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Abstract Objective To evaluate age-specific multimorbidity patterns and morbidity burden on mortality and healthcare expenditure across age groups. Patients and Methods Retrospective observational study between January 1, 2009 to December 31, 2017 using electronic health records in Hong Kong: Individuals were stratified by age (

Objectives: To evaluate the difference between BNT162b2 and CoronaVac in vaccine effectiveness and safety. Methods: This target trial emulation study included individuals aged ≥12 during 2022. Propensity score matching was applied to ensure group balance. The Cox proportional hazard model was used to compare the effectiveness outcomes including COVID-19 infection, severity, 28-day hospitalization, and 28-day mortality after infection. Poisson regression was used for safety outcomes including 32 adverse events of special interests between groups. Results: A total of 639,818 and 1804,388 individuals were identified for the 2-dose and 3-dose comparison, respectively. In 2-dose and 3-dose comparison, the hazard ratios (95% confidence intervals [CI]) were 0.844 [0.833-0.856] and 0.749 [0.743-0.755] for COVID-19 infection, 0.692 [0.656-0.731] and 0.582 [0.559-0.605] for hospitalization, 0.566 [0.417-0.769] and 0.590 [0.458-0.76] for severe COVID-19, and 0.563 [0.456-0.697] and 0.457 [0.372-0.561] for mortality for BNT162b2 recipients versus CoronaVac recipients, respectively. Regarding safety, 2-dose BNT162b2 recipients had a significantly higher incidence of myocarditis (incidence rate ratio [IRR] [95% CI]: 8.999 [1.14-71.017]) versus CoronaVac recipients, but the difference was insignificant in 3-dose comparison (IRR [95% CI]: 2.000 [0.500-7.996]). Conclusion: BNT162b2 has higher effectiveness among individuals aged ≥12 against COVID-19-related outcomes for SARS-CoV-2 omicron compared to CoronaVac, with almost 50% lower mortality risk.

High-altitude illnesses, encompassing a spectrum of health threats including Acute Mountain Sickness (AMS), pose significant challenges to individuals exposed to high altitude environments, necessitating effective prophylaxis and immediate management. Given the variability in individual responses to these conditions, accurate prediction of high-altitude illnesses onset is of paramount importance. This review systematically consolidates recent advancements in research on predicting AMS by evaluating existing cohort data, predictive models, and methodologies, while also delving into the application of emerging technologies. Through a thorough analysis of scholarly literature, we discuss traditional prediction methods anchored in physiological parameters (e.g., heart rate, respiratory frequency, blood pressure) and biochemical markers, as well as the integration and utility of novel technologies such as biosensors, genetic testing, and artificial intelligence within high-altitude prediction research. While conventional pre-diction techniques have been extensively used, they are often constrained by limitations in accuracy, reliability, and multifactorial influences. The advent of these innovative technologies holds promise for more precise individual risk assessments and personalized preventive and therapeutic strategies across various forms of AMS. Future research endeavors must pivot decisively towards the meticulous identification and stringent validation of innovative predictive biomarkers and models. This strategic re-direction should catalyze intensified interdisciplinary cooperation to significantly deepen our mechanistic insights into the pathogenesis of AMS while refining existing prediction methodologies. These groundbreaking advancements harbor the potential to fundamentally transform preventive and therapeutic frameworks for high-altitude illnesses, ultimately securing augmented safety standards and wellbeing for individuals operating at elevated altitudes with far-reaching global implications.

Pancreatic adenocarcinoma (PAAD) is a notably aggressive malignancy with limited treatment options and an unfavorable prognosis for patients. We aimed to investigate molecular mechanisms by which Sam's pointed domain-containing ETS transcription factor (SPDEF) exerts effects on PAAD progression. We analyzed differentially expressed genes (DEGs) and their integration with ETS family members using the The Cancer Genome Atlas (TCGA) database, hence identifying SPDEF as a core gene in PAAD. Kaplan-Meier survival analysis confirmed SPDEF’s prognostic potential. In vitro experiments validated the association with cell proliferation and apoptosis, affecting pancreatic cancer cell dynamics. We detected increased SPDEF expression in PAAD tumor samples. Our in vitro studies revealed that SPDEF regulates mRNA and protein expression levels, and significantly affects cell proliferation. Moreover, SPDEF was associated with reduced apoptosis and enhanced cell migration and invasion. In-depth analysis of SPDEF-targeted genes revealed four crucial genes for advanced prognostic model, among which S100A16 was significantly correlated with SPDEF. Mechanistic analysis showed that SPDEF enhances the transcription of S100A16, which in turn enhances PAAD cell migration, proliferation, and invasion by activating the PI3K/AKT signaling pathway. Our study revealed the critical role of SPDEF in promoting PAAD by upregulating S100A16 transcription and stimulating the PI3K/AKT signaling pathway. This knowledge deepened our understanding of pancreatic cancer's molecular progression and unveiled potential therapeutic strategies targeting SPDEF-driven pathways.

Abstract Background Calcific aortic valve disease (CAVD) is a common valve disease with an increasing incidence, but no effective drugs as of yet. With the development of sequencing technology, non-coding RNAs have been found to play roles in many diseases as well as CAVD, but no circRNA/lncRNA–miRNA–mRNA interaction axis has been established. Moreover, valve interstitial cells (VICs) and valvular endothelial cells (VECs) play important roles in CAVD, and CAVD differed between leaflet phenotypes and genders. This work aims to explore the mechanism of circRNA/lncRNA–miRNA–mRNA network in CAVD, and perform subgroup analysis on the important characteristics of CAVD, such as key cells, leaflet phenotypes and genders. Results We identified 158 differentially expressed circRNAs (DEcircRNAs), 397 DElncRNAs, 45 DEmiRNAs and 167 DEmRNAs, and constructed a hsa-circ-0073813/hsa-circ-0027587–hsa-miR-525-5p–SPP1/HMOX1/CD28 network in CAVD after qRT-PCR verification. Additionally, 17 differentially expressed genes (DEGs) in VICs, 9 DEGs in VECs, 7 DEGs between different leaflet phenotypes and 24 DEGs between different genders were identified. Enrichment analysis suggested the potentially important pathways in inflammation and fibro-calcification during the pathogenesis of CAVD, and immune cell patterns in CAVD suggest that M0 macrophages and memory B cells memory were significantly increased, and many genes in immune cells were also differently expressed. Conclusions The circRNA/lncRNA–miRNA–mRNA interaction axis constructed in this work and the DEGs identified between different characteristics of CAVD provide a direction for a deeper understanding of CAVD and provide possible diagnostic markers and treatment targets for CAVD in the future.

