High-resolution Power Doppler Using Null Subtraction Imaging.

To improve the spatial resolution of power Doppler (PD) imaging, we explored null subtraction imaging (NSI) as an alternative beamforming technique to delay-and-sum (DAS). NSI is a nonlinear beamforming approach that uses three different apodizations on receive and incoherently sums the beamformed envelopes. NSI uses a null in the beam pattern to improve the lateral resolution, which we apply here for improving PD spatial resolution both with and without contrast microbubbles. In this study, we used NSI with three types of singular value decomposition (SVD)-based clutter filters and noise equalization to generate high-resolution PD images. An element sensitivity correction scheme was also proposed as a crucial component of NSI-based PD imaging. First, a microbubble trace experiment was performed to evaluate the resolution improvement of NSI-based PD over traditional DAS-based PD. Then, both contrast-enhanced and contrast free ultrasound PD images were generated from the scan of a rat brain. The cross-sectional profile of the microbubble traces and microvessels were plotted. FWHM was also estimated to provide a quantitative metric. Furthermore, iso-frequency curves were calculated to provide a resolution evaluation metric over the global field of view. Up to six-fold resolution improvement was demonstrated by the FWHM estimate and four-fold resolution improvement was demonstrated by the iso-frequency curve from the NSI-based PD microvessel images compared to microvessel images generated by traditional DAS-based beamforming. A resolvability of 39 µm was measured from the NSI-based PD microvessel image. The computational cost of NSI-based PD was only increased by 40 percent over the DAS-based PD.

Context-aware deep learning enables high-efficacy localization of high concentration microbubbles for super-resolution ultrasound localization microscopy.

Ultrasound localization microscopy (ULM) enables deep tissue microvascular imaging by localizing and tracking intravenously injected microbubbles circulating in the bloodstream. However, conventional localization techniques require spatially isolated microbubbles, resulting in prolonged imaging time to obtain detailed microvascular maps. Here, we introduce LOcalization with Context Awareness (LOCA)-ULM, a deep learning-based microbubble simulation and localization pipeline designed to enhance localization performance in high microbubble concentrations. In silico, LOCA-ULM enhanced microbubble detection accuracy to 97.8% and reduced the missing rate to 23.8%, outperforming conventional and deep learning-based localization methods up to 17.4% in accuracy and 37.6% in missing rate reduction. In in vivo rat brain imaging, LOCA-ULM revealed dense cerebrovascular networks and spatially adjacent microvessels undetected by conventional ULM. We further demonstrate the superior localization performance of LOCA-ULM in functional ULM (fULM) where LOCA-ULM significantly increased the functional imaging sensitivity of fULM to hemodynamic responses invoked by whisker stimulations in the rat brain.

Accuracy of death risk prediction models for acute coronary syndrome patients: a systematic review and meta-analysis.

