ABSTRACT
Separation membranes play a crucial role in the functioning of artificial organs, such as hemodialysis machines, membrane oxygenators, and artificial liver models. The current COVID-19 pandemic has highlighted the importance of these technologies in the medical community. However, membrane technology in artificial organs faces significant challenges, such as the clearance of low-middle-molecule and protein-bound toxins and limited blood compatibility. In this review, we will discuss the separation mechanisms, separation performance, and biocompatibility of different types of separation membranes used in artificial organs. We will also highlight the opportunities and challenges for next-generation membrane technology in this field, including the need for improved clearance of toxins and increased blood compatibility, as well as the potential for microfluidic devices.
ABSTRACT
Prurigo nodularis (PN) is a chronic inflammatory skin disease characterized by severe itching accompanied by multiple nodules throughout the body. There is currently no efective drug-targeted treatment for PN. Dupilumab is a fully human monoclonal antibody which can suppress the Th2 inflammatory reaction. We aimed to assess the efficacy and safety of dupilumab in PN. There were 29 PN patients who received dupilumab treatment for four months. Serum total immunoglobulin E (Ig E), eosinophil counts, dermatology life quality index (DLQI), and numeric rating scale (NRS) were assessed on patients before and after treatment. We count the vaccination of novel coronavirus pneumonia (COVID-19) in patients and the impact on PN and treatment measures after vaccination. Plotting was performed using GraphPad Prism8, and the statistical analysis was performed using PASW Statistics18. The eosinophil counts in patients higher decreased to normal, and the Ig E levels gradually decreased and tended to normal levels after receiving dupilumab injection. The average DLQI score at the baseline was 23.93 +/- 0.66 and decreased to 11.66 +/- 0.55 (P < 0.01) and 1.83 +/- 0.22 (P < 0.01) at 1-month and 6-monthfollow-up of treatment, respectively. The average NRS score at the baseline was 9.79 +/- 0.08 and decreased to 3.52 +/- 0.23 (P < 0.01) and 0.31 +/- 0.15 (P < 0.01) at the 1-month and 6monthfollow-up of treatment, respectively. Our study shows that dupilumab has achieved good efficacy in PN with few adverse reactions and high safety. We can recommend that patients follow the advice of specialists to be vaccinated and under the condition of stable disease, separated from dupilumab treatment for one week.
ABSTRACT
This review aimed at portraying a nuanced picture of the trajectory of teacher well-being research during 54 years from 1968 to 2021. This review used descriptive quantitative analysis with a dataset of 774 journal articles. The developmental trend demonstrates a considerable change in the volume of publications conducted during the most recent 14 years. Findings of the current review identify that research foci have covered the antecedents, nature, and effects of teacher well-being in a descending manner. Quantitative methods were observed as the most frequently used method in research studies. Moreover, the use of qualitative and mixed research methods increased in recent times. However, due to the considerable absence of mixed methods, longitudinal, and experimental research designs in this review's corpus, our interpretation has been restricted to the ways in which teacher well-being can affect as well as be affected by associated constructs. Research on teacher well-being positions itself at the intermediate stage focusing on fusion relations combining new and recognized structures and adopting qualitative as well as quantitative practices. This review supports the evolution of the teacher well-being literature and poses recommendations for future research.
ABSTRACT
Visual detection of nucleic acids is important to diagnose the serious acute infectious diseases such as coronavirus disease 2019 (COVID-19). During this pandemic, reliable visual detection kits have been in high demand for screening and prevention of the virus. While developing these visual detection kits, a real-time monitoring platform is usually applied to study the amplification and detection processes of nucleic acids and optimize the detecting conditions. Herein, we developed a real-time monitoring platform of colorimetric loop-mediated isothermal amplification (LAMP) to investigate the amplification and detection processes of nucleic acids. Using this platform, we could obtain the real-time amplification curves, and optimize the reaction temperature, color change, and detection time. Based on the optimized conditions, a visual detection kit for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was successfully developed with a sensitivity of 102 copies µL−1 in 12 min. This real-time monitoring platform has advantages of simple construction, steady performance, high sensitivity, and outstanding anti-pollution capability, and could replace the traditional colorimetric methods by photographing and reading values. This platform would accelerate the development of visual detection kits for colorimetric LAMP, help to explore the amplification and transcription of nucleic acids, and provide support for the prevention of emerging biological threats. © 2023
ABSTRACT
Due to the global influenza pandemic and the increasing health problems of the elderly, healthcare for the elderly has become an important application of Internet of Things(IoT) technology. However, under the condition of smart healthcare, the privacy of the elderly is not well protected. Therefore, to ensure the information security of the elderly, this paper proposes a management method for elderly chronic diseases based on the IoT security environment, establishes a security protection mechanism, and uses the IoT technology to monitor the vital signs of the elderly in real-time. At the same time, the Disease Immune Rehabilitation Algorithm (DIRA) is constructed based on the physiological data collected by the device to identify healthy people and reduce the number of infected people. Through experiments on several generations, the results show that this method can increase the proportion of the immune population to a certain extent and reduce the number of infected people.
