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BACKGROUND: Numerous studies have linked fine particulate matter (PM2.5) to increased cardiovascular mortality. Less is known how the PM2.5-cardiovascular mortality association varies by use of cardiovascular medications. This study sought to quantify effect modification by statin use status on the associations between long-term exposure to PM2.5 and mortality from any cardiovascular cause, coronary heart disease (CHD), and stroke. METHODS: In this nested case-control study, we followed 1.2 million community-dwelling adults aged ≥66 years who lived in Ontario, Canada from 2000 through 2018. Cases were patients who died from the three causes. Each case was individually matched to up to 30 randomly selected controls using incidence density sampling. Conditional logistic regression models were used to estimate odds ratios (ORs) for the associations between PM2.5 and mortality. We evaluated the presence of effect modification considering both multiplicative (ratio of ORs) and additive scales (the relative excess risk due to interaction, RERI). RESULTS: Exposure to PM2.5 increased the risks for cardiovascular, CHD, and stroke mortality. For all three causes of death, compared with statin users, stronger PM2.5-mortality associations were observed among non-users [e.g. for cardiovascular mortality corresponding to each interquartile range increase in PM2.5, OR = 1.042 (95% CI, 1.032-1.053) vs OR = 1.009 (95% CI, 0.996-1.022) in users, ratio of ORs = 1.033 (95% CI, 1.019-1.047), RERI = 0.039 (95% CI, 0.025-0.050)]. Among users, partially adherent users exhibited a higher risk of PM2.5-associated mortality than fully adherent users. CONCLUSIONS: The associations of chronic exposure to PM2.5 with cardiovascular and CHD mortality were stronger among statin non-users compared to users.

BackgroundSocial media is a powerful tool for the dissemination of health messages. However, few studies have focused on the factors that improve the influence of health messages on social media. ObjectiveTo explore the influence of goal-framing effects, information organizing, and the use of pictures or videos in health-promoting messages, we conducted a case study of Sina Weibo, a popular social media platform in China. MethodsLiterature review and expert discussion were used to determine the health themes of childhood obesity, smoking, and cancer. Web crawler technology was employed to capture data on health-promoting messages. We used the number of retweets, comments, and likes to evaluate the influence of a message. Statistical analysis was then conducted after manual coding. Specifically, binary logistic regression was used for the data analyses. ResultsWe crawled 20,799 Sina Weibo messages and selected 389 health-promoting messages for this study. Results indicated that the use of gain-framed messages could improve the influence of messages regarding childhood obesity (P

Abstract Background Many studies have demonstrated the association between intestinal microbiota and joint diseases. The “gut-joint axis” also has potential roles in chikungunya virus (CHIKV) infection. Pro-inflammatory arthritis after CHIKV infection might disrupt host homeostasis and lead to dysbacteriosis. This study investigated the characteristics of fecal and gut microbiota, intestinal metabolites, and the changes in gene regulation of intestinal tissues after CHIKV infection using multi-omics analysis to explore the involvement of gut microbiota in the pathogenesis of CHIKV infection. Results CHIKV infection increases the systemic burden of inflammation in the GI system of infected animals. Moreover, infection-induced alterations in GI microbiota and metabolites may be indirectly involved in the modulation of GI and bone inflammation after CHIKV infection, including the modulation of inflammasomes and interleukin-17 inflammatory cytokine levels. Conclusion Our results suggest that the GI tract and its microbes are involved in the modulation of CHIKV infection, which could serve as an indicator for the adjuvant treatment of CHIKV infection. Video Abstract

Abstract Zika virus, a mosquito-borne arbovirus, has repeatedly caused large pandemics with symptoms worsening from mild and self-limiting diseases to Guillain–Barré syndrome in adults and fetal microcephaly in newborns. In recent years, Zika virus diseases have posed a serious threat to human health. The shortage of susceptible small animal models makes it difficult to study pathogenic mechanisms and evaluate potential therapies for Zika virus infection. Therefore, we chose immunocompromised mice (AG129 mice) deficient in IFN-α/β and IFN-γ receptors, which can abolish the innate immune system that prevents Zika virus infection early. AG129 mice were infected with the Zika virus, and this mouse model exhibited replication dynamics, tissue tropism, pathological lesion and immune activation of the Zika virus. Our results suggest that the inoculum dose of Zika virus can affect the viral replication dynamics, cytokine responses and survival rate in AG129 mice. By testing the potential antiviral drug favipiravir, several critical indicators, including replication dynamics and survival rates, were identified in AG129 mice after Zika virus infection. It is suggested that the model is reliable for drug evaluation. In brief, this model provides a potential platform for studies of the infectivity, virulence, and pathogenesis of the Zika virus. Moreover, the development of an accessible mouse model of Zika virus infection will expedite the research and deployment of therapeutics and vaccines.

