Organoid-Based Assessment of Metal-Organic Framework (MOF) Nanomedicines for Ex Vivo Cancer Therapy.

Nanomaterials have been extensively exploited in tumor treatment, leading to numerous innovative strategies for cancer therapy. While nanomedicines present immense potential, their application in cancer therapy is characterized by significant complexity and unpredictability, especially regarding biocompatibility and anticancer efficiency. These considerations underscore the essential need for the development of ex vivo research models, which provide invaluable insights and understanding into the biosafety and efficacy of nanomedicines in oncology. Fortunately, the emergence of organoid technology offers a novel approach to the preclinical evaluation of the anticancer efficacy of nanomedicines in vitro. Hence, in this study, we constructed intestine and hepatocyte organoid models (Intestine-orgs and Hep-orgs) for assessing intestinal and hepatic toxicity at the microtissue level. We utilized three typical metal-organic frameworks (MOFs), ZIF-8, ZIF-67, and MIL-125, as nanomedicines to further detect their interactions with organoids. Subsequently, the MIL-125 with biocompatibility loaded methotrexate (MTX), forming the nanomedicine (MIL-125-PEG-MTX), indicated a high loading efficiency (82%) and a well-release capability in an acid microenvironment. More importantly, the anticancer effect of the nanomedicine was investigated using an in vitro patient-derived organoids (PDOs) model, achieving inhibition rates of 48% and 78% for PDO-1 and PDO-2, respectively, demonstrating that PDOs could predict clinical response and facilitate prospective therapeutic selection. These achievements presented great potential for organoid-based ex vivo models for nano theragnostic evaluation in biosafety and function.

Formative assessment in K-12 English as a foreign language education: A systematic review.

The surge of globalisation and the recognition of English as a lingua franca have propelled the worldwide advancement of English as a foreign language (EFL) education. Formative assessment can support the English language teaching and learning in classroom settings. This systematic review critically analyses recent research on formative assessment within K-12 EFL education, underscoring its substantial role in enhancing teaching efficacy and student outcomes. Specifically, it addresses three key aspects: the beliefs, perceptions and literacy of teachers and students regarding formative assessment, the effectiveness of different assessment practices on learners' performance and the contextual challenges in implementing these strategies. A comprehensive analysis of qualitative and quantitative studies reveals that the attitudes of teachers and students considerably influence the effectiveness of formative assessment. Innovative methods, such as technology-assisted and peer assessments, show promise in enhancing learner outcomes. However, the education system and cultural contexts present persistent challenges in implementing formative assessment in EFL classes. The findings highlight the importance of ensuring professional training for educators and raising student awareness regarding the benefits of formative assessment. This study emphasises the need for aligning adaptable assessment practices with diverse educational environments and calls for a closer integration of theoretical and practical approaches in EFL education. Moreover, this review provides valuable insights into formative assessment strategies in EFL learning, offering guidance for educators, policymakers and stakeholders in language education.

Lamb wave-based damage assessment for composite laminates using a deep learning approach.

With the increasing utilization of composite materials due to their superior properties, the need for efficient structural health monitoring techniques rises rapidly to ensure the integrity and reliability of composite structures. Deep learning approaches have great potential applications for Lamb wave-based damage detection. However, it remains challenging to quantitatively detect and characterize damage such as delamination in multi-layered structures. These deep learning architectures still lack a certain degree of physical interpretability. In this study, a convolutional sparse coding-based UNet (CSCUNet) is proposed for ultrasonic Lamb wave-based damage assessment in composite laminates. A low-resolution image is generated using delay-and-sum algorithm based on Lamb waves acquired by transducer array. The encoder-decoder framework in the proposed CSCUNet enables the transformation of low-resolution input image to high-resolution damage image. In addition, the multi-layer convolutional sparse coding block is introduced into encoder of the CSCUNet to improve both performance and interpretability of the model. The proposed method is tested on both numerical and experimental data acquired on the surface of composite specimen. The results demonstrate its effectiveness in identifying the delamination location, size, and shape. The network has powerful feature extraction capability and enhanced interpretability, enabling high-resolution imaging and contour evaluation of composite material damage.

