Understanding adsorptive interactions between organic contaminants and carbon nanotubes is critical to both the environmental application of carbon nanotubes as special adsorbents and the assessment ...of the potential impact of carbon nanotubes on the fate and transport of organic contaminants in the environment. The adsorption of organic compounds with varied physical−chemical properties (hydrophobicity, polarity, electron polarizability, and size) to one single-walled carbon nanotube (SWNT) and two multiwalled carbon nanotubes (MWNTs) was evaluated. For a given carbon nanotube, the adsorption affinity correlated poorly with hydrophobicity but increased in the order of nonpolar aliphatic < nonpolar aromatics < nitroaromatics, and within the group of nitroaromatics, the adsorption affinity increased with the number of nitro-functional groups. We propose that the strong adsorptive interaction between carbon nanotubes and nitroaromatics was due to the π−π electron-donor–acceptor (EDA) interaction between nitroaromatic molecules (electron acceptors) and the highly polarizable graphene sheets (electron donors) of carbon nanotubes. Additionally, we attribute the stronger adsorption of nonpolar aromatics compared to that of nonpolar aliphatics to the π-electron coupling between the flat surfaces of both aromatic molecules and carbon nanotubes. For tetrachlorobenzene, the bulkiest adsorbate, adsorption affinity (on a unit surface area basis) to the SWNT was much stronger than to the two MWNTs, indicating a probable molecular sieving effect.
Scene classification in very high-resolution (VHR) remote sensing (RS) images is a challenging task due to the complex and diverse content of the images. Recently, convolution neural networks (CNNs) ...have been utilized to tackle this task. However, CNNs cannot fully meet the needs of scene classification due to clutters and small objects in VHR images. To handle these challenges, this letter presents a novel multilevel feature fusion (MLFF) network with adaptive channel dimensionality reduction for RS scene classification. Specifically, an adaptive method is designed for channel dimensionality reduction of high-dimensional features. Then, an MLFF module is introduced to fuse the features in an efficient way. Experiments on three widely used data sets show that our model outperforms several state-of-the-art methods in terms of both accuracy and stability.
•Gallic acid (GA) showed protective effects against ethanol-induced gastric ulcer in rats.•The gastroprotective effect of GA could be partly related to the stimulations of gastric nitric oxide and ...prostaglandin.•Nrf2/HO-1 signaling activation is involved in the protection of GA against ethanol-induced gastric mucosa injury.•Pretreatment with GA could inhibit the apoptosis of gastric mucosal cells.
Gallic acid (3,4,5-trihydroxybenzoic acid, GA) is a phenolic compound found in many medicinal plants traditionally used in China or patent medicine such as Feiyangchangweiyan capsule (FY capsule) for the treatment of gastrointestinal diseases for decades. However, the evidence for the gastroprotective effect of GA is deficient and the pharmacological mechanisms remain limited. The present investigation was initiated to demonstrate the gastroprotective effect and to understand potential underlying mechanism of GA on ethanol-induced gastric ulcer in rats. Gastric ulcers were induced by absolute ethanol (5 mL/kg, i.g.) in male Sprague-Dawley rats, GA (10, 30, and 50 mg/kg), FY capsule (0.4 g/kg) and 30 mg/kg Lansoprazole was administered orally. Physiological saline and lansoprazole were used as negative and positive control, respectively. Induction of rats with ethanol resulted in a significant rise in ulcer index, serum levels of inflammatory cytokines markers (IL-1β, IL-6 and TNF-α), TBARS, protein expression of Bax and Caspase-3 and a significant reduction in the activities or levels of endogenous antioxidants (SOD, CAT and GSH), gastric mucosal protective factors (PGE2 and NO) and protein expression of Bcl-2. Pretreatment with GA showed a remarkable decrease in ulcer index, inflammatory cytokines markers, TBARS, protein expression of Bax and Caspase-3 and a significant increase in the activities of endogenous antioxidants, levels of PGE2 and NO, and protein expression of Bcl-2, Nrf2 and HO-1 when compared with ethanol treated groups. This study demonstrated the gastroprotective effect of Gallic acid and FY capsule on ethanol-induced gastric ulcer in rats. The underlying mechanism of GA and FY capsule against gastric ulcer in rats caused by ethanol might be involved in Nrf2/HO-1 anti-oxidative pathway and ultimately played an anti-apoptotic role through regulating Bax, Bcl-2 and Caspase-3.
