Metabolomics-driven discovery of therapeutic targets for cancer cachexia.

Cancer cachexia (CC) is a devastating metabolic syndrome characterized by skeletal muscle wasting and body weight loss, posing a significant burden on the health and survival of cancer patients. Despite ongoing efforts, effective treatments for CC are still lacking. Metabolomics, an advanced omics technique, offers a comprehensive analysis of small-molecule metabolites involved in cellular metabolism. In CC research, metabolomics has emerged as a valuable tool for identifying diagnostic biomarkers, unravelling molecular mechanisms and discovering potential therapeutic targets. A comprehensive search strategy was implemented to retrieve relevant articles from primary databases, including Web of Science, Google Scholar, Scopus and PubMed, for CC and metabolomics. Recent advancements in metabolomics have deepened our understanding of CC by uncovering key metabolic signatures and elucidating underlying mechanisms. By targeting crucial metabolic pathways including glucose metabolism, amino acid metabolism, fatty acid metabolism, bile acid metabolism, ketone body metabolism, steroid metabolism and mitochondrial energy metabolism, it becomes possible to restore metabolic balance and alleviate CC symptoms. This review provides a comprehensive summary of metabolomics studies in CC, focusing on the discovery of potential therapeutic targets and the evaluation of modulating specific metabolic pathways for CC treatment. By harnessing the insights derived from metabolomics, novel interventions for CC can be developed, leading to improved patient outcomes and enhanced quality of life.

Evolution of H7N9 highly pathogenic avian influenza virus in the context of vaccination.

Human infections with the H7N9 influenza virus have been eliminated in China through vaccination of poultry; however, the H7N9 virus has not yet been eradicated from poultry. Carefully analysis of H7N9 viruses in poultry that have sub-optimal immunity may provide a unique opportunity to witness the evolution of highly pathogenic avian influenza virus in the context of vaccination. Between January 2020 and June 2023, we isolated 16 H7N9 viruses from samples we collected during surveillance and samples that were sent to us for disease diagnosis. Genetic analysis indicated that these viruses belonged to a single genotype previously detected in poultry. Antigenic analysis indicated that 12 of the 16 viruses were antigenically close to the H7-Re4 vaccine virus that has been used since January 2022, and the other four viruses showed reduced reactivity with the vaccine. Animal studies indicated that all 16 viruses were nonlethal in mice, and four of six viruses showed reduced virulence in chickens upon intranasally inoculation. Importantly, the H7N9 viruses detected in this study exclusively bound to the avian-type receptors, having lost the capacity to bind to human-type receptors. Our study shows that vaccination slows the evolution of H7N9 virus by preventing its reassortment with other viruses and eliminates a harmful characteristic of H7N9 virus, namely its ability to bind to human-type receptors.

Advancing urban traffic accident forecasting through sparse spatio-temporal dynamic learning.

Traffic accidents have emerged as one of the most public health safety matters, raising concerns from both the public and urban administrators. The ability to accurately predict traffic accident not only supports the governmental decision-making in advance but also enhances public confidence in safety measures. However, the efficacy of traditional spatio-temporal prediction models are compromised by the skewed distributions and sparse labeling of accident data. To this end, we propose a Sparse Spatio-Temporal Dynamic Hypergraph Learning (SST-DHL) framework that captures higher-order dependencies in sparse traffic accidents by combining hypergraph learning and self-supervised learning. The SST-DHL model incorporates a multi-view spatiotemporal convolution block to capture local correlations and semantics of traffic accidents, a cross-regional dynamic hypergraph learning model to identify global spatiotemporal dependencies, and a two-supervised self-learning paradigm to capture both local and global spatiotemporal patterns. Through experimentation on New York City and London accident datasets, we demonstrate that our proposed SST-DHL exhibits significant improvements compared to optimal baseline models at different sparsity levels. Additionally, it offers enhanced interpretability of results by elucidating complex spatio-temporal dependencies among various traffic accident instances. Our study demonstrates the effectiveness of the SST-DHL framework in accurately predicting traffic accidents, thereby enhancing public safety and trust.

High-risk event prone driver identification considering driving behavior temporal covariate shift.

