Search

Abstract Background Seasonal influenza and other respiratory tract infections are serious public health problems that need to be further addressed and investigated. Internet search data are recognized as a valuable source for forecasting influenza or other respiratory tract infection epidemics. However, the selection of internet search data and the application of forecasting methods are important for improving forecasting accuracy. The aim of the present study was to forecast influenza epidemics based on the long short-term memory neural network (LSTM) method, Baidu search index data, and the influenza-like-illness (ILI) rate. Methods The official weekly ILI% data for northern and southern mainland China were obtained from the Chinese Influenza Center from 2018 to 2021. Based on the Baidu Index, search indices related to influenza infection over the corresponding time period were obtained. Pearson correlation analysis was performed to explore the association between influenza-related search queries and the ILI% of southern and northern mainland China. The LSTM model was used to forecast the influenza epidemic within the same week and at lags of 1–4 weeks. The model performance was assessed by evaluation metrics, including the mean square error (MSE), root mean square error (RMSE) and mean absolute error (MAE). Results In total, 24 search queries in northern mainland China and 7 search queries in southern mainland China were found to be correlated and were used to construct the LSTM model, which included the same week and a lag of 1–4 weeks. The LSTM model showed that ILI% + mask with one lag week and ILI% + influenza name were good prediction modules, with reduced RMSE predictions of 16.75% and 4.20%, respectively, compared with the estimated ILI% for northern and southern mainland China. Conclusions The results illuminate the feasibility of using an internet search index as a complementary data source for influenza forecasting and the efficiency of using the LSTM model to forecast influenza epidemics.

Hsin-Hung Chen,1 Wen-Chia Huang,2 Wen-Hsuan Chiang,2 Te-I Liu,2 Ming-Yin Shen,2,3 Yuan-Hung Hsu,4 Sung-Chyr Lin,1 Hsin-Cheng Chiu2 1Department of Chemical Engineering, National Chung Hsing University, Taichung, 2Department of Biomedical Engineering and Environmental Sciences, National Tsing Hua University, 3Department of Surgery, National Taiwan University Hospital-Hsinchu Branch, 4Pharmaceutical Optimization Technology Division, Biomedical Technology and Device Research Laboratory, Industrial Technology Research Institute, Hsinchu, Taiwan Abstract: In this study, a novel pH-responsive cholesterol-PEG adduct-coated solid lipid nanoparticles (C-PEG-SLNs) carrying doxorubicin (DOX) capable of overcoming multidrug resistance (MDR) breast cancer cells is presented. The DOX-loaded SLNs have a mean hydrodynamic diameter of ~100 nm and a low polydispersity index (under 0.20) with a high drug-loading efficiency ranging from 80.8% to 90.6%. The in vitro drug release profiles show that the DOX-loaded SLNs exhibit a pH-controlled drug release behavior with the maximum and minimum unloading percentages of 63.4% at pH 4.7 and 25.2% at pH 7.4, respectively. The DOX-loaded C-PEG-SLNs displayed a superior ability in inhibiting the proliferation of MCF-7/MDR cells. At a DOX concentration of 80 µM, the cell viabilities treated with C-PEG-SLNs were approximately one-third of the group treated with free DOX. The inhibition activity of C-PEG-SLNs could be attributed to the transport of C-PEG to cell membrane, leading to the change of the composition of the cell membrane and thus the inhibition of permeability glycoprotein activity. This hypothesis is supported by the confocal images showing the accumulation of DOX in the nuclei of cancer cells and the localization of C-PEG on the cell membranes. The results of in vivo study further demonstrated that the DOX delivered by the SLNs accumulates predominantly in tumor via enhanced permeability and retention effect, the enhanced passive tumor accumulation due to the loose intercellular junctions of endothelial cells lining inside blood vessels at tumor site, and the lack of lymphatic drainage. The growth of MCF-7/MDR xenografted tumor on Balb/c nude mice was inhibited to ~400 mm3 in volume as compared with the free DOX treatment group, 1,140 mm3, and the group treated with 1,2 distearoyl-sn-glycero-3-phosphoethanolamine-N-[methoxy(polyethylene glycol)] solid lipid nanoparticles, 820 mm3. Analysis of the body weight of nude mice and the histology of organs and tumor after the administration of DOX-loaded SLNs show that the SLNs have no observable side effects. These results indicate that the C-PEG-SLN is a promising platform for the delivery of therapeutic agents for MDR cancer chemotherapy. Keywords: pH-responsive, solid lipid nanoparticles, multidrug resistance, permeability glycoprotein

