Facile Preparation and Study of Self-healing Water-absorbing Expanding Elastomers.

The absorbent expansion elastomer plays a crucial role in engineering sealing and holds a promising future in this field as infrastructure continues to develop. Traditional materials have their limitations, especially when used in large construction projects where the integrity and reliability of the material are of utmost importance. In this work, a self-healing water-absorbing expansion elastomer was developed for continuous production at a large scale to monitor the sealing conditions of massive infrastructure projects. At room temperature, the material exhibited a repairing efficiency of 57.77% within 2 h, which increased to 84.02% after 12 h. This extended reaction time allowed for effective repairs when defects were detected. The material's strength reached approximately 3 MPa, making it suitable for a wide range of applications. The volume expansion rate of the material reached 200-400% for effective sealing, and the fictionalization of the packing made it have a good external force sensing effect and prevent heat build-up effect. The conductive detection performance of the absorbent expansion elastomer was improved by utilizing triple self-healing strategies, including dipole-dipole interaction, ion cross-linked network, and externally-aided restoration materials.

Major bleeding increases the risk of subsequent cardiovascular events in patients with atrial fibrillation: insights from the SAKURA AF registry and RAFFINE registry.

BACKGROUND: Bleeding events are one of the major concerns in patients using oral anticoagulants (OACs). We aimed to evaluate the association between major bleeding and long-term clinical outcomes in atrial fibrillation (AF) patients taking OACs. METHODS: We analyzed a database comprising two large-scale prospective registries of patients with documented AF: the RAFFINE and SAKURA registries. The primary outcome was major adverse cardiac and cerebrovascular events (MACCE), defined as the composite of all-cause death, ischemic stroke, and myocardial infarction. Major bleeding was defined in accordance with the criteria of the International Society on Thrombosis and Hemostasis. Cox multivariate analysis was used to determine the impact of major bleeding on the incidence of MACCE. RESULTS: The median follow-up period was 39.7 (interquartile range, 33.1-48.1) months. Among 6,633 patients with AF who were taking OAC, 298 (4.5%) had major bleeding and 737 (11.1%) had MACCE. The incidence of MACCE was higher in patients with bleeding than in those without (18.33 and 3.22, respectively, per 100 patient-years; log-rank p < 0.0001). Multivariate logistic regression analysis revealed older age, vitamin K antagonist use, and antiplatelet drug use as independent predictors of major bleeding. Median duration of MACCE occurrence after major bleeding was 41 (interquartile range, 3-300) days. Multivariate Cox hazard regression analysis showed that the risk of MACCE was significantly higher in patients with major bleeding compared to those without (hazard risk, 4.64; 95% confidence interval, 3.62-5.94; p < 0.0001). CONCLUSIONS: Major bleeding was associated with long-term adverse cardiovascular events among AF patients taking OAC. Therefore, reducing the risk of bleeding is important for improving clinical outcomes in patients with AF.

Large-scale cultivation of Synechocystis sp. PCC6803 for the production of Poly(3-hydroxybutyrate) and its potential applications in the manufacturing of bulk and medical prototypes.

Polyhydroxyalkanoates (PHAs) are biopolymers produced by microorganisms under nutrient limiting conditions and in the presence of excess carbon source. PHAs have gained popularity as a sustainable alternative to traditional plastics. However, large scale production of PHAs is economically challenging due to the relatively high costs of organic carbon. Alternative options include using organisms capable of phototrophic or mixotrophic growth. This study aimed at the production of poly(3-hydroxybutyrate) P(3HB), a type of PHA, at pilot scale using the freshwater cyanobacterium Synechocystis sp. PCC6803. First, to identify optimal conditions for P(3HB) production from Synechocystis sp. PCC6803, different supplemental carbon source concentrations and salinity levels were tested at laboratory scale. The addition of 4 g/L acetate with no added NaCl led to P(3HB) accumulation of 10.7 % dry cell weight on the 28th day of cultivation. Although acetate additions were replicated in an outdoor 400 L serpentine photobioreactor, P(3HB) content was lower, implying uncontrolled conditions impact on biopolymer production efficiency. An optimized P(3HB) extraction methodology was developed to remove pigments, and the biopolymer was characterized and subjected to 3D printing (fused deposition modelling) to confirm its processability. This study thus successfully led to the large-scale production of P(3HB) using sustainable and environmentally friendly cyanobacterial fermentation.

