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Abstract Despite the considerable advancements in fabricating polymeric-based scaffolds for tissue engineering, the clinical transformation of these scaffolds remained a big challenge because of the difficulty of simulating native organs/tissues’ microenvironment. As a kind of natural tissue-derived biomaterials, decellularized extracellular matrix (dECM)-based scaffolds have gained attention due to their unique biomimetic properties, providing a specific microenvironment suitable for promoting cell proliferation, migration, attachment and regulating differentiation. The medical applications of dECM-based scaffolds have addressed critical challenges, including poor mechanical strength and insufficient stability. For promoting the reconstruction of damaged tissues or organs, different types of dECM-based composite platforms have been designed to mimic tissue microenvironment, including by integrating with natural polymer or/and syntenic polymer or adding bioactive factors. In this review, we summarized the research progress of dECM-based composite scaffolds in regenerative medicine, highlighting the critical challenges and future perspectives related to the medical application of these composite materials. Graphical Abstract

Given the scarcity of annotated data, current deep learning methods face challenges in the field of document-level chemical-disease relation extraction, making it difficult to achieve precise relation extraction capable of identifying relation types and comprehensive extraction tasks that identify relation-related factors. This study tests the abilities of three large language models (LLMs), GPT3.5, GPT4.0, and Claude-opus, to perform precise and comprehensive extraction in document-level chemical-disease relation extraction on a self-constructed dataset. Firstly, based on the task characteristics, this study designs six workflows for precise extraction and five workflows for comprehensive extraction using prompting engineering strategies. The characteristics of the extraction process are analyzed through the performance differences under different workflows. Secondly, this study analyzes the content bias in LLMs extraction by examining the extraction effectiveness of different workflows on different types of content. Finally, this study analyzes the error characteristics of extracting incorrect examples by the LLMs. The experimental results show that: (1) The LLMs demonstrate good extraction capabilities, achieving the highest F1 scores of 87% and 73% respectively in the tasks of precise extraction and comprehensive extraction; (2) In the extraction process, the LLMs exhibit a certain degree of stubbornness, with limited effectiveness of prompting engineering strategies; (3) In terms of extraction content, the LLMs show a content bias, with stronger abilities to identify positive relations such as induction and acceleration; (4) The essence of extraction errors lies in the LLMs’ misunderstanding of the implicit meanings in biomedical texts. This study provides practical workflows for precise and comprehensive extraction of document-level chemical-disease relations and also indicates that optimizing training data is the key to building more efficient and accurate extraction methods in the future.

Electrochemical alcohol oxidation (EAO) represents an effective method for the production of high-value carbonyl products. However, its industrial viability is hindered by suboptimal efficiency stemming from low reaction rates. Here, we present a synergistic electrocatalysis approach that integrates an active electrode and aminoxyl radical to enhance the performance of EAO. The optimal aminoxyl radical (4-acetamido-2,2,6,6-tetramethylpiperidine 1-oxyl) and Ni 0.67 V 0.33 -layered double hydroxide (LDH) are screen as cooperative electrocatalysts by integrating theoretical predictions and experiments. The Ni 0.67 V 0.33 -LDH facilitates the adsorption and activation of N -(1-hydroxy-2,2,6,6-tetramethylpiperidin-4-yl)acetamide (ACTH) via interactions with ketonic oxygen, thereby improving selectivity and yield at high current densities. The electrolysis process is scaled up to produce 200 g of the steroid carbonyl product 8b (19-Aldoandrostenedione), achieving a yield of 91% and a productivity of 243 g h -1 . These results represent a promising method for accelerating electron transfer to enhance alcohol oxidation, highlighting its potential for practical electrosynthesis applications. The industrial viability of alcohol electrooxidation is hindered due to low reaction rates. Here, the authors report a synergistic approach that integrating an active electrode (Ni 0.67 V 0.33 -layered double hydroxide) and aminoxyl radical to enhance the oxidation of alcohols at high current densities.