Clinical Assessment of Magnetic Resonance Spectroscopy and Diffusion-Weighted Imaging in Diffuse Glioma: Insights Into Histological Grading and IDH Classification.

PURPOSE: To assess the diagnostic utility of clinical magnetic resonance spectroscopy (MRS) and diffusion-weighted imaging (DWI) in distinguishing between histological grading and isocitrate dehydrogenase (IDH) classification in adult diffuse gliomas. METHODS: A retrospective analysis was conducted on 247 patients diagnosed with adult diffuse glioma. Experienced radiologists evaluated DWI and MRS images. The Kruskal-Wallis test examined differences in DWI and MRS-related parameters across histological grades, while the Mann-Whitney U test assessed molecular classification. Receiver Operating Characteristic (ROC) curves evaluated parameter effectiveness. Survival curves, stratified by histological grade and IDH classification, were constructed using the Kaplan-Meier test. RESULTS: The cohort comprised 141 males and 106 females, with ages ranging from 19 to 85 years. The Kruskal-Wallis test revealed significant differences in ADC mean, Cho/NAA, and Cho/Cr concerning glioma histological grade (P < .01). Subsequent application of Dunn's test showed significant differences in ADC mean among each histological grade (P < .01). Notably, Cho/NAA exhibited a marked distinction between grade 2 and grade 3/4 gliomas (P < .01). The Mann-Whitney U test indicated that only ADC mean showed statistical significance for IDH molecular classification (P < .01). ROC curves were constructed to demonstrate the effectiveness of the specified parameters. Survival curves were also delineated to portray survival outcomes categorized by histological grade and IDH classification. Conclusions: Clinical MRS demonstrates efficacy in glioma histological grading but faces challenges in IDH classification. Clinical DWI's ADC mean parameter shows significant distinctions in both histological grade and IDH classification.

Measurement and spatial correlation analysis of high-quality development Level: A case study of the Yangtze River Delta urban agglomeration in China.

Against the backdrop of slowing economic growth and increasing environmental pressure, the Yangtze River Delta city cluster, as one of the largest city clusters in the world, has become more driven in its pursuit of high-quality development. We constructed a system of 24 evaluation indexes and used entropy-weighted TOPSIS to calculate and study the high-quality development index of urban agglomerations in the region. First, the level of high quality development (HQD) of the Yangtze River Delta city cluster generally improved from 2010 to 2021, with 2017 was the best year, while 2010 was the worst year. Second, in the multidimensional evaluation of HQD, Jiangsu excels in innovation and people's livelihood with 0.524 and 0.534, respectively; Shanghai (0.531) excels in coordinated development; Zhejiang excels in green and economic development with 0.557 and 0.484, respectively; and Anhui lags behind in all aspects. Third, the development process of HQD in the Yangtze River Delta region is uneven, and the level of HQD development varies greatly among the city clusters in the province. The measurement results show that Shanghai (0.511) has the highest score, followed by Zhejiang (0.484), Jiangsu (0.440) and Anhui (0.435). Fourth, spatial correlation analysis shows that Shanghai and Jiangsu are mainly distributed in the double-high region, Zhejiang is distributed in the high-low region, while Anhui is concentrated in the low-low region. The results of this study help us understand more deeply the characteristics and challenges of high-quality development in the Yangtze River Delta urban agglomerations and provide a scientific basis for more precise urban development policies.

A platform for microplastic assessment in aquatic environments based on the protein corona-induced aggregation effect.

The environmental hazards of microplastics have received widespread attention. However, the in-situ detection of microplastics, particularly in aquatic environments, has been challenged by the limitations of detection methods, the large-scale instruments, and small size. Herein, a photoelectrochemical sensor based on the protein corona-induced aggregation effect is designed for the detection of polystyrene microplastics. The sensor has advantages of high sensitivity, reproducibility, and detection capability. A linear detection range of 0.5-500 µg mL-1, a method detection limit of 0.06 µg mL-1, and a limit of quantification of 0.14 µg mL-1 are achieved. Furthermore, the relative standard deviations of intra-day and inter-day precision, ranging from 0.56% to 4.63% and 0.84%-3.36% are obtained. A digital multimeter was employed to construct a platform for the real-time detection in real water samples, streamlining the detection process and yielding clear results. We believe this sensor provides new insight for the in-situ real-time detection of microplastics and has broad applications for the analysis of microplastic pollution in aquatic environments.