Drivers who perform frequent high-risk events (e.g., hard braking maneuvers) pose a significant threat to traffic safety. Existing studies commonly estimated high-risk event occurrence probabilities based upon the assumption that data collected from different time periods are independent and identically distributed (referred to as i.i.d. assumption). Such approach ignored the issue of driving behavior temporal covariate shift, where the distributions of driving behavior factors vary over time. To fill the gap, this study targets at obtaining time-invariant driving behavior features and establishing their relationships with high-risk event occurrence probability. Specifically, a generalized modeling framework consisting of distribution characterization (DC) and distribution matching (DM) modules was proposed. The DC module split the whole dataset into several segments with the largest distribution gaps, while the DM module identified time-invariant driving behavior features through learning common knowledge among different segments. Then, gated recurrent unit (GRU) was employed to conduct time-invariant driving behavior feature mining for high-risk event occurrence probability estimation. Moreover, modified loss functions were introduced for imbalanced data learning caused by the rarity of high-risk events. The empirical analyses were conducted utilizing online ride-hailing services data. Experiment results showed that the proposed generalized modeling framework provided a 7.2% higher average precision compared to the traditional i.i.d. assumption based approach. The modified loss functions further improved the model performance by 3.8%. Finally, benefits for the driver management program improvement have been explored by a case study, demonstrating a 33.34% enhancement in the identification precision of high-risk event prone drivers.

Correction: Phospholipid scramblase 1 interacts with influenza A virus NP, impairing its nuclear import and thereby suppressing virus replication.

[This corrects the article DOI: 10.1371/journal.ppat.1006851.].

Green synthesis of epigallocatechin gallate-ferric complex nanoparticles for photothermal enhanced antibacterial and wound healing.

Bacterial infections are a significant global health concern, particularly in the context of skin infections and chronic wounds, which was further exacerbated by the emerging of antibiotic resistance. Therefore, there are urgent needs to develop alternative antibacterial strategies without inducing significant resistance. Photothermal therapy (PTT) is a promising alternative approach but usually faces limitations such as the need for stable and environmental-friendly PTT agents and ensuring biocompatibility with living tissues, necessitating ongoing research for its clinical advancement. Herein, in this study, with the aim to develop a green synthesized PTT agent for photothermal enhanced antibacterial and wound healing, we proposed a facile one-pot method to prepare epigallocatechin gallate-ferric (EGCG-Fe) complex nanoparticles. The obtained nanoparticles showed improved good size distribution and stability with high reproducibility. More importantly, EGCG-Fe complex nanoparticles have additional photothermal conversion ability which can give photothermal enhanced antibacterial effect on various pathogens, including Gram-positive Staphylococcus aureus (S. aureus) and Gram-negative Escherichia coli (E. coli) strains. EGCG-Fe complex nanoparticles also showed powerful biofilm prevention and destruction effects with promoted antibacterial and wound healing on mice model. In conclusion, EGCG-Fe complex nanoparticles can be a robust green material with effective and novel light controllable antibacterial properties for photothermal enhanced antibacterial and wound healing applications.

Alternative approaches to modeling heterogeneity to analyze injury severity sustained by motorcyclists in two-vehicle crashes.

