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With the continuous development of modified starch, people's demand for the functional characteristics, safety, and convenience of modified starch has increased significantly. Pre-gelatinized starch (PGS) is a type of starch modified by physical methods, which is a modified starch particle without obvious crystallization obtained by rapid dehydration and drying after starch gelatinization. PGS has a porous and hydrogen-bonded fracture structure, which has the advantages of good cold water solubility, strong water retention ability and high viscoelasticity. Cold water can be mixed into paste during application, eliminating the step of heating and gelatinization. Therefore, as a biodegradable raw material with excellent performance, PGS has important application value in the fields of flour products, low-temperature meat products, condiments and other leisure starch-based foods. This paper briefly introduces the definition, preparation methods, properties, and influencing factors of PGS performance, focuses on the current application status of PGS in starch-based foods, and points out the future research direction of PGS in the field of food, aiming to provide some references for promoting the application of PGS in the food industry.

Starch provides essential calories for the body's vital activities, but it usually has a high glycemic index. The digestion of starch tends to lead to a steep increase in blood sugar levels, insulin resistance and health risk. The digestibility of starch is related to its multi-scale structure. The regulations of starch digestibility can be achieved by altering its multi-scale structure through the modifications. However, the physical modification has received more and more attention due to its safety, simplicity and environmental friendliness. Based on this, the digestibility of starch is outlined. The relationship between the multi-scale structure and digestive properties of starch is analyzed. The digestive properties of starch regulated by physical modifications, such as hydrothermal treatment, microwave, extrusion, high-pressure homogenization, ultrasonic, cold plasma and physical encapsulation are reviewed. The future research directions on starch digestibility are also provided.

Abstract Fluctuations in gas emission or concentration at working face represent primary indicators of impending coal and gas outbursts, making them essential for monitoring processes. However, the direct use of original signals for predictive purposes may cause false warnings due to the inability to recognize valuable potential information. This can result in decreased prediction accuracy. Additionally, gas emission or concentration is affected by potential hazards such as sensor calibration, borehole spraying, and air duct damage, leading to complexity of their behavior and recognition process. This paper proposes an intelligent recognition method leveraging YOLOv8 neural network to discern coal and gas outburst precursors and potential hazards. By employing continuous wavelet transform (CWT) on the gas concentration signal processed by critical slowing down (CSD) method, a two-dimensional time–frequency representation is generated. This representation is then fed into YOLOv8 model to recognize the outburst precursor characteristics and potential hazards. The research results show that YOLOv8-based intelligent recognition adeptly identifies the relevant precursor characteristics and potential hazards, enhancing both coal mine safety protocols and the accuracy of early outburst warning mechanisms.

Abstract Background Airway epithelial cell (AEC) necroptosis contributes to airway allergic inflammation and asthma exacerbation. Targeting the tumor necrosis factor-like ligand 1 A (TL1A)/death receptor 3 (DR3) axis has a therapeutic effect on asthmatic airway inflammation. The role of TL1A in mediating necroptosis of AECs challenged with ovalbumin (OVA) and its contribution to airway inflammation remains unclear. Methods We evaluated the expression of the receptor-interacting serine/threonine-protein kinase 3(RIPK3) and the mixed lineage kinase domain-like protein (MLKL) in human serum and lung, and histologically verified the level of MLKL phosphorylation in lung tissue from asthmatics and OVA-induced mice. Next, using MLKL knockout mice and the RIPK3 inhibitor GSK872, we investigated the effects of TL1A on airway inflammation and airway barrier function through the activation of necroptosis in experimental asthma. Results High expression of necroptosis marker proteins was observed in the serum of asthmatics, and necroptosis was activated in the airway epithelium of both asthmatics and OVA-induced mice. Blocking necroptosis through MLKL knockout or RIPK3 inhibition effectively attenuated parabronchial inflammation, mucus hypersecretion, and airway collagen fiber accumulation, while also suppressing type 2 inflammatory factors secretion. In addition, TL1A/ DR3 was shown to act as a death trigger for necroptosis in the absence of caspases by silencing or overexpressing TL1A in HBE cells. Furthermore, the recombinant TL1A protein was found to induce necroptosis in vivo, and knockout of MLKL partially reversed the pathological changes induced by TL1A. The necroptosis induced by TL1A disrupted the airway barrier function by decreasing the expression of tight junction proteins zonula occludens-1 (ZO-1) and occludin, possibly through the activation of the NF-κB signaling pathway. Conclusions TL1A-induced airway epithelial necroptosis plays a significant role in promoting airway inflammation and barrier dysfunction in asthma. Inhibition of the TL1A-induced necroptosis pathway could be a promising therapeutic strategy.

Metal Nanoclusters (MNCs) have shown great potential applications in the field of food safety due to their unique physical and chemical properties. Through the template synthesis method, the specific size, shape and dispersion of MNCs can be regulated, thereby improving their functional characteristics. Based on the recent studies on MNCs, the mechanism of template-assisted synthesis of MNCs and their applications in the detection of food contaminants, food packaging materials, antimicrobial agents and food additives are reviewed. It provides a scientific basis for template synthesis of MNCs, and to provide insights and directions for MNCs in food safety monitoring.

The island mining face in coal mines often encounters stress concentration and significant air leakage, elevating the risk of rock burst and spontaneous combustion of residual coal in the goaf. This study centers on the investigation of the 9305 island mining face within a mine situated in Shandong Province, China. Leveraging the features of coal spontaneous combustion and data from microseismic and gas monitoring throughout the mining process, a novel method for calculating safe mining speeds under compound disasters is introduced. The safe mining speed of the 9305 island mining face is stratified, and a prevention and control technology integrating long distance directional drilling for impact-spontaneous combustion compound disasters is proposed. Findings suggest that to mitigate spontaneous combustion of residual coal, considering the seam's spontaneous combustion tendency, the safe mining speed should exceed 3.7 m per day. Accounting for the distribution of microseismic events during coal seam extraction, the safe mining speed is maintained below 4.8 m per day in the unprotected mining area and capped at 8 m per day in the protected zone. Through a comprehensive analysis considering coal seam spontaneous combustion tendency, impact tendency, and time to coal seam oxidation to critical temperature, the safe mining speed for the island mining face ranges from 3.7 m per day to 4.8 m per day in the unprotected area and from 5.14 m per day to 8 m per day in the protected area. The proposed long distance directional drilling 'one hole, multiple purposes' scheme enables an integrated approach encompassing pressure relief before coal seam extraction, water injection during mining, and grouting post-mining. This method effectively prevents and controls the compound disasters of rock burst and coal spontaneous combustion, presenting innovative technical solutions for the safe extraction of coal resources.

Granulomatous lobular mastitis (GLM) presents significant challenges, including high rates of morbidity, recurrence, and disability, ultimately impacting women’s health and quality of life. Local autoimmune imbalance involving dysregulated cytokines and immune cells has been recognized to play a key role in the pathology of GLM. Traditional Chinese medicine (TCM), with its multi-component, multi-pathway and multi-target characteristics, offers unique advantages and broad prospects in the treatment of GLM. Here, we review the relationship between immune dysregulation and GLM, as well as the regulatory mechanisms of TCM-based interventions, with the aim of providing new insights and foundational knowledge for the clinical treatment of GLM, while promoting the further application and development of TCM-based strategies for the treatment of GLM.

Breast cancer is one of the most prevalent cancers in women globally. Its treatment and prognosis are significantly influenced by the tumor microenvironment and tumor heterogeneity. Precision therapy enhances treatment efficacy, reduces unwanted side effects, and maximizes patients’ survival duration while improving their quality of life. Spatial transcriptomics is of significant importance for the precise treatment of breast cancer, playing a critical role in revealing the internal structural differences of tumors and the composition of the tumor microenvironment. It offers a novel perspective in studying the spatial structure and cell interactions within tumors, facilitating more effective personalized treatments for breast cancer. This article will summarize the latest findings in the diagnosis and treatment of breast cancer from the perspective of spatial transcriptomics, focusing on the revelation of the tumor microenvironment, identification of new therapeutic targets, enhancement of disease diagnostic accuracy, comprehension of tumor progression and metastasis, assessment of drug responses, creation of high-resolution maps of tumor cells, representation of tumor heterogeneity, and support for clinical decision-making, particularly in elucidating the tumor microenvironment, tumor heterogeneity, immunotherapy and their correlation with clinical outcomes.

