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Background Understanding patients’ knowledge, attitudes, and practices is essential for effective heart failure (HF) management, as these factors significantly influence self-care behaviors, treatment adherence, and clinical outcomes. This study aimed to investigate the knowledge, attitudes, and practices (KAP) of patients with heart failure. Methods A web-based cross-sectional study was conducted from April to May 2023 among 483 heart failure patients recruited through convenience sampling. Inclusion criteria encompassed a confirmed HF diagnosis and the ability to independently use mobile devices. The research employed a self-administered questionnaire, validated through expert review and pilot testing, to assess knowledge, attitudes, and practices. The overall reliability was robust (Cronbach’s α = 0.875), with subscale alphas of 0.819 for knowledge, 0.582 for attitudes, and 0.846 for practices. Results The findings from 483 HF patients revealed limited knowledge (mean score: 10.75 out of 20), moderately negative attitudes (mean score: 22.93 out of 40), and relatively proactive practices (mean score: 32.21 out of 45). Higher knowledge levels and having mid-range ejection fraction emerged as significant predictors of proactive health behaviors (all P  < 0.05). Structural equation modeling further demonstrated that education and marital status positively influenced knowledge, while attitudes unexpectedly showed a negative association with practices (all P  < 0.05). Conclusion This study highlights critical gaps in knowledge and attitudes among HF patients, despite some proactive practices. By leveraging a web-based platform, this research contributes to the growing literature on digital health approaches in chronic disease management, suggesting that scalable, technology-enabled education and support strategies should be integrated into HF care. Additionally, this study provides novel insights into the unexpected negative relationship between attitudes and practices, underscoring the complexity of behavioral change in HF management. Clinical trial number Not applicable.

With the increasingly strict limitations on emission standards of vehicles, deep desulfurization in fuel is indispensable for social development worldwide. In this study, a series of hybrid materials based on SiO2-supported polyoxometalate ionic liquid were successfully prepared via a facile ball milling method and employed as catalysts in the aerobic oxidative desulfurization process. The composition and structure of prepared samples were studied by various techniques, including FT-IR, UV-vis DRS, wide-angle XRD, BET, XPS, and SEM images. The experimental results indicated that the synthesized polyoxometalate ionic liquids were successfully loaded on SiO2 with a highly uniform dispersion. The prepared catalyst (C16PMoV/10SiO2) exhibited good desulfurization activity on different sulfur compounds. Moreover, the oxidation product and active species in the ODS process were respectively investigated via GC-MS and ESR analysis, indicating that the catalyst can activate oxygen to superoxide radicals during the reaction to convert DBT to its corresponding sulfone in the fuel.

As a ubiquitous mesoscale phenomenon, ocean eddies significantly impact ocean energy and mass exchange. Detecting these eddies accurately and efficiently has become a research focus in ocean remote sensing. Many traditional detection methods, rooted in physical principles, often encounter challenges in practical applications due to their complex parameter settings, while effective, deep learning models can be limited by the high computational demands of their extensive parameters. Therefore, this paper proposes a new approach to eddy detection based on the altimeter data, the Ghost Attention Deeplab Network (GAD-Net), which is a lightweight and efficient semantic segmentation model designed to address these issues. The encoder of GAD-Net consists of a lightweight ECA+GhostNet and an Atrous Spatial Pyramid Pooling (ASPP) module. And the decoder integrates an Efficient Attention Network (EAN) module and an Efficient Ghost Feature Integration (EGFI) module. Experimental results show that GAD-Net outperforms other models in evaluation indices, with a lighter model size and lower computational complexity. It also outperforms other segmentation models in actual detection results in different sea areas. Furthermore, GAD-Net achieves detection results comparable to the Py-Eddy-Tracker (PET) method with a smaller eddy radius and a faster detection speed. The model and the constructed eddy dataset are publicly available.

A green and facile pathway was described using Viburnum odoratissimum leaf extract in the presence of sodium thiosulfate for the synthesis of sulfidated iron oxide nanocomposites (S-Fe NCs) adsorbents. The prepared S-Fe NCs can be used for the efficient removal of Malachite Green (MG) and Rhodamine B (RhB) from aqueous solution. Analytical techniques by scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDS), transmission electron microscopy (TEM), X-ray diffraction (XRD), Fourier transform infrared (FTIR), and X-ray photoelectron spectroscopy (XPS) were applied to understand the morphologies and compositions of S-Fe NCs. The stability of the adsorption capacity on S-Fe NCs was studied. Results from the characterization studies showed that S-Fe NCs were mainly composed of iron oxides, iron sulfides and biomolecules. The S-Fe NCs displayed high adsorption capacity for a wide range of pH values. The Koble-Corrigan isotherm model and Elovich model well described the adsorption process. The maximum adsorption capacity for MG and RhB was 4.31 mmol g−1 and 2.88 mmol g−1 at 303 K, respectively. The adsorption mechanism may be attributed to the electrostatic interaction, the hydrogen bonding, the π-π stacking interactions, the inner-sphere surface complexation or the cation bridging among the S-Fe NCs and dye molecules.

