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Underground engineering frequently encounters the challenges of high temperatures and high confining pressures. The combined effects of temperature and confining pressure can significantly alter the mechanical properties of rock. Evaluating the impact of these factors on rock is a crucial aspect of engineering. This study investigates the mechanical properties of rocks under various temperature and stress conditions, focusing on the deformation and failure characteristics of four typical rock types: granite, red sandstone, gray sandstone, and shale under temperature-confining pressure coupling. The results indicate that high temperatures cause internal structural damage and crack propagation in rocks, leading to a reduction in compressive strength and elastic modulus. Conversely, high confining pressures can inhibit crack propagation and enhance rock deformation capacity. Additionally, significant differences were observed in the mechanical responses of different rocks; red sandstone and shale predominantly exhibited shear failure under the coupled effects of temperature and confining pressure, whereas granite and gray sandstone exhibited bulging failure. Based on the experimental results, an elastic modulus fitting model considering the temperature-confining pressure coupling effect was proposed, and the parameters of the Drucker–Prager criterion were modified. A constitutive model was constructed to accurately reflect the stress–strain state of rocks under the coupled effects of temperature and confining pressure. The constitutive model results show good agreement with the experimental findings.

Microbial-induced calcium carbonate precipitation (MICP) has attracted much attention as a promising technology for soil improvement in the infrastructures of marine engineering. This paper introduces a novel numerical sample preparation technique for MICP-treated sand, with particular attention paid to the distribution patterns of calcium carbonate, including contact cementing, bridging, and grain coating. The effect of calcium carbonate content (CCC) on the deformation and failure mechanism is studied at macroscopic and granular scales. The findings show that a small amount of calcium carbonate can quickly increase the strength of sand. The strength improvement and deformation control of MICP technology are better than those of traditional compaction treatment. As the calcium carbonate content increases, the mechanical coordination number of the sand also increases, indicating a more stable microstructure of the sand phase. In the contact bonding mode, initial failure occurs as shear failure between sand and calcium carbonate. In the bridge mode, initial failure manifests as shear failure between calcium carbonate particles. In the coating mode, initial failure occurs as tensile failure between sand and calcium carbonate. Calcium carbonate contributes to a reduction in both sliding and rolling movements among sand particles.

The misconception that “green is ecological” is widespread, causing the public to overlook severe ecological problems in nature and life. This study aimed to reveal how cognitive biases affect residents’ willingness to participate in ecological restoration and provide a valuable reference for policy design. Based on survey data from 655 urban residents, this study quantified the residents’ cognitive biases regarding the eco-consequences of natural rubber expansion and used a double-hurdle (DH) model to estimate the effect of cognitive biases on their willingness to participate (WP) and their degree of willingness to participate (DWP) in natural rubber plantation ecological restoration (RPER) programs. We further analyzed how ecological consciousness moderates the effects of cognitive biases on residents’ WP and DWP. The results indicate that there are widespread cognitive biases regarding the eco-consequences of rubber expansion among urban residents in Hainan. Cognitive biases cause a decrease in residents’ WP and DWP in the RPER programs. WP was positively influenced by residents’ educational level, and negatively influenced by age. DWP was positively correlated with residents’ household income, and industry cognition and negatively correlated with age, educational level, and residence duration. The moderating effect of the residents’ ecological consciousness significantly weakens the negative effects of cognitive biases on WP and DWP. Moreover, ecological attention exerts a negative moderating effect, whereas ecological identity and ecological practice exert a positive moderating effect. These results have profound implications for policymakers in implementing programs to restore ecosystems.