BackgroundAccording to some recent observational studies, the gut microbiota influences atherosclerosis via the gut microbiota-artery axis. However, the causal role of the gut microbiota in atherosclerosis remains unclear. Therefore, we used a Mendelian randomization (MR) strategy to try to dissect this causative link.MethodsThe biggest known genome-wide association study (GWAS) (n = 13,266) from the MiBioGen collaboration was used to provide summary data on the gut microbiota for a two-sample MR research. Data on atherosclerosis were obtained from publicly available GWAS data from the FinnGen consortium, including cerebral atherosclerosis (104 cases and 218,688 controls), coronary atherosclerosis (23,363 cases and 187,840 controls), and peripheral atherosclerosis (6631 cases and 162,201 controls). The causal link between gut microbiota and atherosclerosis was investigated using inverse variance weighting, MR-Egger, weighted median, weighted mode, and simple mode approaches, among which inverse variance weighting was the main research method. Cochran’s Q statistic was used to quantify the heterogeneity of instrumental variables (IVs), and the MR Egger intercept test was used to assess the pleiotropy of IVs.ResultsInverse-variance-weighted (IVW) estimation showed that genus Ruminiclostridium 9 had a protective influence on cerebral atherosclerosis (OR = 0.10, 95% CI: 0.01–0.67, P = 0.018), while family Rikenellaceae (OR = 5.39, 95% CI: 1.50–19.37, P = 0.010), family Streptococcaceae (OR = 6.87, 95% CI: 1.60–29.49, P = 0.010), genus Paraprevotella (OR = 2.88, 95% CI: 1.18–7.05, P = 0.021), and genus Streptococcus (OR = 5.26, 95% CI: 1.28–21.61, P = 0.021) had pathogenic effects on cerebral atherosclerosis. For family Acidaminococcaceae (OR = 0.87, 95% CI: 0.76–0.99, P = 0.039), the genus Desulfovibrio (OR = 0.89, 95% CI: 0.80–1.00, P = 0.048), the genus RuminococcaceaeUCG010 (OR = 0.80, 95% CI: 0.69–0.94, P = 0.006), and the Firmicutes phyla (OR = 0.87, 95% CI: 0.77–0.98, P = 0.023) were protective against coronary atherosclerosis. However, the genus Catenibacterium (OR = 1.12, 95% CI: 1.00–1.24, P = 0.049) had a pathogenic effect on coronary atherosclerosis. Finally, class Actinobacteria (OR = 0.83, 95% CI: 0.69–0.99, P = 0.036), family Acidaminococcaceae (OR = 0.76, 95% CI: 0.61–0.94, P = 0.013), genus Coprococcus2 (OR = 0.76, 95% CI: 0.60–0.96, P = 0.022), and genus RuminococcaceaeUCG010 (OR = 0.65, 95% CI: 0.46–0.92, P = 0.013), these four microbiota have a protective effect on peripheral atherosclerosis. However, for the genus Lachnoclostridium (OR = 1.25, 95% CI: 1.01–1.56, P = 0.040) and the genus LachnospiraceaeUCG001 (OR = 1.22, 95% CI: 1.04–1.42, P = 0.016), there is a pathogenic role for peripheral atherosclerosis. No heterogeneity was found for instrumental variables, and no considerable horizontal pleiotropy was observed.ConclusionWe discovered that the presence of probiotics and pathogens in the host is causally associated with atherosclerosis, and atherosclerosis at different sites is causally linked to specific gut microbiota. The specific gut microbiota associated with atherosclerosis identified by Mendelian randomization studies provides precise clinical targets for the treatment of atherosclerosis. In the future, we can further examine the gut microbiota’s therapeutic potential for atherosclerosis if we have a better grasp of the causal relationship between it and atherosclerosis.

With the increasing integration of AI technology in the food industry, deep learning has demonstrated its immense potential in the domain of plant disease image recognition. However, there remains a gap in research between models capable of continual learning of new diseases and addressing the inherent catastrophic forgetting issue in neural networks. This study aims to comprehensively evaluate various learning strategies based on advanced computer vision models for multi-disease continual learning tasks in food disease recognition. To cater to the benchmark dataset requirements, we collected the PlantDiseaseCL dataset, sourced from the internet, encompassing diverse crop diseases from apples, corn, and more. Utilizing the Vision Transformer (ViT) model, we established a plant disease image recognition classifier, which, in joint learning, outperformed several comparative CNN architectures in accuracy (0.9538), precision (0.9532), recall (0.9528), and F1 score (0.9560). To further harness the potential of ViT in food disease defect recognition, we introduced a mathematical paradigm for crop disease recognition continual learning. For the first time, we proposed a novel ViT-TV architecture in the multi-disease image recognition scenario, incorporating a Total Variation (TV) distance-based loss (TV-Loss) to quantify the disparity between current and previous attention distributions, fostering attention consistency and mitigating the catastrophic forgetting inherent in ViT without prior task samples. In the incremental learning of the PlantDiseaseCL dataset across 3-Steps and 5-Steps, our strategy achieved average accuracies of 0.7077 and 0.5661, respectively, surpassing all compared Zero-Exemplar Approaches like LUCIR, SI, MAS, and even outperforming exemplar-based strategies like EEIL and ICaRL. In conclusion, the ViT-TV approach offers robust support for the long-term intelligent development of the agricultural and food industry, especially showcasing significant applicability in continual learning for crop disease image recognition.

Summary: Studies across a diverse group of metazoan embryos indicate that Wnt signaling often activates the transcription factor Sp5, forming a signaling ‘cassette’ that plays critical roles in many developmental processes. This study explores the role of Wnt/Sp5 signaling during the specification and patterning of the primary germ layers during early anterior-posterior axis formation in the deuterostome sea urchin embryo. Our functional analyses show that Sp5 is critical for endomesoderm specification downstream of Wnt/β-catenin in posterior cells as well as anterior neuroectoderm patterning downstream of non-canonical Wnt/JNK signaling in anterior cells. Interestingly, expression and functional data comparisons show that Wnt/Sp5 signaling often plays similar roles in posterior endomesoderm as well as neuroectoderm patterning along the AP axis of several deuterostome embryos, including vertebrates. Thus, our findings provide strong support for the idea that Wnt-Sp5 signaling cassettes were critical for the establishment of early germ layers in the common deuterostome ancestor.