Acidic deep eutectic solvents (DESs) have been considered desirable extractants and catalysts for desulfurization. However, their hydrogen bond donors (HBDs) are usually sole organic acids, which are not conducive to efficient green catalysis. Herein, a novel inorganic–organic dual-acid DES (DADES) was reported for efficient extractive and oxidative desulfurization. Benefiting from the physical interaction among the three components in a DADES, a transparent homogeneous liquid can be obtained even though inorganic acid (boric acid, BA) and organic acid (acetic acid, AA) can be immiscible. Furthermore, the dual-acid HBD can increase the acidity of the DADES and reduce its viscosity, accelerating its mass transfer efficiency and enhancing its catalytic activity. With 1-butyl-3-methylimidazolium chloride ([Bmim]Cl) as the hydrogen bond acceptor, [Bmim]Cl/BA/0.3AA effectively activated hydrogen peroxide and achieved sulfur removal of 96.6% at 40 °C. Furthermore, the universality of the synergistic effect in various DADESs was confirmed by the modulation of the types of organic acids. This study not only motivates the construction of more intriguing novel DESs based on the DADES concept but also highlights their potential in clean fuel production.

Background Paclitaxel is commonly used as a cancer chemotherapy drug that frequently causes peripheral neuropathic pain. Inflammasome is a multiprotein complex consisting of Nod-like receptor proteins (NLRPs), apoptosis-associated speck-like protein, and caspase-1, which functions to switch on the inflammatory process and the release of interleukin-1β. Growing evidences have supported that peripheral interleukin-1β is critical in enhancing paclitaxel-induced neuropathic pain. However, whether activation of NLRP3 inflammasome in peripheral nerve contributes to paclitaxel-induced neuropathic pain is still unclear. Results Paclitaxel induced mechanical allodynia of rats from day 3 and worsened gradually till 3 weeks after injection. Paclitaxel resulted in expression of NLRP3 and activated fragments of caspase-1 and interleukin-1β in L4-6 dorsal root ganglia and sciatic nerve three weeks after injection, indicating activation of NLRP3 inflammasome. The expression of NLRP3 was located in CD68-labeled macrophages infiltrating in L4-6 dorsal root ganglia and sciatic nerve, and paclitaxel increased the expression of NLRP3 in macrophage. Moreover, the paclitaxel elicited mitochondria damage, which became swollen and enlarged in macrophages and axons of sciatic nerve three weeks after injection. In vitro, paclitaxel increased the number of damaged mitochondria and mitochondrial reactive oxygen species production in the rat alveolar macrophage cell line NR8383. The administration of a non-specific reactive oxygen species scavenger, phenyl-N-tert-butylnitrone, markedly alleviated mechanical allodynia and inhibited the activation of NLRP3 inflammasome in L4-6 dorsal root ganglia and sciatic nerve of the paclitaxel-induced neuropathic pain model. Conclusions Paclitaxel induced mechanical allodynia and activation of NLRP3 inflammasome in infiltrated macrophages of L4-6 dorsal root ganglia and sciatic nerve. Paclitaxel elicited mitochondria damage and reactive oxygen species production may result in activation of NLRP3 inflammasome in peripheral nerve, which contributes to paclitaxel-induced neuropathic pain.

Background Reverse Transcription quantitative polymerase chain reaction (RT-qPCR) is a sensitive and reliable method for mRNA quantification and rapid analysis of gene expression from a large number of starting templates. It is based on the statistical significance of the beginning of exponential phase in real-time PCR kinetics, reflecting quantitative cycle of the initial target quantity and the efficiency of the PCR reaction (the fold increase of product per cycle). Results We used the large clinical biomarker dataset and 94-replicates-4-dilutions set which was published previously as research tools, then proposed a new qPCR curve analysis method——C q MAN, to determine the position of quantitative cycle as well as the efficiency of the PCR reaction and applied in the calculations. To verify algorithm performance, 20 genes from biomarker and partial data with concentration gradients from 94-replicates-4-dilutions set of MYCN gene were used to compare our method with various publicly available methods and established a suitable evaluation index system. Conclusions The results show that C q MAN method is comparable to other methods and can be a feasible method which applied to our self-developed qPCR data processing and analysis software, providing a simple tool for qPCR analysis.