IntroductionThe rapid evolution of online health services (OHS) has significantly improved healthcare accessibility. However, the impact of air pollution on individuals’ willingness to engage with OHS remains underexplored in the existing literature. This study addresses this gap by investigating how air pollution threats influence the intention to use OHS, grounded in Protection Motivation Theory (PMT) and Rational Choice Theory (RCT).MethodsUsing cross-sectional survey data, a theoretical model was developed and tested to examine the relationships between threat appraisal, coping appraisal, perceived benefits, perceived costs, and OHS usage intentions.ResultsThe findings indicate that threat appraisal, coping appraisal, and perceived benefits positively affect the intention to use OHS, while perceived costs negatively impact usage intention. Additionally, age significantly moderates the relationship between coping appraisal and the intention to use OHS.DiscussionThis study extends the application of PMT and RCT in the context of OHS. It provides valuable insights into the factors influencing users’ intentions to utilize these services, with implications for improving OHS adoption in environments affected by air pollution.

Abstract The classification of coal bursting liability (CBL) is essential for the mitigation and management of coal bursts in mining operations. This study establishes an index system for CBL classification, incorporating dynamic fracture duration (DT), elastic strain energy index (W ET), bursting energy index (K E), and uniaxial compressive strength (R C). Utilizing a dataset comprising 127 CBL measurement groups, the impacts of various optimization algorithms were assessed, and two prominent machine learning techniques, namely the back propagation neural network (BPNN) and the support vector machine (SVM), were employed to develop twelve distinct models. The models’ efficacy was evaluated based on accuracy, F1-score, Kappa coefficient, and sensitivity analysis. Among these, the Levenberg–Marquardt back propagation neural network (LM-BPNN) model was identified as superior, achieving an accuracy of 96.85%, F1-score of 0.9113, and Kappa coefficient of 0.9417. Further validation in Wudong Coal Mine and Yvwu Coal Industry confirmed the model, achieving 100% accuracy. These findings underscore the LM-BPNN model’s potential as a viable tool for enhancing coal burst prevention strategies in coal mining sectors.

Abstract Background Airway epithelium is an important component of airway structure and the initiator of airway remodeling in asthma. The changes of extracellular matrix (ECM), such as collagen deposition and structural disturbance, are typical pathological features of airway remodeling. Thus, identifying key mediators that derived from airway epithelium and capable of modulating ECM may provide valuable insights for targeted therapy of asthma. Methods The datasets from Gene Expression Omnibus database were analyzed to screen differentially expressed genes in airway epithelium of asthma. We collected bronchoscopic biopsies and serum samples from asthmatic and healthy subjects to assess lysyl oxidase like 2 (LOXL2) expression. RNA sequencing and various experiments were performed to determine the influences of LOXL2 knockdown in ovalbumin (OVA)-induced mouse models. The roles and mechanisms of LOXL2 in bronchial epithelial cells were explored using LOXL2 small interfering RNA, overexpression plasmid and AKT inhibitor. Results Both bioinformatics analysis and further experiments revealed that LOXL2 is highly expressed in airway epithelium of asthmatics. In vivo, LOXL2 knockdown significantly inhibited OVA-induced ECM deposition and epithelial-mesenchymal transition (EMT) in mice. In vitro, the transfection experiments on 16HBE cells demonstrated that LOXL2 overexpression increases the expression of N-cadherin and fibronectin and reduces the expression of E-cadherin. Conversely, after silencing LOXL2, the expression of E-cadherin is up-regulated. In addition, the remodeling and EMT process that induced by transforming growth factor-β1 could be enhanced and weakened after LOXL2 overexpression and silencing in 16HBE cells. Combining the RNA sequencing of mouse lung tissues and experiments in vitro, LOXL2 was involved in the regulation of AKT signaling pathway. Moreover, the treatment with AKT inhibitor in vitro partially alleviated the consequences associated with LOXL2 overexpression. Conclusions Taken together, the results demonstrated that epithelial LOXL2 plays a role in asthmatic airway remodeling partly via the AKT signaling pathway and highlighted the potential of LOXL2 as a therapeutic target for airway remodeling in asthma.

In order to investigate the effects of different degrees of substitution (DS) on physicochemical properties of rice starch malate and on the quality of noodles, rice starch malate with different degrees of substitution (DS=0~0.603) was prepared. The solubility, swelling, gelatinization and rheological properties of rice starch malate were investigated, as well as its improvement effect on the cooking characteristics, texture characteristics and sensory characteristics of noodles. The results indicated that a new absorption peak appeared at a spectral wavenumber of approximately 1720 cm−1 on the infrared spectrum curve of malic acid starch esters. After esterification, the solubility of starch significantly increased (P

Abstract Background CDC6 is an oncogenic protein whose expression level fluctuates during the cell cycle. Although several E3 ubiquitin ligases responsible for the ubiquitin-mediated proteolysis of CDC6 have been identified, the deubiquitination pathway for CDC6 has not been investigated. Methods The proteome-wide deubiquitinase (DUB) screening was used to identify the potential regulator of CDC6. Immunofluorescence, protein half-life and deubiquitination assays were performed to determine the protein stability of CDC6. Gain- and loss-of-function experiments were implemented to analyse the impacts of OUTD6A-CDC6 axis on tumour growth and chemosensitivity in vitro. N-butyl-N-(4-hydroxybutyl) nitrosamine (BBN)-induced conditional Otud6a knockout (CKO) mouse model and tumour xenograft model were performed to analyse the role of OTUD6A-CDC6 axis in vivo. Tissue specimens were used to determine the association between OTUD6A and CDC6. Results OTUD6A interacts with, depolyubiquitinates and stabilizes CDC6 by removing K6-, K33-, and K48-linked polyubiquitination. Moreover, OTUD6A promotes cell proliferation and decreases sensitivity to chemotherapy by upregulating CDC6. CKO mice are less prone to BCa tumorigenesis induced by BBN, and knockdown of OTUD6A inhibits tumour progression in vivo. Furthermore, OTUD6A protein level has a positive correlation with CDC6 protein level, and high protein levels of OTUD6A and CDC6 are associated with poor prognosis in patients with bladder cancer. Conclusions We reveal an important yet missing piece of novel DUB governing CDC6 stability. In addition, our findings propose a model for the OTUD6A-CDC6 axis that provides novel insights into cell cycle and chemosensitivity regulation, which may become a potential biomarker and promising drug target for cancer treatment.

Abstract To achieve the transient overvoltage suppression of doubly fed induction generator (DFIG) terminals and the point of common coupling (PCC) in the wind farm (WF), an adaptive overvoltage control strategy based on data‐driven approach is proposed. Firstly, the PCC reactive power demand and DFIG overvoltage threshold are derived, respectively. Secondly, three regression models are developed through surface fitting to determine three variables: the maximum overvoltage of each DFIG, the PCC maximum overvoltage, and the reactive power demand of each DFIG. Subsequently, the K‐means clustering method is adopted to group all DFIGs, and then the reactive power requirement of the PCC is proportionally distributed to each group of DFIGs. Lastly, a coordinated control strategy of DFIGs and static var generator (SVG) is designed based on the values of three regression models, which can adaptively adjust the reactive power absorbed by each DFIG and SVG. In particular, the de‐load control is adopted to enlarge the maximum reactive power capacity of DFIG. The simulation results show that the proposed control strategy can timely and effectively restore the transient overvoltage of the wind farm to the normal range during the gird failure, ensuring the stable operation of the wind farm.

