used to complete the rapid and accurate positioning of personnel, so as to realize the comprehensive supervision of the implementation of home isolation measures. DA14699 chip supports Bluetooth BLE5.1 protocol. Epidemic prevention personnel can transmit and read the data recorded in the smart collar from a long distance, and display it directly on the intelligent Bluetooth handheld terminal, effectively avoid the risk of infection caused by close contact. Through the actual test, the monitoring function of the key parameters of the collar is reliable and has high application value.
Humans ; Monitoring, Physiologic ; Pneumonia, Viral

Visual object recognition in humans and nonhuman primates is achieved by the ventral visual pathway (ventral occipital-temporal cortex, VOTC), which shows a well-documented object domain structure. An on-going question is what type of information is processed in the higher-order VOTC that underlies such observations, with recent evidence suggesting effects of certain visual features. Combining computational vision models, fMRI experiment using a parametric-modulation approach, and natural image statistics of common objects, we depicted the neural distribution of a comprehensive set of visual features in the VOTC, identifying voxel sensitivities with specific feature sets across geometry/shape, Fourier power, and color. The visual feature combination pattern in the VOTC is significantly explained by their relationships to different types of response-action computation (fight-or-flight, navigation, and manipulation), as derived from behavioral ratings and natural image statistics. These results offer a comprehensive visual feature map in the VOTC and a plausible theoretical explanation as a mapping onto different types of downstream response-action systems.

Visual object recognition in humans and nonhuman primates is achieved by the ventral visual pathway (ventral occipital-temporal cortex, VOTC), which shows a well-documented object domain structure. An on-going question is what type of information is processed in the higher-order VOTC that underlies such observations, with recent evidence suggesting effects of certain visual features. Combining computational vision models, fMRI experiment using a parametric-modulation approach, and natural image statistics of common objects, we depicted the neural distribution of a comprehensive set of visual features in the VOTC, identifying voxel sensitivities with specific feature sets across geometry/shape, Fourier power, and color. The visual feature combination pattern in the VOTC is significantly explained by their relationships to different types of response-action computation (fight-or-flight, navigation, and manipulation), as derived from behavioral ratings and natural image statistics. These results offer a comprehensive visual feature map in the VOTC and a plausible theoretical explanation as a mapping onto different types of downstream response-action systems.
Animals ; Brain Mapping ; Humans ; Magnetic Resonance Imaging ; Occipital Lobe ; Pattern Recognition, Visual ; Photic Stimulation ; Temporal Lobe ; Visual Pathways/diagnostic imaging* ; Visual Perception

Objective To evaluate the application of three deep learning algorithms in automatic segmentation of clinical target volumes (CTVs) in high-dose-rate brachytherapy after surgery for endometrial carcinoma. Methods A dataset comprising computed tomography scans from 306 post-surgery patients with endometrial carcinoma was divided into three subsets: 246 cases for training, 30 cases for validation, and 30 cases for testing. Three deep convolutional neural network models, 3D U-Net, 3D Res U-Net, and V-Net, were compared for CTV segmentation. Several commonly used quantitative metrics were employed, i.e., Dice similarity coefficient, Hausdorff distance, 95th percentile of Hausdorff distance, and Intersection over Union. Results During the testing phase, CTV segmentation with 3D U-Net, 3D Res U-Net, and V-Net showed a mean Dice similarity coefficient of 0.90 ± 0.07, 0.95 ± 0.06, and 0.95 ± 0.06, a mean Hausdorff distance of 2.51 ± 1.70, 0.96 ± 1.01, and 0.98 ± 0.95 mm, a mean 95th percentile of Hausdorff distance of 1.33 ± 1.02, 0.65 ± 0.91, and 0.40 ± 0.72 mm, and a mean Intersection over Union of 0.85 ± 0.11, 0.91 ± 0.09, and 0.92 ± 0.09, respectively. Segmentation based on V-Net was similarly to that performed by experienced radiation oncologists. The CTV segmentation time was < 3.2 s, which could save the work time of clinicians. Conclusion V-Net is better than other models in CTV segmentation as indicated by quantitative metrics and clinician assessment. Additionally, the method is highly consistent with the ground truth, reducing inter-doctor variability and treatment time.