Background Mechanical damage to plants triggers local and systemic electrical signals that are eventually decoded into plant defense responses. These responses are constantly affected by other environmental stimuli in nature, for instance, light fluctuation. In recent years, studies on decoding plant electrical signals powered by various machine learning models are increasing in a sense of early prediction or detection of different environmental stresses that threaten plant growth or crop yields. However, the main bottleneck is the low-throughput nature of plant electrical signals, making it challenging to obtain a substantial amount of training data. Consequently, training these models with small datasets often leads to unsatisfactory performance. Results In the present work, we set out to decode wound-induced electrical signals (also termed slow wave potentials, SWPs) from plants that are deprived of light to different extents. Using non-invasive electrophysiology, we separately collected sets of local and distal SWPs from the treated plants. Then, we proposed a workflow based on few-shot learning to automatically identify SWPs. This workflow incorporates data preprocessing, feature extraction, data augmentation and classifier training. We established the integral and the first-order derivative as features for efficiently classifying SWPs. We then proposed an Adversarial Autoencoder (AAE) structure to augment the SWP samples. Combining them, the Random Forest classifier allowed remarkable classification accuracies of 0.99 for both local and systemic SWPs. In addition, in comparison to two other reported methods, our proposed AAE structure enabled better classification results using our tested features and classifiers. Conclusions The results of this study establish new features for efficiently classifying wound-induced electrical signals, which allow for distinguishing dark-affected local and systemic plant wound responses. We also propose a new data augmentation structure to generate virtual plant electrical signals. The methods proposed in this study could be further applied to build models for crop plants using electrical signals as inputs, and also to process other small-scale signals.

This study investigated the effects of different delivery modes on oral microflora in healthy newborns immediately post-partum, and provided evidence for microbial colonization disruption induced by medical procedures. Eighteen infants delivered by cesarean section and 74 by vaginal delivery were included in the study. High-throughput sequencing of 16S bacterial rRNA was performed on oral samples collected immediately after birth. All data were analyzed using bioinformatics approaches. Our results indicated that different oral bacteria were found between infants delivered by cesarean section compared to vaginal delivery group. Lactobacillus, Prevotella and Gardnerella were the most abundant genera in the vaginal group, while Petrimonas, Bacteroides, Desulfovibrio, Pseudomonas, Staphylococcus, Tepidmicrobium, VadinCA02, and Bifidobacterium were dominant bacteria in the cesarean section (C-section) group. Furthermore, bacteria isolated from 27 vaginally-delivered infants were not clustered into the vaginal group. Most of them spent more than 24 hours in the delivery room and this led to repeated sterilization procedures. We hypothesized that repeated sterilization might have influenced oral microflora in those cases. To conclude, this study suggested that different modes of birth delivery affect oral microflora in healthy infants. In addition, attention shall be paid to the clinical practice of repeated sterilization of the vulva that possibly obstructs the colonization of vaginal bacterial.

Immune composition is commonly heterogeneous and varies among colorectal cancer (CRC) patients. A comprehensive immune classification may act as important characteristics to predict CRC prognosis. Thus, we aimed to identify novel immune specific subtypes to guide future therapies. Unsupervised clustering was used to classify CRC samples into different immune subtypes based on abundances of immune cell populations, during which TCGA and GSE17536 datasets were used as training and validation sets, respectively. The associations between the immune subtypes and patient prognosis were investigated. Further, we identified differentially expressed genes (DEGs) between immune high and low subtypes, followed by functional enrichment analyses of DEGs. The expression levels of 74 immunomodulators (IMs) across immune subtypes were analyzed. As a result, we clustered CRC samples into three distinct immune subtypes (immune high, moderate, and low). Patients with immune-high subtype showed the best prognosis, and patients with immune-low subtype had the worst survival in both TCGA and GSE17536 cohorts. A group of 2735 up-regulated DEGs were identified across immune high and low subtypes. The main DEGs were the members of complement components, chemokines, immunoglobulins, and immunosuppressive genes that are involved in immune modulation-related pathways (e.g., cytokine-cytokine receptor interaction) or GO terms (e.g., adaptive immune response and T cell activation). The expression levels of 63 IMs were significantly varied across immune subtypes. In conclusion, this study provides a conceptual framework and molecular characteristics of CRC immune subtypes, which may accurately predict prognosis and offer novel targets for personalized immunotherapy through modifying subtype-specific tumor immune microenvironment.