Cereal foods are the primary source of daily energy, protein, and dietary fiber intake for humans. With the continuous improvement of people's living standards, cereal foods' nutritional value, safety, and food quality have attracted much attention. Cereal metabonomics, as a new and rapidly developing branch of "metabonomics", has made rapid progress in quantifying and analyzing various metabolites. This article reviews the analysis process of metabolomics technology, and its application in the detection of component identification, the identification of flavor substances, the traceability of origin, and the product of new developments in cereal foods, and the development trend of metabonomics in cereal food field has prospected, to provide a reference for the in-depth development and application of cereal foods.

Cyclic disturbance loading affects the continuous operation of concrete structures. This study aims to better understand the failure behavior of concrete structures under cyclic disturbance loading. This study combined acoustic emission (AE) and digital image correlation (DIC) technologies to study mechanical failure behavior and damage evolution of concrete materials under cyclic disturbance loading and unloading. The AE event time-frequency characteristics, surface principal strain program, and damage parameters of samples with different cyclic disturbance amplitudes were obtained by theoretical calculation. The results reveal that when minor disturbance amplitudes are applied, the elastic modulus of concrete specimens tends to increase. When the disturbance amplitude is too large, the elastic modulus of the specimens drops, leading to faster specimen failure. The AE characteristic parameters exhibit a Felicity effect after the sample is subjected to a cyclic disturbance loading. Moreover, the larger the amplitude of the disturbance load, the more frequent the low-frequency and high-amplitude events generated during the final rupture, the higher the concentration of the principal strain on the sample's surface, and the wider the crack band distribution. AE energy and Effective crack are consistent in characterizing damage, and the changing trend of these two damage parameters can better reflect the deterioration process of concrete. The research results will provide practical and theoretical insights for safely operating and maintaining concrete structures.

In this study, some loading experiments were conducted on coal specimens using the established weak current testing system to investigate the effects of weak currents in coals. The response characteristics of weak currents during the coal deformation process were analyzed, and a quantitative relationship between weak currents and the mechanical behaviors of coal was established. The principles for predicting coal and rock dynamic disasters based on the weak current method were proposed. Furthermore, drawing upon the results obtained from on-site practical applications, a forward-looking assessment of the potential applications of this methodology is presented. The results indicate that the deformation of loaded coal generates weak currents. The fluctuations in these weak currents exhibit a notable alignment with variations in stress, with the magnitude closely tied to the coal mechanical characteristics like stress, strain, and strain rate. Notably, in the compaction and plastic deformation stages, the weak current rises in tandem with the strain rate, while in the elastic deformation stage, the weak current exhibits a linear increase in correlation with stress and strain. Under perturbation loading, the coal produces some periodic weak currents, referred to as the pulsating direct current (PDC). And the variations in the PDC align with stress changes. Furthermore, the weak current demonstrates a reliable precursor response to coal damage, with some unusual fluctuations observed during the accelerated increase process (plastic deformation stage) serving as the precursor indicators for the progressive failure of coal. Conversely, the pulse-like fluctuations observed during the attenuation process can be regarded as the precursor features for the coal's creep failure. The phenomenon of weak currents in stressed coal serves as a pivotal foundation for conducting weak current measurements in mines. The intimate correlation between weak currents and the mechanical behaviors of coal underscores a crucial prerequisite for employing the weak current technology in monitoring stress within coal and rock formations. Furthermore, the discernible precursor response of weak currents to coal damage provides a vital assurance for predicting coal and rock dynamic disasters through the utilization of weak current technology. The weak current technology possesses some advantages such as high responsiveness, clear precursor signals, and robust anti-interference capabilities. These attributes make it highly promising for some applications in the stress monitoring within coal/rock mass and the early warning for coal and rock dynamic disasters. In the future, some further researches on the fundamental theory and applied technology of weak current in stressed coal and rock are needed to provide a support for the development of precise spatiotemporal monitoring and early warning of coal rock dynamic disasters using the weak current methods.

Abstract Objective Tumors are highly heterogeneous, and within their parenchyma, a small population of tumor-stem cells possessing differentiation potential, high oncogenicity, and self-renewal capabilities exists. These cells are pivotal in mediating tumor development, chemotherapy resistance, and recurrence. Ovarian cancer shares characteristics with tumor stem cells, making it imperative to investigate molecular markers associated with these cells. Methods Stem cell-related genes were collected, and molecular subtypes were established based on gene expression profiles from The Cancer Genome Atlas using the R package tool “ConsensusClusterPlus.” Multi-gene prognostic markers were identified using LASSO regression analysis. Gene set enrichment analysis was employed to gain insights into the potential molecular mechanisms of these identified markers. The robustness of these prognostic markers was analyzed across different cohorts, and their clinical independence was determined through multivariate Cox analysis. A nomogram was constructed to assess the model’s clinical applicability. Immunohistochemistry was performed to validate the expression of hub genes. Results Utilizing 49 tumor stem cell-related genes associated with prognosis, 362 ovarian cancer samples were divided into two distinct clusters, revealing significant prognostic disparities. A seven-gene signature (GALP, CACNA1C, COL16A1, PENK, C4BPA, PSMA2, and CXCL9), identified through LASSO regression, exhibited stability and robustness across various platforms. Multivariate Cox regression analysis confirmed the signature’s independence in predicting survival in patients with ovarian cancer. Furthermore, a nomogram combining the gene signature demonstrated strong predictive abilities. Immunohistochemistry results indicated significantly elevated GALP, CACNA1C, COL16A1, PENK, C4BPA, PSMA2, and CXCL9 expression in cancer tissues. Conclusion The seven-gene signature holds promise as a valuable tool for decision-making and prognosis prediction in patients with ovarian cancer.

Objective: In this work, the optimal preparation process parameters of selenium-curcumin nanoparticles (SeNPs@Cur) were explored and its protective effect on alcoholic liver injury was evaluated. Methods: On the basis of single factor experiment, with particle size as the index, curcumin dosage, molar ratio of VC to Na2SeO3, reaction time and reaction temperature were selected as influence factors. The process parameters of SeNPs@Cur were optimized by response surface methodology, and its structure was characterized by scanning electron microscope (SEM), fourier transform infrared spectrometer (FT-IR) and energy dispersive spectrometer (EDS). The protective effect of SeNPs@Cur on ethanol-induced LO2 cell injury was investigated by MTT method. Results: The optimal conditions of SeNPs@Cur were list as follows: Curcumin dosage of 5.92 mg/mL, molar ratio of VC to Na2SeO3 of 10.39:1, the reaction time of 1.98 h, and the reaction temperature of 34.00 ℃. Under these conditions, the SeNPs@Cur with a minimum size of (100.79±3.46) nm was obtained. The loading rate of curcumin and the selenium content in the sample were 12.80%±0.80% and 23.32%±0.07%, respectively. Further structural identification showed that the prepared SeNPs@Cur was dispersed spherical nanoparticles. Moreover, compared with the model group, after treatments with different dosages of SeNPs@Cur, the cell viabilities increased from 58.06%±0.43% to 71.43%±1.39%, 77.33%±3.54%, and 85.41%±4.61%, respectively, indicating that SeNPs@Cur had a protective effect on LO2 cells damaged by ethanol in a concentration-dependent manner. Conclusion: The SeNPs@Cur with anti-alcoholic liver injury effect was successfully obtained, which provided new technical means and theoretical reference data for formulation improvement and application of curcumin.

Abstract Background Oculocutaneous albinism (OCA) is a group of rare genetic disorders characterized by a reduced or complete lack of melanin in the skin, hair, and eyes. Patients present with colorless retina, pale pink iris, and pupil, and fear of light. The skin, eyebrows, hair, and other body hair are white or yellowish-white. These conditions are caused by mutations in specific genes necessary for the production of melanin. OCA is divided into eight clinical types (OCA1-8), each with different clinical phenotypes and potential genetic factors. This study aimed to identify the genetic causes of non-syndromic OCA in a Chinese Han family. Methods We performed a comprehensive clinical examination of family members, screened for mutation loci using whole exome sequencing (WES) technology, and predicted mutations using In silico tools. Results The patient’s clinical manifestations were white skin, yellow hair, a few freckles on the cheeks and bridge of the nose, decreased vision, blue iris, poorly defined optic disk borders, pigmentation of the fundus being insufficient, and significant vascular exposure. The WES test results indicate that the patient has compound heterozygous mutations in the OCA2 gene (c.1258G > A (p.G420R), c.1441G > A (p.A481T), and c.2267-2 A > C), respectively, originating from her parents. Among them, c.1258G > A (p.G420R) is a de novo mutation with pathogenic. Our analysis suggests that compound heterozygous mutations in the OCA2 gene are the primary cause of the disease in this patient. Conclusions The widespread application of next-generation sequencing technologies such as WES in clinical practice can effectively replace conventional detection methods and assist in the diagnosis of clinical diseases more quickly and accurately. The newly discovered c.1258G > A (p.G420R) mutation can update and expand the gene mutation spectrum of OCA2-type albinism.

