Development and validation of Parents' Health Literacy Scale on Preventing Road Traffic Injuries for children aged 0-6 years in China.

BACKGROUND: Road traffic injuries are the leading cause of death in children. Parents' health literacy (HL) is closely related to child safety, especially for the young children. This study aimed to develop an intervention-oriented parent HL scale: Parents' Health Literacy Scale on Preventing Road Traffic Injuries (PHLS-PRTIs). METHODS: We developed the scale including four parts: (1) definition and conceptualisation of PHLS-PRTIs; (2) domain and item development; (3) instrument construction; and (4) psychometric property validation. 685 parents participated in process of development (n=333) and validation (n=352). Cronbach's α and test-retest correlation were used to assess reliability. Content validity and construct validity were used to assess validity. RESULTS: The scale consisted of five domains (access, understand, belief, communication and use) and 16 subdomains. The Cronbach's α for each domain and the whole scale was 0.85, 0.70, 0.87, 0.80, 0.79 and 0.85, respectively. The test-retest reliability was acceptable (intraclass correlations >0.70). Content validity was good (item-level content validity index >0.79, average of the scale-level content validity index >0.80, kappa >0.74). For construct validity for domain understand, χ2/df=1.723, p=0.009, standardised root mean square residual (SRMR)=0.0404, root mean square error of approximation (RMSEA)=0.045, Comparative Fit Index (CFI)=0.942, Tucker-Lewis Index (TLI)=0.910; and for the other four domains, χ2/df=1.840, p<0.001, SRMR=0.043, RMSEA=0.049, CFI=0.958, TLI=0.952. CONCLUSIONS: PHLS-PRTIs was developed and validated by a rigorous process, providing a tool for community doctors to measure parents' HL on child road traffic safety and develop targeted health education interventions.

Graph explicit pooling for graph-level representation learning.

Graph pooling has been increasingly recognized as crucial for Graph Neural Networks (GNNs) to facilitate hierarchical graph representation learning. Existing graph pooling methods commonly consist of two stages: selecting top-ranked nodes and discarding the remaining to construct coarsened graph representations. However, this paper highlights two key issues with these methods: (1) The process of selecting nodes to discard frequently employs additional Graph Convolutional Networks or Multilayer Perceptrons, lacking a thorough evaluation of each node's impact on the final graph representation and subsequent prediction tasks. (2) Current graph pooling methods tend to directly discard the noise segment (dropped) of the graph without accounting for the latent information contained within these elements. To address the first issue, we introduce a novel Graph explicit Pooling (GrePool) method, which selects nodes by explicitly leveraging the relationships between the nodes and final representation vectors crucial for classification. The second issue is addressed using an extended version of GrePool (i.e., GrePool+), which applies a uniform loss on the discarded nodes. This addition is designed to augment the training process and improve classification accuracy. Furthermore, we conduct comprehensive experiments across 12 widely used datasets to validate our proposed method's effectiveness, including the Open Graph Benchmark datasets. Our experimental results uniformly demonstrate that GrePool outperforms 14 baseline methods for most datasets. Likewise, implementing GrePool+ enhances GrePool's performance without incurring additional computational costs. The code is available at https://github.com/LiuChuang0059/GrePool.

Molecular characteristics of sea spray aerosols during aging with the participation of marine volatile organic compounds.

Sea spray aerosols (SSAs) are one of the largest natural sources of aerosols globally, known to affect the earth's radiation budget and to play a pivotal role in air quality and climate. The physical and chemical properties of organic components in SSA change during long-distance atmospheric transport over the ocean. To characterize the evolution of organic components during the aging process of SSA, in this study, we use a flow reactor to simulate the oxidation processes of SSA produced by authentic seawater via OH radicals (in the presence of organic gases evaporated from seawater) and to present the molecular signatures of the nascent and aged SSA. We found, under our experimental conditions, that oxidation of headspace organic gases during aging leads to significant formation of new particles and changes in the chemical constituents of SSA. In the nascent and aged SSA samples, we retained 129 and 340 products, respectively. The formation of high O/C and low carbon-number products was observed during the aging process, corresponding to functionalization and fragmentation reactions. Moreover, the significant contributions of compounds containing multiple nitrogen atoms and sulfate groups were observed in aged SSA for the first time, which can be attributed to the accretion reaction driven by OH heterogeneous oxidation and the formation of organic sulfur compounds, respectively. These findings provide additional insights into the atmospheric transformation of organic components in marine aerosols, which is important for understanding the global carbon cycle.

