Polycyclic aromatic hydrocarbons exposure and arterial stiffness-related plasma miRNAs: A panel study.

The underlying mechanisms between polycyclic aromatic hydrocarbons (PAHs) exposure and arterial stiffness are poorly understood. We carried out a panel study involving three repeated surveys to examine the associations of individual and mixture of PAHs exposure with arterial stiffness-related miRNAs among 123 community adults. In linear mixed-effect (LME) models, we found that urinary 9-hydroxyfluorene (9-OHFlu), 2-hydroxyphenanthrene (2-OHPh), 9-hydroxyphenanthrene (9-OHPh) at lag 0 day were positively linked to miR-146a and/or miR-222. The Bayesian kernel machine regression (BKMR) analyses revealed positive overall associations of PAHs mixture at lag 0 day with miR-146a and miR-222, and urinary 9-OHFlu contributed the most. In addition, an inter-quartile range (IQR) increase in urinary 9-OHFlu at lag 0 day was associated with elevated miR-146a and miR-222 by 0.16 (95% CI: 0.02, 0.30) to 0.34 (95% CI: 0.13, 0.54). Accordingly, exposure to PAHs, especially 9-OHFlu at lag 0 day, was related to elevated arterial stiffness-related plasma miRNAs.

Periodontal disease increases the severity of chronic obstructive pulmonary disease: a Mendelian randomization study.

BACKGROUND: Recent research suggests that periodontitis can increase the risk of chronic obstructive pulmonary disease (COPD). In this study, we performed two-sample Mendelian randomization (MR) and investigated the causal effect of periodontitis (PD) on the genetic prediction of COPD. The study aimed to estimate how exposures affected outcomes. METHODS: Published data from the Gene-Lifestyle Interaction in the Dental Endpoints (GLIDE) Consortium's genome-wide association studies (GWAS) for periodontitis (17,353 cases and 28,210 controls) and COPD (16,488 cases and 169,688 controls) from European ancestry were utilized. This study employed a two-sample MR analysis approach and applied several complementary methods, including weighted median, inverse variance weighted (IVW), and MR-Egger regression. Multivariable Mendelian randomization (MVMR) analysis was further conducted to mitigate the influence of smoking on COPD. RESULTS: We chose five single-nucleotide polymorphisms (SNPs) as instrumental variables for periodontitis. A strong genetically predicted causal link between periodontitis and COPD, that is, periodontitis as an independent risk factor for COPD was detected. PD (OR = 1.102951, 95% CI: 1.005-1.211, p = 0.039) MR-Egger regression and weighted median analysis results were coincident with those of the IVW method. According to the sensitivity analysis, horizontal pleiotropy's effect on causal estimations seemed unlikely. However, reverse MR analysis revealed no significant genetic causal association between COPD and periodontitis. IVW (OR = 1.048 > 1, 95%CI: 0.973-1.128, p = 0.2082) MR Egger (OR = 0.826, 95%CI:0.658-1.037, p = 0.1104) and weighted median (OR = 1.043, 95%CI: 0.941-1.156, p = 0.4239). The results of multivariable Mendelian randomization (MVMR) analysis, after adjusting for the confounding effect of smoking, suggest a potential causal relationship between periodontitis and COPD (P = 0.035). CONCLUSION: In this study, periodontitis was found to be independent of COPD and a significant risk factor, providing new insights into periodontitis-mediated mechanisms underlying COPD development.

HRD-Net: High resolution segmentation network with adaptive learning ability of retinal vessel features.