Abstract Background Hepatocellular carcinoma (HCC) is the most common type of malignant liver tumor with poor prognosis. In this study, we investigated the expression of transforming growth factor beta regulator 4 (TBRG4) in HCC and its effects on the proliferation, invasion, and metastasis of HCC cells, and analyzed the possible molecular mechanisms. Method Downloading the expression and clinical information of HCC samples in the TCGA database, analyzing the expression differences of TBRG4 by bioinformatics methods, analyzing the clinical relevance and prognostic significance. Performing GO, KEGG and GSEA enrichment analysis on the TBRG4-related gene set in patient HCC tissues. Applying cell counting, scratch test and Transwell experiment to study the biological function of TBRG4 in HCC. Mitochondrial membrane potential, apoptosis and ROS levels were evaluated to assess cell iron death. Western blot, RT-PCR, laser confocal microscopy and co-immunoprecipitation were used to detect and analyze the downstream signaling pathways and interacting molecules of TBRG4. Results Bioinformatics analysis revealed that TBRG4 was abnormally highly expressed in HCC tumor tissues and was associated with poor prognosis and metastasis in HCC patients. GO and KEGG functional enrichment analysis showed that TBRG4 was related to oxidative stress and NADH dehydrogenase (ubiquinone) activity. GSEA enrichment analysis showed that TBRG4 was associated with Beta catenin independent wnt signaling and B cell receptor. Functional experiments confirmed that knocking down TBRG4 could inhibit the proliferation, migration, and invasion of HCC cells. Mechanistically, TBRG4 inhibited the function of HCC cells through the DDX56/p-AKT/GSK3β signaling pathway. In addition, interference with TBRG4 expression could reduce the mitochondrial membrane potential and accumulate ROS in HCC cells, leading to increased ferroptosis. Co-IP analysis showed that TBRG4 specifically bound to Beclin1. Conclusion TBRG4 is highly expressed in HCC tumor tissues and is associated with poor prognosis. It may regulate the proliferation, invasion, and metastasis of HCC cells through the DDX56/p-AKT/GSK3β signaling pathway. TBRG4 may interact with Beclin1 to regulate the ferroptosis of HCC cells.

Microseism, acoustic emission and electromagnetic radiation (M-A-E) data are usually used for predicting rockburst hazards. However, it is a great challenge to realize the prediction of M-A-E data. In this study, with the aid of a deep learning algorithm, a new method for the prediction of M-A-E data is proposed. In this method, an M-A-E data prediction model is built based on a variety of neural networks after analyzing numerous M-A-E data, and then the M-A-E data can be predicted. The predicted results are highly correlated with the real data collected in the field. Through field verification, the deep learning-based prediction method of M-A-E data provides quantitative prediction data for rockburst monitoring.

Abstract Background Extensive local invasion of glioblastoma (GBM) cells within the central nervous system (CNS) is one factor that severely limits current treatments. The aim of this study was to uncover genes involved in the invasion process, which could also serve as therapeutic targets. For the isolation of invasive GBM cells from non-invasive cells, we used a three-dimensional organotypic co-culture system where glioma stem cell (GSC) spheres were confronted with brain organoids (BOs). Using ultra-low input RNA sequencing (ui-RNA Seq), an invasive gene signature was obtained that was exploited in a therapeutic context. Methods GFP-labeled tumor cells were sorted from invasive and non-invasive regions within co-cultures. Ui-RNA sequencing analysis was performed to find a gene cluster up-regulated in the invasive compartment. This gene cluster was further analyzed using the Connectivity MAP (CMap) database. This led to the identification of SKF83566, an antagonist of the D1 dopamine receptor (DRD1), as a candidate therapeutic molecule. Knockdown and overexpression experiments were performed to find molecular pathways responsible for the therapeutic effects of SKF83566. Finally, the effects of SKF83566 were validated in orthotopic xenograft models in vivo. Results Ui-RNA seq analysis of three GSC cell models (P3, BG5 and BG7) yielded a set of 27 differentially expressed genes between invasive and non-invasive cells. Using CMap analysis, SKF83566 was identified as a selective inhibitor targeting both DRD1 and DRD5. In vitro studies demonstrated that SKF83566 inhibited tumor cell proliferation, GSC sphere formation, and invasion. RNA sequencing analysis of SKF83566-treated P3, BG5, BG7, and control cell populations yielded a total of 32 differentially expressed genes, that were predicted to be regulated by c-Myc. Of these, the UHRF1 gene emerged as the most downregulated gene following treatment, and ChIP experiments revealed that c-Myc binds to its promoter region. Finally, SKF83566, or stable DRD1 knockdown, inhibited the growth of orthotopic GSC (BG5) derived xenografts in nude mice. Conclusions DRD1 contributes to GBM invasion and progression by regulating c-Myc entry into the nucleus that affects the transcription of the UHRF1 gene. SKF83566 inhibits the transmembrane protein DRD1, and as such represents a candidate small therapeutic molecule for GBMs.

In the present study, a heteromannan primarily composed of mannose, fucose, xylose, glucose, and arabinose at a molar ratio of 4.78:1.18:1:0.82:0.11 containing a low proportion of glucuronic acid with weight-average molecular weights of 3.6 × 106 Da, named NTP, was prepared from the fruiting body of Tremella fuciformis. The anti-skin-aging effects of NTP on d-Galactose-induced aging mice and the biological mechanisms were investigated by an iTRAQ-based proteomics approach. NTP substantially mitigated skin aging characterized by a decreased loss of hydroxyproline and hyaluronic acid and reduced oxidative stress in the skin. Moreover, 43 differentially expressed proteins (DEPs) were identified in response to NTP, of which 23 were up-regulated and 20 were down-regulated. Bioinformatics analysis revealed that these DEPs were mainly involved in the biological functions of cellular and metabolic regulations, immune system responses, and structural components. The findings provided new insights into the biological mechanisms underlying the anti-skin-aging actions of T. fuciformis-derived polysaccharides and facilitated NTP applications in naturally functional foods.

Objectives: Oral insulin delivery has received much attention over the past 20 years due to its high compliance. The aim of this study is to prepare nanoparticles for the oral delivery of insulin; Methods: Fucoidan and protamine were used to prepare a pH-sensitive nanoparticle via self-assembly. The secondary structure and in vitro stability of the nanoparticles were characterized using FTIR, XRD, ITC, and TEM. the nanoparticles had a controlled release effect on insulin in simulated intestinal fluid. The pre-liminary therapeutic effect on high-fat-fed type 2 diabetic mice; Results: When the fucoidan/protamine mass ratio was 10:3 (w/w), the particle size and zeta potential were 140.83 ± 1.64 nm and −48.13 ± 0.61 mV.The encapsulation efficiency of insulin was 62.97 ± 0.59%. The preliminary therapeutic effect on type 2 diabetic mice showed that the fasting blood glucose of diabetic mice decreased from 10.28 ± 0.88 mmol/L to 9.22 ± 0.64 mmol/L, the area under the curve value of oral glucose tolerance test was reduced by 11.70%, and the insulin se-cretion of diabetic mice was increased by 13.3%; Conclusions: The nanoparticles were prepared successfully by self-assembly. The empty and insulin-loaded nanoparticles remained stable in simulated gastric fluid, and the nanoparticles had a controlled release effect on insulin in simulated intestinal fluid. Moreover, insulin-loaded nanoparticles could relieve on type 2 diabetic mice.