The presence of unobserved factors in the motorcycle involved two-vehicle crashes (MV) data could lead to heterogenous associations between observed factors and injury severity sustained by motorcyclists. Capturing such heterogeneities necessitates distinct methodological approaches, of which random and scale heterogeneity models are paramount. Herein, we undertake an empirical evaluation of random and scale heterogeneity models, exploring their efficacy in delineating the influence of external determinants on the degree of injury severity in crashes. Within the effects of scale heterogeneity, this study delves into two dominant models: the scaled multinomial logit model (S-MNL) and its generalized counterpart, the G-MNL, which encompasses both the S-MNL and the random parameters multinomial logit model (RPL). While the random heterogeneity domain is represented by the random parameters multinomial logit and an upgraded variant - the random parameters multinomial logit model with heterogeneity in means and variances (RPLHMV). Motorcycle involved two-vehicle crashes data were extracted from the UK STATS19 dataset from 2016 to 2020. Likelihood ratio tests are computed to assess the temporal variability of the significant factors. The test result demonstrates the temporal variations over a five-year study period. Some very important differences started to show up across the years based on the model estimation results: that the RPLHMV model statistically outperforms the G-MNL model in the 2016, 2018, and 2019 models, while the S-MNL model is statistically superior in the 2017 and 2020 years. These important findings suggest that the origin of heterogeneity in explaining factor weights can be captured by scale effects, not just random heterogeneity. In addition, the model results further show that motorcyclists' injury severities are significantly affected by motorcycle-related characteristics; there is the added factor of external influences, such as non-motorcycle drivers (males, young drivers, and elderly drivers) and vehicles (the moving status, age, and types of vehicles) that collide with motorcycles. The results of this paper are anticipated to help policymakers develop effective strategies to mitigate motorcycle involved two-vehicle crashes by implementing appropriate measures.

Evolution and biological characterization of H5N1 influenza viruses bearing the clade 2.3.2.1 hemagglutinin gene.

H5N1 avian influenza viruses bearing the clade 2.3.2.1 hemagglutinin (HA) gene have been widely detected in birds and poultry in several countries. During our routine surveillance, we isolated 28 H5N1 viruses between January 2017 and October 2020. To investigate the genetic relationship of the globally circulating H5N1 viruses and the biological properties of those detected in China, we performed a detailed phylogenic analysis of 274 representative H5N1 strains and analyzed the antigenic properties, receptor-binding preference, and virulence in mice of the H5N1 viruses isolated in China. The phylogenic analysis indicated that the HA genes of the 274 viruses belonged to six subclades, namely clades 2.3.2.1a to 2.3.2.1f; these viruses acquired gene mutations and underwent complicated reassortment to form 58 genotypes, with G43 being the dominant genotype detected in eight Asian and African countries. The 28 H5N1 viruses detected in this study carried the HA of clade 2.3.2.1c (two strains), 2.3.2.1d (three strains), or 2.3.2.1f (23 strains), and formed eight genotypes. These viruses were antigenically well-matched with the H5-Re12 vaccine strain used in China. Animal studies showed that the pathogenicity of the H5N1 viruses ranged from non-lethal to highly lethal in mice. Moreover, the viruses exclusively bound to avian-type receptors and have not acquired the ability to bind to human-type receptors. Our study reveals the overall picture of the evolution of clade 2.3.2.1 H5N1 viruses and provides insights into the control of these viruses.

Emissions reduction strategy in a three-stage agrifood value chain: A dynamic differential game approach.

Agrifood systems account for 31% of global greenhouse gas emissions. Substantial emissions reduction in agrifood systems is critical to achieving the temperature goal set by the Paris Agreement. A key challenge in reducing GHG emissions in the agrifood value chain is the imbalanced allocation of benefits and costs associated with emissions reduction among agrifood value chain participants. However, only a few studies have examined agrifood emissions reduction from a value chain perspective, especially using dynamic methods to investigate participants' long-term emissions reduction strategies. This paper helps fill this gap in the existing literature by examining the impact of collaborations among agrifood value chain participants on correcting those misallocations and reducing emissions in agrifood systems. We develop a dynamic differential game model to examine participants' long-term emissions reduction strategies in a three-stage agrifood value chain. We use the Hamilton-Jacobi-Bellman equation to derive the Nash equilibrium emissions reduction strategies under non-cooperative, cost-sharing, and cooperative mechanisms. We then conduct numerical analysis and sensitivity analysis to validate our model. Our results show that collaboration among value chain participants leads to higher emissions reduction efforts and profits for the entire value chain. Specifically, based on our numerical results, the cooperative mechanism results in the greatest emissions reduction effort by the three participants, which leads to a total that is nearly three times higher than that of the non-cooperative mechanism and close to two times higher than the cost-sharing mechanism. The cooperative mechanism also recorded the highest profits for the entire value chain, surpassing the non-cooperative and cost-sharing mechanisms by around 37% and 16%, respectively. Our results provide valuable insights for policymakers and agrifood industry stakeholders to develop strategies and policies encouraging emissions reduction collaborations in the agrifood value chain and reduce emissions in the agrifood systems.