INTRODUCTION: This study systematically evaluates the accuracy of several death risk prediction models for patients with acute coronary syndrome (ACS) through evidence-based methods. We identify the most accurate and effective ACS death risk prediction model and provide an evidence-based basis for clinical healthcare personnel to evaluate their choice of death risk prediction model for ACS patients. EVIDENCE ACQUISITION: An evidence-based approach was used to study the current death risk prediction model for ACS. First, a literature search was carried out using computer-based and manual searching. The literature databases searched include Cochrane Library, MEDLINE, EMBASE, PubMed, Web of Science, WanFang Data, CNKI, VPCS, and SinoMed. The search period was limited to 2009 to 2022. Screening, quality evaluation and data extraction were carried out for the included articles. The PROBAST was used to conduct a migration risk assessment. RevMan 5.3 and Meta-DiSc 1.4 were used in combination to determine the model effect sizes. A descriptive analysis was conducted for the data that could not be meta-analyzed. EVIDENCE SYNTHESIS: A total of 8277 articles were initially included in this study. After screening, 25 articles were finally included, involving 11 different risk prediction models. A total of 306,390 patients with ACS were included of which 158,080 (51.6%) were male and 147,793 (48.4%) were female. The patients stemmed from 11 different countries (e.g., China, the USA, Spain, the UK, etc.). The total number of deaths was 23,601. The sensitivity of the GRACE risk prediction model was 0.78, with a specificity of 0.76 and an AUC value of 0.86. The sensitivity of the CAMI risk prediction model was 0.78, with a specificity of 0.70 and an AUC value of 0.85. The sensitivity of the TIMI risk prediction model was 0.51, with a specificity of 0.81, and an AUC value of 0.64. The sensitivity of the REMS risk prediction model was 0.78, with a specificity of 0.46 and an AUC value of 0.41. Eight different risk prediction models (EPICOR, CRUSADE, SAMI, GWTG, LNS, SYNTAX II, APACHE II) that could not be combined with the effect size were also included, with sensitivities ranging from 0.77-0.95, specificities ranging from 0.22-0.99, and AUC values ranging from 0.71-0.92. CONCLUSIONS: The GRACE and CAMI risk prediction models demonstrate good accuracy for evaluating the death risk of ACS patients. The accuracy of the TIMI risk prediction model is similar to that of the REMS risk prediction model. The APACHE II, SYNTAX II, EPICOR, and CAMI risk prediction models also show good accuracy for estimating the risk of death in ACS patients, although further validation is needed due to limited evidence. For improved predictive accuracy and to help advance medical interventions, the author recommends that clinical medical staff use the GRACE model to predict the death risk of ACS patients.

Transcriptomic Signature of 3D Hierarchical Porous Chip Enriched Exosomes for Early Detection and Progression Monitoring of Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is a highly lethal malignant tumor, and the current non-invasive diagnosis method based on serum markers, such as α-fetoprotein (AFP), and des-γ-carboxy-prothrombin (DCP), has limited efficacy in detecting it. Therefore, there is a critical need to develop novel biomarkers for HCC. Recent studies have highlighted the potential of exosomes as biomarkers. To enhance exosome enrichment, a silicon dioxide (SiO2) microsphere-coated three-dimensional (3D) hierarchical porous chip, named a SiO2-chip is designed. The features of the chip, including its continuous porous 3D scaffold, large surface area, and nanopores between the SiO2 microspheres, synergistically improved the exosome capture efficiency. Exosomes from both non-HCC and HCC subjects are enriched using an SiO2-chip and performed RNA sequencing to identify HCC-related long non-coding RNAs (lncRNAs) in the exosomes. This study analysis reveales that LUCAT-1 and EGFR-AS-1 are two HCC-related lncRNAs. To further detect dual lncRNAs in exosomes, quantitative real time polymerase chain reaction (qRT-PCR) is employed. The integration of dual lncRNAs with AFP and DCP significantly improves the diagnostic accuracy. Furthermore, the integration of dual lncRNAs with DCP effectively monitors the prognosis of patients with HCC and detects disease progression. In this study, a liquid biopsy-based approach for noninvasive and reliable HCC detection is developed.

Analysis of Risk Factors for Complications after Percutaneous Coronary Intervention in Patients with Acute ST-segment Elevation Myocardial Infarction.