Trends in gender disparity in the field of Helicobacter pylori research from 2000 to 2020: A cross-sectional study.

Many studies have investigated gender disparity in scientific publications, but this has been poorly studied in the field of digestive diseases. This study aimed to determine the gender difference of first and senior authors in publications related to Helicobacter pylori (H. pylori) during the past 20 years. Data were derived from original articles published in the main journals of digestive diseases (Journal of Hepatology, Gut, Gastroenterology, American Journal of Gastroenterology, Endoscopy, Gastrointestinal Endoscopy, Alimentary Pharmacology and Therapeutics, Digestive Endoscopy, Journal of Gastroenterology, Helicobacter, and Gastric Cancer) in 2000, 2005, 2010, 2015, and 2020. These original articles were classified according to the gender and nationality of the first and senior (last listed) authors. Linear-by-linear association test was used to analyze the proportion of women authors over time. Multivariable logistic regression was applied to explain the factors impacting authorship difference of first and senior authors. A total of 561 original articles on H. pylori were collected for this study, accounting for 10.70% in 2000 to 7.60% in 2020 among all articles. In these original articles, the percentage of women first authors increased from 14.60% in 2000 to 45.0% in 2020 (P < .001). The percentage of women senior authors increased from 5.60% in 2000 to 18.80% in 2020 (P < .001). Women first authors were more likely to perform research with women senior authors (18.42%) than with men senior authors (10.23%, P < .001). The proportion of women first authors from Oceania were higher than that from North and South America (P = .004), whereas there was no statistical difference regarding women senior authors. In the past 2 decades, although the percentage of women authors among both first and senior authors in the field of H. pylori research has increased significantly, women are still a minority in original research.

Individualized Assessment of Brain Aß Deposition With fMRI Using Deep Learning.

PET-based Alzheimer's disease (AD) assessment has many limitations in large-scale screening. Non-invasive techniques such as resting-state functional magnetic resonance imaging (rs-fMRI) have been proven valuable in early AD diagnosis. This study investigated feasibility of using rs-fMRI, especially functional connectivity (FC), for individualized assessment of brain amyloid-ß deposition derived from PET. We designed a graph convolutional networks (GCNs) and random forest (RF) based integrated framework for using rs-fMRI-derived multi-level FC networks to predict amyloid-ß PET patterns with the OASIS-3 (N = 258) and ADNI-2 (N = 291) datasets. Our method achieved satisfactory accuracy not only in Aß-PET grade classification (for negative, intermediate, and positive grades, with accuracy in the three-class classification as 62.8% and 64.3% on two datasets, respectively), but also in prediction of whole-brain region-level Aß-PET standard uptake value ratios (SUVRs) (with the mean square errors as 0.039 and 0.074 for two datasets, respectively). Model interpretability examination also revealed the contributive role of the limbic network. This study demonstrated high feasibility and reproducibility of using low-cost, more accessible magnetic resonance imaging (MRI) to approximate PET-based diagnosis.

Factors influencing the satisfaction of community senior care services in China: an analysis based on an extended Anderson model.

Community-based senior care, as a convenient and promising care model, has gradually been accepted by the public. However, community services developed to facilitate older adults often fail to achieve the expected effect. With the fast-growing aging population in China, the problems of underutilization and low service satisfaction of senior care facilities need to be resolved urgently. In this study, we further developed an extended Anderson behavior model by incorporating social psychological factors, and the vertical and horizontal fairness perceptions. In addition, a binary logistic regression model was used to analyze factors affecting the satisfaction of older adults in life care services, health care services, and mental and spiritual comfort services. The study used data from a survey of 322 urban area seniors in Shaanxi Province. The results showed that factors influencing older adults' satisfaction of different service categories are different. Moreover, with the addition of the social psychological factors, we observed that the vertical fairness perception of the survey respondents affected their satisfaction of senior care services significantly more than the horizontal fairness perception.