Key Amino Acid Residues That Determine the Antigenic Properties of Highly Pathogenic H5 Influenza Viruses Bearing the Clade 2.3.4.4 Hemagglutinin Gene.

The H5 subtype highly pathogenic avian influenza viruses bearing the clade 2.3.4.4 HA gene have been pervasive among domestic poultry and wild birds worldwide since 2014, presenting substantial risks to human and animal health. Continued circulation of clade 2.3.4.4 viruses has resulted in the emergence of eight subclades (2.3.4.4a-h) and multiple distinct antigenic groups. However, the key antigenic substitutions responsible for the antigenic change of these viruses remain unknown. In this study, we analyzed the HA gene sequences of 5713 clade 2.3.4.4 viruses obtained from a public database and found that 23 amino acid residues were highly variable among these strains. We then generated a series of single-amino-acid mutants based on the H5-Re8 (a vaccine seed virus) background and tested their reactivity with a panel of eight monoclonal antibodies (mAbs). Six mutants bearing amino acid substitutions at positions 120, 126, 141, 156, 185, or 189 (H5 numbering) led to reduced or lost reactivity to these mAbs. Further antigenic cartography analysis revealed that the amino acid residues at positions 126, 156, and 189 acted as immunodominant epitopes of H5 viruses. Collectively, our findings offer valuable guidance for the surveillance and early detection of emerging antigenic variants.

Marine biomaterials in biomedical nano/micro-systems.

Marine resources in unique marine environments provide abundant, cost-effective natural biomaterials with distinct structures, compositions, and biological activities compared to terrestrial species. These marine-derived raw materials, including polysaccharides, natural protein components, fatty acids, and marine minerals, etc., have shown great potential in preparing, stabilizing, or modifying multifunctional nano-/micro-systems and are widely applied in drug delivery, theragnostic, tissue engineering, etc. This review provides a comprehensive summary of the most current marine biomaterial-based nano-/micro-systems developed over the past three years, primarily focusing on therapeutic delivery studies and highlighting their potential to cure a variety of diseases. Specifically, we first provided a detailed introduction to the physicochemical characteristics and biological activities of natural marine biocomponents in their raw state. Furthermore, the assembly processes, potential functionalities of each building block, and a thorough evaluation of the pharmacokinetics and pharmacodynamics of advanced marine biomaterial-based systems and their effects on molecular pathophysiological processes were fully elucidated. Finally, a list of unresolved issues and pivotal challenges of marine-derived biomaterials applications, such as standardized distinction of raw materials, long-term biosafety in vivo, the feasibility of scale-up, etc., was presented. This review is expected to serve as a roadmap for fundamental research and facilitate the rational design of marine biomaterials for diverse emerging applications.

Mycobacterium tuberculosis suppresses host DNA repair to boost its intracellular survival.

Mycobacterium tuberculosis (Mtb) triggers distinct changes in macrophages, resulting in the formation of lipid droplets that serve as a nutrient source. We discover that Mtb promotes lipid droplets by inhibiting DNA repair responses, resulting in the activation of the type-I IFN pathway and scavenger receptor-A1 (SR-A1)-mediated lipid droplet formation. Bacterial urease C (UreC, Rv1850) inhibits host DNA repair by interacting with RuvB-like protein 2 (RUVBL2) and impeding the formation of the RUVBL1-RUVBL2-RAD51 DNA repair complex. The suppression of this repair pathway increases the abundance of micronuclei that trigger the cyclic GMP-AMP synthase (cGAS)/stimulator of interferon genes (STING) pathway and subsequent interferon-ß (IFN-ß) production. UreC-mediated activation of the IFN-ß pathway upregulates the expression of SR-A1 to form lipid droplets that facilitate Mtb replication. UreC inhibition via a urease inhibitor impaired Mtb growth within macrophages and in vivo. Thus, our findings identify mechanisms by which Mtb triggers a cascade of cellular events that establish a nutrient-rich replicative niche.