Background: Acute ST segment elevation myocardial infarction (ASTEMI) is the most common and serious type of AMI, percutaneous coronary intervention (PCI) is currently the most commonly used approach for clinical treatment of ASTEMI. After PCI, patients with ASTEMI are very prone to various complications, which will seriously threaten their health and life safety. Objective: This study aims to analyze the risk factors for complications in patients with acute ST-segment elevation myocardial infarction (ASTEMI) treated with percutaneous coronary intervention (PCI). Methods: A total of 107 patients with ASTEMI who were subjected to PCI from October 2021 to December 2022 were selected as study subjects. Patients were divided into a safety group (no complications, n = 63) and a risk group (n = 45) based on the presence of postoperative complications. Baseline data (age, sex, etc.), Killip classification, left ventricular ejection fractions (LVEF), and routine blood test results were collected from patients in both groups for Logistic regression analysis to obtain relevant factors affecting the occurrence of post-PCI complications. Results: There were no differences between the safety group and the risk group in terms of sex, age, body mass index (BMI), Killip classification, and infarct site (P > .05). Compared with the safety group, the risk group exhibited a higher proportion of patients with multiple pre-existing diseases, LVEF < 40%, and number of coronary artery lesions ≥ 1, and higher levels of hs-CRP, NT-proBNP, HbA1c and Scr (P < .05). Logistic regression analysis results showed that multiple pre-existing diseases, hs-CRP, NT-proBNP, HbA1c, and Scr were relevant factors affecting the occurrence of post-PCI complications (P < .05). Conclusion: Multiple pre-existing diseases, hs-CRP, NT-proBNP, HbA1c, and Scr were all independent risk factors for the occurrence of post-PCI complications. Future clinical attention should be paid to these indicators in patients with ASTEMI in order to prevent post-PCI complications.

High-Quality Circularly Polarized Organic Afterglow from Nonconjugated Amorphous Chiral Copolymers.

Organic materials featuring circularly polarized luminescence (CPL) and/or afterglow emission represent an active research frontier with promising applications in various fields, but the achievement of high-performance CPL organic afterglow (CPOA) remains a huge challenge due to the intrinsic contradictions between the luminescent lifetime/dissymmetry factor (glum) and phosphorescent quantum efficiency (PhQY). Herein, we report a simple and universal approach to design efficient CPOA from amorphous copolymers by incorporating chiral chromophores into a nonconjugated clusterization-triggered emissive polymer with plenty of hydron-bonding interactions, followed by aggregation engineering using water dissolution and evaporation. With this chiral copolymerization and aggregation engineering (CCAE) strategy, high-performance CPOA polymers with PhQYs of up to 6.32%, ultralong lifetimes of over 650 ms, glum values of 3.54 × 10-3, and the highest figure-of-merit were achieved at room temperature. Given the impressive CPOA performance of these polymers, the applications in multilevel data anticounterfeiting and reversible displays with high stability were demonstrated. These findings through the CCAE strategy to overcome the inherent restraints of CPOA materials lay the foundation for the development of amorphous polymers with superior CPOA, significantly expanding the understanding of CPL and the design of organic afterglow materials.

Nitrogen removal in freshwater sediments of riparian zone: N-loss pathways and environmental controls.

The riparian zone is an important location of nitrogen removal in the terrestrial and aquatic ecosystems. Many studies have focused on the nitrogen removal efficiency and one or two nitrogen removal processes in the riparian zone, and less attention has been paid to the interaction of different nitrogen transformation processes and the impact of in situ environmental conditions. The molecular biotechnology, microcosm culture experiments and 15N stable isotope tracing techniques were used in this research at the riparian zone in Weinan section of the Wei River, to reveal the nitrogen removal mechanism of riparian zone with multi-layer lithologic structure. The results showed that the nitrogen removal rate in the riparian zone was 4.14-35.19 µmol·N·kg-1·h-1. Denitrification, dissimilatory reduction to ammonium (DNRA) and anaerobic ammonium oxidation (anammox) jointly achieved the natural attenuation process of nitrogen in the riparian zone, and denitrification was the dominant process (accounting for 59.6%). High dissolved organic nitrogen and nitrate ratio (DOC:NO3-) would promote denitrification, but when the NO3- content was less than 0.06 mg/kg, DNRA would occur in preference to denitrification. Furthermore, the abundances of functional genes (norB, nirS, nrfA) and anammox bacterial 16S rRNA gene showed similar distribution patterns with the corresponding nitrogen transformation rates. Sedimentary NOX-, Fe(II), dissolved organic carbon (DOC) and the nitrogen transformation functional microbial abundance were the main factors affecting nitrogen removal in the riparian zone. Fe (II) promoted NO3- attenuation through nitrate dependent ferrous oxidation process under microbial mediation, and DOC promotes NO3- attenuation through enhancing DNRA effect. The results of this study can be used for the management of the riparian zone and the prevention and control of global nitrogen pollution.