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) Omicron subvariants raises concerns regarding the effectiveness of immunity acquired from previous Omicron subvariants breakthrough infections (BTIs) or reinfections (RIs) against the current circulating Omicron subvariants. In this study, we prospectively investigate the dynamic changes of virus‐specific antibody and T cell responses among 77 adolescents following Omicron BA.2.3 BTI with or without subsequent Omicron BA.5 RI. Notably, the neutralizing antibodies (NAbs) titers against various detected SARS‐CoV‐2 variants, especially the emerging Omicron CH.1.1, XBB.1.5, XBB.1.16, EG.5.1, and JN.1 subvariants, exhibited a significant decrease along the time. A lower level of IgG and NAbs titers post‐BTI was found to be closely associated with subsequent RI. Elevated NAbs levels and shortened antigenic distances were observed following Omicron BA.5 RI. Robust T cell responses against both Omicron BA.2‐ and CH.1.1‐spike peptides were observed at each point visited. The exposure to Omicron BA.5 promoted phenotypic differentiation of virus‐specific memory T cells, even among the non‐seroconversion adolescents. Therefore, updated vaccines are needed to provide effective protection against newly emerging SARS‐CoV‐2 variants among adolescents. [ABSTRACT FROM AUTHOR]

After the termination of zero‐COVID‐19 policy, the populace in China has experienced both Omicron BA.5 and XBB waves. Considering the poor antibody responses and severe outcomes observed among the elderly following infection, we conducted a longitudinal investigation to examine the epidemiological characteristics and antibody kinetics among 107 boosted elderly participants following the Omicron BA.5 and XBB waves. We observed that 96 participants (89.7%) were infected with Omicron BA.5, while 59 (55.1%) participants were infected with Omicron XBB. Notably, 52 participants (48.6%) experienced dual infections of both Omicron BA.5 and XBB. The proportion of symptomatic cases appeared to decrease following the XBB wave (18.6%) compared to that after the BA.5 wave (59.3%). Omicron BA.5 breakthrough infection induced lower neutralizing antibody titers against XBB.1.5, BA.2.86, and JN.1, while reinfection with Omicron XBB broadened the antibody responses against all measured Omicron subvariants and may alleviate the wild type‐vaccination induced immune imprinting. Boosted vaccination type and comorbidities were the significant factors associated with antibody responses. Updated vaccines based on emerging severe acute respiratory syndrome coronavirus 2 variants are needed to control the Coronavirus Disease 2019 pandemic in the elderly. [ABSTRACT FROM AUTHOR]

Colorectal cancer (CRC) is a complex malignancy, influenced not only by cancer cells but also by the tumor microenvironment (TME). Within the TME, emerging evidence highlights the presence and functional roles of diverse microbial entities, referred to as intratumoral microbiota. The distribution of these microbiota exhibits significant heterogeneity and engages in dynamic interactions with tumor cells, forming a unique ecosystem. Certain bacterial strains distinctly influence the TME of CRC, affecting the characteristics and progression of the tumor. This review summarizes the intricate roles of intratumoral microbiota within CRC's TME, emphasizing their importance in the disease's development and progression, and discuss the opportunities and challenges in the field. [ABSTRACT FROM AUTHOR]

Epicoccum latusicollum is a fungus that causes a severe foliar disease on fluecured tobacco in southwest China, resulting in significant losses in tobacco yield and quality. To better understand the organism, researchers investigated its optimal growth conditions and metabolic versatility using a combination of traditional methods and the Biolog Phenotype MicroArray technique. The study found that E. latusicollum exhibited impressive metabolic versatility, being able to metabolize a majority of carbon, nitrogen, sulfur, and phosphorus sources tested, as well as adapt to different environmental conditions, including broad pH ranges and various osmolytes. The optimal medium for mycelial growth was alkyl ester agar medium, while oatmeal agar medium was optimal for sporulation, and the optimum temperature for mycelial growth was 25°C. The lethal temperature was 40°C. The study also identified arbutin and amygdalin as optimal carbon sources and Ala-Asp and Ala-Glu as optimal nitrogen sources for E. latusicollum. Furthermore, the genome of E. latusicollum strain T41 was sequenced using Illumina HiSeq and Pacific Biosciences technologies, with 10,821 genes predicted using Nonredundant, Gene Ontology, Clusters of Orthologous Groups, Kyoto Encyclopedia of Genes and Genomes, and SWISS-PROT databases. Analysis of the metabolic functions of phyllosphere microorganisms on diseased tobacco leaves affected by E. latusicollum using the Biolog Eco microplate revealed an inability to efficiently metabolize a total of 29 carbon sources, with only tween 40 showing some metabolizing ability. The study provides new insights into the structure and function of phyllosphere microbiota and highlights important challenges for future research, as well as a theoretical basis for the integrated control and breeding for disease resistance of tobacco Epicoccus leaf spot. This information can be useful in developing new strategies for disease control and management, as well as enhancing crop productivity and quality. [ABSTRACT FROM AUTHOR]

