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Categorical color constancy has been widely investigated and found to be very robust. As one of object material properties, the surface gloss was found to barely contribute to color constancy in a natural viewing condition. In this study, the effect of surface gloss on categorical color constancy was investigated by asking eight observers to categorize 208 Munsell matte surfaces and 260 Munsell glossy surfaces under D65, F, and TL84 illuminants in a viewing chamber with a uniform gray background. A color constancy index based on the centroid shift of the color category was used to evaluate color constancy degree of each color category across illumination changes from D65 to F or TL84 illuminant. The result showed that both matte and glossy surfaces showed almost perfect color constancy on all color categories under F and TL84 illuminants, and there was no significant difference between them. This result suggests that surface gloss has little effect on categorical color constancy in a uniform gray background where the local surround cue was present, which is consistent with the previous findings.

The Ru(dppbsa)-catalyzed reductive amination of ketones with nitroarenes and nitriles using H2 as the hydrogen source is developed. This study provides a novel design platform for homogeneous catalysts in constructing motifs of secondary amines.

In this study, we developed four clinopyroxene (cpx) and five orthopyroxene (opx) samples as reference materials for SIMS water content analysis.

This paper presents a novel Adaptive Mutation Quantum-inspired Squirrel Search Algorithm (AM-QSSA). Firstly, based on the population mutation, a location-update of quantum state correlative and attractor method is proposed. By introducing a random process to modify the sliding factor of the local attractor, the inevitable lack of diversity in the population renewal method is solved. The premature convergence problem of adaptive mutation rate improvement algorithm based on squirrel position update mode is introduced. Meanwhile, the paper decomposes the location update process of the SSA, and improves it with quantum-behavior. Furthermore, it proposes a novel quantum-inspired squirrels search algorithm. This method finds the complementary effect between quantum behavior and squirrel search algorithm, and solves the problem of premature convergence probability of SSA. In addition, it improves population diversity, and achieves the balance between global and local search. The efficiency of the proposed AM-QSSA is evaluated using exploitation analysis, exploration analysis, success rate analysis, convergence rate analysis on classical benchmark functions as well as Congress on Evolutionary Computation (CEC) 2017 test functions. For further study, AM-QSSA optimizes an image registration problem for an extensive study to check its applicability. The results reveal that AM-QSSA is more efficient and stable than SSA. And it is comparable to the most advanced optimization algorithms.

Multiparous women are at a higher risk of gestational diabetes mellitus (GDM) than primiparas. Physical activity during pregnancy has been shown to be beneficial for GDM, but there is little evidence on the association between physical activity and glycemic control among women with GDM, whether primiparas or multiparas. Thus, the objective of the present study was to examine the association between physical activity and glycemic control in women with GDM and to determine what, if any, effects result from number of parous events.A prospective cohort of 1162 women with GDM was recruited, with 604 multiparas (51.98%). The general linear model was used to calculate the risk difference and its 95% confidence interval (95%CI) to quantify the impact of parous events on glycemic control in pregnancy as well as the association between physical activity time and glycemic control.Among 1162 women with GDM, the median daily activity time was 65 min (interquartile range (IQR): 45-90 min), and the abnormal plasma glucose (PG) percentage, calculated as number of abnormal PG tests divided by the total number of PG tests, was 40.00% (IQR: 22.22%-66.67%). The percentage of abnormal PG was stabilized and statistically lower with daily physical activity time exceeding 60 min among primiparas (IQR: 30.89%-44.43%) and exceeding 90 min among multiparas (ranged from 27.76% to 38.82%). After adjusting for potential confounders, primiparas tended to have a lower percentage of abnormal PG than do multiparas (rate difference = -0.39, 95%CI: -3.61 to 2.84). The same amount of physical activity time was significantly less effective for multiparas than for primiparas (trend p-value0.01).In women with GDM, being multiparous is associated with less effective glycemic control through physical activity, such that multiparas need more physical activity to achieve glycemic control at a similar level to primiparas.