Longitudinal Awake Imaging of Deep Mouse Brain Microvasculature with Super-resolution Ultrasound Localization Microscopy.

Super-resolution ultrasound localization microscopy (ULM) is an emerging imaging modality that resolves capillary-scale microvasculature in deep tissues. However, existing preclinical ULM applications are largely constrained to anesthetized animals, introducing confounding vascular effects such as vasodilation and altered hemodynamics. As such, ULM quantifications (e.g., vessel diameter, density, and flow velocity) may be confounded by the use of anesthesia, undermining the usefulness of ULM in practice. Here we introduce a method to address this limitation and achieve ULM imaging in awake mouse brain. Pupillary monitoring was used to confirm the awake state during ULM imaging. ULM revealed that veins showed a greater degree of vascularity reduction from anesthesia to awake states than did arteries. The reduction was most significant in the midbrain and least significant in the cortex. ULM also revealed a significant reduction in venous blood flow velocity across different brain regions under awake conditions. Serial in vivo imaging of the same animal brain at weekly intervals demonstrated the highly robust longitudinal imaging capability of the proposed technique. This is the first study demonstrating longitudinal ULM imaging in the awake mouse brain, which is essential for many ULM brain applications that require awake and behaving animals.

Simultaneously Achieving Large Piezoelectricity and Low Dielectric Loss in High-Temperature BiScO3-PbTiO3-Based Ceramics.

High-temperature piezoelectric materials are pivotal to technology applications fields including defense, aerospace, nuclear energy, and oil well logging. However, the acquisition of excellent piezoelectric properties is usually at the cost of temperature stability (reduced Curie temperature and increased high-temperature dielectric loss), which hinders the application of piezoelectric ceramics in harsh environments. In this study, we investigated the effect of Nb5+ donor and Mn2+/3+ acceptor doping on the dielectric and piezoelectric properties of BiScO3-PbTiO3 (BS-PT)-based ceramics. In contrast to the acceptor doping, it was found that the donor doping not only enhances the piezoelectric properties but also effectively suppresses the dielectric loss at a high temperature by reducing the oxygen vacancy concentration. Eventually, we simultaneously attained an excellent piezoelectric performance (d33 is 553 pC/N at room temperature and 1528 pC/N at 400 °C, respectively) and a low dielectric loss (less than 2% in the temperature range of 150-300 °C) but still with a high Curie temperature (TC ∼ 445 °C) in Nb5+-doped BS-PT ceramics. Furthermore, different in situ measurements were used to demonstrate the remarkable temperature stability up to a high depolarization temperature of ∼400 °C. This work represents significant progress in high-temperature piezoelectric materials and provides a guideline for future efforts on enhancing the piezoelectricity and suppressing the dielectric loss at high temperature.

Similarity measures-based graph co-contrastive learning for drug-disease association prediction.

MOTIVATION: An imperative step in drug discovery is the prediction of drug-disease associations (DDAs), which tries to uncover potential therapeutic possibilities for already validated drugs. It is costly and time-consuming to predict DDAs using wet experiments. Graph Neural Networks as an emerging technique have shown superior capacity of dealing with DDA prediction. However, existing Graph Neural Networks-based DDA prediction methods suffer from sparse supervised signals. As graph contrastive learning has shined in mitigating sparse supervised signals, we seek to leverage graph contrastive learning to enhance the prediction of DDAs. Unfortunately, most conventional graph contrastive learning-based models corrupt the raw data graph to augment data, which are unsuitable for DDA prediction. Meanwhile, these methods could not model the interactions between nodes effectively, thereby reducing the accuracy of association predictions. RESULTS: A model is proposed to tap potential drug candidates for diseases, which is called Similarity Measures-based Graph Co-contrastive Learning (SMGCL). For learning embeddings from complicated network topologies, SMGCL includes three essential processes: (i) constructs three views based on similarities between drugs and diseases and DDA information; (ii) two graph encoders are performed over the three views, so as to model both local and global topologies simultaneously; and (iii) a graph co-contrastive learning method is introduced, which co-trains the representations of nodes to maximize the agreement between them, thus generating high-quality prediction results. Contrastive learning serves as an auxiliary task for improving DDA predictions. Evaluated by cross-validations, SMGCL achieves pleasing comprehensive performances. Further proof of the SMGCL's practicality is provided by case study of Alzheimer's disease. AVAILABILITY AND IMPLEMENTATION: https://github.com/Jcmorz/SMGCL.