Copper-induced oxidative stress inhibits asexual reproduction of Aurelia coerulea polyps.

OBJECTIVE: Our research aims to investigate the specific mechanisms by which copper inhibits the asexual proliferation of Aurelia coerulea polyps. METHODS: Aurelia coerulea polyps were exposed to various CuSO4 concentrations to study metamorphosis and budding proliferation. Oxidative stress markers (ROS, MDA, CAT, H2O2, T-AOC, SOD) were measured in polyps and early strobilae. Transcriptomic analysis were used to compare differences in gene expression and enrichment pathways between untreated and copper-exposed polyps. Additionally, RT-qPCR was used to analyze the expression of key molecules. Antioxidant L-Ascorbic acid was applied to determine the role of oxidative stress in asexual reproduction of Aurelia coerulea polyps when exposed to copper. RESULTS: Copper inhibited strobilization and budding of Aurelia coerulea polyps in a dose-dependent manner, in which oxidative stress was involved. Transcriptomic data suggested that the DNA replication pathway was significantly enriched in early strobilae compared to polyps. However, copper treatment repealed the difference of DNA replication pathway between early strobilae compared and polyps. Transcriptomic data suggested that alanine, aspartate, and glutamate metabolism pathways were enriched in untreated budding polyps compared to copper-exposed polyps. After applying the antioxidant L-Ascorbic acid to copper-exposed polyps, various oxidative indicators changed to different extents, with increases in ROS, MDA, CAT, H2O2, and SOD and a decrease in T-AOC. Further more, the time required for polyps to develop into early strobila was shortened, indicating that the delay in metamorphosis caused by copper exposure was effectively alleviated. And the budding rate increased, indicating that the inhibition of budding proliferation caused by copper exposure was effectively alleviated. The expression of key genes were consist with the transcriptomic sequencing results. CONCLUSION: Copper exposure causes oxidative stress resulting in the inhibition of asexual reproduction in Aurelia coerulea polyps, including metamorphosis and budding.

Finding core labels for maximizing generalization of graph neural networks.

Graph neural networks (GNNs) have become a popular approach for semi-supervised graph representation learning. GNNs research has generally focused on improving methodological details, whereas less attention has been paid to exploring the importance of labeling the data. However, for semi-supervised learning, the quality of training data is vital. In this paper, we first introduce and elaborate on the problem of training data selection for GNNs. More specifically, focusing on node classification, we aim to select representative nodes from a graph used to train GNNs to achieve the best performance. To solve this problem, we are inspired by the popular lottery ticket hypothesis, typically used for sparse architectures, and we propose the following subset hypothesis for graph data: "There exists a core subset when selecting a fixed-size dataset from the dense training dataset, that can represent the properties of the dataset, and GNNs trained on this core subset can achieve a better graph representation". Equipped with this subset hypothesis, we present an efficient algorithm to identify the core data in the graph for GNNs. Extensive experiments demonstrate that the selected data (as a training set) can obtain performance improvements across various datasets and GNNs architectures.

[Cloning and functional characterization of the pinoresinol-lariciresinol reductase gene IiPLR2 in Isatis indigotica].

The pinoresinol-lariciresinol reductase (PLR), a crucial enzyme in the biosynthesis of lignans in plants, catalyzes a two-step reaction to produce lariciresinol and secoisolariciresinol. Lignans such as lariciresinol are the effective components of traditional Chinese medicine Radix Isatidis in exerting antiviral activity. In order to study the function of the key enzyme PLR in the biosynthesis of lariciresinol in Isatis indigotica, the original plant of Radix Isatidis, IiPLR2 was cloned from I. indigotica, with a full length of 954 bp, encoding 317 amino acids. Multiple sequence alignment showed that IiPLR2 contained a conserved nicotinamide adenine dinucleotide phosphate (NADPH)-binding motif. The phylogenetic tree showcased that IiPLR2 shared the same clade with AtPrR1 from Arabidopsis thaliana. The prokaryotic expression vector pET32a-IiPLR2 was constructed and then transformed into Escherichia coli BL21(DE3) competent cells for protein expression. The purified enzyme IiPLR2 could catalyze the conversion of pinoresinol to lariciresinol and the conversion of lariciresinol to secoisolariciresinol. The cloning, sequencing, and catalytic functional analysis of IiPLR2 in this study enrich the understanding of this kind of functional proteins in I. indigotica and supplement the biosynthesis pathways of lignans. Moreover, this study provides a functional module for further research on metabolic regulation and synthetic biology and lays a foundation for comprehensively revealing the relationship between the spatial structures and catalytic functions of such proteins.