Facile construction of efficient WO3/V2O5 coupled g-C3N4 ternary composite photocatalyst for environmental emergent aqueous pollutant degradation: Stability, degradation reaction pathway and effect of pH evaluation.

Currently, one of the primary challenges that human society must overcome is the task of decreasing the amount of energy used and the adverse effects that it has on the environment. The daily increase in liquid waste (comprising organic pollutants) is a direct result of the creation and expansion of new companies, causing significant environmental disruption. Water contamination is attributed to several industries such as textile, chemical, poultry, dairy, and pharmaceutical. In this study, we present the successful degradation of methylene blue dye using g-C3N4 (GCN) mixed with WO3 and V2O5 composites (GCN/WO3/V2O5 ternary composite) as a photocatalyst, prepared by a simple mechanochemistry method. The GCN/WO3/V2O5 ternary composite revealed a notable enhancement in photocatalytic performance, achieving around 97% degradation of aqueous methylene blue (MB). This performance surpasses that of the individual photocatalysts, namely pure GCN, GCN/WO3, and GCN/V2O5 composites. Furthermore, the GCN/WO3/V2O5 ternary composite exhibited exceptional stability even after undergoing five consecutive cycles. The exceptional photocatalytic activity of the GCN/WO3/V2O5 ternary composite can be ascribed to the synergistic effect of metal-free GCN and metal oxides, resulting in the alteration of the band gap and suppression of charge recombination in the ternary photocatalyst. This study offers a better platform for understanding the characteristics of materials and their photocatalytic performance under visible light conditions.

Arsenic speciation analysis in human urine for long term epidemiological studies: The Multi-Ethnic Study of Atherosclerosis (MESA).

Arsenic is a ubiquitous toxic metalloid causing serious health problems. Speciation analysis of arsenic in human urine provides valuable insights for large-scale epidemiological studies and informs on sources of exposure as well as human metabolism. The Multi-Ethnic Study of Atherosclerosis (MESA) is a valuable cohort for assessing chronic low-moderate arsenic exposure and health effects in an ethnically diverse US population. We present a state-of-the-art arsenic speciation analysis methodology and its application to 7,677 MESA spot urine samples based on high-performance liquid chromatography coupled to inductively coupled plasma mass spectrometry. This method is fast, robust and detects a total of 11 individual As species at method detection limits of 0.02-0.03 µg arsenic/L urine for each individual species. Our analytical approach features excellent method accuracy (98%) and precision (5%) for the main arsenic species in urine (arsenobetaine, methylarsonic acid, dimethylarsinic acid, and total inorganic As); intra- (3-6%) and inter-day coefficients of variability (5-6%); column recovery (96 ± 7%); and spike recovery (97 ± 6%). The main arsenic species were detectable in ≥95% of urine samples due to the implementation of an oxidation step. Each individual minor arsenic species was detectable in ≤25% of all urines, although at least one of them was detectable in almost half the participants. We identified two minor urinary arsenic species as dimethylarsinoylacetic acid and dimethylarsinoylpropionic acid, potential metabolites of seafood-related arsenicals. We observed differences in individual As species excretion by race/ethnicity, with Asian-American participants featuring 3-4 times higher concentrations compared to other participants. We also found differences by site, body mass index, smoking status, rice intake, and water arsenic levels, potentially indicating different exposures or related to individual bio-metabolism. The proposed approach is suitable for epidemiological studies and the collected data will constitute the base for future research on potential health effects of chronic low-level arsenic exposure.

Assessing Opportunities and Barriers to Improving the Secondary Use of Health Care Data at the National Level: Multicase Study in the Kingdom of Saudi Arabia and Estonia.