Assessment of genetic diversity and population structure of Neocaridina denticulata sinensis in the Baiyangdian drainage area, China, using microsatellite markers and mitochondrial cox1 gene sequences.

Neocaridina denticulata sinensis is a crustacean of major economic significance in the Baiyangdian drainage area. In this study, the first assessment of N. denticulata sinensis genetic diversity and population structure was performed based on sequence analysis of nine polymorphic microsatellite loci and the mitochondrial cytochrome oxidase subunit I (cox1) gene. Samples (n = 192) were collected from four different regions in the Baiyangdian drainage area i.e., Baiyangdian Lake, Jumahe River, Xidayang Reservoir, and Fuhe River. Microsatellite loci analysis identified high levels of genetic diversity represented by observed heterozygosity (Ho) of 0.6865 âˆ¼ 0.9583, expected heterozygosity (He) of 0.7151 âˆ¼ 0.8723, and polymorphism information content (PIC) of 0.6676 âˆ¼ 0.8585. Based on the analysis of cox1 sequences, haplotype diversity (Hd) ranged from 0.568 to 0.853 while nucleotide diversity (π) ranged from 0.0029 to 0.2236. Furthermore, there was no evidence of expansion events in the N. denticulata sinensis populations. Pairwise FST revealed pronounced genetic differentiation, and clustering analyses showed defined genetic structures within the N. denticulata sinensis population. Three groups were identified from four sampled stocks, with Xidayang Reservoir, and Fuhe River populations clustered in the same group. This work identified novel molecular markers and provided an important reference to guide management strategies to assist conservation of N. denticulata sinensis resources.

Quantitative parameters in novel spectral computed tomography: Assessment of Ki-67 expression in patients with gastric adenocarcinoma.

BACKGROUND: The level of Ki-67 expression has served as a prognostic factor in gastric cancer. The quantitative parameters based on the novel dual-layer spectral detector computed tomography (DLSDCT) in discriminating the Ki-67 expression status are unclear. AIM: To investigate the diagnostic ability of DLSDCT-derived parameters for Ki-67 expression status in gastric carcinoma (GC). METHODS: Dual-phase enhanced abdominal DLSDCT was performed preoperatively in 108 patients with gastric adenocarcinoma. Primary tumor monoenergetic CT attenuation value at 40-100 kilo electron volt (kev), the slope of the spectral curve (λHU), iodine concentration (IC), normalized IC (nIC), effective atomic number (Zeff) and normalized Zeff (nZeff) in the arterial phase (AP) and venous phase (VP) were retrospectively compared between patients with low and high Ki-67 expression in gastric adenocarcinoma. Spearman's correlation coefficient was used to analyze the association between the above parameters and Ki-67 expression status. Receiver operating characteristic (ROC) curve analysis was performed to compare the diagnostic efficacy of the statistically significant parameters between two groups. RESULTS: Thirty-seven and 71 patients were classified as having low and high Ki-67 expression, respectively. CT40 kev-VP, CT70 kev-VP, CT100 kev-VP, and Zeff-related parameters were significantly higher, but IC-related parameters were lower in the group with low Ki-67 expression status than the group with high Ki-67 expression status, and other analyzed parameters showed no statistical difference between the two groups. Spearman's correlation analysis showed that CT40 kev-VP, CT70 kev-VP, CT100 kev-VP, Zeff, and nZeff exhibited a negative correlation with Ki-67 status, whereas IC and nIC had positive correlation with Ki-67 status. The ROC analysis demonstrated that the multi-variable model of spectral parameters performed well in identifying the Ki-67 status [area under the curve (AUC) = 0.967; sensitivity 95.77%; specificity 91.89%)]. Nevertheless, the differentiating capabilities of single-variable model were moderate (AUC value 0.630 - 0.835). In addition, the nZeff VP and nICVP (AUC 0.835 and 0.805) showed better performance than CT40 kev-VP, CT70 kev-VP and CT100 kev-VP (AUC 0.630, 0.631 and 0.662) in discriminating the Ki-67 status. CONCLUSION: Quantitative spectral parameters are feasible to distinguish low and high Ki-67 expression in gastric adenocarcinoma. Zeff and IC may be useful parameters for evaluating the Ki-67 expression.