Modifying and transforming natural antibacterial products is a novel idea for developing new efficacious compounds. Phillygenin has an inhibitory effect on H. pylori. The aim of the present study was to prepare a phillygenin derivative (PHI-Der) through demethylation and hydroxylation. The minimum inhibitory concentration of 18 strains of H. pylori from different sources was 8–32 μg/mL in vitro, and the activity increased 2–8 times than that of phillygenin. PHI-Der could significantly inhibit the colonization of H. pylori in vivo, reduce the inflammatory response, and promote the repair of inflammatory damage. Further, we used SwissTargetPrediction to predict that its main targets are ALOX5, MCL1, and SLC6A4, and find that it can inhibit bacterial biofilm formation and reduce bacterial infection of cells. It can enhance the intracellular oxidative capacity of H. pylori to inhibit H. pylori growth. Further, it could prevent the oxidation of H. pylori-infected cells and reduce the inflammatory response, which plays a role in protection. In conclusion, compared to phillygenin, PHI-Der had better antibacterial activity and was more effective in treating H. pylori infection. It has characteristics of high safety, specificity, resistance to drug resistance and better antibacterial activity than phillygenin, it’s a good antioxidant for host cells.

BackgroundPostoperative recurrence of hepatocellular carcinoma (HCC) is associated with low survival rates. While HCC treatment options have expanded substantially, they are accompanied by several challenges. This study assessed the outcomes of repeated hepatectomy (RH) for postoperative intrahepatic recurrence of HCC among patients undergoing initial hepatectomy (IH) as well as independent risk factors for HCC recurrence among patients undergoing repeated hepatectomy (RH).MethodsClinical data from 84 patients undergoing both IH and RH and 66 recurrent HCC patients who had received radiofrequency ablation (RFA) from July 2011 to September 2017 were retrospectively reviewed. The following groups were compared: (1) RH Group A (n = 84), (2) IH Group (n = 84, same individuals as RH Group A), (3) RH Group B (n = 45/84 from RH Group A), and (4) RFA Group (n = 66). The clinical pathology and operative characteristics of the patients in RH Group A were compared to those in the IH Group. Meanwhile, the clinical pathology and pre- and post-treatment features of the patients in RH Group B were compared to those in the RFA Group. The tumor-free survival time was compared between patients in RH Group A and the IH Group as well as between patients in RH Group B and the RFA Group. The independent risk factors for the 1-year postoperative tumor-free survival of RH Group A patients were investigated using univariate and multivariate analysis.ResultsMeasures of clinical pathology, including AFP, Child-Pugh score, HBV-DNA, tumor number, liver cirrhosis, tumor differentiation, surgical approach, and TNM stage differed significantly between patients in RH Group A and the IH Group (all P < 0.05), with the exception of tumor number and tumor size (both P > 0.05). No significant differences were found in these measures between the patients in RH Group B and the RFA Group (all P > 0.05). While patients in the RH Group A had a longer operation time than those in the IH Group (4.35 ± 1.25 h vs. 3.55 ± 0.92 h, P < 0.001), the level of intraoperative bleeding was similar (400.00 ± 199.25 ml vs. 359.40 ± 213.37 ml, P = 0.204). RH Group B patients had a longer hospitalization time than those in the RFA Group (6.5 ± 0.8 d vs. 5.5 ± 1.1 d, P < 0.001), however, the difference in hospitalization costs was not statistically significant (29,009 ± 3,806 CNY vs. 29,944 ± 3,752 CNY, P = 0.202). Five-day post-operative serum biomarker levels, including direct bilirubin (DB) and albumin (ALB), were significantly higher in RH Group B than in the RFA Group (all P < 0.05), with the exception of ALT, AST, and total bilirubin (TB) (all P > 0.05). Patients in RH Group A had a lower tumor-free survival time than those in the IH Group (median: 12 vs. 22 months, P < 0.001), and patients in the RH Group B had a significantly higher tumor-free survival time than those in the RFA group (median: 15 months vs. 8 months, P < 0.001). Age ≥50 y, Child-Pugh class A, and negative HBV-DNA were independent risk factors that positively impacted the 1-year postoperative tumor-free survival rate of postoperative intrahepatic recurrent HCC patients undergoing RH (P < 0.001, respectively).ConclusionDue to the potential of harm related to relapse of recurrent HCC for cancer patients, RH is a superior option. RH could offer better outcomes for recurrent HCC patients undergoing IH. Compared with lesion pathology, the better target organ of the liver will be key to ameliorating tumor-free survival for recurrent HCC patients undergoing RH.