Retinal segmentation is a crucial step in the early warning of human health conditions. However, retinal blood vessels possess complex curvature, irregular distribution, and contain multi-scale fine structures, which make the limited receptive field of regular convolution challenging to process their vascular details efficiently. Additionally, the encoder-decoder based network leads to irreversible spatial information loss because of multiple downsampling, resulting in over-segmentation and missed segmentation of the vessels. For this reason, we develop a high-resolution network based on Deformable Convolution v3, called HRD-Net. By constructing a high-resolution representation, the network allows special attention to be paid to the details of tiny blood vessels. The proposed feature enhancement cascade module based on Deformable Convolution v3 can flexibly adapt and capture the ever-changing morphology and intricate connections of retinal blood vessels, ensuring the continuity of vessel segmentation. In the output phase of the network, the proposed global aggregation module integrates full-resolution feature maps while suppressing redundant features, achieving an effective fusion of high-level semantic information and spatial detail information. In addition, we have re-examined the selection criteria for activation and normalization methods, and also refine the network architectures from a spatial domain perspective to release redundant computational loads. Testing on the DRIVE, STARE, and CHASE_DB1 datasets indicates that HRD-Net, with fewer parameters, outperforms existing segmentation methods on several evaluation metrics such as F1, ACC, SE, SP, AUC, and IOU.

Clinical prediction of microvascular invasion in hepatocellular carcinoma using an MRI-based graph convolutional network model integrated with nomogram.

OBJECTIVES: Based on enhanced MRI, a prediction model of microvascular invasion (MVI) for hepatocellular carcinoma (HCC) was developed using graph convolutional network (GCN) combined nomogram. METHODS: We retrospectively collected 182 HCC patients confirmed histopathologically, all of them performed enhanced MRI before surgery. The patients were randomly divided into training and validation groups. Radiomics features were extracted from the arterial phase (AP), portal venous phase (PVP), and delayed phase (DP), respectively. After removing redundant features, the graph structure by constructing the distance matrix with the feature matrix was built. Screening the superior phases and acquired GCN Score (GS). Finally, combining clinical, radiological and GS established the predicting nomogram. RESULTS: 27.5% (50/182) patients were with MVI positive. In radiological analysis, intratumoural artery (P = 0.007) was an independent predictor of MVI. GCN model with grey-level cooccurrence matrix-grey-level run length matrix features exhibited area under the curves of the training group was 0.532, 0.690, and 0.885 and the validation group was 0.583, 0.580, and 0.854 for AP, PVP, and DP, respectively. DP was selected to develop final model and got GS. Combining GS with diameter, corona enhancement, mosaic architecture, and intratumoural artery constructed a nomogram which showed a C-index of 0.884 (95% CI: 0.829-0.927). CONCLUSIONS: The GCN model based on DP has a high predictive ability. A nomogram combining GS, clinical and radiological characteristics can be a simple and effective guiding tool for selecting HCC treatment options. ADVANCES IN KNOWLEDGE: GCN based on MRI could predict MVI on HCC.

The Active Glucuronide Metabolite of the Brain Protectant IMM-H004 with Poor Blood-Brain Barrier Permeability Demonstrates a High Partition in the Rat Brain via Multiple Mechanisms.

BACKGROUND: Glucuronidation is an essential metabolic pathway for a variety of drugs. IMM-H004 is a novel neuroprotective agent against ischemic stroke, and its glucuronide metabolite IMM-H004G exhibits similar pharmacological activity. Despite possessing a higher molecular weight and polarity, brain exposure of IMM-H004G is much higher than that of IMM-H004. This study aimed to investigate the brain metabolism and transport mechanisms of IMM-H004 and IMM-H004G. METHODS: First, the possibility of IMM-H004 glucuronidation in the brain was evaluated in several human brain cell lines and rat homogenate. Subsequently, the blood-brain barrier carrier-mediated transport mechanism of IMM-H004 and IMM-H004G was studied using overexpression cell models. In addition, intracerebroventricular injection, in situ brain perfusion model, and microdialysis/microinjection techniques were performed to study the distribution profiles of IMM-H004 and IMM-H004G. RESULTS: IMM-H004 could be metabolized to IMM-H004G in both rat brain and HEB cells mediated by UGT1A7. However, IMM-H004G could not be hydrolyzed back into IMM-H004. Furthermore, the entry and efflux of IMM-H004 in the brain were mediated by the pyrilamine-sensitive H+/OC antiporter and P-gp, respectively, while the transport of IMM-H004G from the blood to the brain was facilitated by OATP1A2 and OATP2B1. Ultimately, stronger concentration gradients and OATP-mediated uptake played a critical role in promoting greater brain exposure of IMM-H004G. CONCLUSIONS: The active glucuronide metabolite of the brain protectant IMM-H004 with poor blood-brain barrier permeability demonstrates a high partition in the rat brain via multiple mechanisms, and our findings deepen the understanding of the mechanisms underlying the blood-brain barrier metabolism and transport of active glucuronide conjugates.