Water quality assessment and pollution source apportionment using multivariate statistical and PMF receptor modeling techniques in a sub-watershed of the upper Yangtze River, Southwest China.

Rapid industrial and agricultural development as well as urbanization affect the water environment significantly, especially in sub-watersheds where the contaminants/constituents present in the pollution sources are complex, and the flow is unstable. Water quality assessment and quantitative identification of pollution sources are the primary prerequisites for improving water management and quality. In this work, 168 water samples were collected from seven stations throughout 2018-2019 along the Laixi River, a vital pollution control unit in the upper reaches of the Yangtze River. Multivariate statistics and positive matrix factorization (PMF) receptor modeling techniques were used to evaluate the characteristics of the river-water quality and reveal the pollution sources. Principal component analysis was employed to screen the crucial parameters and establish an optimized water quality assessment procedure to reduce the analysis cost and improve the assessment efficiency. Cluster analysis further illustrates the spatiotemporal distribution characteristics of river-water quality. Results indicated that high-pollution areas are concentrated in the tributaries, and the high-pollution periods are the spring and winter, which verifies the reliability of the evaluation system. The PMF model identified five and six potential pollution sources in the cold and warm seasons, respectively. Among them, pollution from agricultural activities and domestic wastewater shows the highest contributions (33.2% and 30.3%, respectively) during the cold and warm seasons, respectively. The study can provide theoretical support for pollutant control and water quality improvement in the sub-watershed, avoiding the ecological and health risks caused by the deterioration of water quality.

Research Progress on Amide Proton Transfer Imaging in Preoperative and Postoperative Glioma Assessment.

Amide proton transfer (APT) imaging is a technique that reflects the level of amide proton transport of hydrogen ions in water to peptides and proteins within the tumour by using magnetic resonance imaging without contrast administration. This technique can more accurately reflect the protein level, pH value, and other relevant information within the tumour. Currently, APT is widely used in glioma research. In this paper, we briefly describe the research progress on this technique.

Health-Related Quality of Life Among Chinese Pregnant Women in the Third Trimester Based on Theory of Unpleasant Symptoms: A Complex Network Analysis.

Health-related quality of life (HRQOL) is the result of complex interactions between many different factors. But few studies to date have explored the structure of HRQOL. This study aimed to investigate the complex inter-relationship between HRQOL and influencing factors using network analysis. In all, 624 Chinese pregnant women in third trimester were recruited through recruiting sampling. We used regularized network analysis to create a complex network. The relationship with mother-in-law was the most central node followed by relationship with partner. We found the comorbidity of anxiety and depressive symptoms using network analysis. Physical- and mental-related quality of life were a "bridge node" connecting psychological factors with physiologic factors. The present network analysis highlights the strong link between relationship with mother-in-law or partner and HRQOL, which is also a unique phenomenon under Chinese culture. This analysis provides key variables for future intervention or improvement of pregnant women' HRQOL.

Water quality assessment and pollution source apportionment using multivariate statistical techniques: a case study of the Laixi River Basin, China.