Cross-contamination of foodborne pathogenic bacteria is a widespread phenomenon in food processing and production, storage and transportation, and household consumption. It is regarded as one of the important causes of foodborne disease outbreaks. Using mathematical models to simulate the transfer of foodborne pathogenic bacteria in different food media enhances our understanding of the pattern of transmission of the pathogens through cross-contamination. Determining the key links in the microbial cross-contamination by combined use of cross-contamination modelling with microbiological risk assessment and designing novel antimicrobial coatings will effectively prevent and control the occurrence of microbial cross-contamination. Therefore, this article reviews the food safety problems caused by cross-contamination of pathogenic bacteria in the food industry and household settings, and provides a detailed overview of the latest progress in mathematical modelling of cross-contamination of foodborne pathogenic bacteria, and describes the application of microbial risk assessment combined with hazard analysis and critical control point (MRA-HACCP) and antimicrobial coatings in the prevention and control of cross-contamination of foodborne pathogenic bacteria from the perspectives of both global control and source control. It concludes with an outlook on future research and control strategies for foodborne pathogen cross-contamination.

Six Cu–Ni–Si–Cr alloy composites are prepared by explosive welding with the designed parameters. Wavy interface containing the melted pockets and shrinkage cracks is always observed, refining the grains to just several micrometers with the localized deformation. Resultantly, the interface regions show higher hardness and yield strength than elsewhere, reaching HV 190 and over 400 MPa, respectively, but their plasticity is less than half that of the base-plate parts. Theoretical analysis successfully relates the area of the total disrupted regions including the interfacial molten and wavy zones in the composites linearly to the flyer-plate thickness (hf) and collision angle (β) as (hfsin2β2)2. For the composite fabricated at an impact velocity and angle of 310 m/s and 8.0°, respectively, strengthening mechanism calculation for its interface part reveals that grain boundary, dislocation and solid solution hardening are most effective and contribute 68 MPa, 370 MPa and 39 MPa individually, making the dislocation hardening dominant for the enhanced strength.

Mycotoxins are toxic secondary metabolites produced by certain fungal species under specific conditions. Research has found that exposure to various fungal toxins can cause embryonic developmental toxicity, such as embryo arrest, abortion, and intrauterine fetal developmental delay. There are no systematic reports on the impact of exposure to fungal toxins during pregnancy on the toxicity of intrauterine fetal development and biological monitoring. Therefore, in order to prevent and reduce the harm of fungal toxins during pregnancy, this paper summarizes the research status of embryonic developmental toxicity, as well as the research status of biological monitoring technologies based on biomarkers such as exposure biomarkers and biomakers of effect, which are caused by traditional fungal toxins such as aflatoxin, deoxynivalenol, zearalenone, and emerging fungal toxins such as Alternaria toxins, beauverin, and enniatins. The aim of the paper is for construction of the healthy China.