Unearthing new ccr genes and SCC elements in staphylococci through genome mining.

The SCCmec typing is crucial for investigating methicillin-resistant S. aureus, relying primarily on the combination of ccr and mec gene complexes. To date, 19 ccr genes and 10 ccr gene complexes have been identified, forming 15 SCCmec types. With the vast release of bacterial genome sequences, mining the database for novel ccr gene complexes and SCC/SCCmec elements could enhance MRSA epidemiological studies. In this study, we identified 12 novel ccr genes (6 ccrA, 3 ccrB and 3 ccrC) through mining of the NCBI database, which forming 12 novel ccr gene complexes and 10 novel SCC elements. Overexpression of five groups of novel Ccr recombinases (CcrA9B3, CcrA10B1, CcrC3, CcrC4, and CcrC5) in a mutant MRSA strain lacking the ccr gene and extrachromosomal circular intermediate (ciSCC) production significantly promoted ciSCC production, demonstrating their biological activity. This discovery provides an opportunity to advance MRSA epidemiological research and develop database-based bacterial typing methods.

Inhibition of iRhom1 by CD44-targeting nanocarrier for improved cancer immunochemotherapy.

The multifaceted chemo-immune resistance is the principal barrier to achieving cure in cancer patients. Identifying a target that is critically involved in chemo-immune-resistance represents an attractive strategy to improve cancer treatment. iRhom1 plays a role in cancer cell proliferation and its expression is negatively correlated with immune cell infiltration. Here we show that iRhom1 decreases chemotherapy sensitivity by regulating the MAPK14-HSP27 axis. In addition, iRhom1 inhibits the cytotoxic T-cell response by reducing the stability of ERAP1 protein and the ERAP1-mediated antigen processing and presentation. To facilitate the therapeutic translation of these findings, we develop a biodegradable nanocarrier that is effective in codelivery of iRhom pre-siRNA (pre-siiRhom) and chemotherapeutic drugs. This nanocarrier is effective in tumor targeting and penetration through both enhanced permeability and retention effect and CD44-mediated transcytosis in tumor endothelial cells as well as tumor cells. Inhibition of iRhom1 further facilitates tumor targeting and uptake through inhibition of CD44 cleavage. Co-delivery of pre-siiRhom and a chemotherapy agent leads to enhanced antitumor efficacy and activated tumor immune microenvironment in multiple cancer models in female mice. Targeting iRhom1 together with chemotherapy could represent a strategy to overcome chemo-immune resistance in cancer treatment.

Prediction of jaywalker-vehicle conflicts based on encoder-decoder framework utilizing multi-source data.

Jaywalker-vehicle (J-V) conflicts at mid-blocks without crossing facilities in China are frequent and hazardous. Due to the unexpected and sudden nature of jaywalking activity, it is crucial to develop predictive models for J-V conflicts to offer pre-conflict warnings for road users. This study introduces a novel encoder-decoder framework that utilizes multi-source data to predict J-V conflict severity. We define three encoders to represent three types of input data, (1) J-V interaction encoder (Bi-LSTM), (2) jaywalker motion encoder (Bi-LSTM) and (3) background information encoder (MLP). Subsequently, features extracted by these three encoders are concatenated and transferred to the conflict severity decoder (MLP) to obtain the predicted severity level. We further conduct a case study using the surveyed video data at three mid-blocks without crossing facilities in Nanjing, China. The experimental results indicate that, compared to classical models, our proposed encoder-decoder (Proposed ED) model exhibits the best and stable predictive metrics. Furthermore, the results of the ablation study suggest that the incorporation of background information significantly enhances the four evaluative metrics of the Proposed ED model, with an average improvement of 24.291%. Additionally, the results of transferability analysis suggest that, when the ratio of added samples from the new mid-block reaches 40% to 50%, the predictive metrics of the updated models could stabilize at around 80% to 95%, indicating a notably good performance. Eventually, we derive several practical suggestions from the above findings, in order to help with J-V conflict prediction and jaywalking safety improvement.