IntroductionWalking plays a crucial role in promoting physical activity among older adults. Understanding how the built environment influences older adults’ walking behavior is vital for promoting physical activity and healthy aging. Among voluminous literature investigating the environmental correlates of walking behaviors of older adults, few have focused on walking duration across different age groups and life stages, let alone examined the potential nonlinearities and thresholds of the built environment.MethodsThis study employs travel diary from Zhongshan, China and the gradient boosting decision trees (GBDT) approach to disentangle the age and retirement status differences in the nonlinear and threshold effects of the built environment on older adults’ walking duration.ResultsThe results showed built environment attributes collectively contribute 57.37% for predicting older adults’ walking duration, with a higher predicting power for the old-old (70+ years) or the retired. The most influencing built environment attribute for the young-old (60–70 years) is bus stop density, whereas the relative importance of population density, bus stop density, and accessibility to green space or commercial facilities is close for the old-old. The retired tend to walk longer in denser-populated neighborhoods with better bus service, but the non-retired are more active in walking in mixed-developed environments with accessible commercial facilities. The thresholds of bus stop density to encourage walking among the young-old is 7.8 counts/km2, comparing to 6 counts/km2 among the old-old. Regarding the green space accessibility, the effective range for the non-retired (4 to 30%) is smaller than that of the retired (12 to 45%).DiscussionOverall, the findings provide nuanced and diverse interventions for creating walking-friendly neighborhoods to promote walking across different sub-groups of older adults.

It is well acknowledged to all that an active equalization strategy can overcome the inconsistency of lithium-ion cell's voltage and state of charge (SOC) in series-connected lithium-ion battery (LIB) pack in the electric vehicle application. In this regard, a novel dual threshold trigger mechanism based active equalization strategy (DTTM-based AES) is proposed to overcome the inherent inconsistency of cells and to improve the equalization efficiency for a series-connected LIB pack. First, a modified dual-layer inductor equalization circuit is constructed to make it possible for the energy transfer path optimization. Next, based on the designed dual threshold trigger mechanism provoked by battery voltage and SOC, an active equalization strategy is proposed, each single cell's SOC in the battery packs is estimated using the extended Kalman particle filter algorithm. Besides, on the basis of the modified equalization circuit, the improved particle swarm optimization is adopted to optimize the energy transfer path with aiming to reduce the equalization time. Lastly, the simulation and experimental results are provided to validate the proposed DTTM-based AES.

The classification of coal bursting liability is of great significance for the prevention and control of rock burst. To address the shortcomings in existing bursting liability classification methods, a comprehensive evaluation model for bursting liability based on a combination of weighted-fuzzy set theory and three influencing factor analyses is proposed. The model selects four evaluation indicators: dynamic failure time (DT), elastic energy index (WET), bursting energy index (KE), and uniaxial compressive strength (RC). Two types of membership functions, trapezoidal fuzzy numbers (TFN) and Gaussian fuzzy numbers (GFN), are used to quantitatively describe the fuzziness between indicator levels. The Delphi method and a random forest feature identification method are combined to obtain a subjective and objective combined weighting, determining the optimal combination weight of the four indicators. Based on Zadeh operator (ZO), maximum-minimum operator (MMO), weighted-average operator (WAO), and all-around restrictive operator (ARO), calculations are carried out for the synthesis of indicator weights and memberships. Maximal membership principle (MMP) and Credible identification principle (CIP) are utilized as evaluation principle to assess the bursting liability level, constructing 16 fuzzy comprehensive evaluation models. The impact of membership functions, fuzzy operators, and evaluation principle on evaluation results are systematically analyzed based on the discrimination results of 127 sample sets. The results show that the optimal fuzzy comprehensive evaluation model is constructed using the trapezoidal fuzzy numbers, weighted average operator, and maximal membership principle (TFN-WAO-MMP), with a classification accuracy of 97.64%. Finally, the optimal model is applied to 10 engineering instances, and the evaluation results are consistent with the actual situation, verifying the reliability and effectiveness of the model. Overall, these findings contribute to the development of a more sophisticated and accurate method for assessing the rock burst tendency of coal specimens. By leveraging the theory of fuzzy sets, this approach provides a more nuanced and nuanced evaluation of rock burst tendency, and thus offers the potential to improve workplace safety and efficiency in the coal mining industry.

Numerous local herbal extract species have been investigated as potential medicinal ingredients due to their promising anti-cancer properties. However, the primary constraint of the class of plant flavonoids lies in their low solubility and limited membrane permeability, leading to chemical instability and restricted bioavailability that impede biomedical applications. In this study, we have developed an ideal nanozyme-Galazyme, comprising galangin-loaded copper Nanozyme coated by DSPE-PEG, which amplifies oxidative stress to induce apoptosis via the regulation of reactive oxygen species (ROS) generation and mitogen-activated protein kinase (MAPK) activation. Galazyme exhibited significant peroxidase mimetic activity, demonstrating its potential to generate ROS and elevate oxidative stress. Upon uptake by HepG-2 cells, Galazyme efficiently converts excess hydrogen peroxide (H2O2) into highly reactive •OH radicals and upregulates MAPK expression, leading to the activation of Bax and Caspase 3, thereby promoting irreversible tumor cell apoptosis. Both in vitro and in vivo results demonstrate that Galazyme inhibits tumor cell growth and induces apoptosis by generating ample ROS and activating the MAPK pathway. Our study offers novel evidence supporting the enhancement of Galazyme-induced apoptosis through the upregulation of Bax and Caspase 3, along with the elucidation of the interaction between MAPK and apoptosis.

Abstract Aiming at the problems of large consumption of computational resources and insufficient data feature extraction in the current partial discharge fault identification process of GIS equipment, a partial discharge fault identification method of GIS equipment based on improved deep learning is proposed. Firstly, the audio information of GIS equipment is filtered by a simple power normalised cepstral coefficient (SPNCC). Secondly, the spatial correlation between audio data streams is obtained by a convolutional neural network, the temporal correlation of audio is obtained and the next time slice data stream is predicted by using bi‐directional long short‐term memory (BiLSTM) network, and the attention mechanism is designed to extract deeper data features. Finally, the partial discharge fault identification model of GIS equipment based on improved SPNCC‐CNN‐BiLSTM‐Multi‐att is established, which improves the accuracy of the partial discharge identification method of GIS equipment. Experiments show that when the number of iterations is 100, the accuracy, recall, and F1 value of the proposed GIS equipment partial discharge fault recognition method on the dataset are 0.876, 0.812, and 0.843, respectively.

Breast cancer, as one of the most common malignancies in women, exhibits complex and heterogeneous pathological characteristics across different subtypes. Triple-negative breast cancer (TNBC) and HER2-positive breast cancer are two common and highly invasive subtypes within breast cancer. The stability of the breast microbiota is closely intertwined with the immune environment, and immunotherapy is a common approach for treating breast cancer.Tertiary lymphoid structures (TLSs), recently discovered immune cell aggregates surrounding breast cancer, resemble secondary lymphoid organs (SLOs) and are associated with the prognosis and survival of some breast cancer patients, offering new avenues for immunotherapy. Machine learning, as a form of artificial intelligence, has increasingly been used for detecting biomarkers and constructing tumor prognosis models. This article systematically reviews the latest research progress on TLSs in breast cancer and the application of machine learning in the detection of TLSs and the study of breast cancer prognosis. The insights provided contribute valuable perspectives for further exploring the biological differences among different subtypes of breast cancer and formulating personalized treatment strategies.