Abstract Background Regulatory actions are increasingly used to tackle issues such as excessive alcohol or sugar intake, but such actions to reduce sedentary behaviour remain scarce. World Health Organization (WHO) guidelines on sedentary behaviour call for system-wide policies. The Chinese government introduced the world’s first nation-wide multi-setting regulation on multiple types of sedentary behaviour in children and adolescents in July 2021. This regulation restricts when (and for how long) online gaming businesses can provide access to pupils; the amount of homework teachers can assign to pupils according to their year groups; and when tutoring businesses can provide lessons to pupils. We evaluated the effect of this regulation on sedentary behaviour safeguarding pupils. Methods With a natural experiment evaluation design, we used representative surveillance data from 9- to 18-year-old pupils before and after the introduction of the regulation, for longitudinal (n = 7,054, matched individuals, primary analysis) and repeated cross-sectional (n = 99,947, exploratory analysis) analyses. We analysed pre-post differences for self-reported sedentary behaviour outcomes (total sedentary behaviour time, screen viewing time, electronic device use time, homework time, and out-of-campus learning time) using multilevel models, and explored differences by sex, education stage, residency, and baseline weight status. Results Longitudinal analyses indicated that pupils had reduced their mean total daily sedentary behaviour time by 13.8% (95% confidence interval [CI]: -15.9 to -11.7%, approximately 46 min) and were 1.20 times as likely to meet international daily screen time recommendations (95% CI: 1.01 to 1.32) one month after the introduction of the regulation compared to the reference group (before its introduction). They were on average 2.79 times as likely to meet the regulatory requirement on homework time (95% CI: 2.47 to 3.14) than the reference group and reduced their daily total screen-viewing time by 6.4% (95% CI: -9.6 to -3.3%, approximately 10 min). The positive effects were more pronounced among high-risk groups (secondary school and urban pupils who generally spend more time in sedentary behaviour) than in low-risk groups (primary school and rural pupils who generally spend less time in sedentary behaviour). The exploratory analyses showed comparable findings. Conclusions This regulatory intervention has been effective in reducing total and specific types of sedentary behaviour among Chinese children and adolescents, with the potential to reduce health inequalities. International researchers and policy makers may explore the feasibility and acceptability of implementing regulatory interventions on sedentary behaviour elsewhere.

Foodborne diseases are major global public health issues. Among microbial hazards， foodborne diseases are the most serious problem caused by pathogenic bacteria. Under the risk-based food safety management system， identifying the priority hazards and their important food sources is the premise and key to the prevention and control foodborne diseases. Currently， Europe， the United States， and other countries have established two categories of source attribution methods based on microbial subtyping. The results of these methods were transformed on a scientific basis to formulate food safety risk control measures. China is in the preliminary exploration stage of source attribution method research of foodborne disease. This study aimed to systematically sort the methodological principles and models of source attribution methods widely used internationally， combine the basis and current situation of foodborne disease source attribution research in China， and suggest four work directions for constructing the next stage of foodborne disease source attribution system in China. These directions include developing methods， combining data， building platforms and strong exchanges， which involves the development of localized source attribution model based on microbial subtyping to solve the challenges associated with confirming control and preventing and controlling foodborne diseases in China， and achieving food safety and protecting the lives and health of individuals.

ObjectiveTo propose targeted prevention and control measures， improve product quality， and ensure food safety， Salmonella contamination in the production and processing of prefabricated crispy pork in a large food processing factory was analyzed.MethodsA total of 103 meat samples were collected during the production and processing of prefabricated crispy pork， and 165 environmental samples were collected before processing. Qualitative and quantitative analyses of Salmonella were performed. Finally， serological typing， multilocus sequence typing （MLST）， and pulsed filed gel electrophoresis （PFGE） typing of the isolated Salmonella strains were evaluated.ResultsThe isolation rate of Salmonella from meat samples was 47.6% （49/103）. The isolation rate of Salmonella from environmental samples was 1.2% （2/165）. Fifty-one Salmonella strains were divided into 9 serotypes and 9 STs. Among them， Salmonella enteritidis ST11 was dominant in the production and processing of prefabricated crispy pork， and was detected in the environment. Twenty-two strains of Salmonella enteritidis were divided into 7 PFGE bands， with a similarity of over 92.9%.ConclusionA high overall contamination rate of Salmonella was found in meat samples. Therefore， controlling Salmonella contamination of raw meat is key to improving product quality. The amount of Salmonella contamination in fried products can be effectively controlled. Furthermore， strict separation of processing and storage areas for poultry and stored products is necessary to prevent cross-contamination of microorganisms in food.

Genetically modified microorganism （GMM）， also known as recombinant DNA microorganism， means a micro-organism in which the genetic material has been altered in a way that does not occur naturally by mating and/or natural recombination. Given the fact that the safety management requirements for GMM and related products used in food industry differ among countries， different countries adopt different regulations. Chinese government attaches great importance to the management of GMM and related products used in food industry， and has successively issued a series of administrative regulations， departmental rules， announcements， and standards. This study systematically reviews the safety management requirements from international organizations and Western countries for GMM and related products used in food industry， and provides reference and experience for the management of relevant risks in China.