Spherical Assembly of Halloysite Clay Nanotubes as a General Reservoir of Hydrophobic Pesticides for pH-Responsive Management of Pests and Weeds.

The development of smart systems for pesticidal delivery presents a significant advancement in enhancing the utilization efficiency of pesticides and mitigating environmental risks. Here an acid-responsive pesticidal delivery system using microspheres formed by the self-assembly of halloysite clay nanotubes (HNTs) is proposed. Insecticide avermectin (AVM) and herbicide prometryn (PMT) are used as two models of hydrophobic pesticide and encapsulated within the porous microspheres, followed by a coating of tannic acid/iron (TA/FeIII) complex films to generate two controlled-release pesticides, named as HCEAT and HCEPT, resulting in the loading capacity of AVM and PMT being 113.3 and 120.3 mg g-1, respectively. Both HCEAT and HCEPT exhibit responsiveness to weak acid, achieving 24 h-release ratios of 85.8% and 80.5% at a pH of 5.5. The experiment and simulation results indicate that the coordination interaction between EDTA2- and Ca2+ facilitates the spherical aggregation of HNTs. Furthermore, these novel pesticide formulations demonstrate better resistance against ultraviolet (UV) irradiation, higher foliar affinity, and less leaching effect, with negligible impact of the carrier material on plants and terrestrial organisms. This work presents a promising approach toward the development of efficient and eco-friendly pesticide formulations, greatly contributing to the sustainable advancement of agriculture.

Exploring sparsity in graph transformers.

Graph Transformers (GTs) have achieved impressive results on various graph-related tasks. However, the huge computational cost of GTs hinders their deployment and application, especially in resource-constrained environments. Therefore, in this paper, we explore the feasibility of sparsifying GTs, a significant yet under-explored topic. We first discuss the redundancy of GTs based on the characteristics of existing GT models, and then propose a comprehensive Graph Transformer SParsification (GTSP) framework that helps to reduce the computational complexity of GTs from four dimensions: the input graph data, attention heads, model layers, and model weights. Specifically, GTSP designs differentiable masks for each individual compressible component, enabling effective end-to-end pruning. We examine our GTSP through extensive experiments on prominent GTs, including GraphTrans, Graphormer, and GraphGPS. The experimental results demonstrate that GTSP effectively reduces computational costs, with only marginal decreases in accuracy or, in some instances, even improvements. For example, GTSP results in a 30% reduction in Floating Point Operations while contributing to a 1.8% increase in Area Under the Curve accuracy on the OGBG-HIV dataset. Furthermore, we provide several insights on the characteristics of attention heads and the behavior of attention mechanisms, all of which have immense potential to inspire future research endeavors in this domain. Our code is available at https://github.com/LiuChuang0059/GTSP.

Effect of pinoresinol-lariciresinol reductases on biosynthesis of lignans with substrate selectivity in Schisandra chinensis.

Schisandra lignans are the main bioactive compounds found in Schisandra chinensis fruits, such as schisandrol lignans and schisandrin lignans, which play important roles in organ protection or other clinical roles. Pinoresinol-lariciresinol reductase (PLR) plays a pivotal role in plant lignan biosynthesis, however, limited research has been conducted on S. chinensis PLR to date. This study identified five genes as ScPLR, successfully cloned their coding sequences, and elucidated their catalytic capabilities. ScPLR3-5 could recognize both pinoresinol and lariciresinol as substrates, and convert them into lariciresinol and secoisolariciresinol, respectively, while ScPLR2 exclusively catalyzed the conversion of (+)-pinoresinol into (+)-lariciresinol. Transcript-metabolite correlation analysis indicated that ScPLR2 exhibited unique properties that differed from the other members. Molecular docking and site-directed mutagenesis revealed that Phe271 and Leu40 in the substrate binding motif were crucial for the catalytic activity of ScPLR2. This study serves as a foundation for understanding the essential enzymes involved in schisandra lignan biosynthesis.