To clarify the flow characteristics of the misaligned-phase parallel external gear pump with different phases and rotational speeds, the flow pulsation and cavitation of the misaligned-phase parallel external gear pump under different working conditions were systematically studied by combining theoretical analysis with tridimensional numerical simulation. The flow pulsation characteristics of the misaligned-phase parallel external gear pump are analyzed. Compared with the same-phase gear pump, the flow pulsation of the misaligned-phase parallel external gear pump was reduced. When the phase angle is [Formula: see text], the pressure and flow pulsations of the parallel gear pump are minimum, the flow pulsation is less than 1/4 of the same-phase gear pump. The relationship between cavitation and phase angle of the misaligned-phase parallel external gear pump is verified. When the rotational speed is different, with the increase in the rotational speed, the amplitude of the pressure fluctuation increases, and the effect of pressure pulsation overlapping and counteracting each other in the misaligned phase is more ideal. The flow does not fluctuate with the rotational speed and is only related to the phase angle, the cavitation is aggravated with the increase in the rotational speed.

As there is no contrast enhancement, the liver tumor area in nonenhanced MRI exists with blurred edges and low contrast, which greatly affects the speed and accuracy of liver tumor diagnosis. As a result, precise segmentation of liver tumor from nonenhanced MRI has become an urgent and challenging task. In this paper, we propose an edge constraint and localization mapping segmentation model (ECLMS) to accurately segment liver tumor from nonenhanced MRI. It consists of two parts: localization network and dual-branch segmentation network. We build the localization network, which generates prior coarse masks to provide position mapping for the segmentation network. This part enhances the ability of the model to localize liver tumor in nonenhanced images. We design a dual-branch segmentation network, where the main decoding branch focuses on the feature representation in the core region of the tumor and the edge decoding branch concentrates on capturing the edge information of the tumor. To improve the ability of the model for capturing detailed features, sSE blocks and dense upward connections are introduced into it. We design the bottleneck multiscale module to construct multiscale feature representations using kernels of different sizes while integrating the location mapping of tumor. The ECLMS model is evaluated on a private nonenhanced MRI dataset that comprises 215 different subjects. The model achieves the best Dice coefficient, precision, and accuracy of 90.23%, 92.25%, and 92.39%, correspondingly. The effectiveness of our model is demonstrated by experiment results, and our model reaches superior results in the segmentation task of nonenhanced liver tumor compared to existing segmentation methods.

IntroductionAdvances in mobile computing platforms and the rapid development of wearable devices have made possible the continuous monitoring of patients with mild cognitive impairment (MCI) and their daily activities. Such rich data can reveal more subtle changes in patients’ behavioral and physiological characteristics, providing new ways to detect MCI anytime, anywhere. Therefore, we aimed to investigate the feasibility and validity of digital cognitive tests and physiological sensors applied to MCI assessment.MethodsWe collected photoplethysmography (PPG), electrodermal activity (EDA) and electroencephalogram (EEG) signals from 120 participants (61 MCI patients, 59 healthy controls) during rest and cognitive testing. The features extracted from these physiological signals involved the time domain, frequency domain, time-frequency domain and statistics. Time and score features during the cognitive test are automatically recorded by the system. In addition, selected features of all modalities were classified by tenfold cross-validation using five different classifiers.ResultsThe experimental results showed that the weighted soft voting strategy combining five classifiers achieved the highest classification accuracy (88.9%), precision (89.9%), recall (88.2%), and F1 score (89.0%). Compared to healthy controls, the MCI group typically took longer to recall, draw, and drag. Moreover, during cognitive testing, MCI patients showed lower heart rate variability, higher electrodermal activity values, and stronger brain activity in the alpha and beta bands.DiscussionIt was found that patients’ classification performance improved when combining features from multiple modalities compared to using only tablet parameters or physiological features, indicating that our scheme could reveal MCI-related discriminative information. Furthermore, the best classification results on the digital span test across all tasks suggest that MCI patients may have deficits in attention and short-term memory that came to the fore earlier. Finally, integrating tablet cognitive tests and wearable sensors would provide a new direction for creating an easy-to-use and at-home self-check MCI screening tool.