Hoxb5 exhibits preferential expression in hematopoietic stem cells (HSCs) and uniquely marks the long-term HSCs (LT-HSCs). Previous studies have demonstrated the remarkable capability of Hoxb5 to alter cell fates when enforced expression in blood progenitors, such as B cell progenitors and multipotent progenitors. Additionally, Hoxb5 deficiency does not hinder the generation of LT-HSCs. However, the specific impact of Hoxb5 deletion on LT-HSCs has remained unexplored. To address this, we developed a conditional Hoxb5 knockout-reporter mouse model, wherein Hoxb5 was knock out by the Vav-cre recombinase, and the endogenous Hoxb5 promoter drove the expression of the blue fluorescent protein (BFP). Our findings revealed that the primary recipients, who transplanted with HSCs indicating Hoxb5 deficiency by the presence of BFP (BFP-positive HSCs), exhibited comparable levels of donor chimerism and lineage chimerism to recipients transplanted with HSCs that spontaneously did not express Hoxb5 and thus lacked BFP expression (BFP-negative HSCs). However, during the secondary transplantation, recipients receiving total bone marrow (BM) from the primary recipients with BFP-positive HSCs showed significantly higher levels of donor chimerism and more robust multi-lineage chimerism compared to those receiving total BM from the primary recipients with BFP-negative HSCs. Our results indicate that deleting Hoxb5 in LT-HSCs transiently influences their lineage differentiation bias without compromising their long-term self-renewal capacity. These findings highlight the primary role of Hoxb5 in regulating lineage commitment decisions in LT-HSCs, while emphasizing that its presence is not indispensable for the maintenance of long-term self-renewal capacity.

● The water environment of Erhai Lake has shown satisfactory, declining and improving.● Total N and P, and chemical oxygen demand as key water environment indicators.● Pollution load distribution and control measures are key to Erhai Lake management. Elucidating the spatiotemporal pattern of water quality and algal biomass is crucial for accurately tracing pollution sources and reducing the risk of algal blooms in lake systems. This study analyzed the spatiotemporal variability of water quality and algal biomass in Erhai Lake from 1994 to 2021 using water quality index (WQI), Mann-Kendall test and Sen’s slope combined methods. The potential causes of water quality deterioration and algal biomass dynamics were also elucidated. The results showed that the historical changes in the water environment of Erhai Lake mainly had three stages: satisfactory (1994‒2001), deteriorating (2002‒2010) and improving (2011‒2021). The changes in water quality and algal biomass were primarily affected by total nitrogen, total phosphorus and chemical oxygen demand in different stages. The water environment of Erhai Lake is currently improving significantly, starting in the southern area that is furthest from the sources of agricultural pollution, especially in summer and autumn. This is attributed to the implementation of control measures resulting in lower pollutant loads at particular times and places. Therefore, it is necessary to continue to promote standardized livestock farming, to strengthen rural wastewater collection and to investigate measures such as the interruption of the endogenous cycle.

Abstract Introduction Protists are unicellular eukaryotes including important predators, parasites and phototrophs, and play pivotal roles in organic matter decomposition, biogeochemical nutrient cycling and various ecosystem functions. Unravelling the impact of climate warming on soil protists is paramount in predicting how these microorganisms will continue to provide essential ecosystem services in the face of changing climatic conditions. Materials and Methods We examined the effects of 5‐year simulated climate warming scenario, with temperatures increased by 4°C above ambient levels, on the diversity and community composition of soil protists, as well as their interactions with other microorganisms in both natural and plantation forest ecosystems during three seasons: summer, autumn and winter. Results We found a season‐dependent response of protists to climate warming, with a significant decrease in protist diversity during winter in natural forests. Furthermore, we identified significant alterations in the community compositions of protists during summer in both natural and plantation forests, as well as during winter in both forest types, under warming. Our analysis pinpointed specific functional protist taxa, such as consumers, parasites and phototrophs, which exhibited significant shifts in their relative abundances under warming. Additionally, we found that warming facilitated trophic interactions between protists and bacteria, while also strengthening interactions between bacterial and fungal communities. Warming could induce direct modifications in protist community compositions or indirectly affect them by modifying bacterial and fungal communities, as revealed by structural equation modelling. Conclusion These findings demonstrate the substantial impact of warming on the taxonomic and functional compositions of protists in forest ecosystems, with the magnitude of these effects varying across seasons. Our study implicates that ongoing climate warming could have significant consequences for the profiles of soil protists, as well as their trophic interactions with bacteria and fungi, highlighting the importance of considering these effects for the sustainable provision of ecosystem functions.

Abstract Purpose This prospective study with 10-year follow-up aimed to analyze potential impact of body mass index (BMI) and gamma gap on heart failure and mortality rate in older patients with coronary artery disease (CAD). Methods There were 987 consecutive older patients with CAD included and divided into four groups according to BMI and gamma gap levels. Results Median age was 86 years. The highest proportion of heart failure (46.2%) and the highest mortality rate (84.4%) was observed in patients with low BMI and high gamma gap, whereas the lowest proportion of heart failure (18.9%) and the lowest mortality rate (62.9%) was observed in those with high BMI and low gamma gap. After full adjustment in multivariate Logistic regression analysis, heart failure was most common in patients with low BMI and high gamma gap compared with those with high BMI and low gamma gap (hazard ratio [HR]: 2.82, 95% confidence interval [CI]: 1.79–4.48, P

ObjectiveTo assess the effectiveness of 23-valent pneumococcal polysaccharide vaccine (PPV23) against Streptococcus pneumonia (Spn)-associated respiratory tract infection in children with a test-negative design study. MethodsWe recruited totally 3 339 children at ages of 2 – 9 years hospitalized due to acute respiratory illness (ARI) and being detected for Spn isolates in sputum/bronchoalveolar lavage fluid (BALF) during 2017 – 2020 at the Soochow University Affiliated Children's Hospital for a test-negative case-control design study. The collected Spn isolates were serotyped with Quellung test. Of the all the participants, 1 264 being positive for PPV23 serotype Spn (VT-Spn) in sputum/BALF specimens were assigned into a case group; the two control groups were 485 being positive for non-PPV23 serotype (NVT-Spn) and 1 590 being negative for Spn (Spn –). The participants′ information on PPV23 vaccination were collected from the Vaccination Registry Database of Suzhou Municipal Center for Disease Control and Prevention. The adjusted odds ratio of PPV23 vaccination was calculated with logistic regression model to estimate the vaccine effectiveness (VE) of PPV23 against Spn-associated ARI. ResultsAmong the 1 749 Spn strains isolated, the most common serotypes were 19F, 6B, 23F, 6A and 19A; 76.56% and 72.27% were positive for 13-valent pneumococcal conjugate vaccine (PCV13) serotype and PPV23 serotype, respectively. For the participants of VT-Spn, NVT-Spn and Spn – group, the mean age were 3.88 ± 1.27, 4.02 ± 1.43, and 4.52 ± 1.92 years; the vaccination rate of PPV23 were 5.4%, 6.8%, and 5.8%, respectively; and the mean age at the vaccination was 2.53 ± 0.61 years for all the participants vaccinated., When using the NVT-Spn group as the control and after adjusting for age, clinical diagnosis, and other vaccination status, the logistic regression analysis revealed an overall PPV23 effectiveness of 18.3% (95% confidence interval [95%CI]: – 26.3% – 47.1%) for against Spn-associated ARI, an effectiveness of 15.7% for against community acquired pneumonia, and the effectiveness of 32.8, 16.5% and 57.5% for against serotype 19F, 6B and 15B Spn-associated ARI, respectively, and the effectiveness (26.0%) was better in the participants aged 2 – 5 years than that (7.6%) in those aged older than 5 year; while using the Spn – group as the control and after adjusting for age, household registration, other vaccination status, the year of seeking medication, season of seeking medication, and clinical diagnosis, the logistic regression analysis showed a VE of 29.9% (95% CI: – 22.0% – 59.7%) for the participants aged 2 – 5 years. Conclusion PPV23 is of certain protection against Spn-associated respiratory tract infections in 2 – 9 years old children and the effectiveness decreases with the prolonged time after receiving the vaccination

Abstract Background As social networking sites (SNSs) with diverse functions gradually become an important social place for modern people, openness, as a personality trait that represents the willingness to consider diverse things, will be more likely to affect people’s cognitive and emotional experience (e.g., social anxiety) in social interactions. This study examined the relationship between openness and social anxiety and the underlying psychological mechanism in the internet age based on the cognitive-behavioral model of social anxiety. Methods This cross‑sectional survey study conducted a questionnaire survey of 522 college students from two provinces in China (191 male; age range 18–25; M = 20.76, SD = 1.34). Results The results showed that openness is negatively related to social anxiety. Self-evaluation and passive SNS use independently mediate the relationship between openness and social anxiety, respectively. Moreover, openness is associated with social anxiety both through the chain mediating roles of active SNS use and self-evaluation and through the chain mediating roles of passive SNS use and self-evaluation. Conclusions Openness is negatively associated with social anxiety, and the different ways of SNS use and self-evaluation are the underlying mechanisms. These results provide insights into the clinical treatment of social anxiety and how to benefit from online interactions.