Graph structure reforming framework enhanced by commute time distance for graph classification.

As a graph data mining task, graph classification has high academic value and wide practical application. Among them, the graph neural network-based method is one of the mainstream methods. Most graph neural networks (GNNs) follow the message passing paradigm and can be called Message Passing Neural Networks (MPNNs), achieving good results in structural data-related tasks. However, it has also been reported that these methods suffer from over-squashing and limited expressive power. In recent years, many works have proposed different solutions to these problems separately, but none has yet considered these shortcomings in a comprehensive way. After considering these several aspects comprehensively, we identify two specific defects: information loss caused by local information aggregation, and an inability to capture higher-order structures. To solve these issues, we propose a plug-and-play framework based on Commute Time Distance (CTD), in which information is propagated in commute time distance neighborhoods. By considering both local and global graph connections, the commute time distance between two nodes is evaluated with reference to the path length and the number of paths in the whole graph. Moreover, the proposed framework CTD-MPNNs (Commute Time Distance-based Message Passing Neural Networks) can capture higher-order structural information by utilizing commute paths to enhance the expressive power of GNNs. Thus, our proposed framework can propagate and aggregate messages from defined important neighbors and model more powerful GNNs. We conduct extensive experiments using various real-world graph classification benchmarks. The experimental performance demonstrates the effectiveness of our framework. Codes are released on https://github.com/Haldate-Yu/CTD-MPNNs.

Vemurafenib inhibits necroptosis in normal and pathological conditions as a RIPK1 antagonist.

Necroptosis, a programmed cell death with necrotic-like morphology, has been recognized as an important driver in various inflammatory diseases. Inhibition of necroptosis has shown potential promise in the therapy of multiple human diseases. However, very few necroptosis inhibitors are available for clinical use as yet. Here, we identified an FDA-approved anti-cancer drug, Vemurafenib, as a potent inhibitor of necroptosis. Through direct binding, Vemurafenib blocked the kinase activity of receptor-interacting protein kinases 1 (RIPK1), impeded the downstream signaling and necrosome complex assembly, and inhibited necroptosis. Compared with Necrostain-1, Vemurafenib stabilized RIPK1 in an inactive DLG-out conformation by occupying a distinct allosteric hydrophobic pocket. Furthermore, pretreatment with Vemurafenib provided strong protection against necroptosis-associated diseases in vivo. Altogether, our results demonstrate that Vemurafenib is an effective RIPK1 antagonist and provide rationale and preclinical evidence for the potential application of approved drug in necroptosis-related diseases.

Comprehensive Graph Gradual Pruning for Sparse Training in Graph Neural Networks.

Graph neural networks (GNNs) tend to suffer from high computation costs due to the exponentially increasing scale of graph data and a large number of model parameters, which restricts their utility in practical applications. To this end, some recent works focus on sparsifying GNNs (including graph structures and model parameters) with the lottery ticket hypothesis (LTH) to reduce inference costs while maintaining performance levels. However, the LTH-based methods suffer from two major drawbacks: 1) they require exhaustive and iterative training of dense models, resulting in an extremely large training computation cost, and 2) they only trim graph structures and model parameters but ignore the node feature dimension, where vast redundancy exists. To overcome the above limitations, we propose a comprehensive graph gradual pruning framework termed CGP. This is achieved by designing a during-training graph pruning paradigm to dynamically prune GNNs within one training process. Unlike LTH-based methods, the proposed CGP approach requires no retraining, which significantly reduces the computation costs. Furthermore, we design a cosparsifying strategy to comprehensively trim all the three core elements of GNNs: graph structures, node features, and model parameters. Next, to refine the pruning operation, we introduce a regrowth process into our CGP framework, to reestablish the pruned but important connections. The proposed CGP is evaluated over a node classification task across six GNN architectures, including shallow models graph convolutional network (GCN) and graph attention network (GAT), shallow-but-deep-propagation models simple graph convolution (SGC) and approximate personalized propagation of neural predictions (APPNP), and deep models GCN via initial residual and identity mapping (GCNII) and residual GCN (ResGCN), on a total of 14 real-world graph datasets, including large-scale graph datasets from the challenging Open Graph Benchmark (OGB). Experiments reveal that the proposed strategy greatly improves both training and inference efficiency while matching or even exceeding the accuracy of the existing methods.