PmAGAMOUS recruits polycomb protein PmLHP1 to regulate single-pistil morphogenesis in Japanese apricot.

Japanese apricot (Prunus mume Sieb. et Zucc.) is a traditional fruit tree with a long history. Multiple pistils (MP) lead to the formation of multiple fruits, decreasing fruit quality and yield. In this study, the morphology of flowers was observed at 4 stages of pistil development: undifferentiated stage (S1), predifferentiation stage (S2), differentiation stage (S3), and late differentiation stage (S4). In S2 and S3, the expression of PmWUSCHEL (PmWUS) in the MP cultivar was significantly higher than that in the single-pistil (SP) cultivar, and the gene expression of its inhibitor, PmAGAMOUS (PmAG), also showed the same trend, indicating that other regulators participate in the regulation of PmWUS during this period. Chromatin immunoprecipitation-qPCR (ChIP-qPCR) showed that PmAG could bind to the promoter and the locus of PmWUS, and H3K27me3 repressive marks were also detected at these sites. The SP cultivar exhibited an elevated level of DNA methylation in the promoter region of PmWUS, which partially overlapped with the region of histone methylation. This suggests that the regulation of PmWUS involves both transcription factors and epigenetic modifications. Also, the gene expression of Japanese apricot LIKE HETEROCHROMATIN PROTEIN (PmLHP1), an epigenetic regulator, in MP was significantly lower than that in SP in S2 to 3, contrary to the trend in expression of PmWUS. Our results showed that PmAG recruited sufficient PmLHP1 to maintain the level of H3K27me3 on PmWUS during the S2 of pistil development. This recruitment of PmLHP1 by PmAG inhibits the expression of PmWUS at the precise time, leading to the formation of 1 normal pistil primordium.

Impact of the COVID-19 pandemic and the dynamic COVID-zero strategy on HIV incidence and mortality in China.

BACKGROUND: In response to the coronavirus disease 2019 (COVID-19) pandemic, the Chinese government implemented the dynamic COVID-zero strategy. We hypothesized that pandemic mitigation measures might have reduced the incidence, mortality rates, and case fatality ratios (CFRs) of the human immunodeficiency virus (HIV) in 2020-2022. METHOD: We collected HIV incidence and mortality data from the website of the National Health Commission of the People's Republic of China from January 2015 to December 2022. We compared the observed and predicted HIV values in 2020-2022 with those in 2015-2019 using a two-ratio Z-test. RESULTS: From January 1, 2015, to December 31, 2022, a total of 480,747 HIV incident cases were reported in mainland China, of which 60,906 (per year) and 58,739 (per year) were reported in 2015-2019 (pre-COVID-19 stage) and 2020-2022 (post-COVID-19 stage), respectively. The average yearly HIV incidence decreased by 5.2450% (from 4.4143 to 4.1827 per 100,000 people, p <  0.001) in 2020-2022 compared with that in 2015-2019. However, the average yearly HIV mortality rates and CFRs increased by 14.1076 and 20.4238%, respectively (all p <  0.001), in 2020-2022 compared with those in 2015-2019. During the emergency phase in January 2020 to April 2020, the monthly incidence was significantly lower (23.7158%) than that during the corresponding period in 2015-2019, while the incidence during the routine stage in May 2020-December 2022 increased by 27.4334%, (all p <  0.001). The observed incidence and mortality rates for HIV decreased by 16.55 and 18.1052% in 2020, by 25.1274 and 20.2136% in 2021, and by 39.7921 and 31.7535% in 2022, respectively, compared with the predicted values, (all p <  0.001). CONCLUSIONS: The findings suggest that China's dynamic COVID-zero strategy may have partly disrupted HIV transmission and further slowed down its growth. Without China's dynamic COVID-zero strategy, HIV incidence and deaths in the country would have likely remained high in 2020-2022. There is an urgent need to expand and improve HIV prevention, care, and treatment, as well as surveillance in the future.