The challenge of data-driven synthesis of advanced nanomaterials can be minimized by using machine learning algorithms to optimize synthesis parameters and expedite the innovation process. In this study, a high-throughput robotic platform was employed to synthesize over 1356 gold nanorods with varying aspect ratios via a seedless approach. The developed models guided us in synthesizing gold nanorods with customized morphology, resulting in highly repeatable morphological yield with quantifiable structure-modulating precursor adjustments. The study provides insight into the dynamic relationships between key structure-modulating precursors and the structural morphology of gold nanorods based on the expected aspect ratio. The high-throughput robotic platform-fabricated gold nanorods demonstrated precise aspect ratio control when spectrophotometrically investigated and further validated with the transmission electron microscopy characterization. These findings demonstrate the potential of high-throughput robot-assisted synthesis and machine learning in the synthesis optimization of gold nanorods and aided in the development of models that can aid such synthesis of as-desired gold nanorods.

To enhance heat and fluid exchange between main flow and near-wall flow in microchannels, a twisted blade-like fin with an advantage in stimulating both spanwise and normalwise secondary flow is proposed. The cross sections of the fin are low-drag airfoils in different orientations. The flow and heat transfer performances of microchannels with fins are numerically investigated at Re = 50–700. The results show that the twisted blade-like fin improves heat transfer process significantly, especially wall temperature uniformity, and reduces the flow separation region behind the fin, resulting in an obvious small pressure penalty. At small inflow (Re = 50), the best heat transfer and lowest pressure penalty are obtained in the microchannel with a single fin. When Re > 150, better heat transfer is obtained in the microchannel with three fins, and the highest comprehensive thermal performance (TP) reaches 3.58. The twisted direction of fins has a significant effect on heat transfer but less on flow drag. Due to the twisted fins, left and right walls experience an overall decline in temperature, and the temperature uniformity of top and bottom walls is improved. Compared with a smooth microchannel, the average and maximum temperature of the investigated microchannels are reduced by 48.1 K and 49.0 K at most respectively.

Background: Acute Ischemic stroke (AIS) has been a leading cause of long-term disability and even death, accounting for significant economic costs and leading to heavy care and financial burdens for patients and their families. Ginkgo biloba extract is highly valued for its efficacy and broad implementation into clinical practice. Therefore, a Bayesian network meta-analysis (BNMA) was conducted to determine the differences in efficacy and safety among various ginkgo biloba-related agents and to prescribe the optimal medication for decision-making. Methods: Inclusion and exclusion eligibility criteria were determined in advance. From the inception of the literature until December 2022, a systematic literature search was conducted in seven electronic databases. An assessment of the methodological quality of the included studies was carried out according to the Cochrane Collaborations tool. A previous registration (PROSPERO CRD42022319301) of this review protocol was undertaken. Results: There were 94 eligible studies included in the NMA involving 10,071 patients, with the experimental control group of 5,063 and the control group of 5,008, involving five ginkgo biloba-related agents included in the NMA. Findings of the studies revealed that supplementation with ginkgo biloba-related agents significantly improved the clinical effective rate, functional independence, neurological function impairments and activities of daily living function compared with the conventional western medicine (WM) group in a relatively safe manner. Comprehensive therapeutic efficacy analysis of neurological function impairments and activities of daily living function showed that GI+WM and GDLM+WM manifest at the first grade among these five therapeutic strategies. Conclusion: This Bayesian network meta-analysis results showed that the combination of ginkgo biloba-related agents and WM was more effective for AIS patients than WM alone. GI+WM and GDLM+WM stand out for the comprehensive therapeutic efficacy of patients with stroke through comparisons among various therapeutic approaches. Moreover, SXN+WM, GK+WM and GDLM+WM might be among the most effective in improving the functional efficacy outcome, while GI+WM might be among the least effective. GI+WM, GDLM+WM, SXN+WM and GK+WM might be among the most effective in improving neurofunctional deficits, while GNT+WM might be among the least effective. In terms of improving activities of daily living function, five ginkgo biloba-related agents might be among the most effective. However, well-designed researches with standardized functional outcome measures for AIS, further adequately powered, blinding and suitable comparators are still required to contribute to the current clinical practice.

BackgroundUntil now, few articles have revealed the potential roles of innate lymphoid cells (ILCs) in cardiovascular diseases. However, the infiltration of ILC subsets in ischemic myocardium, the roles of ILC subsets in myocardial infarction (MI) and myocardial ischemia-reperfusion injury (MIRI) and the related cellular and molecular mechanisms have not been described with a sufficient level of detail.MethodIn the current study, 8-week-old male C57BL/6J mice were divided into three groups: MI, MIRI and sham group. Single-cell sequencing technology was used to perform dimensionality reduction clustering of ILC to analyze the ILC subset landscape at a single-cell resolution, and finally flow cytometry was used to confirm the existence of the new ILC subsets in different disease groups.ResultsFive ILC subsets were found, including ILC1, ILC2a, ILC2b, ILCdc and ILCt. It is worth noting that ILCdc, ILC2b and ILCt were identified as new ILC subclusters in the heart. The cellular landscapes of ILCs were revealed and signal pathways were predicted. Furthermore, pseudotime trajectory analysis exhibited different ILC statuses and traced related gene expression in normal and ischemic conditions. In addition, we established a ligand–receptor–transcription factor–target gene regulatory network to disclose cell communications among ILC clusters. Moreover, we further revealed the transcriptional features of the ILCdc and ILC2a subsets. Finally, the existence of ILCdc was confirmed by flow cytometry.ConclusionCollectively, by characterizing the spectrums of ILC subclusters, our results provide a new blueprint for understanding ILC subclusters’ roles in myocardial ischemia diseases and further potential treatment targets.

As a response to climate changes, permafrost has deteriorated and the hydrologic process has undergone significant alterations in high-cold regions. The response mechanism still remains unknown. The characteristic contribution was calculated using the random forest (RF) algorithm, AdaBoost algorithm, and gradient-boosted decision tree (GBDT) algorithm. A comprehensive evaluation model was constructed to evaluate the contribution of climate changes to the headwaters of the Yellow River and the influence of permafrost degradation as well as climate-permafrost cooperation on runoff changes. The selected characteristic vectors were chosen as datasets for the support vector machine (SVM) and RF algorithms. A model was constructed for the prediction of permafrost degradation and runoff changes based on climate data. Results demonstrated that climate variables influencing the mean depth-to-permafrost table (DPT) were ranked according to their contributions: air temperature > evapotranspiration > wind speed > relative humidity (RHU) > sunshine duration > precipitation. The descending rank of climate and permafrost variables according to their contributions to runoff was the following: precipitation > sunshine duration > permafrost coverage > evapotranspiration > relative humidity (RHU) > mean DPT > wind speed > maximum DPT > air temperature. The model demonstrated good prediction results. The outputs can provide scientific references in applications related to water resources and the protection of ecologically vulnerable areas in high-cold regions.