Glutathione metabolism is conserved in response to excessive copper exposure between mice liver and Aurelia coerulea polyps.

BACKGROUND: Copper (Cu) is a trace element that is engaged in various routine physiological processes. Excessive copper exposure can cause damage to organisms; however, it is unknown if the mechanisms underlying the response to Cu2+ among different species are conserved. METHODS: Aurelia coerulea polyps and mice models were exposed to Cu2+ to assess its effects on survival status and organ damage. Transcriptomic sequencing, BLAST, structural analysis, and real-time quantitative PCR were carried out to analyze the similarities and differences in the molecular composition and response mechanisms between two species when exposed to Cu2+. RESULTS: Excessive Cu2+ exposure led to toxic effects on both A. coerulea polyps and mice. The polyps were injured at a Cu2+ concentration of 3.0 mg L-1. In the mice, increasing Cu2+ concentrations were correlated with, the degree of liver damage, which manifested as hepatocyte apoptosis. In the 300 mg L-1 Cu2+ group of mice, livers cell death was primarily triggered by the phagosome and Toll-like signaling pathways. We found the glutathione metabolism was significantly altered in response to copper stress in both A. coerulea polyps and mice. Moreover, the similarity of gene sequences enriched at the two same sites in this pathway was as high as 41.05 %-49.82 % and 43.61 %-45.99 % respectively. Among them, there was a conservative region in the structure of A. coerulea polyps GSTK1 and mice Gsta2, but the overall difference is large. CONCLUSION: Glutathione metabolism is a conserved copper response mechanism in evolutionary distant organisms such as A. coerulea polyps and mice, although mammals have a more complex regulatory network when it comes to copper-induced cell death.

Identification and investigation of a novel NADP+-dependent secoisolariciresinol dehydrogenase from Isatis indigotica.

Cofactors are crucial for the biosynthesis of natural compounds, and cofactor engineering is a useful strategy for enzyme optimization due to its potential to enhance enzyme efficiency. Secoisolariciresinol dehydrogenase (SIRD) was reported to convert secoisolariciresinol into matairesinol in an NAD+-dependent reaction. Here, a SIRD designated as IiSIRD2 identified from Isatis indigotica was found to utilize NADP+ as the cofactor. To explore the structural basis for this unique cofactor preference, model-based structural analysis was carried out, and it was postulated that a variation at the GXGGXG glycine-rich motif of IiSIRD2 alters its cofactor preference. This study paves way for future investigations on SIRD cofactor specificity and cofactor engineering to improve SIRD's catalytic efficiency.

Effectiveness of WeChat-group-based parental health education in preventing unintentional injuries among children aged 0-3: randomized controlled trial in Shanghai.

BACKGROUND: Unintentional injuries to children are a major public health problem. The online social media is a potential way to implement health education for caregivers in online communities. Using WeChat, a free and popular social media service in China, this study evaluated the effectiveness of social online community-based parental health education in preventing unintentional injuries in children aged 0-3. METHODS: We recruited 365 parents from two community health centers in Shanghai and allocated them into intervention and control groups randomly. Follow-up lasted for one year. The intervention group received and followed their WeChat group and a WeChat official account for dissemination of reliable medical information. The control group received only the WeChat group. RESULTS: Between the intervention and control groups, changes in unintentional injuries (OR = 1.71, 95% CI: 1.02-2.87, P = .04), preventability (ß = 0.344, 95% CI: 0.152-0.537, P < .001), daily supervision behavior (ß = 0.503, 95% CI: 0.036-0.970, P = .04), and behaviors for preventing specific injuries (ß = 2.198, 95% CI: 1.530-2.865, P < .001) were significantly different, and change in first-aid skills for treating a tracheal foreign body were nearly significant (P = .06). CONCLUSIONS: The WeChat-group-based parental health education can reduce the occurrence of unintentional child injuries by improving parents' skills, beliefs, and behaviors. Online social communities promote health education and reduce unintentional injuries among children. TRIAL REGISTRATION: ChiCTR1900020753. Registered on January 17, 2019.