The in-pipe robot is the most commonly used technique in offshore pipelines. The use of rubber sealing discs is important for in-pipe robots to ensure that the robots are moved by fluid pressures inside offshore pipelines. This paper focuses on the measuring and modeling of the wax–oil gel-breaking process at the soft frictional area between sealing discs and the pipe wall. In this study, a detailed characterization of the gel-scraping process and in situ probing portable microscopy are performed. Two contributions are made in this study. First, a direct observation of wax–oil deposition breaking is employed to detect the minute changes at the in-pipe robot. Second, we find that a simple function is possible to describe the relationship between the wax contents and dewaxing efficiency, in which the debris material removal ratio (DRR) is discussed. Thus, the gel deposition-breaking phenomena are quite different under the influence of rubber sealing discs. This result is further confirmed by the real contact ratio measurements. It is important to research the sealing disc further and apply it more in the petroleum industry, especially in in-pipe robots for deepwater pipeline systems.

This paper analyzes the green production efficiency (GPE) and spatial divergence of the hog breeding industry, with the aim of providing a foundation for the rational layout of hog breeding and promoting the industry's high-quality development. The paper uses the SBM model to estimate GPE in 29 provinces, cities, and districts of China from 2006 to 2019. Furthermore, it analyzes the spatial divergence of GPE and its driving factors using divergence indexes and the Geodetector. The results show that China's GPE of the hog breeding industry increased from 0.409 to 0.496 between 2006 and 2019. The highest efficiency occurred during the I-period, while the lowest efficiency was observed during the II-period. The highest efficiency was in the key development region, and the lowest efficiency was in the potential growth region. The spatial divergence of GPE in the hog breeding industry expanded, with labor input, non-point source pollution, resource endowment, and environmental load bearing being the main driving factors for the expansion in each period. The potential growth region had the largest spatial divergence, mainly affected by resource endowment. In contrast, the constrained development region had the smallest spatial divergence, mainly affected by resource endowment and pollutant emissions. The spatial divergence of moderate and key development regions was considerable, mainly affected by environmental investment, environmental load bearing, and pollutant emissions. Therefore, the key to improving the GPE of the hog breeding industry is to promote the adoption of advanced technology, such as labor-saving, material-saving, and emission reduction technologies. Moreover, several actions should be taken to reduce the spatial divergence among different regions, such as integrated breeding, clean standards, large-scale breeding, and high-end boutique.

Cultivated land protection and quality improvement have become inevitable requirements for alleviating ecological and environmental pressure and sustainable agricultural development. It is of practical significance to explore the differences and causes of cultivated land protection behaviors (CLPB) between smallholders and professional farmers for formulating targeted protection policies and improving their effectiveness. Based on 422 mango farmers' survey data in Hainan Province, this paper explored the internal and external characteristics between smallholders and professional farmers, and used the Fairlie decomposition method to compare and analyze the sources of differences in farmers' CLPB. The results showed that: (1) the CLPB of smallholders and professional farmers differ significantly; (2) the sources of differences in CLPB between smallholders and professional farmers are different, including differences in internal characteristics and differences in external characteristics; (3) differences in internal characteristics are the main cause of the differences in farmer's CLPB, and the contribution of differences in external characteristics was smaller, of which planting years, annual household income and planting scale are the top three factors. It is suggested that differential protection policies should be designed for smallholders and professional farmers, such as guiding smallholders to carry out large-scale operations and improve their organizational level, encouraging and guiding professional farmers to sign long-term contracts to stabilize the land tenure, and formulating subsidy policies for cultivated land protection.