ABSTRACT
With the development of modern technology, numerous economic losses are incurred by various spreading phenomena. Thus, it is of great significance to identify the initial sources triggering such phenomena. The investigation of source localization in social networks has gained substantial attention and become a popular topic of study. For practical spreading phenomena on social networks, the infection rates are relatively low. Hence, a high uncertainty of spreading trace might be incurred, which further incurs the reduction of localization accuracy obtained through existed source localization methods, especially the observer-based ones. Aiming to solve the source localization problem with a low infection rate, we propose a novel localization algorithm, i.e., path-based source identification (PBSI). First, a small number of nodes are selected and designated as observers. After the propagation process triggered by sources, we can obtain a snapshot. Later, a label is assigned to represent whether a node is infected or not, and observers are supposed to record the paths through which nodes are successfully infected. Based on source centrality theory, observers make the labels flow in the direction recorded during the label iteration process, which ensures the labels of nodes in the direction of the source increase gradually. Extensive experiments indicate that the proposed PBSI can handle source localization problems for both single and multi-source scenarios with better performance than that of state-of-the-art algorithms under different propagation models.
ABSTRACT
The structured data collected by the Internet of Things can be encrypted for protecting the user's privacy. Range query can be used to get the expected data with some specific attributes among the encrypted data, that is, given the upper and lower limits $(x, y)$ of a certain attribute, the range query will get all the records whose corresponding attribute values are in $(x, y)$. However, in the structured encryption scheme with range query, there is a certain amount of information leakage, which will lead to the so-called inference attacks, i.e., the attacker can obtain the user's attribute values. To hide attribute values and their relationship, this article transformed the problem of the numerical comparison between two attribute values into the problem of the intersection of two sets. By using the Bloom filter, the elements in the attribute value collection are recorded and determined whether the intersection of the two sets is empty. This ensures that our scheme effectively resists inference attacks. Besides, by multiplying the endpoints of the range interval by a large number, we gave an improved scheme to hide the user's search pattern. In the query process, our scheme will not leak the upper and lower limits of the range value and will not leak the relationship between the attribute value and the range interval. This will prevent the attacker from inducing the relationship of attribute values by multiple range queries. Finally, we conducted a simulation evaluation of the scheme by using the published novel coronavirus pneumonia data, and the results show that our scheme has a better performance than the existing schemes.
ABSTRACT
In response to the impact of the Novel coronavirus infection in 2020, State Grid Corporation has introduced 12 important measures to fully promote enterprises to resume work and production. The evaluation method based on the ratio of user's current daily electricity consumption to the average electricity consumption of December 2019 is straightforward and effective, but difficult to scientifically defining the judgment threshold. In view of the problem of lacking rationality in the evaluation of resumption of work and production, this project deeply analyzes the current enterprise resumption index, then further proposes a K-means clustering analysis method. By extracting power consumption data of enterprises, characteristic indexes reflecting their production shutdown, resumption and peak periods are established. K-means clustering algorithm is applied to cluster characteristic samples, accurately analyzing the resumption situation of enterprises consequently, which helps improve the accuracy of the evaluation on resumption of work and production and achieve significant management, economic and social benefits.
ABSTRACT
Background: Studies have shown that, Shengmai injection (Shengmai decoction) which has been included in the diagnosis and treatment of coronavirus disease 2019 (COVID-19), is effective in the early treatment of patients with severe COVID-19. However, the mechanism of its intervention in severe stage of COVID-19 at molecular level is still unclear. Therefore, it is necessary to further explore the mechanism of Shengmai decoction in the treatment of patients with severe COVID-19 based on network pharmacology. Methods: The Traditional Chinese Medicine Systems Pharmacology, BATMAN, UniProt databases and the published literatures which contain the reported compounds that have therapeutic effects on COVID-19 were used to screen out the active ingredients and targets of Shengmai decoction, and the "drug-active compound-target" network was constructed. The GeneCards database was used to screen out the targets of COVID-19. The protein-protein interaction network map was constructed by mapping two genes, and the network of active ingredients, targets and disease was constructed using Cytoscape software. Thereafter, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses of the key targets were performed using the Metascape Site Maintenance website and David databases. The Gene Ontology function and Kyoto Encyclopedia of Genes and Genomes pathway enrichment of the mapping targets were analyzed. Results: The active component-target network contained 73 main compounds and 457 targets. The first five main active components (hydroquinone, guanosine, ophiopogon C, ophiopogon B, ophiopogon D) were obtained by topological analysis of 33 common targets. Using the Matthews correlation coefficient algorithm, the key targets included albumin, tumor necrosis factor, and cyclic adenosine response element binding. Compounds in Shengmai decoction were found to have good binding effect and strong interaction with caspase-8, caspase-3, apoptosis regulator Bcl-2, tumor necrosis factor, C-C motif chemokine 3, to inhibit the inflammatory response and improve lung injury. Additionally, 31 biological processes were obtained by Gene Ontology enrichment, mainly related to lipopolysaccharide response and cytokine-mediated signal pathways, and 71 biological processes were obtained by Kyoto Encyclopedia of Genes and Genomes enrichment, mainly related to pulmonary tuberculosis, hepatitis B, and mitogen-activated protein kinase signal pathways. Conclusion: Shengmai decoction has multicomponent, multitarget and multichannel characteristics, which can provide an important theoretical basis to treat patients with severe COVID-19.