Water, in trace amounts, can greatly alter chemical and physical properties of mantle minerals and exert primary control on Earth’s dynamics. Quantifying how water is retained and distributed in Earth’s deep interior is essential to our understanding of Earth’s origin and evolution. While directly sampling Earth’s deep interior remains challenging, the experimental technique using laser-heated diamond anvil cell (LH-DAC) is likely the only method available to synthesize and recover analog specimens throughout Earth’s lower mantle conditions. The recovered samples, however, are typically of micron sizes and require high spatial resolution to analyze their water abundance. Here we use nano-scale secondary ion mass spectrometry (NanoSIMS) to characterize water content in bridgmanite, the most abundant mineral in Earth’s lower mantle. We have established two working standards of natural orthopyroxene that are likely suitable for calibrating water concentration in bridgmanite, i.e., A119(H2O) = 99 ± 13 μg/g (1SD) and A158(H2O) = 293 ± 23 μg/g (1SD). We find that matrix effect among orthopyroxene, olivine, and glass is less than 10%, while that between orthopyroxene and clinopyroxene can be up to 20%. Using our calibration, a bridgmanite synthesized by LH-DAC at 33 ± 1 GPa and 3,690 ± 120 K is measured to contain 1,099 ± 14 μg/g water, with partition coefficient of water between bridgmanite and silicate melt ∼0.025, providing the first measurement at such condition. Applying the unique analytical capability of NanoSIMS to minute samples recovered from LH-DAC opens a new window to probe water and other volatiles in Earth’s deep mantle.

Aiming at the problems of long time, high cost, invasive sampling damage, and easy emergence of drug resistance in lung cancer gene detection, a reliable and non-invasive prognostic method is proposed. Under the guidance of weakly supervised learning, deep metric learning and graph clustering methods are used to learn higher-level abstract features in CT imaging features. The unlabeled data is dynamically updated through the k-nearest label update strategy, and the unlabeled data is transformed into weak label data and continue to update the process of strong label data to optimize the clustering results and establish a classification model for predicting new subtypes of lung cancer imaging. Five imaging subtypes are confirmed on the lung cancer dataset containing CT, clinical and genetic information downloaded from the TCIA lung cancer database. The successful establishment of the new model has a significant accuracy rate for subtype classification (ACC = 0.9793), and the use of CT sequence images, gene expression, DNA methylation and gene mutation data from the cooperative hospital in Shanxi Province proves the biomedical value of this method. The proposed method also can comprehensively evaluate intratumoral heterogeneity based on the correlation between the final lung CT imaging features and specific molecular subtypes.

In this study, we developed a method for measuring Sm–Nd isotopic ratios in samples with low Nd contents (−1) by LA-MC-ICP-MS.

The neutron flux monitor is a critical part of the process of 40Ar/39Ar dating. We report a new protocol to prepare a neutron flux monitor from the sanidine contained within obsidian, a rock which has undergone rapid eruption and cooling on the surface.

The application of a 1012 Ω amplifier Faraday cup improved the secondary ion mass spectrometry analytical precision of the apatite Cl isotope by a factor of about two.

The stability assessment of a large power system in real-time is very necessary after it encounters fault. The paper proposes a new model (SVM-GDPSO) for assessing the large power system. In order to enhance SVM, taking tangent vector of power flow Jacobian (PFJ) as the goal of machine learning was used for improving the precision. Besides, particle swarm optimization (PSO) with Gaussian disturbance (GD) is taken for setting the key parameters of SVM, and metalearning was utilized to decrease the search space of PSO. The experiment on the standard test system of IEEE 118-bus demonstrated that this model could reflect the status of large power system in time. Besides, the method could locate the fault area and rank the fault level by the observation of critical bus. The proposed method has the reliability rate 97.22 %, which is superior to the back propagation neural network (BPNN) and SVM-GA, as well as determines the fault area with the success rate of 96.61 %.