A combined protein toxin screening based on the transcriptome and proteome of Solenopsis invicta.

BACKGROUND: Multi-omics technology provides a good tool to analyze the protein toxin composition and search for the potential pathogenic factors of Solenopsis invicta, under the great harm of the accelerated invasion in southern China. METHODS: Species collection, functional annotation, toxin screening, and 3D modeling construction of three interested toxins were performed based on the successfully constructed transcriptome and proteome of S. invicta. RESULTS: A total of 33,231 unigenes and 721 proteins were obtained from the constructed transcriptome and proteome, of which 9,842 (29.62%) and 4,844 (14.58%) unigenes, as well as 469 (65.05%) and 71 (99.45%) proteins were annotated against the databases of Gene Ontology and Kyoto Encyclopedia of Genes and Genomes, respectively. After comparing with the uniprot toxin database, a total of 316 unigenes and 47 proteins (calglandulin, venom allergen 3, and venom prothrombin activator hopsarin-D, etc.) were successfully screened. CONCLUSIONS: The update of annotations at the transcriptome and proteome levels presents a progression in the comprehension of S. invicta in China. We also provide a protein toxin list that could be used for further exploration of toxicity as well as its antagonistic strategy by S. invicta.

The medusa of Aurelia coerulea is similar to its polyp in molecular composition and different from the medusa of Stomolophus meleagris in toxicity.

BACKGROUND: The incidents of Aurelia sp. stinging have recently increased because of a bloom in offshore area. However, their symptoms are much milder than those from another scyphozoan jellyfish, Stomolophus meleagris. METHODS: The molecular composition of the medusa and polyp of Aurelia coerulea was analyzed by sequencing the transcriptome and proteome. The toxicity of tentacle extract from A. coerulea medusa (A-TE) and S. meleagris medusa (S-TE) was measured by the survival rates of mice, their blood indexes, and integrity of red blood cells. RESULTS: The medusa and polyp of A. coerulea are similar in molecular composition, while their gene expressions are significantly different at both transcriptome and proteome levels. A-TE displayed no in vitro hemolysis and caused mild damage to the liver, heart and kidney instead of lethality. In contrast, S-TE showed strong hemolytic toxicity, and lethal effect with serious damage to the liver, heart and kidney. The toxin screening in the medusae showed that there were similar toxin categories though the number of toxin species in A. coerulea was larger than that in S. meleagris. Among them, lactotransferrin and venom prothrombin activator were the two predominant protein toxins in the medusae of A. coerulea and S. meleagris, respectively. CONCLUSIONS: A. coerulea medusa and polyp have similar molecular compositions, though there are observable morphological differences. The toxicity of A. coerulea medusa is significantly weaker than that of S. meleagris medusa of which the variation in toxin expressions is feasibly an important reason.

Using online social networks to provide a parental health-education intervention for preventing unintentional injuries among children aged 0-3 years: A randomized controlled trial and social network analysis in Shanghai, China.