ObjectiveThe pathogenic potential of Shiga toxin-producing Escherichia coli （STEC） isolated from different sources in China was classified to provide reference for risk management of STEC.MethodsThis study used hazard classification method，porposed by the Food and Agriculture Organization of the United Nations and the World Health Organization’s report， what name is Shiga toxin-producing Escherichia coli （STEC） and food： attribution， characterization and monitoring. We used to classify and analyze the published STEC data in China from 2018 to 2022.ResultsSTEC isolates were classified as low-risk （72.9%）， high-hazard （26.0%） and highest-hazard （1.1%） strains. The high-hazard strains were mainly from cattle or beef food （95.3%）， only 8 highest hazard strains were from cattle and patients.ConclusionThe serotypes of STEC strains are not enough to evaluate the virulence of STEC strains. The virulence gene combination is a more reliable method to classify the risk of STEC infection. This method could provide reference for STEC surveillance and risk assessment in China.

Background: The most effective method for improving the prognosis of lung cancer is the application of low-dose computed tomography (LDCT) for pulmonary nodule screening in populations at high risk. Timely diagnosis and treatment of early-stage lung cancer can contribute to higher long-term survival rates. However, it remains difficult to differentiate malignant from benign pulmonary nodules measuring 8–15 mm, and avoid overtreatment on the one hand and delayed diagnosis on the other hand. In this consensus paper, we aimed to clarify the definition of “high-risk indeterminate pulmonary nodules (IPNs)” and discuss appropriate evaluation and management to facilitate timely diagnosis of lung cancer to improve lung cancer outcome. Direction for future research was discussed. Methods: A multi-disciplinary panel of doctors and IT experts from Asia, Canada and the U.S. were invited to participate. Published evidence and consensus guidelines were used to develop this consensus was clarified. Their evaluation and management were discussed. Findings: The experts believed that the prevalence of pulmonary nodules was very high, and it that was difficult to diagnose early-stage lung cancer due to the small size of the nodules, often leading to delayed diagnosis or overtreatment. To address this issue and to improve long-term outcome, the panel considered important to revise the classification of high-risk IPNs, (1) as pulmonary nodules that cannot be clearly diagnosed with non-surgical biopsy procedures, but is highly suspicious for early-stage lung cancer. The panel also recommends the most responsible should arrange imaging evaluations and follow-ups, taking new technologies into account. Artificial intelligence (AI) assessment based on the Medical Internet of Things (MIoT) can be combined with expert opinion to form a human–computer multidisciplinary team (MDT) that can fully implement the three core procedures of the MIoT, namely, comprehensive perception, reliable transmission, and intelligent processing. This will help to upgrade the non-standard diagnosis and treatment, the so-called “handicraft workshop model”, to a modern assembly-line model that meets international standards. The MIoT technology, which has the potential to realize “simplification of complex problems, digitalization of simple problems, programming of digital problems, and systematization of programming problems”, can promote the homogeneous evaluation of pulmonary nodules by enhancing both the sensitivity and the specificity of detecting early-stage lung cancer, in order to avoid delayed diagnosis and overtreatment. Conclusion: To optimize the evaluation of early-stage lung cancer, and to avoid delayed diagnosis and overtreatment, it is necessary to propose and promote the concept of “high-risk IPNs”. The application of current technologies, AI, and a human–computer MDT, will facilitate improvement in nodule evaluation, transforming the current diagnosis and treatment model, which is akin to production in handicraft workshops, into a modern assembly-line model that meets international standards, and will ultimately result in better prognosis.

Hybridization was performed between the rice materials carrying the rice blast resistance genes Pi1, Pi2, and Pi-ta (R283) as the maternal parent and the broad compatibility restorer line R110 as the paternal parent. Molecular marker-assisted breeding techniques were used to detect the target genes in segregating generations. Through field selection and resistance identification, three stable lines with multiple resistance, strong restorer ability, and excellent agronomic traits were bred. Their agronomic and economic traits were analyzed along with their resistance to different physiological races of rice blast. These three stable lines were then crossed with seven different sterile lines (Chunjiang 23A, FA1, FA3, FA4, 81, 57, and 212A) to analyze the agronomic traits and resistance to rice blast pathogens in the hybrid combinations. JR11, JR12, and JR13 were stable, homozygous polymerized lines carrying Pi1, Pi2, and Pi-ta resistance genes and exhibited resistance frequencies ranging from 90.19 to 96.08% against different physiological races of rice blast disease. This indicates the excellent resistance of these three stable lines to rice blast disease. The seed-set frequencies in the hybrid combinations of the seven sterile lines with the three stable lines ranged from 66.4 to 84.1%, showing strong recovery ability. The yield of the selected combination of 23 A/JR11, 81 A/JR11, and 212 A/JR13 was 2.2–2.3% higher than that of the control Yongyou 1540. The research results showed that the three stable aggregated rice blast resistance genes Pi1, Pi2, and Pi-ta rice restorer lines created have good practical value in production.

