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Abstract Objectives House dust mites, including Der p1, are common allergens. The current study was designed to explore the allergen-specific immune tolerance effects of Der p1-modified dendritic cells (DCs) through IL-4, IL-10 and IL-13 on an allergic rhinitis (AR) mouse model. Methods A lentivirus was modified to express Derp1. Then, immature DCs from mice were infected with this modified lentivirus to generate a lenti- Derp1 -GFP DCs. 24 mice were random divided into four groups (n = 6 each), AR mouse were sensitized by Derp1 allergens and treated with lenti-GFP DCs (GFP-DC/AR group), or lenti- Derp1 -GFP DCs (Der p1-DC/AR group) and dexamethasone (Dex/AR group), mice in the control group were treated with PBS instead of Der p1 then also intraperitoneally injected with 5 × 106 lenti-GFP DCs/mouse. AR symptoms expressed by each mouse were recorded. The proportions of CD4+CD25+Foxp3+ regulatory T cells among CD4+ T cells in the peripheral blood, and mRNA and protein expression levels of IL-4, IL-10, and IL-13 were measured. Results DCs infected with lenti- Derp1 -GFP stimulated the maturation of DCs. Compared with the GFP-DC/AR group, mice in the Der p1-DC/AR group showed an ameliorated allergic response, a significant decrease in the levels of serum IgE, IgG1, and histamine, and a decrease in the expression of IL-4 and IL-13 mRNA and protein in the nasal mucosa. The expression of IL-10 increased in the Der p1-DC/AR group to a level similar to that observed in the Dex/AR group. Conclusions These results indicate that Der p1-modified DCs have therapeutic potential for AR via downregulation of IL-4 and IL-13, and upregulation of IL-10. Highlights • Immature DCs were infected with Der p1 gene change into Derp1 -DC. • Der p1 -DC ameliorated allergic response of AR via regulatory effects on IL-4, IL-10 and IL-13. • Der p1 -DC increased the percentage of CD4+CD25+Foxp3+ Tregs. • Der p1 -DC has the same therapeutic effect of glucocorticoid treatment in AR. [ABSTRACT FROM AUTHOR]

• Feature-block proposed for reduction of dimension based on Hellinger Distance. • Label-links Classifier Chain proposed for performance improvement of MLC. • A novel fusion approach DS Theory based proposed for improvement of accuracy. Rhinitis is a common chronic inflammation of the upper respiratory tract with multiple symptoms and signs. The clinical classification of rhinitis is a multi-label classification, characterized by high dimensionality, poor correlation and class imbalance. Low recognition rate and poor generalization performance often occur for minority class instances. Therefore, this paper proposes a Feature-Block classification model, MBLCC based on Label-Links Classifier Chain. We apply kernel density estimation and Deborah Hellinger distance to partition allergic rhinitis instance with similar characteristics, calculate the correlation matrix of label characteristics, build an ordered classification chain for each block, and integrate the predictions of each block classifiers by evidence theory as the outcome. The cross-validation experiments conducted on 2231 cases of clinical rhinitis show that the evaluation indicators of MBLCC, i.e. sensitivity, specificity, accuracy, F1-score, and G-Mean, are 91.80 %, 96.8 %, 96.9 %, 0.925, and 0.941 respectively. In comparison with the other baselines, MBLCC achieves better generalization performance and is more effective and rapid in early clinical diagnosis of rhinitis. In addition, we calculate the feature importance ranking for rhinitis features via Label-Links Classifier Chain on the grounds of the purity of nodes in decision-making tree inside Random Forest and study the correlation between rhinitis features and classification that can provides the reference for the clinical rhinitis diagnosis. [ABSTRACT FROM AUTHOR]

In present work, the degradation of two N-heteroaromatic pollutants, i.e., pyridine and quinoline was investigated by gamma irradiation in the presence of TiO 2 nanoparticle. The experimental results showed that quinoline has a higher degradation rate than pyridine. The removal efficiency of the pollutants, TOC and TN reached 93.0%, 11.9% and 12.0% for quinoline, 71.0%, 10.6% and 4.4% for pyridine, respectively at 7.0 kGy and initial concentration of 50 mg/L. Ammonium was detected for both pyridine and quinoline within the absorbed doses, suggesting that the organic nitrogen was transformed into ammonium. The degradation rate constant of pyridine and quinoline was increased by 1.1–1.5 times with addition of TiO 2 . TiO 2 nanoparticles were especially effective to enhance the mineralization. The removal efficiency of TOC and TN was increased by 15–12% for pyridine and 23–25% for quinoline, respectively in the presence of 2.0 g/L TiO 2 . Following gamma irradiation, 2-hydroxypyridine, 3-hydroxypyridine, oxalic acid and formic acid were identified for pyridine and the hydroxyl quinoline and formic acid were detected for quinoline. Accordingly, the degradation mechanism of pyridine and quinoline by gamma irradiation was tentatively proposed. [ABSTRACT FROM AUTHOR]