ABSTRACT With the overmining of actinomycetes for compounds acting against Gram-negative pathogens, recent efforts to discover novel antibiotics have been focused on other groups of bacteria. Teixobactin, the first antibiotic without detectable resistance that binds lipid II, comes from an uncultured Eleftheria terra, a betaproteobacterium; odilorhabdins, from Xenorhabdus, are broad-spectrum inhibitors of protein synthesis, and darobactins from Photorhabdus target BamA, the essential chaperone of the outer membrane of Gram-negative bacteria. Xenorhabdus and Photorhabdus are symbionts of the nematode gut microbiome and attractive producers of secondary metabolites. Only small portions of their biosynthetic gene clusters (BGC) are expressed in vitro. To access their silent operons, we first separated extracts from a small library of isolates into fractions, resulting in 200-fold concentrated material, and then screened them for antimicrobial activity. This resulted in a hit with selective activity against Escherichia coli, which we identified as a novel natural product antibiotic, 3′-amino 3′-deoxyguanosine (ADG). Mutants resistant to ADG mapped to gsk and gmk, kinases of guanosine. Biochemical analysis shows that ADG is a prodrug that is converted into an active ADG triphosphate (ADG-TP), a mimic of GTP. ADG incorporates into a growing RNA chain, interrupting transcription, and inhibits cell division, apparently by interfering with the GTPase activity of FtsZ. Gsk of the purine salvage pathway, which is the first kinase in the sequential phosphorylation of ADG, is restricted to E. coli and closely related species, explaining the selectivity of the compound. There are probably numerous targets of ADG-TP among GTP-dependent proteins. The discovery of ADG expands our knowledge of prodrugs, which are rare among natural compounds. IMPORTANCE Drug-resistant Gram-negative bacteria have become the major problem driving the antimicrobial resistance crisis. Searching outside the overmined actinomycetes, we focused on Photorhabdus, gut symbionts of enthomopathogenic nematodes that carry up to 40 biosynthetic gene clusters coding for secondary metabolites. Most of these are silent and do not express in vitro. To gain access to silent operons, we first fractionated supernatant from Photorhabdus and then tested 200-fold concentrated material for activity. This resulted in the isolation of a novel antimicrobial, 3′-amino 3′-deoxyguanosine (ADG), active against E. coli. ADG is an analog of guanosine and is converted into an active ADG-TP in the cell. ADG-TP inhibits transcription and probably numerous other GTP-dependent targets, such as FtsZ. Natural product prodrugs have been uncommon; discovery of ADG broadens our knowledge of this type of antibiotic.

Background: Acute myocardial infarction (AMI) is one of the main fatal diseases of cardiovascular diseases. Circular RNA (circRNA) is a non-coding RNA (ncRNA), which plays a role in cardiovascular disease as a competitive endogenous RNA (ceRNA). However, their role in AMI has not been fully clarified. This study aims to explore the mechanism of circRNA-related ceRNA network in AMI, and to identify the corresponding immune infiltration characteristics.Materials and Methods: The circRNA (GSE160717), miRNA (GSE24548), and mRNA (GSE60993) microarray datasets of AMI were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed circRNAs (DEcircRNAs), miRNAs (DEmiRNAs), and mRNAs (DEmRNAs) were identified by the “limma” package. After integrating the circRNA, miRNA and mRNA interaction, we constructed a circRNA-miRNA-mRNA network. The “clusterProfiler” package and String database were used for functional enrichment analysis and protein-protein interaction (PPI) analysis, respectively. After that, we constructed a circRNA-miRNA-hub gene network and validated the circRNAs and mRNAs using an independent dataset (GSE61144) as well as qRT-PCR. Finally, we used CIBERSORTx database to analyze the immune infiltration characteristics of AMI and the correlation between hub genes and immune cells.Results: Using the “limma” package of the R, 83 DEcircRNAs, 54 DEmiRNAs, and 754 DEmRNAs were identified in the microarray datasets of AMI. Among 83 DEcircRNAs, there are 55 exonic DEcircRNAs. Then, a circRNA-miRNA-mRNA network consists of 21 DEcircRNAs, 11 DEmiRNAs, and 106 DEmRNAs were predicted by the database. After that, 10 hub genes from the PPI network were identified. Then, a new circRNA-miRNA-hub gene network consists of 14 DEcircRNAs, 7 DEmiRNAs, and 9 DEmRNAs was constructed. After that, three key circRNAs (hsa_circ_0009018, hsa_circ_0030569 and hsa_circ_0031017) and three hub genes (BCL6, PTGS2 and PTEN) were identified from the network by qRT-PCR. Finally, immune infiltration analysis showed that hub genes were significantly positively correlated with up-regulated immune cells (neutrophils, macrophages and plasma cells) in AMI.Conclusion: Our study constructed a circRNA-related ceRNA networks in AMI, consists of hsa_circ_0031017/hsa-miR-142-5p/PTEN axis, hsa_circ_0030569/hsa-miR-545/PTGS2 axis and hsa_circ_0009018/hsa-miR-139-3p/BCL6 axis. These three hub genes were significantly positively correlated with up-regulated immune cells (neutrophils, macrophages and plasma cells) in AMI. It helps improve understanding of AMI mechanism and provides future potential therapeutic targets.

Objective To investigate a foodborne disease outbreak in a small restaurant, analyze its causes and risk factors, and propose the measures for prevention and control. Methods Using field epidemiology survey to describe the features and analyze the risk factors of the incident. The strains homology was evaluated by pulsed field gel electrophoresis (PFGE). Results The major clinic manifestations of 5 cases were diarrhea and abdominal pain. Dietary survey showed that suspicious food was pot-stewed chicken leg at dinner on June 13, 2019. The result of field hygienic survey indicated that the risk factor of the incident was the long placement time of the pot-stewed chicken leg at normal temperature status and insufficient re-heating time before dinner. Enterococcus faecalis was isolated from 3 anal swabs of cases, the pot-stewed chicken leg and the cutting board swab which sampled in the kitchen, and the strains were 100% homological. Conclusion The outbreak was caused by eating the pot-stewed chicken leg which was contaminated by abundant Enterococcus faecalis. It was suggested to strengthen the food safety awareness of small restaurant employees and the proper cooking method should be mastered.