Purpose It is of great significance to accurately identify the KRAS gene mutation status for patients in tumor prognosis and personalized treatment. Although the computer-aided diagnosis system based on deep learning has gotten all-round development, its performance still cannot meet the current clinical application requirements due to the inherent limitations of small-scale medical image dataset and inaccurate lesion feature extraction. Therefore, our aim is to propose a deep learning model based on T2 MRI of colorectal cancer patients to identify whether KRAS gene is mutated. Methods In this research, a multi-task attentive model is proposed to identify KRAS gene mutations in patients, which is mainly composed of a segmentation sub-network and an identification sub-network. Specifically, at first, the features extracted by the encoder of segmentation model are used as guidance information to guide the two attention modules in the identification network for precise activation of the lesion area. Then the original image of the lesion and the segmentation result are concatenated for feature extraction. Finally, features extracted from the second step are combined with features activated by the attention modules to identify the gene mutation status. In this process, we introduce the inter-layer loss function to encourage the similarity of the two sub-network parameters and ensure that the key features are fully extracted to alleviate the overfitting problem caused by small dataset to some extent. Results The proposed identification model is benchmarked primarily using 15-fold cross validation. 382 images from 36 clinical cases were used to test the model. For the identification of KRAS mutation status, the average accuracy is 89.95±1.23%, the average sensitivity is 89.29±1.79%, the average specificity is 90.53±2.45%, and the average AUC is 95.73±5.02%. For segmentation of lesions, the average dice is 88.11 ± 0.86% CONCLUSIONS: We developed a novel deep learning-based model to identify the KRAS status in colorectal cancer. We demonstrated the excellent properties of the proposed identification through comparison with ground truth gene mutation status of 36 clinical cases. And all these results show that the novel method has great potential for clinical application. This article is protected by copyright. All rights reserved.

Side information is being used extensively to improve the effectiveness of sequential recommendation models. It is said to help capture the transition patterns among items. Most previous work on sequential recommendation that uses side information models item IDs and side information separately, which may fail to fully model the relation between the items and their side information. Moreover, in real-world systems, not all values of item feature fields are available. This hurts the performance of models that rely on side information. Existing methods tend to neglect the context of missing item feature fields, and fill them with generic or special values, e.g., unknown, which might lead to sub-optimal performance. To address the limitation of sequential recommenders with side information, we define a way to fuse side information and alleviate the problem of missing side information by proposing a unified task, namely the missing information imputation (MII), which randomly masks some feature fields in a given sequence of items, including item IDs, and then forces a predictive model to recover them. By considering the next item as a missing feature field, sequential recommendation can be formulated as a special case of MII. We propose a sequential recommendation model, called missing information imputation recommender (MIIR), that builds on the idea of MII and simultaneously imputes missing item feature values and predicts the next item. We devise a dense fusion self-attention (DFSA) mechanism for MIIR to capture all pairwise relations between items and their side information. Empirical studies on three benchmark datasets demonstrate that MIIR, supervised by MII, achieves a significantly better sequential recommendation performance than state-of-the-art baselines., Comment: Under review

Kirsten Ras (KRAS) mutation identification has great clinical significance to formulate the rectal cancer treatment scheme. Recently, the development of deep learning does much help to improve the computer-aided diagnosis technology. However, deep learning models are usually designed for only one task, ignoring the potential benefits in jointly performing both tasks. In this paper, we proposed a joint network named segmentation-based multi-scale attention model (SMSAM) to predict the mutation status of KRAS gene in rectal cancer. More specifically, the network performs segmentation and prediction tasks at the same time. The two tasks mutually transfer knowledge between each other by sharing the same encoder. Meanwhile, two universal multi-scale attention blocks are introduced to ensure that the network more focuses on the region of interest. Besides, we also proposed an entropy branch to provide more discriminative features for the model. Finally, the method is evaluated on internal and external datasets. The results show that the comprehensive performance of SMSAM is better than the existing methods. The code and model have been publicly available.

Accurate measurements of glacier elevation changes play a crucial role in various glaciological studies related to glacier dynamics and mass balance. In this paper, glacier elevation changes of Urumqi Glacier No.1 between August 2015 and August 2017 were investigated using Sentinel-1A data and DInSAR technology. Meanwhile, the atmospheric delay error was corrected with the MODIS MOD05_L2 products. The weight selection iteration method was applied to calibrate the glacier elevation changes in the mass balance years 2015–2016 and 2016–2017. Finally, the geodetic method was employed to calculate the elevation change values of individual stakes of Urumqi Glacier No.1. Moreover, the elevation change values corrected by the weight selection iteration method were verified. Results showed as follows: (1) the elevation of Urumqi Glacier No.1 glacier affected by atmospheric delay was 1.270 cm from 2015 to 2016. The glacier elevation affected by atmospheric delay from 2016 to 2017 was 1.071 cm. (2) The elevation change value of Urumqi Glacier No.1 was −1.101 m from 2015 to 2016, and the elevation of Urumqi Glacier No.1 decreased by 1.299 m from 2016 to 2017. The overall thickness of Urumqi Glacier No. 1 was thinning. (3) By comparing the elevation change results of individual stakes with corresponding points corrected by the weight selection iteration method, the mean squared errors of difference were 0.343 m and 0.280 m between the two mass balance years, respectively. (4) The accuracy of elevation change in non-glaciated areas was 0.039 m from 2015 to 2016 and 0.034 m from 2016 to 2017. Therefore, it is reliable to use Sentinel-1A data and the study method proposed in this paper to calculate the elevation change of mountain glaciers with very low horizontal movement.