Job stress and university faculty members' life satisfaction: The mediating role of emotional burnout.

As one of the leading work-related health problems arising from increasingly fierce competition, work-related stress has become a significant predictor of the reduced wellbeing of university faculty members, especially for non-tenured junior faculty members. In light of this and based on a survey, this research seeks to examine how and why work-related stress impacts the life satisfaction level of university junior faculty members. The results indicate that the three subdivisions of university faculty members' work-related stress, namely, research stress, teaching stress, and administrative stress, are all negatively related to their life satisfaction level. In addition, emotional burnout has been confirmed to function as the psychological mechanism for the aforementioned main effects. The research contributes to the literature mainly by offering a new insight in which the three subdivisions of work-related stress are regarded as independent variables affecting the life satisfaction level of university junior faculty members.

A single-centre, open-label, single-arm, fixed-sequence pharmacokinetic study of SHR3680 on repaglinide and bupropion in prostate cancer patients.

To evaluate the pharmacokinetic effects of SHR3680 on repaglinide and bupropion and its metabolite hydroxybupropion. METHODS: A single-centre, open-label, single-arm, fixed-sequence clinical trial in 18 patients with prostate cancer. RESULTS: After a single oral dose of 0.5 mg repaglinide and SHR3680, geometric mean peak plasma concentration (Cmax ) of plasma repaglinide was 14.240 and 5.887 ng/mL, geometric mean area under the concentration-time curve (AUC0-t )was 20.577 and 7.320 h ng/mL, geometric mean AUC0-∞ was 20.949 and 7.451 h ng/mL, mean half-life (t1/2 ) was 1.629 and 1.195 hours, and geometric mean oral clearance (CL/F) was 23.867 and 67.107 L/h, respectively. After a single oral administration of 150 mg bupropion and SHR3680, geometric mean Cmax of plasma bupropion was 85.430 and 33.747 ng/mL, geometric mean AUC0-t was 1003.896 and 380.158 h ng/mL, geometric mean AUC0-∞ was 1038.054 and 401.387 h ng/mL, mean t1/2 was 22.533 and 17.733 hours, and geometric mean CL/F was 144.501 and 373.705 L/h, respectively. The plasma geometric mean Cmax of its main active metabolic hydroxybupropion was 268.113 and 177.318 ng/mL, geometric mean AUC0-t was 14 283.087 and 5420.219 h ng/mL, geometric mean AUC0-∞ was 15 218.158 and 5364.625 h ng/mL, mean t1/2 were 36.069 and 16.688 hours, and geometric mean CL/F was 8.623 L/h and 27.961 L/h, respectively. CONCLUSION: Coadministration of SHR3680 with repaglinide or bupropion significantly shortened the elimination half-lives, significantly increased the apparent clearance rate, and significantly decreased the in vivo exposure of repaglinide, bupropion and hydroxybupropion compared with single administration of repaglinide or bupropion.

Interaction mechanism between nitrogen conversion and the microbial community in the hydrodynamic heterogeneous interaction zone.