PurposeTo investigate the prevalence of myopic macular degeneration (MMD) in the global population.MethodsAll published literature of population-based studies on MMD prevalence worldwide were searched and only those with clear definitions to diagnose and classify MMD lesions by standardised grading methods were selected. Meta-analysis methods were used to calculate the pooled prevalence of MMD and its 95% CI in a random-effects model. The prevalence of MMD lesions would also be reported, together with the subgroup analysis of age, region and gender. Correlation between MMD prevalence and spherical equivalent levels and axial length were also evaluated.Results12 studies with 58 558 subjects were included in this meta-analysis. The pooled prevalence of MMD in the world population was 2.1% (95% CI: 1.3% to 3.3%). In the subgroup analysis, people with the following characteristics were at higher risk of developing MMD: female, urban life, living in Asia, older age, longer axial length and severer myopia.ConclusionsMMD is a serious public health concern worldwide, particularly in subjects who are women, subjects living in urban areas, subjects living in Asia, and subjects with longer axial lengths and severer myopia. Further studies from other continents/ethnicities are needed for comprehensive estimates of the prevalence of MMD globally.

Compared to various traditional 2D approaches, the scaffold‐based 3D tumor models have emerged as an effective strategy to investigate the complex mechanisms behind cancer progression and responses to drug treatments, by providing biomimetic extracellular matrix and stromal‐like microenvironments including the vascular elements. Herein, the development of a 3D endothelialized hepatic tumor microtissue model based on the fusion of multicellular aggregates of human hepatocellular carcinoma cells and human umbilical vein endothelial cells cocultured in poly(lactic‐co‐glycolic acid)‐based porous microspheres (PLGA PMs) is reported. In contrast to the conventional 2D culture, the cells within the PLGA PMs exhibit significantly higher half‐maximal inhibitory concentration values against anticancer drugs, including doxorubicin and cisplatin. Furthermore, the feasibility of coculturing other cell types, such as fibroblasts (L929) and HepG2 cells, is investigated. Together, the findings emphasize the significance of engineered 3D hepatic tumor microtissue models using PLGA PM‐based multicellular aggregates for drug screening applications. The development of a 3D endothelialized hepatic tumor microtissue model based on the fusion of multicellular aggregates of human hepatocellular carcinoma cells and human umbilical vein endothelial cells cocultured in poly(lactic‐co‐glycolic acid)‐based porous microspheres (PLGA PMs) is reported. The significance of engineered 3D hepatic microtissue models using PLGA PM‐based multicellular aggregates for drug screening applications is demonstrated.

Facial expression is an important way to reflect human emotions and it represents a dynamic deformation process. Analyzing facial movements is an effective means of understanding expressions. However, there is currently a lack of methods capable of analyzing the dynamic details of full-field deformation in expressions. In this paper, in order to enable effective dynamic analysis of expressions, a classic optical measuring method called stereo digital image correlation (stereo-DIC or 3D-DIC) is employed to analyze the deformation fields of facial expressions. The forming processes of six basic facial expressions of certain experimental subjects are analyzed through the displacement and strain fields calculated by 3D-DIC. The displacement fields of each expression exhibit strong consistency with the action units (AUs) defined by the classical Facial Action Coding System (FACS). Moreover, it is shown that the gradient of the displacement, i.e., the strain fields, offers special advantages in characterizing facial expressions due to their localized nature, effectively sensing the nuanced dynamics of facial movements. By processing extensive data, this study demonstrates two featured regions in six basic expressions, one where deformation begins and the other where deformation is most severe. Based on these two regions, the temporal evolutions of the six basic expressions are discussed. The presented investigations demonstrate the superior performance of 3D-DIC in the quantitative analysis of facial expressions. The proposed analytical strategy might have potential value in objectively characterizing human expressions based on quantitative measurement.

The gearbox is one of the key components in wind turbines. Gearbox fault signals are usually nonstationary and highly contaminated with noise. The presence of amplitude-modulated and frequency-modulated (AM-FM) characteristics compound the difficulty of precise fault diagnosis of wind turbines, therefore, it is crucial to develop an effective fault diagnosis method for such equipment. This paper presents an improved diagnosis method for wind turbines via the combination of synchrosqueezing transform and local mean decomposition. Compared to the conventional time-frequency analysis techniques, the improved method which is performed in non-real-time can effectively reduce the noise pollution of the signals and preserve the signal characteristics, and hence is suitable for the analysis of nonstationary signals with high noise. This method is further validated by simulated signals and practical vibration data measured from a 1.5 MW wind turbine. The results confirm that the proposed method can simultaneously control the noise and increase the accuracy of time-frequency representation.