In Wi-Fi fingerprint positioning, what we should most care about is the distance relationship between the user and the reference points (RP). However, most of the existing weighted k-nearest neighbor (WKNN) algorithms use the Euclidean distance of received signal strengths (RSS) as distance measure for fingerprint matching, and the RSS Euclidean distance is not consistent with the position distance. To address this issue, this paper analyzes the relationship between RSS similarity and position distance, propose a novel WKNN based on signal similarity and spatial position. Firstly, we obtain the weighted Euclidean distance (WED) by balancing the size between the RSS difference and the signal propagation distance difference according to the attenuation law of the spatial signal. Then, we obtain the approximate position distance (APD) by making full use of the position distances and WEDs between RPs. Finally, the nearest RPs can be selected more accurately based on the APDs between the user and different RPs, and the position of user can be estimated by the proposed WKNN based on the APD (APD-WKNN) algorithm. In order to fully evaluate the proposed algorithm, we use three fingerprint databases for comparison experiments with eight fingerprint positioning algorithms. The results show that the proposed algorithm can significantly improve the positioning accuracy of WKNN algorithm.

Growing evidence has indicated that the characteristics of gut microbiota are associated with acute ischemic stroke (AIS). Phlegm-heat syndrome (PHS), a specific pathological state of the AIS, is one of the common traditional Chinese syndromes of stroke. The long duration of PHS in patients with AIS could lead to poor clinical outcomes. Gut microbiota characteristics in patients with both AIS and PHS, and their relationship remains unknown. This study was designed to investigate the alterations in gut microbiota in patients with AIS and PHS through a cross-sectional study. Fecal samples were collected from 10 patients with AIS and non-PHS (ntAIS), 7 patients with AIS and PHS (tAIS), and 10 healthy controls (HC). Samples were profiled via Illumina sequencing of the 16S rRNA V3-V4. Stroke severity was assessed at admission by the National Institutes of Health Stroke Scale (NIHSS) and modified Rankin scale (mRS); their correlation with gut microbiota was investigated. The alpha-diversity of the bacterial communities was significantly higher in the fecal samples of patients with tAIS than in patients with ntAIS (Shannon index, P = 0.037). In addition, the combined tAIS and ntAIS group (tntAIS) exhibited higher microbiotic diversity when compared with HC (chao1, P = 0.019). The structure of intestinal microbiota was effectively distinguished between the tAIS and ntAIS group (ANOSIM, r = 0.337, P = 0.007). Additionally, the gut microbiota structure was significantly different between the tntAIS and HC groups (ANOSIM, r = 0.217, P = 0.005). The genera, Ruminococcaceae_ UCG_002 and Christensenellaceae_R-7_group, were implicated in the discrimination of PHS from non-PHS. The order Lactobacillales and family Lachnospiraceae were significantly negatively correlated with NIHSS and mRS at admission (P < 0.05). By contrast, the order Desulfovibrionales, families Christensenellaceae and Desulfovibrionaceae, and genera Ruminococcaceae UCG-014 and Ruminococcaceae UCG-002 were significantly positively correlated with NIHSS and mRS at admission (P < 0.05). This study is the first to profile the characteristics of gut microbiota in patients with AIS and PHS, compared with those with non-PHS. The genera, Ruminococcaceae_ UCG_002 and Christensenellaceae_R-7_group, may be objective indicators of this traditional Chinese medicine (TCM) syndrome in AIS. Furthermore, it provides a microbe-inspired biological basis for TCM syndrome differentiation.

The water content of the foundation soil will change dynamically as a result of rainfall, snowfall, and ground surface evaporation, leading to a significant change in frost heave properties of the foundation soil in cold regions. One-dimensional freezing tests of the Qinghai-Tibet silty clay with three different initial water contents in an open system were carried out using CCD image acquisition technology and computed tomography (CT) scanning technology in combination with the traditional soil freeze-thaw test system. The heat conduct process, cryostructure formation, frost heave development, unfrozen zone consolidation, ice segregation, water migration, and redistribution during soil freezing are studied comprehensively. The results show that increasing the sample’s initial water content will lead to the increase of frozen depth, ladder-like cryostructures in vertical sections, more structural polygons in horizontal sections, and also more obvious ice lens segregation, unfrozen zone consolidation, and water migration during the freezing of soil samples.

Mushrooms are the fleshy, spore-bearing structure of certain fungi, produced by a group of mycelia and buried in a substratum. Mushrooms are classified as edible, medicinal, and poisonous. However, many poisoning incidents occur yearly by consuming wild mushrooms. Thousands of poisoning incidents are reported each year globally, and 80% of these are from unidentified species of mushrooms. Mushroom poisoning is one of the most serious food safety issues worldwide. Motivated by this problem, this study uses an open-source mushroom dataset and employs several data augmentation approaches to decrease the probability of model overfitting. We propose a novel deep learning pipeline (ViT-Mushroom) for mushroom classification using the Vision Transformer large network (ViT-L/32). We compared the performance of our method against that of a convolutional neural network (CNN). We visualized the high-dimensional outputs of the ViT-L/32 model to achieve the interpretability of ViT-L/32 using the t-distributed stochastic neighbor embedding (t-SNE) method. The results show that ViT-L/32 is the best on the testing dataset, with an accuracy score of 95.97%. These results surpass previous approaches in reducing intraclass variability and generating well-separated feature embeddings. The proposed method is a promising deep learning model capable of automatically classifying mushroom species, helping wild mushroom consumers avoid eating toxic mushrooms, safeguarding food safety, and preventing public health incidents of food poisoning. The results will offer valuable resources for food scientists, nutritionists, and the public health sector regarding the safety and quality of mushrooms.

In order to address global warming, most countries in the world have established carbon neutral targets and are continuously taking action to achieve carbon neutrality. The building sector accounts for 36% of end-use energy consumption and 37% of energy-related CO2 emissions globally, so carbon mitigation in building sector is considered to be the most critical step in completing the “last mile” of global carbon neutrality. Low-carbon buildings and communities are the foundation for achieving low-carbon cities and the key transition to reach the goal of carbon neutrality. Therefore, this review aimed to: (a) provide a comprehensive review of countries’ policies on low-carbon buildings and communities and a theoretical basis for improving the corresponding laws and regulations; (b) investigate low-carbon technologies related to building and community construction and operation, as well as identify the current technology gaps; (c) provide a comprehensive overview of low-carbon buildings and communities assessment systems to analyze and evaluate the economic, technical, environmental and social benefits of current building and community energy systems; and (d) identify enablers and barriers in low-carbon buildings and communities to provide direction for future research. The results of this paper can provide comprehensive insights in to further achieving low-carbon buildings and communities.