Identifying potential sources of pollution in tributaries and determining their contribution rates are critical to the treatment of water pollution in main streams. In this paper, we conducted a multivariate statistical analysis on the water quality data of 12 parameters for 3 years (2018-2020) at six sampling sites in the Laixi River to qualitatively identify potential pollution sources and quantitatively calculate the contribution rates to reveal the tributaries' pollution status. Spatio-temporal cluster analysis (CA) divided 12 months into two parts, corresponding to the lightly polluted season (LPS) and highly polluted season (HPS), and six sampling sites were divided into two regions, corresponding to the lightly polluted region (LPR) and highly polluted region (HPR). Principal component analysis (PCA) was used to determine the potential sources of contamination, identifying four and three potential factors in the LPS and HPS, respectively. The absolute principal component score-multiple linear regression (APCS-MLR) receptor model quantitatively analyzed the contribution rates of identified pollution sources, and the importance of the different pollution sources in LPS can be ranked as domestic sewage and industrial wastewater and breeding pollution (33.80%) > soil weathering (29.02%) > agricultural activities (20.95%) > natural influence (13.03%). HPS can be classified as agricultural cultivation (41.23%), domestic sewage and industrial wastewater and animal waste (33.19%), and natural variations (21.43%). Four potential sources were identified in LPR ranked as rural domestic sewage (31.01%) > agricultural pollution (26.82%) > industrial effluents and free-range livestock and poultry pollution (25.13%) > natural influence (14.82%). Three identified latent pollution sources in HPR were municipal sewage and industrial effluents (37.96%) > agricultural nonpoint sources and livestock and poultry wastewater (33.55%) > natural sources (25.23%). Using multivariate statistical tools to identify and quantify potential pollution sources, managers may be able to enhance water quality in tributary watersheds and develop future management plans.

Ecological risk assessment and identification of the distinct microbial groups in heavy metal-polluted river sediments.

To assess the health of river ecosystems, it is essential to quantify the ecological risk of heavy metals in river sediments and the structure of microbial communities. As important tributaries of the Tuo River in the upper reaches of the Yangtze River, the Mianyuan River and the Shiting River, are closely related to the economic development and human daily life in the region. This study assessed the ecological risks of heavy-metal-polluted river sediments, the heavy-metal-driven bacterial communities were revealed, and the relationships between the ecological risks and the identical bacterial communities were discussed. The Cd content was significantly greater than the environmental background value, leading to a serious pollution and very high ecological risk at the confluence of the two rivers and the upper reaches of the Mianyuan River. Microbial community analysis showed that Rhodobacter, Nocardioides, Sphingomonas, and Pseudarthrobacter were the dominant bacterial genera in the sediments of the Shiting River. However, the dominant bacterial genera in the Mianyuan River were Kouleothrix, Dechloromonas, Gaiella, Pedomicrobium, and Hyphomicrobium. Mantel test results showed (r = 0.5977, P = 0.005) that the Cd, As, Zn, Pb, Cr, and Cu were important factors that influenced differences in the distribution of sediment bacterial communities Mianyuan and Shiting rivers. A correlation heatmap showed that heavy metals were negatively correlated for most bacterial communities, but some bacterial communities were tolerant and showed a positive correlation. Overall, the microbial structure of the river sediments showed a diverse spatial distribution due to the influence of heavy metals. The results will improve the understanding of rivers contaminated by heavy metals and provide theoretical support for conservation and in situ ecological restoration of river ecosystems.

Exploring spatiotemporal pattern in the association between short-term exposure to fine particulate matter and COVID-19 incidence in the continental United States: a Leroux-conditional-autoregression-based strategy.

Background: Numerous studies have demonstrated that fine particulate matter (PM2.5) is adversely associated with COVID-19 incidence. However, few studies have explored the spatiotemporal heterogeneity in this association, which is critical for developing cost-effective pollution-related policies for a specific location and epidemic stage, as well as, understanding the temporal change of association between PM2.5 and an emerging infectious disease like COVID-19. Methods: The outcome was state-level daily COVID-19 cases in 49 native United States between April 1, 2020 and December 31, 2021. The exposure variable was the moving average of PM2.5 with a lag range of 0-14 days. A latest proposed strategy was used to investigate the spatial distribution of PM2.5-COVID-19 association in state level. First, generalized additive models were independently constructed for each state to obtain the rough association estimations, which then were smoothed using a Leroux-prior-based conditional autoregression. Finally, a modified time-varying approach was used to analyze the temporal change of association and explore the potential causes spatiotemporal heterogeneity. Results: In all states, a positive association between PM2.5 and COVID-19 incidence was observed. Nearly one-third of these states, mainly located in the northeastern and middle-northern United States, exhibited statistically significant. On average, a 1 µg/m3 increase in PM2.5 concentration led to an increase in COVID-19 incidence by 0.92% (95%CI: 0.63-1.23%). A U-shaped temporal change of association was examined, with the strongest association occurring in the end of 2021 and the weakest association occurring in September 1, 2020 and July 1, 2021. Vaccination rate was identified as a significant cause for the association heterogeneity, with a stronger association occurring at a higher vaccination rate. Conclusion: Short-term exposure to PM2.5 and COVID-19 incidence presented positive association in the United States, which exhibited a significant spatiotemporal heterogeneity with strong association in the eastern and middle regions and with a U-shaped temporal change.