Critical safety management driver identification based upon temporal variation characteristics of driving behavior.

Identifying critical safety management drivers with high driver-level risks is essential for traffic safety improvement. Previous studies commonly evaluated driver-level risks based upon aggregated statistical characteristics (e.g., driving exposure and driving behavior), which were obtained from long-period driving monitoring data. However, given the great advancements of the connected vehicle and in-vehicle data instrumentation technologies, there has been a notable increase in the collection of short-period driving data, which has emerged as a prominent data source for analysis. In this data environment, traditionally employed aggregated behavior characteristics are unstable due to the time-varying feature of driving behavior coupled with insufficient data sampling periods. Thus, traditional modeling methods based upon aggregated statistical characteristics are no longer feasible. Instead of utilizing such unreliable statistical information to represent driver-level risks, this study employed temporal variation characteristics of driving behavior to identify critical safety management drivers in the short-period driving data environment. Specifically, the relationships between driving behavior temporal variation characteristics and individual crash occurrence probability were developed. To eliminate the impacts of drivers' driving behavior heterogeneity on model performance, "traffic entropy" index that could quantify the abnormal degrees of driving behavior was proposed. Deep learning models including convolutional neural network (CNN) and long short-term memory (LSTM) were employed to conduct the temporal variation feature mining. Empirical analyses were conducted using data obtained from online ride-hailing services. Experiment results showed that temporal variation characteristics based models outperformed traditional aggregated statistical characteristics based models. The area under the curve (AUC) index was improved by 4.1%. And the proposed traffic entropy index further enhanced the model performance by 5.3%. The best model achieved an AUC of 0.754, comparable to existing approaches utilizing long-period driving data. Finally, applications of the proposed method in driver management program development and its further investigations have been discussed.

Analysis of avian influenza A (H3N8) viruses in poultry and their zoonotic potential, China, September 2021 to May 2022.

BackgroundTwo human cases of avian influenza A (H3N8) virus infection were reported in China in 2022.AimTo characterise H3N8 viruses circulating in China in September 2021-May 2022.MethodsWe sampled poultry and poultry-related environments in 25 Chinese provinces. After isolating H3N8 viruses, whole genome sequences were obtained for molecular and phylogenetic analyses. The specificity of H3N8 viruses towards human or avian receptors was assessed in vitro. Their ability to replicate in chicken and mice, and to transmit between guinea pigs was also investigated.ResultsIn total, 98 H3N8 avian influenza virus isolates were retrieved from 38,639 samples; genetic analysis of 31 representative isolates revealed 17 genotypes. Viruses belonging to 10 of these genotypes had six internal genes originating from influenza A (H9N2) viruses. These reassorted viruses could be found in live poultry markets and comprised the strains responsible for the two human infections. A subset of nine H3N8 viruses (including six reassorted) that replicated efficiently in mice bound to both avian-type and human-type receptors in vitro. Three reassorted viruses were shed by chickens for up to 9 days, replicating efficiently in their upper respiratory tract. Five reassorted viruses tested on guinea pigs were transmissible among these by respiratory droplets.ConclusionAvian H3N8 viruses with H9N2 virus internal genes, causing two human infections, occurred in live poultry markets in China. The low pathogenicity of H3N8 viruses in poultry allows their continuous circulation with potential for reassortment. Careful monitoring of spill-over infections in humans is important to strengthen early-warning systems and maintain influenza pandemic preparedness.

The level of macrophage migration inhibitory factor is negatively correlated with the efficacy of PD-1 blockade immunotherapy combined with chemotherapy as a neoadjuvant therapy for esophageal squamous cell carcinoma.