The combined finite–discrete element method (FDEM) based on the cohesive zone model (the CZM) achieves cracking simulation by inserting cohesive elements between solid elements. In this study, three-point bending fracture tests of frozen soil were simulated by using the FDEM based on the CZM. Firstly, the sensitivity of the cohesive model parameters was analyzed. Secondly, through a series of simulations of the three-point bending test of frozen soil, it was found that the model with reasonable values of the CZM parameters had a good adaptability to the three-point bending cracking test of frozen soil, as the model not only reflects the load-displacement curve, but also has good correspondence with the fracture pattern compared with the test. Finally, the relationship between the CZM parameters and the specimens’ temperature under two loading rates of 1 mm/min and 0.1 mm/min was analyzed, and it was found that the CZM parameters had a good linear relationship with the specimens’ temperature. This paper is expected to provide a new possibility for the numerical simulation of frozen soil cracking.

In many collaborative tasks, the need for joint attention arises when one of the users wants to guide others to a specific location or target in space. If the collaborators are co-located and the target position is in close range, it is almost instinctual for users to refer to the target location by pointing with their bare hands. While such pointing gestures can be efficient and effective in real life, performance will be impacted if the target is in augmented reality (AR), where depth cues like occlusion may be missing if the pointer’s hand is not tracked and modeled in 3D. In this paper, we present a study utilizing head-worn AR displays to examine the effects of incorrect occlusion cues on spatial target identification in a collaborative barehanded referencing task. We found that participants’ performance in AR was reduced compared to a real-world condition, but also that they developed new strategies to cope with the limitations of AR. Our work also identified mixed results of the effect of spatial relationships between users.

Global Satellite Navigation System (GNSS) can't provide normal location service in the indoor environment because the signal can be blocked by buildings. The pseudolites system can supplement GNSS, especially in the indoor environments. A new indoor array pseudolites system is introduced, its multi-channel transmitter has an identical clock source. The high-precision Doppler positioning method based on Z-fixed Known Point Initialization (Z-KPI) is developed without ambiguity resolution. Some static and dynamic tests are conducted to evaluate the positioning accuracy of this new method. In the static testing, the average Doppler measurement error is 1x10-5 m/s, which lays the foundation of the high precision velocity measurement, and the average positioning error is 0.02 m in X axis, 0.004 m in Y axis, and the average velocity error is 3.28x10-5 m/s in X axis, 1.94x10-5 m/s in Y axis. In the dynamic test, the maximum positioning error is 0.39 m in X axis, 0.34 m in Y axis. Finally, the reason for adopting the Z-fixed KPI algorithm is explained by the horizontal dilution of precision (HDOP), meanwhile, the influence of the known initialization point error on indoor positioning accuracy is analyzed, when a deviation from -1.5 m to 0.9 m is added to the Z-axis of the KPI, the maximum positioning error is 0.311 m in X axis, 0.08 m in Y axis; when the horizontal deviation tolerance of the KPI is set from - 0.5 m to 0.5 m, the maximum horizontal positioning error is less than 0.5 m.

Background/Aims: Recently, studies have shown that interleukin-37 (IL-37) is involved in atherosclerosis-related diseases. However, the regulatory mechanisms of IL-37 in atherosclerosis remain unknown. This study aims to determine the role of IL-37 in atherosclerosis and to investigate the underlying mechanisms involved. Methods: IL-37 expression in human atherosclerotic plaques was detected by immunohistochemical staining and real-time reverse transcription polymerase chain reaction (RT-PCR). Oil Red O staining was used to measure the size of plaques. Cell apoptosis in vitro and in vivo was tested by flow cytometric analysis and terminal deoxynucleotidyl-transferase mediated dUTP nick-end labeling (TUNEL) staining, respectively. Protein expression levels of IL-37, IL-18Rα and p-Smad3 were measured by Weston blotting. Results: Immunohistochemical staining revealed that IL-37 was highly expressed in human atherosclerotic plaques. Intracellular cytokine staining revealed that infiltrated CD4+ T lymphocytes and vascular smooth muscle cells (VSMCs), but not macrophages, were the major sources of IL-37. Mice that overexpressed IL-37 exhibited significant improvements in their atherosclerotic burden, as demonstrated by reduced plaque size, increased collagen levels, and reduced numbers of apoptotic cells in vivo. Subsequently, mechanistic studies showed that IL-37 played an anti-atherosclerotic role, at least partially, through reducing inflammation by promoting the differentiation of the T helper cell anti-inflammatory phenotype, and through increasing plaque stability by decreasing matrix metalloproteinase (MMP)-2/13-mediated degradation of collagen and inhibiting VSMCs apoptosis. Conclusion: IL-37 may be a novel potential therapeutic target in patients with atherosclerotic heart disease.

Calcium silicate hydrate seeding is a promising accelerator for cement hydration. In this work, calcium silicate hydrate nanoparticles with various CaO/SiO 2 -ratios were prepared by means of mechanochemical approach. X-ray diffractometry, scanning electron microscopy and laser granulometry were used to study the properties of the calcium silicate hydrate particles. The evaluation of the effectiveness of calcium silicate hydrate seeds on the cement hydration was conducted by heat flow calorimetry and compressive strength tests using ordinary Portland cement and early strength white cement. Results show that the main crystalline phase in the final product is crystalline calcium silicate hydrate (Ca 1.5 SiO 3.5 ·xH 2 O). An acceleration of 6.08 h or 28.03% on the hydration of P.O 42.5 type Portland cement as well as a 78% increment of compressive strength 24 h after water addition can be achieved using calcium silicate hydrate seeds. Compared to the P.O cement, calcium silicate hydrate nanoparticles are less effective in the P.W white cement, which indicates the nature of the cement is a crucial factor to influence the effectiveness of the calcium silicate hydrate seeds as well.

The ubiquitylation of histone H2B on lysine 120 is an important modification with roles in a diverse range of nuclear processes. Here, the authors use 'designer' chromatin to show that H2B-ub orients hDot1L into the correct position for activation.