Objectives Hemodialysis patients with end-stage renal disease (ESRD) are susceptible to infections and dysbiosis. Catheter-related infections are typically caused by opportunistic skin pathogens. This study aims to compare the skin microbiota changes around the exit site of tunneled cuffed catheters (peri-catheter group) and the contralateral site (control group).Methods ESRD patients on hemodialysis were recruited. The skin microbiota were collected with moist skin swabs and analyzed using high-throughput sequencing of the 16S rDNA V3–V4 region. After denoising, de-replication, and removal of chimeras, the reads were assigned to zero-radius operational taxonomic units (ZOTU).Results We found significantly reduced alpha diversity in the peri-catheter group compared to the control group, as indicated by the Shannon, Jost, and equitability indexes, but not by the Chao1 or richness indexes. Beta diversity analysis revealed significant deviation of the peri-catheter microbiota from its corresponding control group. There was an overrepresentation of Firmicutes and an underrepresentation of Actinobacteria, Proteobacteria, and Acidobacteria at the phylum level in the peri-catheter group. The most abundant ZOTU (Staphylococcus spp.) drastically increased, while Cutibacterium, a commensal bacterium, decreased in the peri-catheter group. Network analysis revealed that the skin microbiota demonstrated covariance with both local and biochemical factors.Conclusions In conclusion, there was significant skin microbiota dysbiosis at the exit sites compared to the control sites in ESRD dialysis patients. Managing skin dysbiosis represents a promising target in the prevention of catheter-related bacterial infections.

ABSTRACTGastrointestinal (GI) infection is evidenced with involvement in COVID-19 pathogenesis caused by SARS-CoV-2. However, the correlation between GI microbiota and the distinct pathogenicity of SARS-CoV-2 Proto and its emerging variants remains unclear. In this study, we aimed to determine if GI microbiota impacted COVID-19 pathogenesis and if the effect varied between SARS-CoV-2 Proto and its variants. We performed an integrative analysis of histopathology, microbiomics, and transcriptomics on the GI tract fragments from rhesus monkeys infected with SARS-CoV-2 proto or its variants. Based on the degree of pathological damage and microbiota profile in the GI tract, five of SARS-CoV-2 strains were classified into two distinct clusters, namely, the clusters of Alpha, Beta and Delta (ABD), and Proto and Omicron (PO). Notably, the abundance of potentially pathogenic microorganisms increased in ABD but not in the PO-infected rhesus monkeys. Specifically, the high abundance of UCG-002, UCG-005, and Treponema in ABD virus-infected animals positively correlated with interleukin, integrins, and antiviral genes. Overall, this study revealed that infection-induced alteration of GI microbiota and metabolites could increase the systemic burdens of inflammation or pathological injury in infected animals, especially in those infected with ABD viruses. Distinct GI microbiota and metabolite profiles may be responsible for the differential pathological phenotypes of PO and ABD virus-infected animals. These findings improve our understanding the roles of the GI microbiota in SARS-CoV-2 infection and provide important information for the precise prevention, control, and treatment of COVID-19.