A two-dimensional electron-rich fused-ring moiety (ClBDSe) based on benzo[1,2-b:4,5-b']diselenophene is synthesized. Three copolymers (PBDT-Se, PBDSe-T, and PBDSe-Se) are obtained by manipulating the connection types and number of selenophene units on the conjugated main chains with two 2D fused-ring units and two different π-bridges, respectively. In comparison with PBDT-Se and PBDSe-Se, PBDSe-T with benzo[1,2-b:4,5-b']diselenophene unit and thiophene π-bridge exhibits the deepest HOMO energy level and the strongest crystallinity in neat films. The PBDSe-T:Y6 blend film exhibits the best absorption complementarity, the most distinctive face-on orientation with proper phase separation, the highest carrier mobilities, and the lowest charge recombination among three blend films. Finally, the PBDSe-T:Y6-based device delivers an impressive power conversion efficiency (PCE) of 14.50 %, which is higher than those of PBDT-Se:Y6 and PBDSe-Se:Y6. Moreover, a decent open-circuit voltage (Voc ) of 0.89 V with a remarkably small energy loss of 0.44 eV is achieved for PBDSe-T:Y6. The efficiency of 14.50 % is the highest value for selenophene-containing copolymer-based binary organic solar cells (OSCs). This study provides evidence that introduction of 2D-benzo[1,2-b:4,5-b']diselenophene as a fused electron-rich unit with π-bridging into copolymeric donors is a valid strategy for providing high Voc and excellent PCE simultaneously in selenophene-based OSCs.

The prediction of pulmonary nodules being canceration is of vital importance to the diagnosis and treatment of early lung adenocar-cinoma. However, most traditional research paradigms only focus on image data at a single point in time, which makes it easy to ignore the correlation of lung computer tomography (CT) images at multiple points in time. Based on the the correlation between longitudinal images and medical characteristics of patients with pulmonary nodules, a novel multimodal feature analysis framework is proposed in this paper. Firstly, we collect 925 patients with pulmonary nodules from cooperative hospitals, utilize a Convolutional Long Short Term Memory (ConvLSTM) to extract the deep features of pulmonary nod-ules at dierent time points, and radiomics tool kits to extract their radiomics features. After that, the deep features, radiomics features, and risk factor are fused by Multimodal Trilinear Pooling (MTP), and the optimal feature is obtained using the Maximum Correlation and Minimum Redundancy (MCMR) selection. In the end, a total of 260-dimensional features were selected. The validity of the extracted features was veried by the leave-one-out cross-validation method on the data set. The experiment shows that the accuracy of nodule cancera-tion prediction reached 89.15%, which proved the eectiveness of the method proposed in this study. The multimodal nodule feature analysis framework can provide reference values for nodule canceration prediction.