In order to improve the quality of rice bread and reveal the effects of transglutaminase (TGase) on the quality of rice flour, rice dough and rice bread, and reveal the relationship among the indexes. The silty properties and pasting properties of TGase containing different mass fractions in rice flour, the water distribution, rheological properties and fermentation capability of rice dough, and the specific volume, gas cells and texture of bread were tested. The results showed that 0.60% TGase could improve the silty and pasting properties of rice flour, and increase the stabilization time of rice flour to 8.00±0.77 min. The peak viscosity (PV), valley viscosity (VV) and final viscosity (FV) were increased to 5801.00±30.41, 3805.67±76.14 and 5836.33±106.17 cP, respectively. Breakdown (BD) and setback (SB) of rice flour were reduced to 1990.00±128.11 and 1991.67±34.08 cP. At the same time, TGase increased the tightness of water binding, improved the rheological properties and fermentation capability of rice dough, increased S21 to 9.24%±13.07%, decreased T21 and T22 to 1.40±1.80 and 30.45±2.28 ms, and increased Hm to 8.60±0.11 mm. The porosity was increased to 50.51%±0.90%, which reduced the hardness, adhesiveness and chewiness of rice bread, improved the springiness of rice bread and improved the textural properties of rice bread. Correlation analysis showed that T23 was negatively correlated with the formation time, and Hm was positively correlated with C3, C3~C2 and VV. And BD and SB were negatively correlated, chewiness was positively correlated with C1, specific volume was positively correlated with FV and PV were positively correlated. The results showed that TGase improved the quality of rice flour, rice dough and rice bread, and there were different degrees of correlation among the improved indexes.

With economic growth and improved living standards, the incidence of metabolic diseases such as diabetes mellitus caused by over-nutrition has risen sharply worldwide. Elevated blood glucose and complications in patients seriously affect the quality of life and increase the economic burden. There are limitations and side effects of current hypoglycemic drugs, while probiotics, which are safe, economical, and effective, have good application prospects in disease prevention and remodeling of intestinal microecological health and are gradually becoming a research hotspot for diabetes prevention and treatment, capable of lowering blood glucose and alleviating complications, among other things. Probiotic supplementation is a microbiologically based approach to the treatment of type 2 diabetes mellitus (T2DM), which can achieve anti-diabetic efficacy through the regulation of different tissues and metabolic pathways. In this study, we summarize recent findings that probiotic intake can achieve blood glucose regulation by modulating intestinal flora, decreasing chronic low-grade inflammation, modulating glucagon-like peptide-1 (GLP-1), decreasing oxidative stress, ameliorating insulin resistance, and increasing short-chain fatty acids (SCFAs) content. Moreover, the mechanism, application, development prospect, and challenges of probiotics regulating blood glucose were discussed to provide theoretical references and a guiding basis for the development of probiotic preparations and related functional foods regulating blood glucose.

A large number of mines have been closed due to resource depletion, failure to meet safety production requirements, and other reasons. To effectively ensure the safety of the ecological environment above these closed mines along with the safety of engineering construction, it is necessary to monitor the secondary deformation of closed mines. Based on TerraSAR-X, Sentinel-1A data, and InSAR technology, this study obtained high-density secondary surface deformation data on the Jiahe Coal Mine and Pangzhuang Coal Mine in the western Xuzhou area. Combining mining geological data, we analyzed the spatiotemporal variation patterns and mechanisms of secondary deformation in multi-seam mining of closed mines. It was found that when mining multiple seams involves large interlayer spacing, the secondary deformation pattern shows a “W” shape. In this situation, the deformation can be divided into five stages: subsidence, uplift, re-subsidence, re-uplift, and relative stability. This study provides technical support for the evaluation and prevention of secondary deformation hazards in closed mines.

Acidovorax citrulli (Ac) is an important pathogenic bacterium causing bacterial fruit blotch (BFB) in Cucurbitaceae plants and is an important quarantine pest in China. This study was conducted to establish a rapid, convenient, and accurate visual method for detecting A. citrulli. A. citrulli-specific primers and a prober were designed based on the conserved region of the YD-repeat protein gene. Loop-mediated isothermal amplification combined with lateral flow dipstick (LAMP-LFD) was used to establish an assay for the rapid visual detection of A. citrulli by optimizing the reaction temperature and time. The specificity, sensitivity, and performance of the optimized LAMP-LFD assay were evaluated using the genomic DNA of the tested isolates, A. citrulli pure culture, infested seeds, commercial seeds, and leaf samples. The optimal assay temperature and time were 64 °C and 60 min, respectively. The assay specifically detected A. citrulli, and no cross-reactions were observed with the genomic DNA of other closely related species. The detection sensitivity of the LAMP-LFD for detecting pure genomic DNA, the bacterial suspension, bacterial amount on seeds (colony-forming units (CFU)·g−1), and infection rate of seeds (%) were 1 fg·μL−1, 8 CFU·mL−1, 5 CFU·g−1, and 0.05% infestation per reaction, respectively. The positive detection rate of the LAMP-LFD assay was 20–100% in seed samples (n = 1000 seeds) with 0.05–0.1% infestation. The LAMP-LFD assay rapidly and accurately detected A. citrulli in seeds and leaf tissues carrying pathogens. This assay thus offers the advantages of easy operation, rapidity, high specificity and sensitivity, low cost (no need for complex and expensive precision instruments), visualization of detection results, good stability, and strong applicability, which can be used for epidemiological studies and disease management.

【Objective】Based on the method of SSR molecular markers, this study conducted phylogenetic analysis on 12 germplasms and identified true and false hybrids from F1 hybrid progenies of some germplasms in Gerbera hybrida, in order to offer a technological support for the evaluation and innovative utilization of germplasm resources in G. hybrida.【Method】Twelve Gerbera hybrida germplasm resources were collected and their genomic DNA was extracted. PCR amplification was performed using 50 pairs of SSR primers. Cluster analysis and fingerprint construction were conducted by detecting the results of related polymorphisms, as well as identification of hybrid F1 offspring of some varieties.【Result】A total of 74 polymorphic alleles were amplified by 18 pairs of primers in 12 varieties of G. hybrida, with an average of 4.1 polymorphic alleles amplified by each pair of primers. A phylogenetic tree of 12 germplasm resources was constructed using 74 alleles in G. hybrida. UPGMA clustering analysis results showed that when the genetic coefficient was 0.75, the tested 12 germplasm resources could be divided into 5 groups, with Group Ⅰ including 'Yunnanhong', 'Da088', and 'Qingren'. Group Ⅱ including 'Fenjiaren', 'Fenqiu', 'Zhenai', and 'Fuwa'. Group Ⅲ including 'Xiuse', 'Shunv', and 'Huangqiu', both Group Ⅳ and Group Ⅴ containing only one variety, namely 'Zishuijing' and 'Shenzhen 5', respectively. Four pairs of primers were screened, including GHSSR-1, GHSSR-18, GHSSR-20, and GHSSR-21, which can completely distinguish the 12 germplasms with each other. Among them, the last three pairs of primers identified lines with parental specific heterotopies as Gh-5, Gh-4, and Gh-1, respectively. The identification rate of a single pair of primers was 33%. All three tested individual plants are true hybrids, with a true hybrid rate of 100%.【Conclusion】Twelve fingerprint maps of G.hybrida varieties were successfully constructed. At the same time, 3 hybrid F1 offspring seedlings were identified as true hybrids, providing dependable SSR marker primers for the evaluation and innovative utilization of resources in G. hybrida.