Spatial and temporal prediction of secondary crashes combining stacked sparse auto-encoder and long short-term memory.

Secondary crashes occur within the spatial and temporal impact area of primary crashes, resulting in traffic delays and safety problems. While most existing studies focus on the likelihood of secondary crashes, predicting the spatio-temporal location of secondary crashes could offer valuable insights for implementing prevention strategies. This includes guiding the deployment of emergency response measures and determining appropriate speed limits. The main objective of this study is to develop a prediction method for the spatial and temporal locations of secondary crashes. A hybrid deep learning model SSAE-LSTM is proposed by combining stacked sparse auto-encoder (SSAE) and long short-term memory network (LSTM). Traffic and crash data on the California I-880 highway covering the period of 2017-2021 are collected. The identification of secondary crashes is performed by the speed contour map method. The time and distance gaps between primary and secondary crashes are modeled using multiple 5-minute interval traffic variables as inputs. Multiple models are developed for benchmarking purposes, including PCA-LSTM, which incorporates principal component analysis (PCA) and LSTM, SSAE-SVM, which incorporates SSAE and support vector machine (SVM), and back propagation neural network (BPNN). The performance comparison indicates that the hybrid SSAE-LSTM model outperforms the other models in terms of both spatial and temporal prediction. In particular, SSAE4-LSTM1 (with 4 SSAE layers and 1 LSTM layer) demonstrates superior spatial prediction performance, while SSAE4-LSTM2 (with 4 SSAE layers and 2 LSTM layers) excels in temporal prediction. A joint spatio-temporal evaluation is also conducted to measure the overall accuracy of the optimal models over different permitted spatio-temporal ranges. Finally, practical suggestions are provided for secondary crash prevention.

Investigating jaywalker crossing risks from the sequential-conflict perspective: A grouped random parameters generalized ordered probit model.

Jaywalking at the mid-blocks without crossing facilities has been widely acknowledged as a dangerous crossing behavior. Due to the "unexpected" nature of jaywalking activities, jaywalkers may encounter multiple conflicts in sequence, called sequential jaywalker-vehicle (J-V) conflicts in this study. Sequential conflicts are usually complex and hazardous, but receive little attention in previous studies. To fill such research gaps, this study investigates the jaywalking risks from the sequential-conflict perspective, providing a more comprehensive depiction of the jaywalking hazards. Moreover, the "lagged effects" of the previous conflict-related factors on the subsequent conflict are also explored. Such considerations could help with predicting J-V conflicts in advance, further assisting the drivers to adopt precautionary actions. The results indicate that, as the conflict sequence increases, a higher proportion of jaywalkers would behave aggressively, and the proportion of slight conflicts increases accordingly. Furthermore, the grouped random parameters generalized ordered probit model is established to identify the influencing factors of sequential conflicts, which could account for the panel effects and unobserved heterogeneity simultaneously. The model results indicate that jaywalkers' previous evasive actions, such as "decelerating while swerving behind the vehicle", could lead to severer subsequent conflicts. In addition, jaywalkers' previous risky actions (i.e. rushing before the vehicle, using phones) are prone to convey to the subsequent conflicts, further increasing the subsequent conflict severity level. Moreover, non-motor vehicles are found to be a threat to the jaywalkers at mid-blocks without crossing facilities. In light of the above-mentioned results, practical suggestions are provided to reduce the jaywalking activities and prevent the jaywalker-involved accidents.

Genetic Predisposition to Periodontitis and Risk of Migraine: A Two-Sample Mendelian Randomization Study.