Introduction: Unintentional injury among children represents a major public health problem. Online-social-network-based parental-health-education is a potential way to reduce child unintentional injuries. The study aimed to explore the mechanisms by which online-social-network-based health education may reduce the unintentional injuries among children aged 0-3 years. Methods: We conducted a participant-blinded, randomized controlled, online-social-network-based health-education intervention study from March 2019 to February 2020 in Shanghai. We established four WeChat groups (two intervention groups and two control groups). For the intervention groups, a doctor's assistant regularly delivered information regarding unintentional injuries among children, and community childcare doctors answered parents' questions concerning their children's health, including unintentional injuries. Meanwhile, the control groups did not receive any information from the assistant. The study selected one intervention group and one control group and compared the ego network and whole network indicators to determine the differences between the intervention and control groups. Results: In the intervention and control groups, 64.5% and 31.9% of the members, respectively, engaged in communication, and 1,736 and 273 records, respectively, were obtained. Regarding ego network, the doctor showed the largest network in the intervention group, and the size of the intervention group's network was twice that of the control group; the number of ties in the intervention group was nine times that of the control group. Fourteen and four parents in the intervention and control group played an active role, respectively. Regarding centrality, all WeChat groups were loose and multiple centers existed. Regarding subgroup cohesion, the intervention group had 28 cliques with 27 members, and the control group had 4 cliques with 4 members. For structural hole, 23.7% and 7.5% members in the intervention and control group actively participated in interactions, respectively, having strong control and influence over other parents; 69.2% and 59.1% members in the intervention and control group, respectively, had values of < 1.000, showing that they had strong ability to cross-jump structural holes. Discussion: Online-social-networks-based health education interventions could enhance communication among parents, and between parents and community childcare doctors, and also shorten the social distance between them. Thus, online-social-network-based parental-health-education-intervention can be a feasible and generalizable means of preventing unintentional injuries among children.

Cadmium accelerates bacterial oleic acid production to promote fat accumulation in Caenorhabditis elegans.

Environmental cadmium, with a high dietary intake and long biological half-life, is a severe health risk by harming physiological function directly or through gut microbiota. However, the toxicity mechanisms of environmental cadmium on microbes and host systems remain unclear. Herein, we established three C. elegans and E. coli cultivated systems to investigate the vital role of microorganisms in cadmium-induced lipid toxicity and depict the interaction between environmental cadmium, bacteria, and the host. We found that only nematodes in the system with live bacteria, rather than UV-killed bacteria or no bacteria, could be induced to fat accumulation by cadmium exposure, suggesting that bacteria mediated the effect of environmental cadmium on body fat. Cadmium caused perturbation of metabolite in bacteria, most notably oleic acid, elevated the synthesis genes expression, and enhanced the bacterial oleic acid production, which further promoted the expression of lipid metabolism-related genes and fat deposition in C. elegans regardless of the cultivated system. Finally, we showed the potential protective effect of Vitamin D3 which prevented cadmium- or oleic acid-induced fat storage significantly. In conclusion, this study illustrates the mechanism underlying cadmium-induced lipid accumulation in body through bacterial metabolites and reveals the interplay between environmental cadmium, microorganisms, and the host.

Parents' attitudes as mediators between knowledge and behaviours in unintentional injuries at home of children aged 0-3 in Shanghai, Eastern China: a cross-sectional study.

OBJECTIVE: Parental behaviours are important in preventing unintentional injury at home among young children. Previous research showed an inconsistent relationship between knowledge and behaviours, indicating that the mechanisms may vary for different behaviours. This study aimed to examine the mediating roles of different attitudes in the mechanism of knowledge acting on different behaviours. DESIGN: Cross-sectional study. SETTING: Eastern China. PARTICIPANTS: Participants were recruited using stratified community-based sampling. A total of 488 parents of children aged 0-3 years participated in the study and 476 (97.5%) valid questionnaires were recovered. PRIMARY OUTCOME MEASURES: Parents' knowledge, attitudes (including injury attribution, preventability and responsibility) and behaviours (including supervision behaviours, risky behaviours and providing a safe home environment). RESULTS: The results of mediation analysis showed that the mediator variables were different for different behaviours and that all associations were positive. Parents' knowledge (ß 0.19, 95% CI 0.13 to 0.24) and attitude of injury attribution (ß 0.37, 95% CI 0.21 to 0.46) were directly associated with risky behaviours. Attitude of preventability was directly associated with parents' supervision behaviour (ß 0.27, 95% CI 0.14 to 0.40). Parents' attitude of preventability mediated the positive association between knowledge, attitudes of injury attribution and responsibility, and supervision behaviours, as well as providing a safe home environment. In addition, the occurrence of child injuries at home was directly associated with home environment (ß -0.41, 95% CI -0.82 to -0.01). CONCLUSIONS: The current findings confirm that attitudes play varying mediating roles between knowledge and different behaviours. An important recommendation is that parents' attitudes, especially towards preventability and responsibility, need to be considered when health providers develop health education programmes targeted at improving parental supervision behaviours and providing a safe home environment.