To study the inorganic nitrogen in the process of interaction of river and groundwater and the changes in the microbial community, a vertical simulation device was used to simulate groundwater recharge to river water (upwelling) and river water recharge to groundwater (downwelling). The inorganic nitrogen concentrations in the soil and water solution as well as the characteristics of the microbial community were assessed to determine the inorganic nitrogen transformation and microbial community response in the heterogeneous interaction zone under hydrodynamic action, and the interaction mechanism between nitrogen transformation and the microbial community in the interaction zone was revealed. The removal rates of NO3--N in the simulated solution reached 99.1% and 99.3% under the two fluid-groundwater conversion modes, and the prolonged hydraulic retention time (HRT) of the oxidization-reduction layer in the fine clay area and the high organic matter content made the inorganic nitrogen transformation process dominated by microorganisms more complete. The denitrification during upwelling, dominated by denitrifying bacteria in Sphingomonas, Pseudomonas, Bacillus, and Arthrobacter, was stronger than that during downwelling. Dissimilatory nitrate reduction to ammonium (DNRA), controlled by some aerobic bacteria in Pseudomonas, Bacillus, and Desulfovibrio, was more intense in downflow mode than upflow mode.

FIRE: knowledge-enhanced recommendation with feature interaction and intent-aware attention networks.

To solve the information overload issue and enhance the user experience of various web applications, recommender systems aim to better model user interests and preferences. Knowledge Graphs (KGs), consisting of real-world objective facts and fruitful entities, play a vital role in recommender systems. Recently, a technological trend has been to develop end-to-end Graph Neural Networks (GNNs)-based knowledge-aware recommendation (a.k.a., Knowledge Graph Recommendation, KGR) models. Unfortunately, current GNNs-based KGR approaches focus on how to capture high-order feature information on KGs while neglecting the following two crucial limitations: 1) The explicitly high-order feature interaction and fusion mechanism and 2) The valid user intent modelling mechanism. As such, these issues lead to insufficient user/item representation learning capability and unsatisfactory KGR performance. In this work, we present a novel Knowledge-enhanced Re commendation with F eature I nteraction and Intent-aware Attention Networks (FIRE) to address the latent intent modelling and high-order feature interaction deficiencies ignored by existing KGR methods. Based on the prototype user/item representation learning leveraging the GNNs-based approach, our model offers the following major improvements: One is the innovative use of Convolutional Neural Networks (CNNs) that perform vertical convolutional (a.k.a., bit-level convolutional) and horizontal convolutional (a.k.a., vector-level convolutional) processes to model multi-granular high-order feature interactions to enhance item-side representation learning. Another is to model users' latent intent factors by utilizing a two-level attention mechanism (i.e., node- and intent-level attention mechanism) to enhance user-side representation learning. Extensive experiments on three KGs domain public datasets demonstrate that our method outperforms the existing state-of-the-art baseline. Last but not least, numerous ablation- and model studies demystify the working mechanism and elucidate the plausibility of the proposed model.

Who Is Affecting Who: The New Changes of Personal Influence in the Social Media Era.

With the development of social media, some individuals who have a great influence on the Internet have become opinion leaders, which means that the traditional agenda-setting theory cannot explain the mechanism of social consensus generation in the social media era. Therefore, the individual agenda is a new perspective to studying social consensus and personal influence in social media. This study defined the concept of the "individual agenda," and conducted an empirical study on the relationship between the media agenda, the opinion leaders' agenda, and the individual agenda, based on 71.77 million tweets sampled from the Twitter platform in 2015 with the approach of topic modeling. This study found that (1) most individual agendas are not consistent with the traditional public agenda, and the intrapersonal issue salience is highly related to the interpersonal issue salience; therefore, the concept of "individual agenda" has been validated empirically; (2) the media agenda has a significant positive correlation with 30.3% of the individual agendas, which means that professional media influences only a small number of individuals; and (3) the opinion leaders' agenda has no significant correlation with the media agenda, while it has a significant positive correlation with 31.1% of the individual agendas, which means that opinion leaders have become strong competitors of traditional professional media in agenda-setting. This study also discussed the relationship between individual agenda-setting and public agenda-setting and the potential research directions in the future.

Handheld volumetric photoacoustic/ultrasound imaging using an internal scanning mechanism.

