First detected in Wuhan, China in December 2019, the COVID-19 pandemic stretched the medical system in Wuhan and posed a challenge to the state's risk communication efforts. Timely access to quality ...health care information during outbreaks of infectious diseases can be effective to curtail the spread of disease and feelings of anxiety. Although existing studies have extended our knowledge about online health information-seeking behavior, processes, and motivations, rarely have the findings been applied to an outbreak. Moreover, there is relatively little recent research on how people in China are using the internet for seeking health information during a pandemic.
The aim of this study is to explore how people in China are using the internet for seeking health information during a pandemic. Drawing on previous research of online health information seeking, this study asks the following research questions: how was the "#COVID-19 Patient Seeking Help" hashtag being used by patients in Wuhan seeking health information on Weibo at the peak of the outbreak? and what kinds of health information were patients in Wuhan seeking on Weibo at the peak of the outbreak?
Using entity identification and textual analysis on 10,908 posts on Weibo, we identified 1496 patients with COVID-19 using "#COVID-19 Patient Seeking Help" and explored their online health information-seeking behavior.
The curve of the hashtag posting provided a dynamic picture of public attention to the COVID-19 pandemic. Many patients faced difficulties accessing offline health care services. In general, our findings confirmed that the internet is used by the Chinese public as an important source of health information. The lockdown policy was found to cut off the patients' social support network, preventing them from seeking help from family members. The ability to seek information and help online, especially for those with young children or older adult members during the pandemic. A high proportion of female users were seeking health information and help for their parents or for older adults at home. The most searched information included accessing medical treatment, managing self-quarantine, and offline to online support.
Overall, the findings contribute to our understanding of health information-seeking behaviors during an outbreak and highlight the importance of paying attention to the information needs of vulnerable groups and the role social media may play.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Tea green leafhopper
(
)
Matsuda is one of the most devastating pests of tea plants (
), greatly impacting tea yield and quality. A thorough understanding of the interactions between the tea green ...leafhopper and the tea plant would facilitate a better pest management. To gain more insights into the molecular and biochemical mechanisms behind their interactions, a combined analysis of the global transcriptome and metabolome reconfiguration of the tea plant challenged with tea green leafhoppers was performed for the first time, complemented with phytohormone analysis. Non-targeted metabolomics analysis by ultra-performance liquid chromatography quadrupole time-of-flight mass spectrometry (UPLC-QTOF MS), together with quantifications by ultra-performance liquid chromatography triple quadrupole mass spectrometry (UPLC-QqQ MS), revealed a marked accumulation of various flavonoid compounds and glycosidically bound volatiles but a great reduction in the level of amino acids and glutathione upon leaf herbivory. RNA-Seq data analysis showed a clear modulation of processes related to plant defense. Genes pertaining to the biosynthesis of phenylpropanoids and flavonoids, plant-pathogen interactions, and the biosynthesis of cuticle wax were significantly up-regulated. In particular, the transcript level for a
homolog involved in cuticular wax alkane formation was most drastically elevated and an increase in C29 alkane levels in tea leaf waxes was observed. The tea green leafhopper attack triggered a significant increase in salicylic acid (SA) and a minor increase in jasmonic acid (JA) in infested tea leaves. Moreover, transcription factors (TFs) constitute a large portion of differentially expressed genes, with several TFs families likely involved in SA and JA signaling being significantly induced by tea green leafhopper feeding. This study presents a valuable resource for uncovering insect-induced genes and metabolites, which can potentially be used to enhance insect resistance in tea plants.
Divorced and unwed single motherhood is heavily stigmatized in Chinese cultural context, preventing Chinese single mothers from actively seeking the information and support needed and negatively ...impacting their wellbeing. Drawing on the theory of motivated information management (TMIM), this study tested how perceived stigma and cultural norms influenced Chinese single mothers' search for information and social support from families, friends as well as from online communities. Using two-wave data collected from 226 single mothers, findings support the utility of the TMIM in explaining information management and support seeking behaviors and contribute to situating the TMIM process within larger socio-cultural contexts. Practical implications regarding how to facilitate more effective uncertainty management and enhance Chinese single mothers' wellbeing in interpersonal vs. online contexts are discussed.
The functional pool of canonical amino acids (cAAs) has been enriched through the emergence of non-canonical amino acids (ncAAs). NcAAs play a crucial role in the production of various ...pharmaceuticals. The biosynthesis of ncAAs has emerged as an alternative to traditional chemical synthesis due to its environmental friendliness and high efficiency. The breakthrough genetic code expansion (GCE) technique developed in recent years has allowed the incorporation of ncAAs into target proteins, giving them special functions and biological activities. The biosynthesis of ncAAs and their incorporation into target proteins within a single microbe has become an enticing application of such molecules. Based on that, in this study, we first review the biosynthesis methods for ncAAs and analyze the difficulties related to biosynthesis. We then summarize the GCE methods and analyze their advantages and disadvantages. Further, we review the application progress of ncAAs and anticipate the challenges and future development directions of ncAAs.
Human protein interaction prediction studies occupy an important place in systems biology. The understanding of human protein interaction networks and interactome will provide important insights into ...the regulation of developmental, physiological and pathological processes. In this study, we propose a method based on feature engineering and integrated learning algorithms to construct protein interaction prediction models. Principal Component Analysis (PCA) and Locally Linear Embedding (LLE) dimensionality reduction methods were used to extract sequence features from the 174-dimensional human protein sequence vector after Normalized Difference Sequence Feature (NDSF) encoding, respectively. The classification performance of three integrated learning methods (AdaBoost, Extratrees, XGBoost) applied to PCA and LLE features was compared, and the best combination of parameters was found using cross-validation and grid search methods. The results show that the classification accuracy is significantly higher when using the linear dimensionality reduction method PCA than the nonlinear dimensionality reduction method LLE. the classification with XGBoost achieves a model accuracy of 99.2%, which is the best performance among all models. This study suggests that NDSF combined with PCA and XGBoost may be an effective strategy for classifying different human protein interactions.