BACKGROUND: Digitization shall improve the secondary use of health care data. The Government of the Kingdom of Saudi Arabia ordered a project to compile the National Master Plan for Health Data Analytics, while the Government of Estonia ordered a project to compile the Person-Centered Integrated Hospital Master Plan. OBJECTIVE: This study aims to map these 2 distinct projects' problems, approaches, and outcomes to find the matching elements for reuse in similar cases. METHODS: We assessed both health care systems' abilities for secondary use of health data by exploratory case studies with purposive sampling and data collection via semistructured interviews and documentation review. The collected content was analyzed qualitatively and coded according to a predefined framework. The analytical framework consisted of data purpose, flow, and sharing. The Estonian project used the Health Information Sharing Maturity Model from the Mitre Corporation as an additional analytical framework. The data collection and analysis in the Kingdom of Saudi Arabia took place in 2019 and covered health care facilities, public health institutions, and health care policy. The project in Estonia collected its inputs in 2020 and covered health care facilities, patient engagement, public health institutions, health care financing, health care policy, and health technology innovations. RESULTS: In both cases, the assessments resulted in a set of recommendations focusing on the governance of health care data. In the Kingdom of Saudi Arabia, the health care system consists of multiple isolated sectors, and there is a need for an overarching body coordinating data sets, indicators, and reports at the national level. The National Master Plan of Health Data Analytics proposed a set of organizational agreements for proper stewardship. Despite Estonia's national Digital Health Platform, the requirements remain uncoordinated between various data consumers. We recommended reconfiguring the stewardship of the national health data to include multipurpose data use into the scope of interoperability standardization. CONCLUSIONS: Proper data governance is the key to improving the secondary use of health data at the national level. The data flows from data providers to data consumers shall be coordinated by overarching stewardship structures and supported by interoperable data custodians.

Preliminary assessment of TNM classification performance for pancreatic cancer in Japanese radiology reports using GPT-4.

PURPOSE: A large-scale language model is expected to have been trained with a large volume of data including cancer treatment protocols. The current study aimed to investigate the use of generative pretrained transformer 4 (GPT-4) for identifying the TNM classification of pancreatic cancers from existing radiology reports written in Japanese. MATERIALS AND METHODS: We screened 100 consecutive radiology reports on computed tomography scan for pancreatic cancer from April 2020 to June 2022. GPT-4 was requested to classify the TNM from the radiology reports based on the General Rules for the Study of Pancreatic Cancer 7th Edition. The accuracy and kappa coefficient of the TNM classifications by GPT-4 was evaluated with the classifications by two experienced abdominal radiologists as gold standard. RESULTS: The accuracy values of the T, N, and M factors were 0.73, 0.91, and 0.93, respectively. The kappa coefficients were 0.45 for T, 0.79 for N, and 0.83 for M. CONCLUSION: Although GPT is familiar with the TNM classification for pancreatic cancer, its performance in classifying actual cases in this experiment may not be adequate.

Predictions of PM2.5 using air pollutants and meteorological factors with COVID-19 cases in Malaysia and Indonesia: a comparative study using feature selection and robust regression.

The study examines the relationship between air quality, meteorological factors, and COVID-19 cases in Cheras, Kuala Lumpur, and Kelapa Gading, North Jakarta. Analyzing data from 2020 and 2021, the research found notable correlations: COVID-19 cases in Cheras were positively associated with relative humidity (RH) and carbon monoxide (CO) but negatively with ozone (O3) and RH in different years. In Kelapa Gading, COVID-19 cases were positively correlated with pollutants like sulfur dioxide (SO2) and CO, while ambient temperature (AT) showed a negative correlation. The enforcement of social restrictions notably reduced air pollution, affecting COVID-19 spread. Predictive models for PM2.5 levels using robust regression techniques showed strong performance in Kuala Lumpur (R² > 0.9) but exhibited overfitting tendencies in Jakarta, suggesting the need for a longer study period for more accurate results.