INTRODUCTION: Previous observational studies have associated periodontitis (PD) with migraine; however, the results are inconclusive and the causality of the association between PD and migraine remains unclear. This two-sample Mendelian randomization (MR) study was performed to explore the bi-directional causal relationship between PD and migraine. METHODS: To investigate the relationship between PD (17,353 cases; 28,210 controls) and migraine (1072 cases; 360,122 controls), we used genetic tools from the largest available genome-wide association study of European descent. Inverse variance-weighted (IVW) and a series of sensitivity analyses were used to explore the association between migraine and PD. We performed an MR study using seven SNPs (single nucleotide polymorphisms) as instrumental variables for PD to investigate the causal relationship between migraine and PD. RESULTS: We found no significant causal relationship between PD and migraine (odds ratio, OR = 1.000; 95% confidence interval, CI = 0.99-1.00; p = 0.65). Similarly, no evidence supported a causal relationship between migraine and PD (OR = 0.07; CI = 2.04 × 10-9-2.65 × 106; p = 0.77). A sensitivity analysis revealed that no potential polymorphic effect (p = 0.356) and heterogeneity (p = 0.652) exists for the variants used in constructing the genetic instrument. CONCLUSIONS: Based on the results of our MR study, there is no causal relationship between PD and migraines or migraines and PD.

Urinary phthalate metabolites and heart rate variability: A panel study.

Phthalates exposure is linked with cardiovascular disease. Decreased heart rate variability (HRV) is an early indicator of cardiac autonomic imbalance. We conducted a longitudinal panel study in 127 Chinese adults with 3 repeated visits to explore the associations of individual and mixtures of phthalates exposure with HRV. We quantified 10 urinary phthalate metabolites by gas chromatograph-tandem mass spectrometer (GC-MS/MS) and 6 HRV indices by 3-channel digital Holter monitors. Linear mixed-effect (LME) models and Bayesian kernel machine regression (BKMR) models were separately implemented to evaluate the associations. After multivariate adjustments, we found that urinary mono-ethyl phthalate (MEP), mono-iso-butyl phthalate (MiBP), and mono-n-butyl phthalate (MBP) at lag 0 day were inversely associated with low-frequency power (LF) or total power (TP) (all P-FDR <0.05). In mixture analysis, we observed negative overall associations of phthalate mixtures at lag 0 day with LF or TP, and MiBP was the major contributor. Moreover, stratified analysis suggested that the inverse relationships of MiBP at lag 0 day with LF and TP were more prominent in subjects aged >50 years (all Pinteraction < 0.01). Our findings revealed that exposure to individual and mixtures of phthalates, especially MiBP, were related to decreased HRV.

Modeling of Fiber-Constrained Planar PVC Gel Actuators.

In recent years, plasticized poly (vinyl chloride) (PVC) gel has attracted increasing attention in soft robotics. However, there is scarce research on the deformation mechanism and modeling of PVC gel actuators. In this study, to investigate the deformation mechanism of fiber-constrained planar PVC gel actuators, we propose a complex nonlinear model based on traditional thermodynamic electroactive polymer (EAP) multi-field coupling theory. The proposed model can reveal the dielectric breakdown strength of PVC gels and predict the deformation of planar PVC gel actuators with varying levels of pre-stretching. The theoretical results were in good agreement with the experimental results, indicating the feasibility of the proposed model.

Mitochondrial Inhibitor Rotenone Triggers and Enhances Neuronal Ferroptosis Following Intracerebral Hemorrhage.