Qi Zheng,1,&ast; Le Kuai,2,&ast; Wencheng Jiang,1,&ast; Yan Qiang,3 Lei Wei,2 Siting Chen,2 Bin Li,1 Ruiping Wang3,4 1Department of Traditional Chinese Medicine, Shanghai Skin Diseases Hospital, Tongji University, Shanghai, People’s Republic of China; 2Department of Dermatology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, People’s Republic of China; 3Clinical Research and Innovation Transformation Center, Shanghai Skin Diseases Hospital, Tongji University, Shanghai, People’s Republic of China; 4School of Public Health, Shanghai University of Traditional Chinese Medicine, Shanghai, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Bin Li; Ruiping Wang, Email 18930568129@163.com; w19830901@126.comBackground: Gender difference is prevalent in clinical feature, disease severity for noncommunicable diseases (NCD), but studies on gender disparity in clinical feature, disease severity and NCD comorbidity among psoriasis patients are limited. This cross-sectional study explores gender differences in clinical feature, lifestyle behavior and NCD comorbidity among psoriasis patients.Methods: Psoriasis patients were recruited through cluster survey method in two hospitals, and questionnaire interviews were applied to collect the demographic feature, lifestyle habits, clinical feature and NCD among patients.Results: A total of 2102 psoriasis patients included 1332 males (63.4%), 70% were over 35 years old and approximately 50% of them were overweight or obesity. The median value for psoriasis initiation age and disease duration was 33 years old (34 for male and 32 for female) and 9 years (10 for male and 7 for female), respectively. The psoriasis recurrence was mainly in winter (73.4%) and autumn (34.2%) both for patients. The prevalence of tobacco smoking and alcohol drinking was 31.2% and 12.6%. Male patients had higher prevalence of tobacco smoking (odds ratio (OR) = 13.26, 95% confidence interval (CI): 9.54– 18.44) and alcohol drinking (OR = 14.44, 95% CI: 7.90– 26.40). The prevalence of diabetes, hypertension, hyperlipidemia, and metabolic syndrome were 13.2%, 28.5%, 23.4% and 21.5%, respectively. Male patients had higher prevalence of diabetes (OR = 1.53, 95% CI: 1.16– 2.02), hypertension (OR = 1.87, 95% CI: 1.52– 2.30), hyperlipidemia (OR = 2.34, 95% CI: 1.85– 2.95) and metabolic syndrome (OR = 2.06, 95% CI: 1.63– 2.62) than female patients. The proportions for 4 types of NCDs diagnosed after psoriasis onset were over 58%, which were also higher in males than females.Conclusion: Female patients had shorter disease duration and with less NCD, and male patients had more body weight issue, with fewer sleep time and higher prevalence of tobacco smoking and alcohol drinking and NCDs. We recommend that dermatologist should notice the gender disparity in psoriasis patients, which is helpful for the disease diagnosis and treatment.Keywords: psoriasis, gender disparity, tobacco smoking, alcohol drinking, overweight, metabolic diseases, comorbidity

In this study, we developed a sustained-release transdermal delivery system containing losartan potassium (LP) and verapamil hydrochloride (VPH). LP and VPH have low bioavailability and long half-life. Therefore, the development of an optimum administration mode is necessary to overcome these drawbacks and enhance the antihypertensive effect. A transdermal diffusion meter was used to determine the optimal formulation of LP-VPH transdermal drug delivery systems (TDDS). Based on in vitro results, a sustained-release patch was prepared. Physical characteristics, including quality, stickiness, and appearance, were evaluated in vitro, while pharmacokinetics and skin irritation were evaluated in vivo. The results showed that 8.3% polyvinyl alcohol, 74.7% polyvinylpyrrolidone K30, 12% oleic acid-azone, and 5% polyacrylic acid resin II provided an optimized TDDS product for effective administration of LP and VPH. Furthermore, in vitro and in vivo release tests showed that the system continuously released LP and VPH for 24 h. The pharmacokinetic results indicated that although the maximum concentration was lower, both the area under the curve from 0–time and the mean residence time of the prepared patch were significantly higher than those of the oral preparations. Furthermore, the prepared LP-VPH transdermal patch showed good stability and no skin irritation. The developed LP-VPH TDDS showed a sustained-release effect and good characteristics and pharmacokinetics; therefore, it is an ideal formulation.

Background The drug resistance rate of clinical Helicobacter pylori (H. pylori) isolates has increased. However, the mechanism of drug resistance remains unclear. In this study, drug-resistant H. pylori strains were isolated from different areas and different populations of Chinese for genomic analysis. Aim To investigate drug-resistant genes in H. pylori and find the genes for the early diagnosis of clarithromycin resistance. Methods Three drug-resistant H. pylori strains were isolated from patients with gastritis in Bama County, China. Minimal inhibitory concentrations of clarithromycin, metronidazole, and levofloxacin were determined and complete genome sequencing was performed with annotation. Hp1181 and hp1184 genes were found in these strains and then detected by reverse transcription polymerase chain reaction. The relationships between hp1181 or hp1184 and clarithromycin resistance were ascertained with gene mutant and drug-resistant strains. The homology of the strains with hp26695 was assessed through complete genome detection and identification. Differences in genome sequences, gene quantity, and gene characteristics were detected amongst the three strains. Prediction and analysis of the function of drug-resistant genes indicated that the RNA expression of hp1181 and hp1184 increased in the three strains, which was the same in the artificially induced clarithromycin-resistant bacteria. After gene knockout, the drug sensitivity of the strains was assessed. Results The strains showing a high degree of homology with hp26695, hp1181, and hp1184 genes were found in these strains; the expression of the genes hp1184 and hp1181 was associated with clarithromycin resistance. Conclusion Hp1181 and hp1184 mutations may be the earliest and most persistent response to clarithromycin resistance, and they may be the potential target genes for the diagnosis, prevention, and treatment of clarithromycin resistance.