Asthma is one of the common chronic respiratory diseases in children, which poses a serious threat to children's quality of life. Respiratory infection is a risk factor for asthma. Compared with ...healthy children, children with early respiratory infections have a higher risk of asthma and an increased chance of developing severe asthma. Many clinical studies have confirmed the correlation between respiratory infections and the pathogenesis of asthma, but the underlying mechanism is still unclear. The gut microbiome is an important part of maintaining the body's immune homeostasis. The imbalance of the gut microbiome can affect the lung immune function, and then affect lung health and cause respiratory diseases. A large number of evidence supports that there is a bidirectional regulation between intestinal flora and respiratory tract infection, and both are significantly related to the development of asthma. The changes of intestinal microbial components and their metabolites in respiratory tract infection may affect the occurrence and development of asthma through the immune pathway. By summarizing the latest advancements in research, this review aims to elucidate the intricate connection between respiratory tract infections and the progression of asthma by highlighting its bridging role of the gut microbiome. Furthermore, it offers novel perspectives and ideas for future investigations into the mechanisms that underlie the relationship between respiratory tract infections and asthma.
Obesity is an important risk factor and common comorbidity of childhood asthma. Simultaneously, obesity-related asthma, a distinct asthma phenotype, has attracted significant attention owing to its ...association with more severe clinical manifestations, poorer disease control, and reduced quality of life. The establishment of the gut microbiota during early life is essential for maintaining metabolic balance and fostering the development of the immune system in children. Microbial dysbiosis influences host lipid metabolism, triggers chronic low-grade inflammation, and affects immune responses. It is intimately linked to the susceptibility to childhood obesity and asthma and plays a potentially crucial transitional role in the progression of obesity-related asthma. This review article summarizes the latest research on the interplay between asthma and obesity, with a particular focus on the mediating role of gut microbiota in the pathogenesis of obesity-related asthma. This study aims to provide valuable insight to enhance our understanding of this condition and offer preliminary evidence to support the development of therapeutic interventions.
Protein interactions are the foundation of all metabolic activities of cells, such as apoptosis, the immune response, and metabolic pathways. In order to optimize the performance of protein ...interaction prediction, a coding method based on normalized difference sequence characteristics (NDSF) of amino acid sequences is proposed. By using the positional relationships between amino acids in the sequences and the correlation characteristics between sequence pairs, NDSF is jointly encoded. Using principal component analysis (PCA) and local linear embedding (LLE) dimensionality reduction methods, the coded 174-dimensional human protein sequence vector is extracted using sequence features. This study compares the classification performance of four ensemble learning methods (AdaBoost, Extra trees, LightGBM, XGBoost) applied to PCA and LLE features. Cross-validation and grid search methods are used to find the best combination of parameters. The results show that the accuracy of NDSF is generally higher than that of the sequence matrix-based coding method (MOS) coding method, and the loss and coding time can be greatly reduced. The bar chart of feature extraction shows that the classification accuracy is significantly higher when using the linear dimensionality reduction method, PCA, compared to the nonlinear dimensionality reduction method, LLE. After classification with XGBoost, the model accuracy reaches 99.2%, which provides the best performance among all models. This study suggests that NDSF combined with PCA and XGBoost may be an effective strategy for classifying different human protein interactions.
The grinding of spiral bevel gears is a nonlinear and dynamic complex process. It is significant to explore the complex grinding mechanism of spiral bevel gears by simulating the grinding process ...based on finite element method. A finite element model of composite abrasive grinding was established based on material constitutive equation of Johnson-Cook force-thermal coupling. And the changes in the tooth surface temperature field, grinding force and abrasive wear were compared and analyzed under different grinding speeds, grinding depths and abrasive spacing condition. The simulation results were also verified by the experimental results.The results show that the grinding depth is the most important factor to affect the grinding surface performance. The influence of the compound abrasive grain interference on the tooth surface temperature field and the resulting grinding vibration cannot be ignored. It could provide theoretical guidance for the research and practical processing of grinding surface performance
Recently, graph neural networks (GNNs) have revolutionized the field of chemical property prediction and achieved state-of-the-art results on benchmark data sets. Compared with the traditional ...descriptor- and fingerprint-based QSAR models, GNNs can learn task related representations, which completely gets rid of the rules defined by experts. However, due to the lack of useful prior knowledge, the prediction performance and interpretability of the GNNs may be affected. In this study, we introduced a new GNN model called RG-MPNN for chemical property prediction that integrated pharmacophore information hierarchically into message-passing neural network (MPNN) architecture, specifically, in the way of pharmacophore-based reduced-graph (RG) pooling. RG-MPNN absorbed not only the information of atoms and bonds from the atom-level message-passing phase, but also the information of pharmacophores from the RG-level message-passing phase. Our experimental results on eleven benchmark and ten kinase data sets showed that our model consistently matched or outperformed other existing GNN models. Furthermore, we demonstrated that applying pharmacophore-based RG pooling to MPNN architecture can generally help GNN models improve the predictive power. The cluster analysis of RG-MPNN representations and the importance analysis of pharmacophore nodes will help chemists gain insights for hit discovery and lead optimization.
Graphical Abstract
PDF