To study the effects of the neuro-microenvironment on the mass of mitochondria in hematopoietic stem and progenitor cells (HSPC), and to understand the potential mechanisms how nerve regulates HSPC.
...6-hydroxydopamine (6-OHDA) and capsaicin were used to interfere with the function of sympathetic nerve and nociceptive nerve in mitochondria-GFP reporter mice, respectively. The fluorescence intensity of GFP in bone marrow and spleen was measured by flow cytometry. The GFP median fluorescence intensity (MFI) of HSPC in normal bone marrow and spleen was analyzed and compared. The changes of the mitochondrial mass in HSPCs in each group after denervation were compared.
Hematopoietic stem cells (HSC) had the highest mito-GFP MFI in steady-state (49 793±1 877), and the mito-GFP MFI gradually decreased during the differentiation of HSCs. Compared with control group, pharmaceutical nociceptive denervation significantly increased the mito-GFP MFI of bone marrow multipotent progenitor-1 (MPP1, 50 751±420
44 020±510) and L
Remote sensing (RS) scene classification plays an important role in a wide range of RS applications. Recently, convolutional neural networks (CNNs) have been applied to the field of scene ...classification in RS images and achieved impressive performance. However, to classify RS scenes, most of the existing CNN methods either utilize the high-level features from the last convolutional layer of CNNs, missing much important information existing at the other levels, or directly fuse the features at different levels, bringing redundant and/or mutually exclusive information. Inspired by the attention mechanism of the human visual system, in this article, we explore a novel relation-attention model and design an end-to-end relation-attention network (RANet) to learn powerful feature representations of multiple levels to further improve the classification performance. First, we propose to extract convolutional features at different levels by pretrained CNNs. Second, a multiscale feature computation module is constructed to connect features at different levels and generate multiscale semantic features. Third, a novel relation-attention model is designed to focus on the critical features whilst avoiding the use of redundant and even distractive ones by exploiting the scale contextual information. Finally, the resulting relation-attention features are concatenated and fed into a softmax layer for the final classification. Experiments on four well-known RS scene classification datasets (UC-Merced, WHU-RS19, AID, and OPTIMAL-31) show that our method outperforms some state-of-the-art algorithms.
Significant concerns have been raised over the presence of antibiotics including tetracyclines in aquatic environments. We herein studied single-walled carbon nanotubes (SWNT) and multi-walled carbon ...nanotubes (MWNT) as potential effective adsorbents for removal of tetracycline from aqueous solution. In comparison, a nonpolar adsorbate, naphthalene, and two other carbonaceous adsorbents, pulverized activated carbon (AC) and nonporous graphite, were used. The observed adsorbent-to-solution distribution coefficient (K d, L/kg) of tetracycline was in the order of 104−106 L/kg for SWNT, 103−104 L/kg for MWNT, 103−104 L/kg for AC, and 103−105 L/kg for graphite. Upon normalization for adsorbent surface area, the adsorption affinity of tetracycline decreased in the order of graphite/SWNT > MWNT ≫ AC. The weaker adsorption of tetracycline to AC indicates that for bulky adsorbates adsorption affinity is greatly affected by the accessibility of available adsorption sites. The remarkably strong adsorption of tetracycline to the carbon nanotubes and to graphite can be attributed to the strong adsorptive interactions (van der Waals forces, π−π electron-donor−acceptor interactions, cation-π bonding) with the graphene surface. Complexation between tetracycline and model graphene compounds (naphthalene, phenanthrene, pyrene) in solution phase was verified by ring current-induced 1H NMR upfield chemical shifts of tetracycline moieties.