Background Dementia is a growing concern in China. Primary care screening is proposed as a timely and cost-effective way to identify potential cases, while its implementation relies on primary care providers’ (PCPs’) knowledge, attitudes, and skills. Our study aimed to investigate whether dementia screening outcomes vary according to the PCPs’ occupations and how PCPs’ knowledge, attitudes, and skills are associated with screening outcomes. Methods A two-stage dementia screening of residents aged 65 years and over in Guangdong, China was conducted using multistage cluster sampling. 252 PCPs, who completed questionnaires, were included in the analysis, along with 2823 older adults screened by them. Screening outcomes included the positive rate of screening, and positive predictive value (PPV). PCPs’ knowledge was assessed by the Dementia Knowledge Assessment Scale, attitudes by the Dementia Care Attitudes Scale, and skills by a validated self-designed questionnaire. Multilevel logistic regression was used to assess the associations of PCPs’ occupations, knowledge, attitudes, and skills with dementia screening outcomes. Results Knowledge scores in dementia care differed significantly ( P  = 0.005) among PCPs of different occupations, while attitudes and skills did not. The positive screening rate had no significant association with PCPs’ occupations. Public health physicians (OR = 2.927, 95%CI: 1.091–7.854) and nurses (OR = 3.712, 95%CI: 1.141–12.069) had a higher PPV than general practitioners (GPs). Higher dementia-care skills score (OR = 1.024, 95%CI: 1.004–1.046) was associated with a higher positive rate of screening. Positive attitudes towards dementia care was associated with a lower positive rate of screening (OR = 0.948, 95% CI: 0.905–0.994) and a higher PPV (OR = 1.114, 95% CI: 1.007–1.234). Conclusions In community settings, involving different occupations of PCPs besides GPs in dementia screening and systematically promoting dementia-care skills training and positive attitudes towards dementia care are important to improve the effectiveness of dementia screening and management.

In the context of a high proportion of new energy grid connections, demand-side resources have become an inevitable choice for constructing new power systems due to their high flexibility and fast response speed. However, the response capability of demand-side resources is decentralized and fluctuating, which makes it difficult for them to effectively participate in power market trading. Therefore, this paper proposes a robust transaction decision model for demand-side resource aggregators considering multi-objective clustering aggregation optimization. First, a demand-side resource aggregation operation model is designed to aggregate dispersed demand-side resources into a coordinated aggregated response entity through an aggregator. Second, the demand-side resource aggregation evaluation indexes are established from three dimensions of response capacity, response reliability, and response flexibility, and the multi-objective aggregation optimization model of demand-side resources is constructed with the objective function of the larger potential market revenue and the smallest risk of deviation penalty. Finally, robust optimization theory is adopted to cope with the uncertainty of demand-side resource responsiveness, the robust transaction decision model of demand-side resource aggregator is constructed, and a community in Henan Province is selected for simulation analysis to verify the validity and applicability of the proposed model. The findings reveal that the proposed cluster aggregation optimization method reduces the bias penalty risk of the demand-side resource aggregators by about 33.12%, improves the comprehensive optimization objective by about 18.10%, and realizes the optimal aggregation of demand-side resources that takes into account both economy and risk. Moreover, the robust trading decision model can increase the expected net revenue by about 3.1% under the ‘worst’ scenario of fluctuating uncertainties, which enhances the resilience of demand-side resource aggregators to risks and effectively fosters the involvement of demand-side resources in the electricity market dynamics.

Background The traditional treatment for diabetes usually requires frequent insulin injections to maintain normoglycemia, which is painful and difficult to achieve blood glucose control. Results To solve these problems, a non-invasive and painless oral delivery nanoparticle system with bioadhesive ability was developed by amphipathic 2-nitroimidazole– l -cysteine–alginate (NI–CYS–ALG) conjugates. Moreover, in order to enhance blood glucose regulation, an intelligent glucose-responsive switch in this nanoparticle system was achieved by loading with insulin and glucose oxidase (GOx) which could supply a stimulus-sensitive turnover strategy. In vitro tests illustrated that the insulin release behavior was switched “ ON ” in response to hyperglycemic state by GOx catalysis and “ OFF ” by normal glucose levels. Moreover, in vivo tests on type I diabetic rats, this system displayed a significant hypoglycemic effect, avoiding hyperglycemia and maintaining a normal range for up to 14 h after oral administration. Conclusion The stimulus-sensitive turnover strategy with bioadhesive oral delivery mode indicates a potential for the development of synthetic GR-NPs for diabetes therapy, which may provide a rational design of proteins, low molecular drugs, as well as nucleic acids, for intelligent releasing via the oral route.