Photoacoustic/ultrasound (PA/US) dual-modality imaging has been evolving rapidly for the last two decades. Handheld PA/US probes with different implementations have attracted particular attention due to their convenience and high applicability. However, developing a volumetric dual-modality PA/US imaging probe with a compact design remains a challenge. Here, we develop a handheld volumetric PA/US imaging probe with a special light-ultrasound coupling design and an internal scanning mechanism. A coaxial design for the excitation and detection paths in a customized 3D-printed housing with a size of 110 × 90 × 64 mm3 is proposed to optimize the signal-to-noise ratio (SNR) of the handheld probe for deep tissue imaging. Two parallel and synchronously rotational acoustic reflectors allow for volumetric imaging with an effective field of view (FOV) of more than 30 mm × 20 mm × 8 mm. In addition to simulation and phantom validations, in vivo human trials are successfully carried out, demonstrating the high imaging quality and stability of the system for potential clinical translations.

Analysis of Influencing Factors of Unplanned Readmission in Patients With Acute Coronary Syndrome Within 30 Days After PCI.

OBJECTIVE: The purpose of this study is to identify the influencing factors of unplanned readmission in patients with the acute coronary syndrome (ACS) within 30 days after percutaneous coronary intervention (PCI). METHODS: From November 1, 2018, to October 31, 2019, the clinical data of 1277 patients with acute coronary syndrome and percutaneous coronary intervention retrospectively were collected. After screening by exclusion and rejection criteria, a total of 936 patients finally entered the study. Patients were divided into the readmission group (57 cases) and the non-readmission group (879 cases), according to whether unplanned readmission occurred within 30 days after PCI. To analyze the influence of patients' age, past disease history, medication history, laboratory data, vascular diseases, and other factors on readmission and the clinical characteristics of readmission patients. RESULTS: Fifty-seven patients had unplanned readmission within 30 days, and the readmission rate was 6.09%. The clinical features of readmission patients are older age, longer hospitalization days, more emergency percutaneous coronary intervention, more patients with diabetes history, and more patients diagnosed with ST-segment elevation myocardial infarction and non-ST-segment elevation myocardial infarction. Logistic regression analysis revealed that smoking index, number of diseased vessels, ACEF score, diabetes, and PCI status were the influencing factors of unplanned readmission of ACS patients within 30 days after PCI. CONCLUSION: Smoking index, number of diseased vessels, ACEF score, diabetes, and PCI status are the influencing factors of unplanned readmission within 30 days after percutaneous coronary intervention for patients with acute coronary syndrome.

Application of physiologically based pharmacokinetic modelling for the prediction of drug-drug interactions involving anlotinib as a perpetrator of cytochrome P450 enzymes.

Anlotinib is a small molecule of novel tyrosine kinase inhibitor initially approved to treat non-small cell lung cancer in China. Drug-drug interaction (DDI) is an extrinsic factor important for the appropriate use of anlotinib in clinical practice. In vitro experiments demonstrated that anlotinib is a substrate of cytochrome P450 (CYP) enzymes and moderate inhibitor of several common ones; however, no clinical DDI studies have been performed to investigate inhibitory effects of anlotinib on these CYP enzymes. Thus, its drug label recommends avoiding co-administration with substrates of these enzymes, which have narrow therapeutic windows. In this study, we performed a CYP450 inhibition study, followed by gathering in vitro and clinical pharmacokinetic data to build the first physiologically based pharmacokinetic (PBPK) model of anlotinib. The verified model was subsequently used to predict the DDI mediated by anlotinib. As a result, the marginal plasma exposure changes of typical CYP3A and CYP2C9 substrates were less than the bioequivalence threshold, indicating that anlotinib has a very low potential of causing clinically meaningful DDI through the inhibition of several major CYP enzymes. According to the FDA's latest guideline on DDI, the established model with the simulation results may support the revision of anlotinib labelling without further clinical studies, lifting unnecessary restrictions on anlotinib regimens.