Large-scale pretrained frame generative model enables real-time low-dose DSA imaging: An AI system development and multi-center validation study.

BACKGROUND: Digital subtraction angiography (DSA) devices are commonly used in numerous interventional procedures across various parts of the body, necessitating multiple scans per procedure, which results in significant radiation exposure for both doctors and patients. Inspired by generative artificial intelligence techniques, this study proposes GenDSA, a large-scale pretrained multi-frame generative model-based real-time and low-dose DSA imaging system. METHODS: GenDSA was developed to generate 1-, 2-, and 3-frame sequences following each real frame. A large-scale dataset comprising ∼3 million DSA images from 27,117 patients across 10 hospitals was constructed to pretrain, fine-tune, and validate GenDSA. Two other datasets from 25 hospitals were used for evaluation. Objective evaluations included SSIM and PSNR. Five interventional radiologists independently assessed the quality of the generated frames using the Likert scale and visual Turing test. Scoring consistency among the radiologists was measured using the Kendall coefficient of concordance (W). The Fleiss' kappa values were used for inter-rater agreement analysis for visual Turing tests. FINDINGS: Using only one-third of the clinical radiation dose, videos generated by GenDSA were perfectly consistent with real videos. Objective evaluations demonstrated that GenDSA's performance (PSNR = 36.83, SSIM = 0.911, generation time = 0.07 s/frame) surpassed state-of-the-art algorithms. Subjective ratings and statistical results from five doctors indicated no significant difference between real and generated videos. Furthermore, the generated videos were comparable to real videos in overall quality (4.905 vs. 4.935) and lesion assessment (4.825 vs. 4.860). CONCLUSIONS: With clear clinical and translational values, the developed GenDSA can significantly reduce radiation damage to both doctors and patients during DSA-guided procedures. FUNDING: This study was supported by the National Key R&D Program and the National Natural Science Foundation of China.

Tibetan lake change linked to large-scale atmospheric oscillations via hydroclimatic trajectory.

Lakes are known as sentinels of climate change, but their responses may differ from one to another leading to different strategies in lake protection. It is particularly the case in the Tibetan Plateau (TP) of multiple hydrological processes. We employed the Budyko framework to study Tibetan lakes from two lake-basins of contrasting climates for the period between 1980 and 2022: Taro Co Basin (TCB) in a sub-arid climate, and Ranwu Lake Basin (RLB) in a sub-humid climate. Our results showed that total lake area, surface air temperature, evapotranspiration, and potential evapotranspiration increased in both lake-basins, while precipitation and soil moisture increased in the TCB but decreased in the RLB. In the Budyko space, two basins had contrast hydroclimatic trajectories in terms of aridity and evaporative index. The TCB shifted from wetting to drying trend, while the RLB from drying to wetting in early 2000s. Notably, lake change was generally consistent with the drying/wetting phases in the TCB, but in contrast with that in the RLB, which can be attributed to warming-induced glacier melting. Despite of significant correlation with the large-scale atmospheric oscillations, it turned to be more plausible if lake area changes were substituted with basin's hydroclimatic trajectories. Among the large-scale oscillations, El Niño-Southern Oscillation (ENSO) is the most dominant control of lake trends and their drying/wetting shifts. Our findings offer a valuable insight into lake responses to climate change in the TP and other regions.

Score Images as a Modality: Enhancing Symbolic Music Understanding through Large-Scale Multimodal Pre-Training.

Symbolic music understanding is a critical challenge in artificial intelligence. While traditional symbolic music representations like MIDI capture essential musical elements, they often lack the nuanced expression in music scores. Leveraging the advancements in multimodal pre-training, particularly in visual-language pre-training, we propose a groundbreaking approach: the Score Images as a Modality (SIM) model. This model integrates music score images alongside MIDI data for enhanced symbolic music understanding. We also introduce novel pre-training tasks, including masked bar-attribute modeling and score-MIDI matching. These tasks enable the SIM model to capture music structures and align visual and symbolic representations effectively. Additionally, we present a meticulously curated dataset of matched score images and MIDI representations optimized for training the SIM model. Through experimental validation, we demonstrate the efficacy of our approach in advancing symbolic music understanding.