PURPOSE: This study aimed to screen biomarkers to predict the efficacy of programmed cell death 1 (PD-1) blockade immunotherapy combined with chemotherapy as neoadjuvant therapy for esophageal squamous cell carcinoma (ESCC). METHODS: In the first stage of the study, the baseline concentrations of 40 tumor-related chemokines in the serum samples of 50 patients were measured to screen for possible biomarkers. We investigated whether the baseline concentration of the selected chemokine was related to the therapeutic outcomes and tumor microenvironment states of patients treated with the therapy. In the second stage, the reliability of the selected biomarkers was retested in 34 patients. RESULTS: The baseline concentration of macrophage migration inhibitory factor (MIF) was negatively correlated with disease-free survival (DFS) and overall survival (OS) in patients treated with the therapy. In addition, a low baseline expression level of MIF is related to a better tumor microenvironment for the treatment of ESCC. A secondary finding was that effective treatment decreased the serum concentration of MIF. CONCLUSION: Baseline MIF levels were negatively correlated with neoadjuvant therapy efficacy. Thus, MIF may serve as a predictive biomarker for this therapy. The accuracy of the prediction could be improved if the serum concentration of MIF is measured again after the patient received several weeks of treatment.

Advances of Amifostine in Radiation Protection: Administration and Delivery.

Amifostine (AMF, also known as WR-2721) is the only approved broad-spectrum small-molecule radiation protection agent that can combat hematopoietic damage caused by ionizing radiation and is used as an antitumor adjuvant and cell protector in cancer chemotherapy and radiotherapy. Amifostine is usually injected intravenously before chemotherapy or radiotherapy and has been used in the treatment of head and neck cancer. However, the inconvenient intravenous administration and its toxic side effects such as hypotension have severely limited its further application in clinic. In order to reduce the toxic and side effects, scientists are trying to develop a variety of drug administration methods and are devoted to developing a wide application of amifostine in radiation protection. This paper reviews the research progress of amifostine for radiation protection in recent years, discusses its mechanism of action, clinical application, and other aspects, with focus on summarizing the most widely studied amifostine injection administration and drug delivery systems, and explored the correlation between various administrations and drug efficacies.

Supramolecular Architectures Bearing Half-Sandwich Iridium- or Rhodium-Based Carboranes: Design, Synthesis, and Applications.

The utilization of carboranes in supramolecular chemistry has attracted considerable attention. The unique spatial configuration and weak interaction forces of carboranes can help to explore the properties of supramolecular complexes, particularly via host-guest chemistry. Additionally, certain difficulties encountered in carborane development─such as controlled B-H bond activation─can be overcome by judiciously selecting metal centers and their adjacent ligands. However, few studies are being conducted in this nascent research area. With advances in this field, novel carborane-based supramolecular complexes will likely be prepared, structurally characterized, and intrinsically investigated. To expedite these efforts, we present major findings from recent studies, including π-π interactions, host-guest associations, and steric effects, which have been leveraged to implement a regioselective process for activating B(2,9)-, B(2,8)-, and B(2,7)-H bonds of para-carboranes and B(4,7)-H bonds of ortho-carboranes. Future studies should clarify the unique weak interactions of carboranes and their potential for enhancing the utility of supramolecular complexes. Although carboranes exhibit several unique weak interactions (such as dihydrogen-bond [Bδ+-Hδ-···Hδ+-Cδ-], Bδ+-Hδ-···M+, and Bδ+-Hδ-···π interactions), the manner in which they can be utilized remains unclear. Supramolecular complexes, particularly those based on host-guest chemistry, can be utilized as a platform for demonstrating potential applications of these weak interactions. Owing to the importance of alkane separation, applications related to the recognition and separation of alkane isomers via dihydrogen-bond interactions are primarily summarized. Advances in the research of unique weak interactions in carboranes will certainly lead to more possibilities for supramolecular chemistry.

Deciphering pathogenic cellular module at single-cell resolution in checkpoint inhibitor-related pneumonitis.