The CD4 + CD25 + Foxp3 + regulatory T cells (Tregs) are known to regulate Th2-induced allergic rhinitis (AR). In this study, we evaluated the efficacy of Derp1 -modified dendritic cells (DCs) in AR immunotherapy. Derp1 was synthesized and transfected into DCs to generate Derp1 -modified DCs. Phenotypes of Derp1 -modified DCs were analyzed with flow cytometry using antibodies against DC markers CD11c, CD11b, CD59, CD103 and Toll-like receptor 1(TLR1). Four groups of subject mice were formed; the controls were treated with immature DCs, while the AR mice models were sensitized with Derp1(AR) and treated with DCs(DC-AR) or Derp1 -modified DCs (Derp1DC-AR). The frequency of sneezing and scratching, eosinophil cell count, and Th1/Th2 ratio in the spleen were measured for all groups. The percentage of CD4 + CD25 + Foxp3 + Tregs in peripheral blood mononuclear cells was measured using flow cytometry; serum IgE, IgG1, and histamine were measured using enzyme-linked immunosorbent assay; expression levels of transcription factors T-bet, GATA3, Foxp3+ and IL-10 were analyzed using reverse transcription-polymerase chain reaction, and Western blot used in analyzed expression of Foxp3+ and IL-10 in nasal mucosa. Treatment with Derp1 -modified DCs ameliorated the allergic response. The Derp1DC-AR group had significantly lower eosinophil cell count and the IgE, IgG1, and histamine levels than the AR and DC-AR groups, and higher mRNA levels of Th1 transcription factors T-bet, IL-10 and Foxp3 in nasal mucosa than DC-AR mice, but Th2 transcription factors GATA3 mRNA expression level has the opposite results. Furthermore, the Th1/Th2 ratio and percentage of CD4 + CD25 + Foxp3 + Tregs was significantly lower in the AR group (p < 0.05), but higher in the Derp1DC-AR group than in the control group (p < 0.01). Thus, the Derp1 -modified DCs increased the percentage of CD4 + CD25 + Foxp3 + Tregs and influenced the balance of Th1/Th2, showing an immunotherapeutic effect against AR. [ABSTRACT FROM AUTHOR]

In Brief: An efficient image segmentation model, TSE DeepLab is proposed for clincial image segmentation on sinusitis and patellar fracture instances. TSE DeepLab replaces global average pooling with TSE block, which consists of visual Transformer in form of static visual tokens and SE block, in order to improve the ability of global feature extraction. [Display omitted] • Transformer in form of static visual tokens is applied in clincial image to improve global feature extraction. • The hyperparameters of TSE block is optimized to speed up the convergence and improve segmentation performance. Medical image segmentation is the key research of precision medicine. The existing models often ignore some important pixel features and fail to effectively extract global correlation features, which causes poor performance of segmentation. In this paper, we propose TSE DeepLab, which retains the original atrous convolution for extraction of local feature on the basis of DeepLabv3 framework, converts the feature maps after backbone into visual tokens, and further feeds them into Transformer module to enhance the ability of global feature extraction. At the same time, squeeze and excitation components are added to sort the importance of channels after Transformer module, so that the model pays attention to the important pixel features of each channel. In this paper, we apply 5-fold cross-validation to study the clinical sinus instances of Shanghai Tongji Hospital affiliated to Tongji University and the patellar fracture instances of the Sixth People's Hospital affiliated to Shanghai Jiao Tong University. The average of evaluation measures achieves Accuracy of 99.74%, Precision of 93.67%, IOU of 88.10%, Specificity of 99.87%, Fl-score of 93.63%, Sensitivity of 93.82% on sinus dataset and Accuracy of 99.53%, Precision of 85.64%, IOU of 78.47%, Specificity of 99.72%, Fl-score of 87.15%, Sensitivity of 89.95% on patellar fracture dataset. Compared with various typical segmentation models, the proposed model attains better segmentation accuracy and generalization performance, and has better reference value for clinical medical diagnosis. [ABSTRACT FROM AUTHOR]

The decomposition of naphthalene in aqueous solution was studied using gamma irradiation combined with both H 2 O 2 and TiO 2 nanoparticles. Gamma irradiation led to a complete degradation of naphthalene and a partial mineralization. With initial concentration of 5–32 mg/L, more than 98% of naphthalene was removed and TOC reduction reached 28–31% at an absorbed dose of 3.0 kGy. The degradation of naphthalene was faster at neutral pH and the initial degradation rate increased with increasing the initial concentration of naphthalene. Addition of H 2 O 2 and TiO 2 nanoparticles all enhanced the degradation and mineralization of naphthalene. TOC removal efficiency increased from 28% (irradiation alone) to 35% with addition of H 2 O 2 (40 mg/L), and to 48% with addition of TiO 2 (0.8 g/L). The degradation of naphthalene in aqueous solution by gamma irradiation was mainly through the oxidation by · OH radicals. The intermediate naphthol and carboxylic acids such as formic acid and oxalic acid were identified by LC–MS and IC. [ABSTRACT FROM AUTHOR]