ObjectiveTo investigate the relations of serum total homocysteine (tHcy) with cerebral small vessel disease (CSVD) and cognitive function and evaluate whether CSVD mediates the effect of serum tHcy on cognitive function.MethodsA total of 1,033 consecutive eligible participants who received serum tHcy, brain magnetic resonance imaging (MRI), and neuropsychological assessment were included in this retrospective study. White matter hyperintensity, lacune, cerebral microbleed, and enlarged perivascular space were evaluated based on brain MRI. We used multivariate binary logistic regression analysis, multivariate ordinal logistic regression analysis, and mediation analyses to assess the relations of serum tHcy with CSVD and cognitive function.ResultsSerum tHcy levels were higher in patients with cognitive impairment than those with no cognitive impairment. Logistic regression analyses showed elevated serum tHcy was associated with cognitive impairment [odds ratio (OR): 10.475; 95% confidence interval (CI): 4.522 to 24.264; p < 0.001] and a higher CSVD burden score (OR: 17.151; 95% CI: 8.785 to 33.921; p < 0.001) after adjusting potential confounders. Compared with the lowest tHcy quartile, the multivariable-adjusted OR of the highest quartile was 4.851 (95% CI: 3.152 to 7.466; p for the trend < 0.001) for cognitive impairment, 3.862 (95% CI: 2.467 to 6.047; p for the trend < 0.001) for a severe CSVD burden score. Mediation analyses showed significant moderating effects (9.3–23.6%) by different imaging markers of CSVD on the association between higher serum tHcy levels and cognitive impairment.ConclusionElevated serum tHcy is associated with cognitive impairment and the development of CSVD. A proportion of the association between elevated serum tHcy and cognitive impairment may be attributed to the presence of different imaging markers of CSVD, especially the severe CSVD burden score.

Suction caissons are widely used as a promising alternative foundation for offshore structures under variable offshore environments. In this paper, a three-dimensional finite-element model which takes into account soil inelasticity was used to study the seismic behavior of a suction caisson in clay taken from an offshore wind farm in the Hebei province of China. The results showed that the maximum acceleration of the soil increases with increasing depth. There is a distinct amplification for relatively low frequencies when seismic waves propagate to the mudline. It was assumed that the rotation center of suction caisson under seismic conditions was virtually existent. The rotation center was obtained by the proposed formula to explore the movement positions of the suction caisson. The time of the peak of lateral displacement of the lid did not synchronize with that of the changing direction of displacement of the rotation center, indicating that the displacement of the lid can not be a comprehensive assessment of the position state of suction caissons. The point mass at the top of the wind turbine tower was responsible for the increase of vibration amplitude of displacement.

In this study, standard jar tests were conducted using white mustard seed protein (WMSP) as a natural coagulant to remove direct black 19 (DB-19) from its aqueous solution. Comparative coagulation tests were performed using commercial polyaluminum chloride (PAC). The results showed that DB-19 removal by WMSP increased with increasing settling time and reached the maximum removal at 180 min. The DB-19 removal descended from 98.4 to 46.2% as pH increased from 4 to 10. The most effective temperature for DB-19 removal was 25 °C. The removal of DB-19 was weakened by the presence of Na2S2O4. Overall, WMSP was more efficient than PAC for DB-19 removal in all experiments except at pH 4 and 5. The mechanism of the removal of DB-19 by WMSP could be attributed to adsorption and charge neutralization processes.

Fringe projection profilometry (FPP) is widely applied to 3D measurements, owing to its advantages of high accuracy, non-contact, and full-field scanning. Compared with most FPP systems that project visible patterns, invisible fringe patterns in the spectra of near-infrared demonstrate fewer impacts on human eyes or on scenes where bright illumination may be avoided. However, the invisible patterns, which are generated by a near-infrared laser, are usually captured with severe speckle noise, resulting in 3D reconstructions of limited quality. To cope with this issue, we propose a deep learning-based framework that can remove the effect of the speckle noise and improve the precision of the 3D reconstruction. The framework consists of two deep neural networks where one learns to produce a clean fringe pattern and the other to obtain an accurate phase from the pattern. Compared with traditional denoising methods that depend on complex physical models, the proposed learning-based method is much faster. The experimental results show that the measurement accuracy can be increased effectively by the presented method.