Ferroptosis, a form of regulatory non-apoptotic cell death driven by iron-dependent lipid peroxidation, accounts for more than 80% of the total types of neuronal death in the acute phase of intracerebral hemorrhage (ICH). Mitochondria have essential roles in energy production, macromolecule synthesis, cellular metabolism, and cell death regulation. However, its role in ferroptosis remains unclear and somewhat controversial, especially in ICH. This study aimed to investigate whether damaged mitochondria could trigger and enhance neuronal ferroptosis in ICH. The isobaric tag for relative and absolute quantitation proteomics on human ICH samples suggested that ICH caused significant damage to the mitochondria, which presented ferroptosis-like morphology under electron microscopy. Subsequently, use of the mitochondrial special inhibitor Rotenone (Rot) to induce mitochondrial damage showed that it has significant dose-dependent toxicity on primary neurons. Single Rot administration markedly inhibited neuronal viability, promoted iron accumulation, increased malondialdehyde (MDA) contents, decreased total superoxide dismutase (SOD) activity, and downregulated ferroptosis-related proteins RPL8, COX-2, xCT, ASCL4, and GPX4 in primary neurons. Moreover, Rot enhanced these changes via hemin and autologous blood administration in primary neurons and mice, mimicking the in vitro and in vivo ICH models, respectively. Furthermore, Rot exacerbated the ICH-induced hemorrhagic volumes, brain edema, and neurological deficits in mice. Together, our data revealed that ICH induced significant mitochondrial dysfunction and that mitochondrial inhibitor Rot can trigger and enhance neuronal ferroptosis.

Impact of the COVID-19 pandemic on air pollution from jet engines at airports in central eastern China.

Aircraft engine emissions (AEEs) generated during landing and takeoff (LTO) cycles are important air pollutant sources that directly impact the air quality at airports. Although the COVID-19 pandemic triggered an unprecedented collapse in the civil aviation industry, it also relieved some environmental pressure on airports. To quantify the impact of COVID-19 on AEEs, the amounts of three typical air pollutants (i.e., HC, CO, and NOx) from LTO cycles at airports in central eastern China were estimated before and after the pandemic. The study also explored the temporal variation and the spatial autocorrelation of both the emission quantity and the emission intensity, as well as their spatial associations with other socioeconomic factors. The results illustrated that the spatiotemporal distribution pattern of AEEs was significantly influenced by the policies implemented and the severity of COVID-19. The variations of AEEs at airports with similar characteristics and functional positions generally followed similar patterns. The results also showed that the studied air pollutants present positive spatial autocorrelation, and a positive spatial dependence was found between the AEEs and other external socioeconomic factors. Based on the findings, some possible policy directions for building a more sustainable and environment-friendly airport group in the post-pandemic era were proposed. This study provides practical guidance on continuous monitoring of the AEEs from LTO cycles and studying the impact of COVID-19 on the airport environment for other regions or countries.

Targeting Xkr8 via nanoparticle-mediated in situ co-delivery of siRNA and chemotherapy drugs for cancer immunochemotherapy.

Activation of scramblases is one of the mechanisms that regulates the exposure of phosphatidylserine to the cell surface, a process that plays an important role in tumour immunosuppression. Here we show that chemotherapeutic agents induce overexpression of Xkr8, a scramblase activated during apoptosis, at the transcriptional level in cancer cells, both in vitro and in vivo. Based on this finding, we developed a nanocarrier for co-delivery of Xkr8 short interfering RNA and the FuOXP prodrug to tumours. Intravenous injection of our nanocarrier led to significant inhibition of tumour growth in colon and pancreatic cancer models along with increased antitumour immune response. Targeting Xkr8 in combination with chemotherapy may represent a novel strategy for the treatment of various types of cancers.

Overcoming pancreatic cancer immune resistance by codelivery of CCR2 antagonist using a STING-activating gemcitabine-based nanocarrier.