Application of single-molecule analysis to singularity phenomenon of cells.

Single-molecule imaging in living cells is an effective tool for elucidating the mechanisms of cellular phenomena at the molecular level. However, the analysis was not designed for throughput and requires high expertise, preventing it from reaching large scale, which is necessary when searching for rare cells that induce singularity phenomena. To overcome this limitation, we have automated the imaging procedures by combining our own focusing device, artificial intelligence, and robotics. The apparatus, called automated in-cell single-molecule imaging system (AiSIS), achieves a throughput that is a hundred-fold higher than conventional manual imaging operations, enabling the analysis of molecular events by individual cells across a large population. Here, using AiSIS, we demonstrate the single-molecule imaging of molecular behaviors and reactions related to tau protein aggregation, which is considered a singularity phenomenon in neurological disorders. Changes in the dynamics and kinetics of molecular events were observed inside and on the basal membrane of cells after the induction of aggregation. Additionally, to detect rare cells based on the molecular behavior, we developed a method to identify the state of individual cells defined by the quantitative distribution of molecular mobility and clustering. Using this method, cellular variations in receptor behavior were shown to decrease following ligand stimulation. This cell state analysis based on large-scale single-molecule imaging by AiSIS will advance the study of molecular mechanisms causing singularity phenomena.

Stiffness and Interface Engineered Soft Electronics with Large-Scale Robust Deformability.

Skin-like stretchable electronics emerge as promising human-machine interfaces but are challenged by the paradox between superior electronic property and reliable mechanical deformability. Here, a general strategy is reported for establishing robust large-scale deformable electronics by effectively isolating strains and strengthening interfaces. A copolymer substrate is designed to consist of mosaic stiff and elastic areas with nearly four orders of magnitudes modulus contrast and cross-linked interfaces. Electronic functional devices and stretchable liquid metal (LM) interconnects are conformally attached at the stiff and elastic areas, respectively, through hydrogen bonds. As a result, functional devices are completely isolated from strains, and resistances of LM conductors change by less than one time when the substrate is deformed by up to 550%. By this strategy, solar cells, wireless charging antenna, supercapacitors, and light-emitting diodes are integrated into a self-powered electronic skin that can laminate on the human body and exhibit stable performances during repeated multimode deformations, demonstrating an efficient path for realizing highly deformable energy autonomous soft electronics.

A large-scale study and six-month follow-up of an intervention to reduce causal illusions in high school students.

Causal illusions consist of believing that there is a causal relationship between events that are actually unrelated. This bias is associated with pseudoscience, stereotypes and other unjustified beliefs. Thus, it seems important to develop educational interventions to reduce them. To our knowledge, the only debiasing intervention designed to be used at schools was developed by Barberia et al. (Barberia et al. 2013 PLoS One 8, e71303 (doi:10.1371/journal.pone.0071303)), focusing on base rates, control conditions and confounding variables. Their assessment used an active causal illusion task where participants could manipulate the candidate cause. The intervention reduced causal illusions in adolescents but was only tested in a small experimental project. The present research evaluated it in a large-scale project through a collaboration with the Spanish Foundation for Science and Technology (FECYT), and was conducted in schools to make it ecologically valid. It included a pilot study (n = 287), a large-scale implementation (n = 1668; 40 schools) and a six-month follow-up (n = 353). Results showed medium-to-large and long-lasting effects on the reduction of causal illusions. To our knowledge, this is the first research showing the efficacy and long-term effects of a debiasing intervention against causal illusions that can be used on a large scale through the educational system.

When enough is enough: Optimising monitoring effort for large-scale wolf population size estimation in the Italian Alps.