Emissions of fine particulate matter (PM2.5) from human activities have been linked to substantial disease burdens, but evidence regarding how reducing PM2.5 at its sources would improve public health is sparse. We followed a population-based cohort of 2.7 million adults across Canada from 2007 through 2016. For each participant, we estimated annual mean concentrations of PM2.5 and the fractional contributions to PM2.5 from the five leading anthropogenic sources at their residential address using satellite observations in combination with a global atmospheric chemistry transport model. For each source, we estimated the causal effects of six hypothetical interventions on 10-y nonaccidental mortality risk using the parametric g-formula, a structural causal model. We conducted stratified analyses by age, sex, and income. This cohort would have experienced tangible health gains had contributions to PM2.5 from any of the five sources been reduced. Compared with no intervention, a 10% annual reduction in PM2.5 contributions from transportation and power generation, Canada's largest and fifth-largest anthropogenic sources, would have prevented approximately 175 (95%CI: 123-226) and 90 (95%CI: 63-117) deaths per million by 2016, respectively. A more intensive 50% reduction per year in PM2.5 contributions from the two sources would have averted 360 and 185 deaths per million, respectively, by 2016. The potential health benefits were greater among men, older adults, and low-income earners. In Canada, where PM2.5 levels are among the lowest worldwide, reducing PM2.5 contributions from anthropogenic sources by as little as 10% annually would yield meaningful health gains.

Background Long-term exposure to outdoor fine particulate matter (PM2.5) is the leading environmental risk factor for premature mortality worldwide. Characterizing important pathways through which PM2.5 increases individuals' mortality risk can clarify the PM2.5-mortality relationship and identify possible points of interventions. Recent evidence has linked PM2.5 to the onset of diabetes and cardiovascular disease, but to what extent these associations contribute to the effect of PM2.5 on mortality remains poorly understood. Methods and Results We conducted a population-based cohort study to investigate how the effect of PM2.5 on nonaccidental mortality is mediated by its impacts on incident diabetes, acute myocardial infarction, and stroke. Our study population comprised ≈200 000 individuals aged 20 to 90 years who participated in population-based health surveys in Ontario, Canada, from 1996 to 2014. Follow-up extended until December 2017. Using causal mediation analyses with Aalen additive hazards models, we decomposed the total effect of PM2.5 on mortality into a direct effect and several path-specific indirect effects mediated by diabetes, each cardiovascular event, or both combined. A series of sensitivity analyses were also conducted. After adjusting for various individual- and neighborhood-level covariates, we estimated that for every 1000 adults, each 10 μg/m3 increase in PM2.5 was associated with ≈2 incident cases of diabetes, ≈1 major cardiovascular event (acute myocardial infarction and stroke combined), and ≈2 deaths annually. Among PM2.5-related deaths, 31.7% (95% CI, 17.2%-53.2%) were attributable to diabetes and major cardiovascular events in relation to PM2.5. Specifically, 4.5% were explained by PM2.5-induced diabetes, 22.8% by PM2.5-induced major cardiovascular events, and 4.5% through their interaction. Conclusions This study suggests that a significant portion of the estimated effect of long-term exposure to PM2.5 on deaths can be attributed to its effect on diabetes and cardiovascular diseases, highlighting the significance of PM2.5 on deteriorating cardiovascular health. Our findings should raise awareness among professionals that improving metabolic and cardiovascular health may reduce mortality burden in areas with higher exposure to air pollution.

This paper proposes a transfer learning approach to reduce the dependence of the neural network model on dataset size for multi-chip modules (MCMs) thermal estimation. The dataset that resembles the target task is used to pre-train a convolutional neural network model; then, it is fine-tuned using a much smaller dataset belonging to the target task. This process enables the model to adapt and refine its thermal estimation capabilities for the particular MCM under consideration with sparse data. Results show that the model effectively utilizes the geometric shapes and power details of MCMs to precisely predict steady-state temperature fields and junction temperatures. In a complex five-chip configuration, the fine-tuned model achieves a prediction error of only 1.17 % and accelerates inference speed by three orders of magnitude compared to traditional simulations. Additionally, the transfer learning model requires only 1/8 the dataset size and less than 1/3 the training time compared to a model with random initial parameters to achieve similar accuracy. The results indicate that this model can significantly enhance the utilization efficiency of historically accumulated MCM heat transfer datasets for new demands such as real-time thermal estimation and fast optimization of chip configuration.

Abstract To address our climate emergency, “we must rapidly, radically reshape society”—Johnson & Wilkinson, All We Can Save. In science, reshaping requires formidable technical (cloud, coding, reproducibility) and cultural shifts (mindsets, hybrid collaboration, inclusion). We are a group of cross‐government and academic scientists that are exploring better ways of working and not being too entrenched in our bureaucracies to do better science, support colleagues, and change the culture at our organizations. We share much‐needed success stories and action for what we can all do to reshape science as part of the Open Science movement and 2023 Year of Open Science.