In recent years, teachers' teaching anxiety has been widely concerned, while the teaching anxiety of trainee teachers has been rarely studied. Based on Spinoza's affection theory, this study takes semi-structured interview to collect data, and dynamically analyzes the emotional performance and the formation mechanism of trainee teachers' teaching anxiety in combination with programmed root theory and Nvivo11 Plus software. It is found that the teaching anxiety experienced by trainee teachers in teaching practice is mainly manifested as "fear" and "self-shame", which is the intuitive response of trainee teachers stimulated by the external environment and the change of conceptual power. Further tracing, it reveals that the root cause of trainee teachers' teaching anxiety lies in the asymmetric and unbalanced relationship between individuals and the environment, that is, trainee teachers are often in a relatively weak position in the complex and changeable educational environment, and this asymmetry exacerbates their inner anxiety. Therefore, the trainee teachers should enrich themselves, especially with the fulfillment of concepts that trainee teachers should face up to the inevitability of anxiety, replacing simple imagination with the knowledge of necessity; analyze the generationof anxiety, substituting fuzzy cognition with clear idea; lock the transformation node of anxiety, transcending the single perspective with multiple thinking; formulate the guiding creed of anxiety, turning the imagination sequenceinto rational order; seek the absolute restraint of anxiety, pursuing the ultimate love of education.

As the information technology develops, educational resource recommendation system has become an indispensable part of the education field. At the same time, people’s demand for personalised educational resources is also growing; therefore, how to accurately predict user demand and provide personalised recommendations has become an important issue. In this study, a fuzzy predictive control model on the grounds of neural network is proposed to optimise the design of educational resource recommendation in the context of information technology. After experimental testing, the model recommendation fit reached 95.16 and 92.91% on the two test datasets, which are significantly higher than the control model. The average F1 values of the proposed model also reached 95.21 and 88.77%, which are higher than the control model. In other control experiments, the proposed model of this study also has a better performance. The relevant outcomes showcase that the predictive performance and recommendation effect of the model can be further enhanced by improving the structure of the neural network and the parameter optimisation method. Meanwhile, the proposed model has high performance in information overload and personalised demand, which offers a useful reference for the optimal design of educational resource recommendation system.

Background Diabetes has a high incidence rate and many complications, which was an urgent public health problem, but the prevention and management is still not ideal. Objective To observe the effect of Three Division management on metabolic indicators and self-management levels of diabetes patients with different courses of disease. Methods One hundred and 81 type 2 diabetes patients from April 2021 to April 2022 were managed by the mode of Three Division management and followed up for more than 6 months in Shenzhen Hospital Guangzhou University of Chinese Medicine (Futian). The patients were grouped according to the course of disease (Group A: course of disease5-10 years; Group D: course of disease>10 years) ; In outpatient, the three-division team composed of traditional Chinese medicine practitioners, endocrinologists and caregivers carries out comprehensive treatment such as physique identification, complication screening, medicine, food, exercises and emotion intervention; When patients are at home, the caregivers carry out intelligent management through intelligent Glucose meter equipment and mobile phone APP. We collected changes in blood glucose and body weight indicators in patients with different disease courses after six months of management. Additionally, we used a questionnaire to gather common issues in patient self-management and fill out the Summary of Diabetes Self-Care Activities Measure-6 (SDSCA-6) . Results Through the management of the Three Division management, Group A patients showed significant improvement in reducing fasting blood glucose and postprandial 2-hour blood glucose after the intervention (P

The B4C-stainless steel composites with B4C mass fraction of 4.00% were prepared by spark plasma sintering (SPS) method, using B4C microspheres and 304 stainless steel powders as the raw materials. The density of the composites was tested by the drainage method, the microstructures of B4C and stainless steels were analyzed by scanning electron microscope (SEM), and the effects of sintering temperature and pressure on the density and microstructure of the composites were investigated. The results show that, the thickness of the interfacial reaction layer increases with the increase of sintering temperature. Meanwhile, increasing the sintering pressure can significantly reduce the sintering temperature, which inhibits the growth of the interfacial reaction layer between B4C and stainless steels. When the pressure reaches 150 MPa, the relative density of the composites can reach 99.5% after sintering at 950 ℃ for 6 min, and the thickness of the interfacial reaction layer is less than 5 μm.