The COVID-19 pandemic has popularized online learning as a mode of teaching and learning in universities, thereby exacerbating college students' stress levels in multiple ways. To enhance the quality of college student training, it is crucial to investigate the impact of perceived stress on their online self-directed learning. Using questionnaire data from 969 college students across China, this study performed an empirical analysis of the influence of perceived stress on their online self-directed learning, while also exploring the mediating role of phone dependence and the moderating role of self-management ability. The findings indicate that, firstly, perceived stress has a significant and positive effect on college students' online self-directed learning. Secondly, phone dependence masks the positive impact of perceived stress on students' self-directed learning, thereby weakening the effects of employment and learning stress on their online self-directed learning. Thirdly, self-management ability positively moderates the relationship between employment, social, interpersonal, and learning stress and college students' online self-directed learning. Fourthly, rural college students' online self-directed learning is more susceptible to employment, social, and interpersonal stress; vocational college students' self-directed learning is more influenced by employment, social, and learning stress; and students in the "Double First-Class" initiative universities are more likely to develop phone dependence. Therefore, this paper asserts that using stress reasonably, controlling stress appropriately, channeling stress properly, and enhancing self-management abilities can aid college students in learning autonomous online.

Abstract Background With the rapid ageing of the global population, the number of older adults with physical frailty has been gradually increasing, making ageing at home a key strategy for coping with this demographic change. The opinions of older adults regarding their willingness to age at home deserve to be considered respectfully. As a result, this study aimed to investigate willingness to age at home and any associated underlying mechanisms involving physical frailty among older Chinese adults. Methods This study was a national cross-sectional survey. Stratified random and quota sampling were used before and after the individual level respectively. Willingness to age at home was compared between older adults with different characteristics using the Mann–Whitney U test and Kruskal–Wallis H test. A Spearman rank test was conducted to explore the correlations among physical frailty, loneliness, family health, and willingness to age at home. The path hypothesis that loneliness and family health influence the relationship between physical frailty and willingness to age at home among older adults was further tested through sequential multiple mediation analysis. Results A total of 3,837 older adults were included in this study. They returned a median score of 78 in terms of willingness to age at home. Physical frailty (β = − 0.044, P

Abstract Newly diagnosed patients with high-risk acute graft-versus-host disease (aGVHD) often experience poor clinical outcomes and low complete remission rates. Ruxolitinib with corticosteroids showed promising efficacy in improving response and failure free survival in our phase I study. This study (ClinicalTrials.gov: NCT04061876) sought to evaluate the safety and effectiveness of combining ruxolitinib (RUX, 5 mg/day) with corticosteroids (1 mg/kg/day methylprednisolone, RUX/steroids combined group) versus using methylprednisolone alone (2 mg/kg/day, steroids-only group). Newly diagnosed patients with intermediate- or high-risk aGVHD were included, with risk levels classified by either the Minnesota aGVHD Risk Score or biomarker assessment. Patients were randomized in a ratio of 1:1 into 2 groups: 99 patients received RUX combined with methylprednisolone, while the other 99 received methylprednisolone alone as the initial treatment. The RUX/steroids group showed a significantly higher overall response rate (ORR) on day 28 (92.9%) compared to the steroids-only group (70.7%, Odds Ratio [OR] = 5.8; 95% Confidence Interval [CI], 2.4–14.0; P

Abstract Background The current review aims to explore the evidence regarding the effectiveness of mandibular advancement orthodontic appliances with maxillary expansion device in treating pediatric Obstructive Sleep Apnea (OSA). Materials and methods A systematic literature search was conducted across PubMed, Cochrane Central, Web of Science, Embase, Scopus databases, Chinese Biomedical Database, Chinese National Knowledge Infrastructure, and Wanfang. The research involved children and adolescents (under 16 years old) who received mandibular advancement and maxillary expansion functional orthopedic appliances for OSA treatment. We performed narrative reviews and subsequently amalgamated the findings from the studies. Results Six articles were included for review. Although a small number of studies were included, the research suggested the potential advantages of mandibular advancement for children with OSA. Following treatment, there was a decrease in AHI/RDI, an improvement in sleep quality, and the increase in oxygen saturation. Conclusions The limited quantity and quality of existing studies necessitate caution when drawing conclusions about the effectiveness of mandibular advancement and maxillary expansion for OSA. In the future, larger and well-designed randomized controlled trials (RCTs) are needed to provide more robust evidence. Patients should be carefully selected, and their orthodontic indications should be thoroughly evaluated before inclusion in such trials.We encourage researchers to design studies that monitor patients over several years to provide a comprehensive understanding of the long-term effectiveness. Trial registration This study was registered in PROSPERO(CRD42023480407) on November 20, 2023.