BackgroundFunctional dyspepsia is a highly prevalent digestive disorder. The limited effectiveness of current pharmaceutical interventions necessitates the exploration of alternative therapeutic options for functional dyspepsia. Xiangsha liujunzi decoction, a well-known traditional Chinese medicine formulation, has been widely employed in the treatment of functional dyspepsia in China. Nevertheless, the effectiveness of Xiangsha liujunzi decoction in the treatment of functional dyspepsia remains uncertain.ObjectiveTo examine the effectiveness and safety of Xiangsha liujunzi decoction for treating functional dyspepsia.MethodsWe retrieved seven databases containing randomized controlled trials on functional dyspepsia published up until 31 July 2023. The quality of these studies was evaluated using the Cochrane Risk of Bias assessment tool. The analysis of data was performed using the software RevMan 5.4. The total clinical effectiveness rate was evaluated as the primary outcome. In addition, gastric emptying rate, symptom score and safety evaluation were evaluated as the secondary outcomes.ResultsThe meta-analysis included 23 studies, involving 2,101 individuals. Xiangsha liujunzi decoction demonstrated a significantly higher clinical effectiveness rate compared to the control group (RR 1.27; 95% CI 1.21, 1.33; p < 0.00001). Moreover, it exhibited superior gastric emptying rate and symptom score improvement compared to the control group. Nevertheless, no remarkable differences were detected in safety between Xiangsha liujunzi decoction and the control group (RR 0.67; 95% CI 0.16, 2.76; p = 0.58).ConclusionThe findings of this study suggest that Xiangsha liujunzi decoction exhibits effectiveness and no significant adverse events observed. However, because of the low quality of the enrolled studies, more high-quality and strict design randomized controlled trials are required in the future.

ObjectiveTo study the current status of hemophilia B (HB) patients in the central and western regions of China.MethodsThis cross-sectional, multicenter study was conducted in seven provinces in the central and western regions of China from April 2019 to June 2023. Samples were collected for the factor IX activity, inhibitor screen, and gene mutation. Furthermore, the status of six index joints and quality of life (QoL) were assessed.ResultsA total of 185 HB patients (mild 15, moderate 75, and severe 95) with a median age of 12.17 years were enrolled. 30.3% (56/185) of patients had a family history of HB. 34.6% (64/185) of HB patients had diagnostic delay and 38.5% (69/179) experienced treatment delay. The incidence of inhibitors was 6.1% (11/179). We identified 123 genetic variants in this study, with missense mutations being the most common. 84.0% (89/106) of HB mothers were genetically identified as carriers, with 27.7% (13/47) of carriers having clotting factor levels less than 0.40 IU/ml. 71.4% (132/185) of HB patients had a history of joint hemorrhage, with a rate of target joint in these patients was 64.4% (85/132). Lower extremity joints were most often affected in patients. The Hemophilia Joint Health Score (HJHS) score was significantly positively correlated with the Hemophilia Early Arthropathy Detection with Ultrasound in China (HEAD-US-C) (r = 0.542, P

This work develops a model for natural convection in annuli with internal heat sources based on Graph Convolution Network (GCN), achieving rapid prediction by directly establishing the mapping relationship between the geometric features and the temperature field. The GCN learns node features and connections between nodes in data structures. Compared with traditional machine learning networks, GCN exhibits greater advantages in handling complex geometric shapes and unstructured nodes. In this study, numerous data samples were constructed with various geometric features, e.g., varying sizes, positions, and quantities of internal heat sources. The results indicate that a fully trained GCN was capable to adaptively predict temperature field distributions with arbitrary heat source sizes, positions, and quantities. The mean prediction errors of GCN for the temperature field are around 0.17 %, 0.32 %, and 0.85 %, for the single, double, and triple heat sources cases, respectively. Furthermore, the GCN prediction performance was also compared with surrogate models by the convolutional, and fully connected neural networks (CNN and FNN), which shows an improvement of 88.1 % and 84.8 %, respectively. Therefore, compared with numerical results and other surrogate models, the proposed model presents superior geometric adaptability, high prediction accuracy and a remarkable enhancement in computational efficiency (three orders of magnitude).