The ongoing expansion of wolf (Canis lupus) populations in Europe has led to a growing demand for up-to-date abundance estimates. Non-invasive genetic sampling (NGS) is now widely used to monitor wolves, as it allows individual identification and abundance estimation without physically capturing individuals. However, NGS is resource-intensive, partly due to the elusive behaviour and wide distribution of wolves, as well as the cost of DNA analyses. Optimisation of sampling strategies is therefore a requirement for the long-term sustainability of wolf monitoring programs. Using data from the 2020-2021 Italian Alpine wolf monitoring, we investigate how (i) reducing the number of samples genotyped, (ii) reducing the number of transects, and (iii) reducing the number of repetitions of each search transect impacted spatial capture-recapture population size estimates. Our study revealed that a 25% reduction in the number of transects or, alternatively, a 50% reduction in the maximum number of repetitions yielded abundance estimates comparable to those obtained using the entire dataset. These modifications would result in a 2046 km reduction in total transect length and 19,628 km reduction in total distance searched. Further reducing the number of transects resulted in up to 15% lower and up to 17% less precise abundance estimates. Reducing only the number of genotyped samples led to higher (5%) and less precise (20%) abundance estimates. Randomly subsampling genotyped samples reduced the number of detections per individual, whereas subsampling search transects resulted in a less pronounced decrease in both the total number of detections and individuals detected. Our work shows how it is possible to optimise wolf monitoring by reducing search effort while maintaining the quality of abundance estimates, by adopting a modelling framework that uses a first survey dataset. We further provide general guidelines on how to optimise sampling effort when using spatial capture-recapture in large-scale monitoring programmes.

Superfast, large-scale harvesting of cellulose molecules via ethanol pre-swelling engineering of natural fibers.

Cellulose molecules, as the basic unit of biomass cellulose, have demonstrated advancements in versatile engineering and modification of cellulose toward sustainable and promising materials in our low-carbon society. However, harvesting high-quality cellulose molecules from natural cellulosic fibers (CF) remains challenging due to strong hydrogen bonds and unique crystalline structure, which limit solvents (such as ionic liquid, IL) transport and diffusion within CF, making the process energy/time-intensively. Herein, we superfast and sustainably engineer biomass fibers into high-performance cellulose molecules via ethanol pre-swelling of CF followed by IL treatment in the microwave (MW) system. Ethanol-pre-swelled cellulosic fibers (SCF) feature modified morphological and structural distinctions, with improved fiber width, pore size, and specific surface area. The ethanol in the SCF structure is appropriately removed through MW heating and cooling, leaving transport and diffusion pathways of IL within the SCF. Such strategy enables the superfast (140 s) and large-scale (kilogram level) harvesting of cellulose molecules with high molecular weight, resulting in high-performance, versatile cellulose ionogel with a 300 % increase in strength and 1027 % in toughness, monitoring human movement, external pressure, and temperature. Our strategy paves the way for time/energy-effectively, sustainably harvesting high-quality polymer molecules from natural sources beyond cellulose toward versatile and advanced materials.

Controlled Defective Engineering on CuIr Catalyst Promotes Nitrate Selective Reduction to Ammonia.

Electrochemical nitrate reduction reaction (NO3-RR) is a promising low-carbon and environmentally friendly approach for the production of ammonia (NH3). Herein, we develop a high-temperature quenched copper (Cu) catalyst with the aim of inducing nonequilibrium phase transformation, revealing the multiple defects (distortion, dislocations, vacancies, etc.) presented in Cu, which lead to low overpotential for NO3-RR and high efficiency for NH3 production. Further loading a low content of iridium (Ir) species on the Cu surface improves the reactivity and ammonia selectivity. The resultant CuIr electrode exhibits a Faradaic efficiency of 93% and a record yield of 6.01 mmol h-1 cm-2 at -0.22 VRHE exceeding those of state-of-the-art NO3-RR catalysts. Detailed investigations have demonstrated that the synergistic effect between multiple defects and Ir decoration effectively regulate the d-band center of copper, change the adsorption state of the catalyst surface, and promote the adsorption and reduction of intermediates and reactants. The strong H* adsorption ability of the Ir element provides more active hydrogen for the generation of ammonia, promoting the reduction of nitrate to NH3.