Abstract Biophysically detailed multi-compartment models are powerful tools to explore computational principles of the brain and also serve as a theoretical framework to generate algorithms for artificial intelligence (AI) systems. However, the expensive computational cost severely limits the applications in both the neuroscience and AI fields. The major bottleneck during simulating detailed compartment models is the ability of a simulator to solve large systems of linear equations. Here, we present a novel Dendritic Hierarchical Scheduling (DHS) method to markedly accelerate such a process. We theoretically prove that the DHS implementation is computationally optimal and accurate. This GPU-based method performs with 2-3 orders of magnitude higher speed than that of the classic serial Hines method in the conventional CPU platform. We build a DeepDendrite framework, which integrates the DHS method and the GPU computing engine of the NEURON simulator and demonstrate applications of DeepDendrite in neuroscience tasks. We investigate how spatial patterns of spine inputs affect neuronal excitability in a detailed human pyramidal neuron model with 25,000 spines. Furthermore, we provide a brief discussion on the potential of DeepDendrite for AI, specifically highlighting its ability to enable the efficient training of biophysically detailed models in typical image classification tasks.

Abstract Polymer nanocomposites with nanoparticles dispersed in polymer matrices have attracted extensive attention due to their significantly improved overall performance, in which the nanoparticle-polymer interface plays a key role. Understanding the structures and properties of the interfacial region, however, remains a major challenge for polymer nanocomposites. Here, we directly observe the presence of two interfacial polymer layers around a nanoparticle in polar polymers, i.e., an inner bound polar layer (~10 nm thick) with aligned dipoles and an outer polar layer (over 100 nm thick) with randomly orientated dipoles. Our results reveal that the impacts of the local nanoparticle surface potential and interparticle distance on molecular dipoles induce interfacial polymer layers with different polar molecular conformations from the bulk polymer. The bilayer interfacial features lead to an exceptional enhancement in polarity-related properties of polymer nanocomposites at ultralow nanoparticle loadings. By maximizing the contribution of inner bound polar layer via a nanolamination design, we achieve an ultrahigh dielectric energy storage density of 86 J/cm3, far superior to state-of-the-art polymers and nanocomposites.

Abstract Alveolar epithelial barrier is a potential therapeutic target for acute respiratory distress syndrome (ARDS). However, an effective intervention against alveolar epithelial barrier has not been developed. Here, based on single-cell RNA and mRNA sequencing results, death receptor 3 (DR3) and its only known ligand tumor necrosis factor ligand-associated molecule 1A (TL1A) were significantly reduced in epithelium from an ARDS mice and cell models. The apparent reduction in the TL1A/DR3 axis in lungs from septic-ARDS patients was correlated with the severity of the disease. The examination of knockout (KO) and alveolar epithelium conditional KO (CKO) mice showed that TL1A deficiency exacerbated alveolar inflammation and permeability in lipopolysaccharide (LPS)-induced ARDS. Mechanistically, TL1A deficiency decreased glycocalyx syndecan-1 and tight junction-associated zonula occludens 3 by increasing cathepsin E level for strengthening cell-to-cell permeability. Additionally, DR3 deletion aggravated barrier dysfunction and pulmonary edema in LPS-induced ARDS through the above mechanisms based on the analyses of DR3 CKO mice and DR3 overexpression cells. Therefore, the TL1A/DR3 axis has a potential value as a key therapeutic signaling for the protection of alveolar epithelial barrier.

Patient readmission for ischemic stroke significantly strains the healthcare and medical insurance systems. Current understanding of the risk factors associated with these readmissions, as well as their subsequent impact on mortality within China, remains insufficient. This is particularly evident in the context of comprehensive, contemporary population studies. This 4-year retrospective cohort study included 125 397 hospital admissions for ischemic stroke from 838 hospitals located in 22 regions (13 urban and 9 rural) of a major city in western China, between January 1, 2015 and December 31, 2018. The Chi-square tests were used in univariate analysis. Accounting for intra-subject correlations of patients’ readmissions, accelerated failure time (AFT) shared frailty models were used to examine readmission events and pure AFT models for mortality. Risk factors for patient readmission after ischemic stroke include frequent admission history, male gender, employee’s insurance, advanced age, residence in urban areas, index hospitalization in low-level hospitals, extended length of stay (LOS) during index hospitalization, specific comorbidities and subtypes of ischemic stroke. Furthermore, our findings indicated that an additional admission for ischemic stroke increased patient mortality by 16.4% ( P

The coupling effects created by transboundary and local factors on ecosystem services are often difficult to determine. This poses great challenges for ecosystem protection and management in border areas. To decrease uncertainty, it is crucial to quantify and spatialize the impact multiple factors have on ecosystem services within different scenarios. In this study, we identified key transboundary and local factors from a set of 15 sorted factors related to four main ecosystem services. We employed a Bayesian Network—Geographic Information System (BN-GIS) model to simulate 90 scenarios with multiple factors combinations, quantifying and spatializing the coupling effects on the main ecosystem services. These simulations were conducted in the Pu’er region, which is situated alongside three countries, and serves as a representative border area in southwest China. The results showed that: (1) The coupling effects of multiple factors yield significant variations when combined in different scenarios. Managers can optimize ecosystem services by strategically regulating factors within specific areas through the acquisition of various probabilistic distributions and combinations of key factors in positive coupling effect scenarios. The outcome is a positive coupling effect. (2) Among the four main ecosystem services in the Pu’er region, food availability and biodiversity were affected by key transboundary and local factors. This suggests that the coupling of transboundary and local factors is more likely to have a significant impact on these two ecosystem services. Of the 45 combination scenarios on food availability, the majority exhibit a negative coupling effect. In contrast, among the 45 combination scenarios on biodiversity, most scenarios have a positive coupling effect. This indicates that food availability is at a higher risk of being influenced by the coupling effects of multiple factors, while biodiversity faces a lower risk. (3) Transboundary pests & diseases, application of pesticides, fertilizer & filming , population density, and land use were the key factors affecting food availability. Bio-invasion, the normalized differential vegetation index, precipitation, and the landscape contagion index were the key factors affecting biodiversity. In this case, focusing on preventing transboundary factors such as transboundary pests & disease and bio-invasion should be the goal. (4) Attention should also be paid to the conditions under which these transboundary factors combine with local factors. In the areas where these negative coupling effects occur, enhanced monitoring of both transboundary and local factors is essential to prevent adverse effects.

Abstract Colorectal cancer (CRC) is the third most common malignancy worldwide. It is well known that lipid metabolism reprogramming contributes to the tumor progression. However, the lipid metabolic alterations and potential remodeling mechanism underlying the chemoresistance of CRC remain largely unclear. In this study, we compared the gene expression profiles of chemoresistant versus control CRC cells from the GEO database and identified a key factor, Glycerol‐3‐phosphate acyltransferase 3 (GPAT3), that promotes lipid droplet (LD) production and confers chemoresistance of CRC. With applying of HPLC–MS and molecular dynamics simulation, we also demonstrated that the activity of lysophosphatidic acid synthesis by GPAT3 was dependent on its acetylation at K316 site. In particular, GPAT3‐mediated LD accumulation inhibited immunogenic cell death of tumor, and thus facilitated CD8+ T‐cell exhaustion and malignant progression in mouse xenografts and hepatic‐metastasis tumors in CRC patients. High GPAT3 expression turned CRC cells into nonimmunogenic cells after (Oxaliplatin) Oxa treatment, which was supported by a decrease in cytotoxic IFN‐γ release and CD8+ T‐cell exhaustion. In conclusion, these findings revealed the role of GPAT3‐associated LD accumulation, which conferred a malignant phenotype (chemoresistance) and regulated the tumor microenvironment of CRC. These results suggest that GPAT3 is a potential target to enhance CRC chemosensitivity and develop novel therapeutic interventions.