In view of the inability of Global Navigation Satellite System (GNSS) to provide accurate indoor positioning services and the growing demand for location-based services, indoor positioning has become one of the most attractive research areas. Moreover, with the improvement of the smartphone hardware level, the rapid development of deep learning applications on mobile terminals has been promoted. Therefore, this paper borrows relevant ideas to transform indoor positioning problems into problems that can be solved by artificial intelligence algorithms. First, this article reviews the current mainstream pedestrian dead reckoning (PDR) optimization and improvement methods, and based on this, uses the micro-electromechanical systems (MEMS) sensor on a smartphone to achieve better step detection, stride length estimation, and heading estimation modules. In the real environment, an indoor continuous positioning system based on a smartphone is implemented. Then, in order to solve the problem that the PDR algorithm has accumulated errors for a long time, a calibration method is proposed without the need to deploy any additional equipment. An indoor turning point feature detection model based on deep neural network is designed, and the accuracy of turning point detection is 98%. Then, the particle filter algorithm is used to fuse the detected turning point and the PDR positioning result, thereby realizing lightweight cumulative error calibration. In two different experimental environments, the performance of the proposed algorithm and the commonly used localization algorithm are compared through a large number of experiments. In a small-scale indoor office environment, the average positioning accuracy of the algorithm is 0.14 m, and the error less than 1 m is 100%. In a large-scale conference hall environment, the average positioning accuracy of the algorithm is 1.29 m, and 65% of the positioning errors are less than 1.50 m which verifies the effectiveness of the proposed algorithm. The simple and lightweight indoor positioning design scheme proposed in this article is not only easy to popularize, but also provides new ideas for subsequent scientific research in the field of indoor positioning.

As pedestrian dead-reckoning (PDR), based on foot-mounted inertial sensors, suffers from accumulated error in velocity and heading, an improved heuristic drift elimination (iHDE) with a zero-velocity update (ZUPT) algorithm was proposed for simultaneously reducing the error in heading and velocity in complex paths, i.e., with pathways oriented at 45°, curved corridors, and wide areas. However, the iHDE algorithm does not consider the changes in pedestrian movement modes, and it can deteriorate when a pedestrian walks along a straight path without a pre-defined dominant direction. To solve these two problems, we propose enhanced heuristic drift elimination (eHDE) with an adaptive zero-velocity update (AZUPT) algorithm and novel heading correction algorithm. The relationships between the magnitude peaks of the y-axis angular rate and the detection thresholds were established only using the readings of the three-axis accelerometer and the three-axis gyroscopic, and a mechanism for constructing temporary dominant directions in real time was introduced. Real experiments were performed and the results showed that the proposed algorithm can improve the still-phase detection accuracy of a pedestrian at different movement motions and outperforms the iHDE algorithm in complex paths with many straight features.

The autocorrelation function (ACF) of the Binary Offset Carrier modulation (BOC) signal for Global Navigation Satellite System (GNSS) has multiple peaks, ambiguity is easily generated during the synchronization of the baseband signal. Some methods have been proposed to remove the ambiguity, but the performance is not suitable for high-order BOC signals or does not maintain narrow correlation characteristics. This paper proposes a sub-function reconstruction synchronization algorithm to solve this problem, of which the key is to design a new local auxiliary code: the local Pseudo-Random Noise (PRN) code is divided into several new codes with different delays. The auxiliary code performs a coherent integration operation with the received signal. Then, a correlation function without any positive side peaks is obtained by multiplying the two correlation results to make the acquisition/tracking completely unambiguous. The paper gives a design scheme of navigation signal acquisition/tracking and deduces the theoretical analysis of detection performance. The phase discrimination function is provided. The performance of the method is analyzed from both theoretical and simulation aspects. Compared with the Binary phase shift keying-like (BPSK-LIKE) method, Subcarrier Phase Cancellation (SCPC) method and the Autocorrelation Side-Peak Cancellation Technique (ASPeCT) method, the proposed method has the best detection probability for the acquisition, which is 0.5 dB-Hz better than ASPeCT. For tracking, the proposed method performs best in terms of phase-detection curve, anti-multipath performance, and anti-noise performance. For high-order BOC signals, the SRSA technique successfully removes the false lock points, and there is only one multipath error envelope, and the code tracking error is almost the same as the ASPeCT method.

Aims: Thymic stromal lymphopoietin (TSLP) plays an important role in inflammatory diseases and is over-expressed in human atherosclerotic artery specimens. The present study investigated the role of TSLP in platelet activation and thrombosis models in vitro and in vivo, as well as the underlying mechanism and signaling pathway. Methods and Results: Western blotting and flow cytometry demonstrated that the TSLP receptor was expressed on murine platelets. According to flow cytometry, platelet stimulation with TSLP induced platelet degranulation and integrin αIIbβ3 activation. A TSLPR deficiency caused defective platelet aggregation, defective platelet secretion and markedly blunted thrombus growth in perfusion chambers at both low and high shear rates. TSLPR KO mice exhibited defective carotid artery thrombus formation after exposure to FeCl3. TSLP increased Akt phosphorylation, an effect that was abrogated by the PI3K inhibitors wortmannin and LY294002. The PI3K inhibitors further diminished TSLP-induced platelet activation. TSLP-mediated platelet degranulation, integrin αIIbβ3 activation and Akt phosphorylation were blunted in platelets that lacked the TSLP receptor. Conclusion: This study demonstrated that the functional TSLPR was surface-expressed on murine platelets. The inflammatory cytokine TSLP triggered platelet activation and thrombus formation via TSLP-dependent PI3K/Akt signaling, which suggests an important role for TSLP in linking vascular inflammation and thrombo-occlusive diseases.

A Global Satellite Navigation System (GNSS) cannot provide normal location services in an indoor environment because the signals are blocked by buildings. The Beidou satellite navigation system (BDS)/GPS indoor array pseudolite system is proposed to overcome the problems of indoor positioning with conventional pseudolite, such as time synchronization, ambiguity resolution and base stations. At the same time, an algorithm for Doppler differential positioning is proposed to improve the indoor positioning accuracy and the positioning coverage of the system, which uses the Doppler difference equation and Known Point Initialization (KPI) to determinate the velocity and position of the receiver. Experiments were conducted to verify the proposed system under different conditions; the average positioning error of the Doppler differential positioning algorithm was 7.86 mm in the kinematic test and 2.9 mm in the static test. The results show that BDS/GPS indoor array pseudolite system has the potential to make indoor positioning achieve sub-centimeter precision. Finally, the positioning error of the proposed algorithm is also analyzed, and the data tests show that the dilution of precision (DOP) and cycle- slips have a significant impact on the indoor positioning accuracy; a cycle-slip of a half-wavelength can cause positioning errors of tens of millimeters. Therefore, the Doppler-aided cycle-slip detection method (DACS) is proposed to detect cycle-slips of one cycle or greater than one, and the carrier phase double difference cycle-slip detection method (CPDD) is used to detect cycle slips of a half-wavelength.