STING agonist has recently gained much attention for cancer treatment, but the therapeutic potential of STING agonist is hampered by STING-associated tumor immune resistance. In this work, guided by both bioinformatics and computer modeling, we rationally designed a "one stone hits two birds" nanoparticle-based strategy to simultaneously activate STING innate immune response while eliminating STING-associated immune resistance for the treatment of pancreatic ductal adenocarcinoma (PDAC). We discovered that the ultra-small sized micellar system based on gemcitabine-conjugated polymer (PGEM), which showed superior capacity of penetration in pancreatic tumor spheroid model and orthotopic tumor model, could serve as a novel "STING agonist". The activation of STING signaling in dendritic cells (DCs) by PGEM increased both innate nature killer (NK) and adaptive anti-tumor T cell response. However, activation of STING signaling by PGEM in tumor cells also drove the induction of chemokines CCL2 and CCL7, resulting in immune resistance by recruiting tumor associated macrophage (TAM) and myeloid-derived suppressor cells (MDSCs). Through the combination of computer modeling and experimental screening, we developed a dual delivery modality by incorporating a CCR2 (the receptor shared by both CCL2 and CCL7) antagonist PF-6309 (PF) into PGEM micellar system. Our studies demonstrated that PGEM/PF formulation significantly reduced pancreatic tumor burden and induced potent anti-tumor immunity through reversing the CCL2/CCL7-mediated immunosuppression. Moreover, PGEM/PF sensitized PDAC tumors to anti-PD-1 therapy, leading to complete suppression/eradication of the tumors. Our work has shed light to the multi-faceted role of STING activation and provided a novel immunotherapy regimen to maximize the benefit of STING activation for PDAC treatment. In addition, this work paved a new way for bioinformatics and computer modeling-guided rational design of nanomedicine.

Photo-controlled and photo-calibrated nanoparticle enabled nitric oxide release for anti-bacterial and anti-biofilm applications.

After numerous efforts to elucidate the biological role of nitric oxide (NO), NO treatments have become a hotspot at the forefront of medicine. NO-releasing substances are constantly needed, while the direct use of NO gas is unattainable in bio-systems. An ideal NO donor should possess controllable and visible NO-release capability. The reported NO donating nanoparticles, prepared via encapsulating a hydrophobic NO-releasing compound into DSPE-PEG2000, meet the criteria mentioned previously. The localization and flux of NO released from these nanoparticles could be manipulated by UV or blue light. Meanwhile, NOD-NPs emit a dose-dependent fluorescence intensity to calibrate the generation of NO. While the good biocompatibility of NOD-NPs has been validated, the NO from our nanoparticles demonstrates efficient anti-bacterial and anti-biofilm effects toward Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus). Therefore, the NOD-NPs developed in this work have potential application in evaluating the regulation of microbes by NO.

Repair of ATAAD with a 3D-printing assisted pre-windowed coated stent: A case report.

Acute type A aortic dissection (ATAAD) is a life-threatening vascular disease. We report a case of ATAAD treated with interventional therapy using 3D-printing assisted pre-windowing coated stent combined with in situ window-opening technology. There were few complications and the patient experienced an uneventful recovery.

How yielding cameras affect consecutive pedestrian-vehicle conflicts at non-signalized crosswalks? A mixed bivariate generalized ordered approach.

Yielding cameras are considered to be an effective means of preventing drivers' non-yielding behavior. Notably, as pedestrians' perceived risk and behavior change dynamically during the crossing, the safety effectiveness of such facility could also vary across the consecutive conflicts. This study contributes to the literature by assessing the safety effectiveness of yielding camera from a novel perspective, focusing on the consecutive pedestrian-vehicle conflicts (primary conflict and secondary conflict), using Unmanned Aerial Vehicle (UAV) and roadside camera data. Another key contribution lies in the consideration of primary conflict related factors in the secondary conflict analysis, providing new insights into conflict analysis. The mixed bivariate generalized ordered probit model is proposed to analyze the consecutive conflicts simultaneously. The model results indicate that the yielding camera could decrease both slight and severe conflict probability in primary conflict. However, in secondary conflict, the yielding camera would lower severe conflict probability but increase slight conflict probability. Moreover, several primary conflict related factors reveal significant effects on the secondary conflict severity. Specifically, higher pedestrian speed and driver's yielding behavior in primary conflict could lead to higher crossing risks in the secondary conflict. Conversely, more unsuccessful attempts before primary conflict could decrease the severity level of secondary conflict. Based on the results, several practical implications are provided to improve the effectiveness of yielding camera and enhance pedestrian safety.