Dispersion and Disparity: Bibliometric and Visualized Analysis of Research on Climate Change Science Communication.

Research on climate change science communication began in the 1980s and is showing continued vitality and a wider interest at present. In order to track the development of global research on the communication of climate change hot topics and frontier progress since the 21st century, methods such as bibliometrics and co-word network analysis were used to analyze the publication of research papers in this field, and a total of 1175 valid papers published in 2000-2021 in the WOS core database were counted. Different dimensions such as temporal trend, spatial distribution, and author collaboration network were analyzed. The results show that, (1) climate change communication research has become a relatively independent research field and has entered a rapid development stage, and this field still has a broad research prospect in the new understanding of climate change and new international context. (2) At present, research in this field is still dominated by developed countries, but developing countries are actively building their unique climate communication discourse. (3) Public understanding and media information presentation have been hot topics in climate communication research in recent years. In the context of changing international situations and the development of global epidemics and new climate policies, changes in national actions will likely lead to new research topics and dialogues. Research shows that climate change science communication research is increasingly showing a trend of decentralization and differentiation.

Evaluation Study on the Use of Non-Contact Prevention and Protection Products in the Context of COVID-19: A Comprehensive Evaluation Method from AHP and Entropy Weight Method.

In the post-epidemic era, there is an endless supply of epidemic prevention products that cover a wide range of public areas. The introduction of such products has eased the tense pattern of virus proliferation in the context of the epidemic, and effectively demonstrated the initiatives implemented by the Chinese people in response to the outbreak. This paper therefore begins with the study of contactless epidemic prevention products, which appear in a form that meets the needs of contemporary society and offers a new mode of living to it. It enriches the measures for epidemic prevention and control. By obtaining satisfaction ratings from the user community, the performance of such products can be understood in time to provide a substantial basis for the subsequent upgrading and optimization or transformation of such products. This study uses the KJ method and questionnaires to construct an index system for contactless epidemic prevention products, grasp users' needs for epidemic prevention products in real time, classify and identify such products, and select such products as epidemic prevention smart security gates, medical delivery robots, infrared handheld thermometers, thermographic body temperature screening, contactless inductive lift buttons, and contactless medical vending machines. The questionnaire was designed with four dimensions: safety, intelligence, aesthetics and economy. A sample size of 262 was collected through the distribution of questionnaires. We used AHP and entropy weighting methods for the comprehensive evaluation; AHP basically tells us how satisfied most users are with this type of product. The use of the entropy weighting method can achieve objectivity in the weighting process. Combining the two approaches helps to improve the scientific nature of the weighting of the evaluation indexes for contactless and epidemic-proof products. It is clear from the AHP analysis that, firstly, there are differences in the perceptions of the performance of this type of product between different age groups. Secondly, the user group rated the perceived performance of the product presented as high (Bn>0.200), which users can subjectively and directly perceive. Next, the perceived future sustainable economic development of this product category is low (Bn≤0.200), and users place low importance on its economic aspects as an objective additional condition. The entropy method of analysis shows that, under reasonable government control of the market for intelligent products, the safety, intelligence and aesthetic effects of these products are significant (Cm≤0.100); further, the economic presentation of these products has yet to be optimized and upgraded (Cm>0.100).