Since the signals of the global navigation satellite system (GNSS) are blocked by buildings, accurate positioning cannot be achieved in an indoor environment. Pseudolite can simulate similar outdoor satellite signals and can be used as a stable and reliable positioning signal source in indoor environments. Therefore, it has been proposed as a good substitute and has become a research hotspot in the field of indoor positioning. There are still some problems in the pseudolite positioning field, such as: Integer ambiguity of carrier phase, initial position determination, and low signal coverage. To avoid the limitation of these factors, an indoor positioning system based on fingerprint database matching of homologous array pseudolite is proposed in this paper, which can achieve higher positioning accuracy. The realization of this positioning system mainly includes the offline phase and the online phase. In the offline phase, the carrier phase data in the indoor environment is first collected, and a fingerprint database is established. Then a variational auto-encoding (VAE) network with location information is used to learn the probability distribution characteristics of the carrier phase difference of pseudolite in the latent space to realize feature clustering. Finally, the deep neural network is constructed by using the hidden features learned to further study the mapping relationship between different carrier phases of pseudolite and different indoor locations. In the online phase, the trained model and real-time carrier phases of pseudolite are used to predict the location of the positioning terminal. In this paper, by a large number of experiments, the performance of the pseudolite positioning system is evaluated under dynamic and static conditions. The effectiveness of the algorithm is evaluated by the comparison experiments, the experimental results show that the average positioning accuracy of the positioning system in a real indoor scene is 0.39 m, and the 95% positioning error is less than 0.85 m, which outperforms the traditional fingerprint positioning algorithms.

WiFi fingerprint positioning has been widely used in the indoor positioning field. The weighed K-nearest neighbor (WKNN) algorithm is one of the most widely used deterministic algorithms. The traditional WKNN algorithm uses Euclidean distance or Manhattan distance between the received signal strengths (RSS) as the distance measure to judge the physical distance between points. However, the relationship between the RSS and the physical distance is nonlinear, using the traditional Euclidean distance or Manhattan distance to measure the physical distance will lead to errors in positioning. In addition, the traditional RSS-based clustering algorithm only takes the signal distance between the RSS as the clustering criterion without considering the position distribution of reference points (RPs). Therefore, to improve the positioning accuracy, we propose an improved WiFi positioning method based on fingerprint clustering and signal weighted Euclidean distance (SWED). The proposed algorithm is tested by experiments conducted in two experimental fields. The results indicate that compared with the traditional methods, the proposed position label-assisted (PL-assisted) clustering result can reflect the position distribution of RPs and the proposed SWED-based WKNN (SWED-WKNN) algorithm can significantly improve the positioning accuracy.

Background: Thymic stromal lymphopoietin (TSLP) has been shown to be expressed in various inflammatory tissues, such as human atherosclerotic plaques. Many types of myeloid cells involved in atherosclerosis, including mast cells, lymphocytes, dendritic cells and monocytes/macrophages, present TSLP receptors (TSLPR). However, it is unknown whether platelets, which also play important roles in atherothrombosis, express TSLPR. Methods and Results: We applied flow cytometry and western blotting to show that TSLPR was expressed on the surface of human platelets. Following the addition of TSLP to platelets, the expression of CD62P, CD63, PAC-1 and p-Akt as well as aggregation and ATP release were increased significantly. A TSLPR antibody and a PI3K (phosphatidylinositol 3-kinase) enzyme inhibitor (LY294002) significantly inhibited the platelet activation induced by TSLP. The expression of TSLPR, CD62P and CD63 and the increment of the expression of CD62P and CD63 induced by TSLP in the acute coronary syndrome (ACS) group were markedly higher than those in the control group and the stable angina pectoris (SAP) group. The expression and the increment of the expression of CD62P and CD63 induced by TSLP were positively correlated with the expression of TSLPR. Conclusion: Human platelets express functional TSLPR, which can be activated by TSLP to promote platelet activation. TSLP/TSLPR functions via activating the PI3K/AKT pathway, and this signalling pathway may be one of the mechanisms involved in thrombosis in ACS. In coronary disease patients, the determination of TSLPR in platelets may help to identify the risk of ACS.

This paper presents a pedestrian dead reckoning (PDR) approach based on motion mode recognition using a smartphone. The motion mode consists of pedestrian movement state and phone pose. With the support vector machine (SVM) and the decision tree (DT), the arbitrary combinations of movement state and phone pose can be recognized successfully. In the traditional principal component analysis based (PCA-based) method, the obtained horizontal accelerations in one stride time interval cannot be guaranteed to be horizontal and the pedestrian’s direction vector will be influenced. To solve this problem, we propose a PCA-based method with global accelerations (PCA-GA) to infer pedestrian’s headings. Besides, based on the further analysis of phone poses, an ambiguity elimination method is also developed to calibrate the obtained headings. The results indicate that the recognition accuracy of the combinations of movement states and phone poses can be 92.4%. The 50% and 75% absolute estimation errors of pedestrian’s headings are 5.6° and 9.2°, respectively. This novel PCA-GA based method can achieve higher accuracy than traditional PCA-based method and heading offset method. The localization error can reduce to around 3.5 m in a trajectory of 164 m for different movement states and phone poses.

Abnormal expression of thymic stromal lymphopoietin (TSLP) and its receptor (TSLPR) was found in patients with acute coronary syndrome. Ticagrelor, an oral platelet ADP P2Y12 receptor antagonist, is widely used in these patients. The aim of this study was to verify whether different doses of ticagrelor regulated plaque progression and platelet activity by modulating TSLP/TSLPR. Seventy-five ApoE-/- mice were randomly divided into five groups: (1) high-cholesterol diet (HCD, n = 15); (2) HCD plus ticagrelor 25 mg/kg/d (T1, n = 15); (3) HCD plus ticagrelor 50 mg/kg/d (T2, n = 15); (4) HCD plus ticagrelor 100 mg/kg/d (T3, n = 15); and (5) a normal diet group (ND, n = 15). At day 0 and at week 16, blood lipids and serum TSLP levels, expression of TSLPR, CD62, and CD63, platelet aggregation, platelet ATP release, PI3K/Akt signaling pathway, and plaque morphology were assessed. HCD increased TSLPR expression and atherosclerosis progression but high-dose ticagrelor (100 mg/kg) moderated this trend. TSLPR was positively correlated with Akt1, platelet aggregation, corrected plaque area, and vulnerability index in the T3 group (P