The combined effects of hydroxyl/amino functional groups of aromatics and surface O-containing groups of carbon nanotubes on adsorption were evaluated. When normalized for hydrophobicity, ...2,4-dichlorophenol and 2-naphthol exhibited a greater adsorptive affinity to carbon nanotubes and graphite (a model adsorbent without the surface O functionality) than structurally similar 1,3-dichlorobenzene and naphthalene, respectively, and 1-naphthylamine exhibited a much greater adsorptive affinity than all other compounds. Results of the pH-dependency experiments further indicated that the hydroxyl/amino functional groups of the adsorbates and the surface properties of the adsorbents played a combinational role in determining the significance of the nonhydrophobic adsorptive interactions. We propose that the strong adsorptive interaction between hydroxyl-substituted aromatics and carbon nanotubes/graphite was mainly due to the electron-donating effect of the hydroxyl group, which caused a strong electron-donor−acceptor (EDA) interaction between the adsorbates and the π-electron-depleted regions on the graphene surfaces of carbon nanotubes or graphite. In addition to the EDA interaction, Lewis acid−base interaction was likely an extra important mechanism contributing to the strong adsorption of 1-naphthylamine, especially on the O-functionality-abundant carbon nanotubes. The findings of the present study might have significant implications for selective removal of environmental contaminants with carbon nanotubes.
Human motion recognition based on wearable devices plays a vital role in pervasive computing. Smartphones have built-in motion sensors that measure the motion of the device with high precision. In ...this paper, we propose a human lower limb motion capture and recognition approach based on a Smartphone. We design a motion logger to record five categories of limb activities (standing up, sitting down, walking, going upstairs, and going downstairs) using two motion sensors (tri-axial accelerometer, tri-axial gyroscope). We extract the motion features and select a subset of features as a feature vector from the frequency domain of the sensing data using Fast Fourier Transform (FFT). We classify and predict human lower limb motion using three supervised learning algorithms: Naïve Bayes (NB), K-Nearest Neighbor (KNN), and Artificial Neural Networks (ANNs). We use 670 lower limb motion samples to train and verify these classifiers using the 10-folder cross-validation technique. Finally, we design and implement a live detection system to validate our motion detection approach. The experimental results show that our low-cost approach can recognize human lower limb activities with acceptable accuracy. On average, the recognition rate of NB, KNN, and ANNs are 97.01%, 96.12%, and 98.21%, respectively.
Different from the traditional remote sensing (RS) scene classification which uses a single scene label to holistically annotate an image, multilabel RS image classification uses a series of object ...labels to interpret a scene more deeply. For multilabel RS scene classification, there exist two vital problems. First, the objects with different semantic labels have smaller sizes and more scattered arrangements compared to backgrounds, making meaningful semantic feature extraction and representation severely hard. Second, an RS scene usually contains various kinds of objects, leading to exponential magnification of output label space size with the increase of the number of object categories. To simultaneously solve the challenges in features as well as label space and produce significant performance improvements, this article proposes a novel end-to-end deep learning architecture, which we term the global context-based multilevel feature fusion network. We verify the whole framework by conducting a great number of experiments on two publicly available multilabel datasets, and we also provide an ablation study exploring different modules inclusion in the framework. Experimental results demonstrate that the proposed method is superior to some popular networks for multilabel RS image scene classification.
Objective: To investigate the clinical effects of HBV infection on patients with rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE).
Methods: This is a prospective study. Thirty ...patients with RA and 30 patients with SLE admitted to Affiliated Hospital of Hebei University from January 2020 to December 2021 with co-infection of HBV were randomly selected and divided into two groups. Both groups were given anti-HBV treatment. An additional 60 patients with a healthy physical examination during the same period were also selected as a control group. The disease activity, immune function and serum inflammatory factor levels were compared between the RA group and the SLE group before and after treatment.
Results: After anti-HBV treatment, DAS scores in the RA group and SLEDAI scores in the SLE group were significantly lower than before treatment(P<0.05). The levels of IgG, IgA, IgM and CD8+ in the RA group and the SLE group after treatment were significantly lower than those before treatment. The levels of CCP, RF, ESR and CRP in the RA group before and after treatment were higher than those in the control group(P<0.05). The levels of ESR and CRP in the SLE group before and after treatment were higher than those in the control group, with statistically significant differences(P<0.05).
Conclusion: Patients in the RA and SLE groups after HBV infection have an increased degree of inflammatory response in their organism, an altered normal state of immunoglobulin and T-lymphocyte subsets, and a loss of organism immune function, leading to an increase in disease activity.
doi: https://doi.org/10.12669/pjms.39.5.7232
How to cite this: Duan L, Yang C, Cai T, Li W. Clinical Effects of HBV Infection on Patients with Rheumatoid Arthritis and Systemic Lupus Erythematosus. Pak J Med Sci. 2023;39(5):---. doi: https://doi.org/10.12669/pjms.39.5.7232
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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