Multi-Adaptive Strategies-Based Higher-Order Quantum Genetic Algorithm for Agile Remote Sensing Satellite Scheduling Problem.

The agile remote sensing satellite scheduling problem (ARSSSP) for large-scale tasks needs to simultaneously address the difficulties of complex constraints and a huge solution space. Taking inspiration from the quantum genetic algorithm (QGA), a multi-adaptive strategies-based higher-order quantum genetic algorithm (MAS-HOQGA) is proposed for solving the agile remote sensing satellites scheduling problem in this paper. In order to adapt to the requirements of engineering applications, this study combines the total task number and the total task priority as the optimization goal of the scheduling scheme. Firstly, we comprehensively considered the time-dependent characteristics of agile remote sensing satellites, attitude maneuverability, energy balance, and data storage constraints and established a satellite scheduling model that integrates multiple constraints. Then, quantum register operators, adaptive evolution operations, and adaptive mutation transfer operations were introduced to ensure global optimization while reducing time consumption. Finally, this paper demonstrated, through computational experiments, that the MAS-HOQGA exhibits high computational efficiency and excellent global optimization ability in the scheduling process of agile remote sensing satellites for large-scale tasks, while effectively avoiding the problem that the traditional QGA has, namely low solution efficiency and the tendency to easily fall into local optima. This method can be considered for application to the engineering practice of agile remote sensing satellite scheduling for large-scale tasks.

Recent advances of 3-fucosyllactose in health effects and production.

Human milk oligosaccharides (HMOs) have been recognized as gold standard for infant development. 3-Fucosyllactose (3-FL), being one of the Generally Recognized as Safe HMOs, represents a core trisaccharide within the realm of HMOs; however, it has received comparatively less attention in contrast to extensively studied 2'-fucosyllactose. The objective of this review is to comprehensively summarize the health effects of 3-FL, including its impact on gut microbiota proliferation, antimicrobial effects, immune regulation, antiviral protection, and brain maturation. Additionally, the discussion also covers the commercial application and regulatory approval status of 3-FL. Lastly, an organized presentation of large-scale production methods for 3-FL aims to provide a comprehensive guide that highlights current strategies and challenges in optimization.

Heparinized self-healing polymer coating with inflammation modulation for blood-contacting biomedical devices.

The current techniques for antithrombotic coating on blood-contacting biomedical materials and devices are usually complex and lack practical feasibility with weak coating stability and low heparin immobilization. Here, a heparinized self-healing polymer coating with inflammation modulation is introduced through thermal-initiated radical copolymerization of methacrylate esterified heparin (MA-heparin) with methyl methacrylate (MMA) and n-butyl acrylate (nBA), followed by the anchoring of reactive oxygen species (ROS)-responsive polyoxalate containing vanillyl alcohol (PVAX) onto the coating through esterification. The aspirin, which is readily dissolved in the solution of MMA and nBA, is encapsulated within the coating after copolymerization. The copolymerization of MA-heparin with MMA and nBA significantly increases the heparin content of the coating, effectively inhibiting thrombosis and rendering the coating self-healing to help maintain long-term stability. ROS-responsive PVAX and aspirin released in a temperature-dependent manner resist acute and chronic inflammation, respectively. The heparinized self-healing and inflammation-modulated polymer coating exhibits the ability to confer long-term stability and hemocompatibility to blood-contacting biomedical materials and devices. STATEMENT OF SIGNIFICANCE: Surface engineering for blood-contacting biomedical devices paves a successful way to reduce thrombotic and inflammatory complications. However, lack of effectiveness, long-term stability and practical feasibility hinders the development and clinical application of existing strategies. Here we design a heparinized self-healing and inflammation-modulated polymer coating, which possesses high heparin level and self-healing capability to maintain long-term stability. The polymer coating is practically feasible to varied substrates and demonstrated to manipulate inflammation and prevent thrombosis both in vitro and in vivo. Our work provides a new method to develop coatings for blood-contacting biomedical materials and devices with long-term stability and hemocompatibility.