Background Predicting the mutation statuses of 2 essential pathogenic genes [epidermal growth factor receptor (EGFR) and Kirsten rat sarcoma (KRAS)] in non-small cell lung cancer (NSCLC) based on CT is valuable for targeted therapy because it is a non-invasive and less costly method. Although deep learning technology has realized substantial computer vision achievements, CT imaging being used to predict gene mutations remains challenging due to small dataset limitations. Methods We propose a multi-channel and multi-task deep learning (MMDL) model for the simultaneous prediction of EGFR and KRAS mutation statuses based on CT images. First, we decomposed each 3D lung nodule into 9 views. Then, we used the pre-trained inception-attention-resnet model for each view to learn the features of the nodules. By combining 9 inception-attention-resnet models to predict the types of gene mutations in lung nodules, the models were adaptively weighted, and the proposed MMDL model could be trained end-to-end. The MMDL model utilized multiple channels to characterize the nodule more comprehensively and integrate patient personal information into our learning process. Results We trained the proposed MMDL model using a dataset of 363 patients collected by our partner hospital and conducted a multi-center validation on 162 patients in The Cancer Imaging Archive (TCIA) public dataset. The accuracies for the prediction of EGFR and KRAS mutations were, respectively, 79.43% and 72.25% in the training dataset and 75.06% and 69.64% in the validation dataset. Conclusions The experimental results demonstrated that the proposed MMDL model outperformed the latest methods in predicting EGFR and KRAS mutations in NSCLC.

Objective To investigate the effect of Acapella training on respiratory function in patients undergoing video‐assisted thoracic surgery (VATS). Methods A total of 60 patients undergoing VATS surgery for lung cancer in the department of thoracic Surgery of our hospital from August 2018 to February 2019 were selected as study subjects, and were divided into observation group (n = 30) and control group (n = 30) according to the random number table method. The control group received routine pulmonary rehabilitation exercise after surgery, and the patients in the observation group received postoperative respiratory function exercise with Acapella therapeutic apparatus on the basis of the control group. Respiratory function, including forced vital capacity (FVC), forced expiratory capacity in the first second (FEV1), peak expiratory flow (PEF), and length of hospital stay, were observed and compared between the two groups before and after using Acapell trainer. Results There was no significant difference in FEV1, FVC, and PEF between the two groups before treatment with Acapella (P >.05), the difference between the above indicators after treatment and before treatment was statistically significant (P

Subtle changes in fine motor control and quantitative electroencephalography (qEEG) in patients with mild cognitive impairment (MCI) are important in screening for early dementia in primary care populations. In this study, an automated, non-invasive and rapid detection protocol for mild cognitive impairment based on handwriting kinetics and quantitative EEG analysis was proposed, and a classification model based on a dual fusion of feature and decision layers was designed for clinical decision-marking. Seventy-nine volunteers (39 healthy elderly controls and 40 patients with mild cognitive impairment) were recruited for this study, and the handwritten data and the EEG signals were performed using a tablet and MUSE under four designed handwriting tasks. Sixty-eight features were extracted from the EEG and handwriting parameters of each test. Features selected from both models were fused using a late feature fusion strategy with a weighted voting strategy for decision making, and classification accuracy was compared using three different classifiers under handwritten features, EEG features and fused features respectively. The results show that the dual fusion model can further improve the classification accuracy, with the highest classification accuracy for the combined features and the best classification result of 96.3% using SVM with RBF kernel as the base classifier. In addition, this not only supports the greater significance of multimodal data for differentiating MCI, but also tests the feasibility of using the portable EEG headband as a measure of EEG in patients with cognitive impairment.

Metronidazole (Met) is the first choice for treating Helicobacter pylori ( Hp ). However, Hp is easy to resistant, making Met unable to be widely used.