Checkpoint inhibitor pneumonitis (CIP) is the most common fatal immune-related adverse event; however, its pathophysiology remains largely unknown. Comprehensively dissecting the key cellular players and molecular pathways associated with CIP pathobiology is critical for precision diagnosis and develop novel therapy strategy of CIP. Herein, we performed a comprehensive single-cell transcriptome analysis to dissect the complexity of the immunological response in the bronchoalveolar lavage fluid (BALF) microenvironment. CIP was characterized by a dramatic accumulation of CXCL13+ T cells and hyperinflammatory CXCL9+ monocytes. T-cell receptor (TCR) analysis revealed that CXCL13+ T cells exhibited hyperexpanded- TCR clonotypes, and pseudotime analysis revealed a potential differentiation trajectory from naïve to cytotoxic effector status. Monocyte trajectories showed that LAMP3+ DCs derived from CXCL9+ monocytes possessed the potential to migrate from tumors to the BALF, whereas the differentiation trajectory to anti-inflammatory macrophages was blocked. Intercellular crosstalk analysis revealed the signaling pathways such as CXCL9/10/11-CXCR3, FASLG-FAS, and IFNGR1/2-IFNG were activated in CIP+ samples. We also proposed a novel immune signature with high diagnostic power to distinguish CIP+ from CIP- samples (AUC = 0.755). Our data highlighted key cellular players, signatures, and interactions involved in CIP pathogenesis.

Application of f-FeNC@GOx cascade enzyme nanomaterials in the healing of infected wounds.

AIMS: Bacterial infection is a significant factor contributing to the deterioration of wounds, and the misuse of antibiotics has exacerbated bacterial resistance. Therefore, there is an urgent need to develop a novel antibacterial strategy to replace conventional therapies. This study aims to construct a self-activated cascade reaction nanozyme, f-FeNC@GOx, which triggers a cascade reaction in the presence of glucose. This cascade reaction generates highly toxic hydroxyl radicals (OH), thereby achieving the goal of eliminating bacteria and promoting wound healing. MATERIAL AND METHODS: In vitro antibacterial experiments, bacterial spread plate method, Live/Dead staining, and crystal violet staining were conducted to analyze the antibacterial ability and mechanism of f-FeNC@GOx. In vivo experiments, a mouse wound model was established, and H&E and Masson staining of wound tissues were performed to assess the antibacterial activity of the f-FeNC@GOx in vivo. KEY FINDINGS: The in vivo and in vitro experiments confirmed that f-FeNC@GOx exhibited significant antibacterial effect against both Staphylococcus aureus and Escherichia coli in the presence of glucose. Furthermore, it showed optimal wound healing performance in the wound models. SIGNIFICANCE: These findings suggested that f-FeNC@GOx was a novel and effective antibacterial nanomaterial, which provided a promising antibacterial strategy using nanoenzyme based cascade reaction.

Bivariate joint analysis of injury severity of drivers in truck-car crashes accommodating multilayer unobserved heterogeneity.

Truck-involved crashes, especially truck-car crashes, are associated with serious and even fatal injuries, thus necessitating an in-depth analysis. Prior research focused solely on examining the injury severity of truck drivers or developed separate performance models for truck and car drivers. However, the severity of injuries to both drivers in the same truck-car crash may be interrelated, and influencing factors of injury severities sustained by the two parties may differ. To address these concerns, a random parameter bivariate probit model with heterogeneity in means (RPBPHM) is applied to examine factors affecting the injury severity of both drivers in the same truck-car crash and how these factors change over the years. Using truck-car crash data from 2017 to 2019 in the UK, the dependent variable is defined as slight injury and serious injury or fatality. Factors such as driver, vehicle, road, and environmental characteristics are statistically analyzed in this study. According to the findings, the RPBPHM model demonstrated a remarkable statistical fit, and a positive correlation was observed between the two drivers' injury severity in truck-car crashes. More importantly, the effects of the explanatory factors showing relatively temporal stability vary across different types of vehicle crashes. For example, car driver improper actions and lane changing by trucks, have a significant interactive effect on the severity of injuries sustained by drivers involved collisions between trucks and cars. Male truck drivers, young truck drivers, older truck drivers, and truck drivers' improper actions, elevate the estimated odds of only truck drivers; while older car and unsignalized crossing increase the possibility of injury severity of only car drivers. Finally, due to shared unobserved crash-specific factors, the 30-mph speed limit, dark no lights, and head-on collision, significantly affect the severity of injuries sustained by drivers involved in collisions between trucks and cars. The modeling approach provides a novel framework for jointly analyzing truck-involved crash injury severities. The findings will help policymakers take the necessary actions to reduce truck-car crashes by implementing appropriate and accurate safety countermeasures.