Abstract: The synergistic effect of gamma radiation with hydrogen peroxide (H2O2) for p-nitrophenol (PNP) decomposition in aqueous solution was evaluated. The PNP solution with initial concentration of 50mg/L was irradiated in the presence of extra H2O2 at initial concentration of 0, 20, 40, and 80mg/L. The experimental results showed that the decomposition of PNP conformed to the pseudo-first-order reaction kinetics under the applied conditions. When initial H2O2 concentration was in the range of 0–80mg/L, higher concentration of H2O2 was more effective for the decomposition, mineralization and nitrogen release of PNP. However, the removal of total organic carbon (TOC) and total nitrogen (TN) was not as effective as that of PNP. Ammonia and nitrate were detected as the main inorganic nitrogen products of PNP decomposition without extra H2O2, whereas nitrate was considered as a final inorganic nitrogen product with extra H2O2 in the initial concentration range of 0–80mg/L. Major decomposition products, including organic acids were identified by LC/MS and IC. Possible pathways for PNP decomposition by gamma radiation in aqueous solution were proposed. [Copyright &y& Elsevier]

• We propose a cascaded under-sampling ensemble learning method to construct multiple batch classifiers, each of which is composed of a number of base classifiers with different structures. Through batch-by-batch under-sampling, the instances of class imbalance gradually reach the equalization. • The average accuracy, true positive rate, and G-mean of the proposed model were 90.71 %, 87.44 %, and 88.18 %, respectively. Compared to typical classifiers, the proposed model has higher accuracy, true positive rate and lower missed diagnosis rate. • We calculate the feature importance for rhinitis features on the grounds of the purity of nodes in decision-making tree inside Random Forest and study the correlation between rhinitis features and classifications. Rhinitis is a prevalent respiratory disease. Clinical rhinitis instances are characterized by multi-label and class imbalance, which is difficult to be accurately classified by typical machine learning methods. We propose a cascaded under-sampling ensemble learning method (CUEL) to construct multiple batch classifiers, each of which is composed of a few base classifiers with different structures. Through batch-by-batch under-sampling, the correctly classified instances of majority class are removed, and the samples that are difficult to classify are kept to gradually reach the equalization of class imbalance. We assign different weights to each of the batch classifiers to construct the final integrated classifier. Cross validation was performed on 2231 clinical rhinitis instances from Shanghai Tongji Hospital Affiliated to Tongji University. The experiment showed that the average accuracy, true positive rate, and G-mean of the CUEL model were 90.71 %, 87.44 %, and 88.18 %, respectively. Compared to typical classifiers, the CUEL model has higher accuracy, true positive rate and lower missed diagnosis rate, and has stronger generalization performance. It can make full use of all rhinitis instances and effectively reduce the prediction deviation caused by class imbalance. Therefore, it has a good auxiliary effect for the prevention and diagnosis of clinical rhinitis. In addition, we calculate the feature importance for rhinitis features on the grounds of the purity of nodes in decision-making tree inside Random Forest and study the correlation between rhinitis features and classifications. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNNs) can be used for clinical medical image segmentation to improve detection efficiency and accuracy. However, because of the fixed size of convolution kernel and receptive field for existing CNN model, some associated pixel features are ignored and segmentation performance is impaired. Therefore, we propose an effective image segmentation model, COPANet, which uses RepVGG as the backbone and replaces the standard convolution with dilated convolution of multiple kernels to increase the receptive field size, so that COPANet can make full use of pixel correlations and acquire contexture semantic information. We also build skip-connections between global and local features, where a parallel attention (PANet) is employed to extract important location information in the downsampling. PANet is a fused mechanism integrating channel attention with spatial attention in parallel, which can fully extract more global information. We also apply weighted combined loss to reduce the effect of class imbalance of foreground and background pixels on segmentation performance and speed up convergence. In addition, we conduct experiments on 480 cases of clinical CT sinus from Shanghai Tongji Hospital and 236 cases of CT patella fracture from Shanghai Sixth People's Hospital. The evaluation indexes after 5-Fold cross-validation are as follows: Precision is 92.76% and 96.55%, Recall is 88.25% and 57.58%, Specificity is 99.85% and 96.55%, IOU is 91.08% and 77.38%, Hausdorff Distance is 2.8135 and 11.1879, respectively. Compared with the state-of-the-art models, COPANet has higher segmentation accuracy and better generalization performance, which can assist the clinical diagnosis. [ABSTRACT FROM AUTHOR]

In the present study, a novel eco-friendly foam (CS-AMP) was successfully prepared by grafting adenosine 5′-monophosphate (AMP) with high biocompatibility onto environment-friendly monomer chitosan (CS). The CS-AMP foam was used to absorb uranium and exhibited an efficient selective adsorption of uranium from aqueous solution and natural seawater. Due to the phosphate group of AMP, the CS-AMP foam shows an excellent adsorption capacity of 311 mg/g (pH 5.0 and 308 K) for uranium, which is much higher than that of CS-PTA foam without modification by AMP. The adsorption process is fitted well with Sips isothermal and pseudo-second-order kinetic models. As a highly selective uranium adsorbent, it is expected to be applied to extract uranium from wastewater and seawater. [Display omitted] [ABSTRACT FROM AUTHOR]