Maritime Power has gradually increased as a national strategy. In this process, gross marine products continue to grow, and the marine industry has become the most fundamental and critical object. The spatial layout and industrial organization of maritime enterprises are fundamental related tasks. Domestic research can be divided into two main categories, based on the data used. One is to use economic and social statistical data, which have a large spatial scope but large granularity and cannot reflect the details of the industrial layout. The other is to use point-of-interest data, which are often not fully mined owing to the heavy workload of data processing. Therefore, there is little relevant content on departmental and urban comparisons in the existing research. Four representative cities-Dalian, Qingdao, Ningbo, and Xiamen-were selected as the research areas. According to the Industrial Classification for Ocean Industries and Their Related Activities, the research objects were identified as the marine core layer, marine support layer, and marine peripheral layer industries and further refined into eight subcategories. This study is based on the information of maritime enterprises registered for business registration, and uses Python to crawl geographic coordinates to improve the spatial information of enterprises. An innovative task is to identify the industry categories of enterprises. This task was performed using fastText, Convolutional Neural Networks, and Recurrent Neural Network. Thus, a spatial enterprise information database, including multiple marine industry departments, was established. Kernel density analysis, standard deviational ellipse analysis, buffer analysis, and other methods were used. Finally, by comparing the visualization results of the marine industrial spatial layout in the four cities, we delved into the marine industrial spatial differentiation patterns. In the experiment, machine learning models, such as artificial neural networks, exhibited high accuracy and recall when completing human recognition tasks, and these methods were effective. Empirical research on the spatial layout and industrial organization of maritime enterprises revealed the following: 1)From the perspective of spatial pattern, the overall pattern is a balanced pattern of "large dispersion and small agglomeration." By comparing the distribution and organization of different types of marine industries, we found that there is industry agglomeration in the location selection of enterprises, resulting in industry agglomeration characteristics. The land sea relationship is reflected in the high-density single peak or "coastal zone-city center" Multimodal distribution pattern. 2) From the perspective of spatial organization mode, industrial clusters have multilevel hierarchical characteristics corresponding to population size and administrative levels. In addition to single core structures, multi core structures generally exhibit a "primary-secondary dual core" or "primary core-multiple radial" pattern, with spatial connections between core intervals forming a multi node axis or network structure. 3) From the perspective of spatial matching relationships, the elliptical area is positively related to the urban area, the direction of the long axis is close to the coastal direction, and the industrial distribution has a clear matching relationship with the urban center, ports, and other transportation hubs, bay terrain, coastline, and other spatial elements.

The computational power network was aimed to deeply integrate arithmetic resources and network resources, achieving efficient utilization of massive data and heterogeneous resources in ubiquitous discrete deployment. To cope with the complicated multicenter computing collaboration and requirements for data privacy and security in computational power networks, federated learning was recognized for its inherent advantages in privacy protection and edge arithmetic resource utilization. However, federated learning in the computational power network environment faced certain difficulties due to the highly heterogeneous and widely distributed arithmetic edge servers. On one hand, the heterogeneity of data among the massive edge servers in the computational power network caused the Non-IID problem, leading to the deviation of the local model update from the global optimum in federated learning. On the other hand, the generation of low-quality local models could significantly affect the training effect due to the differences in data quality among different edge servers. To solve the above problems, a blockchain-based adaptive federated learning framework AWFL-BC (adaptive weight federated learning-blockchain) was proposed. Initially, the data distribution distances of different edge servers were calculated through smart contracts to generate a similarity matrix that guided aggregation. Concurrently, an adaptive weight aggregation algorithm was designed to alleviate the decrease in model performance and stability caused by differences in data quality, thereby improving the accuracy of the model and accelerating model convergence. Finally, the integration of blockchain technology strengthened the security mechanism, effectively preventing poisoning attacks and inference attacks. Comprehensive experiments on three public standard datasets show that AWFL-BC achieves higher model accuracy and faster model convergence compared to state-of-the-art methods.