The ability to predict tunnel deformation holds great significance for ensuring the reliability, safety, and sustainability of tunnel structures. However, existing deformation prediction models often simplify or overlook the impact of spatial characteristics on deformation by treating it as a time series prediction issue. This study utilizes monitoring data from the Grand Canyon Tunnel and introduces an effective data-driven method for predicting tunnel deformation based on the spatio-temporal characteristics of the historical deformation of adjacent sections. The proposed model, a combination of graph attention network (GAT) and bidirectional long and short-term memory network (Bi-LSTM), is equipped with robust spatio-temporal predictive capabilities. Additionally, the study explores other possible spatial connections and the scalability of the model. The results indicate that the proposed model outperforms other deep learning models, achieving favorable root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2) values of 0.34 mm, 0.23 mm, and 0.94, respectively. The graph structure based on intuitive spatial connections proves more suitable for meeting the challenges of predicting deformation. Integrating GAT-LSTM with transfer learning technology, remains stable performance when extended to other tunnels with limited data.

Abstract Previous research has established associations between amygdala functional connectivity abnormalities and major depressive disorder (MDD). However, inconsistencies persist due to limited sample sizes and poorly elucidated transcriptional patterns. In this study, we aimed to address these gaps by analyzing a multicenter magnetic resonance imaging (MRI) dataset consisting of 210 first-episode, drug-naïve MDD patients and 363 age- and sex-matched healthy controls (HC). Using Pearson correlation analysis, we established individualized amygdala functional connectivity patterns based on the Automated Anatomical Labeling (AAL) atlas. Subsequently, machine learning techniques were employed to evaluate the diagnostic utility of amygdala functional connectivity for identifying MDD at the individual level. Additionally, we investigated the spatial correlation between MDD-related amygdala functional connectivity alterations and gene expression through Pearson correlation analysis. Our findings revealed reduced functional connectivity between the amygdala and specific brain regions, such as frontal, orbital, and temporal regions, in MDD patients compared to HC. Importantly, amygdala functional connectivity exhibited robust discriminatory capability for characterizing MDD at the individual level. Furthermore, we observed spatial correlations between MDD-related amygdala functional connectivity alterations and genes enriched for metal ion transport and modulation of chemical synaptic transmission. These results underscore the significance of amygdala functional connectivity alterations in MDD and suggest potential neurobiological mechanisms and markers for these alterations.

Abstract Severe traumatic bleeding may lead to extremely high mortality rates, and early intervention to stop bleeding plays as a critical role in saving lives. However, rapid hemostasis in deep non-compressible trauma using a highly water-absorbent hydrogel, combined with strong tissue adhesion and bionic procoagulant mechanism, remains a challenge. In this study, a DNA hydrogel (DNAgel) network composed of natural nucleic acids with rapid water absorption, high swelling and instant tissue adhesion is reported, like a band-aid to physically stop bleeding. The excellent swelling behavior and robust mechanical performance, meanwhile, enable the DNAgel band-aid to fill the defect cavity and exert pressure on the bleeding vessels, thereby achieving compression hemostasis for deep tissue bleeding sites. The neutrophil extracellular traps (NETs)-inspired DNAgel network also acts as an artificial DNA scaffold for erythrocytes to adhere and aggregate, and activates platelets, promoting coagulation cascade in a bionic way. The DNAgel achieves lower blood loss than commercial gelatin sponge (GS) in male rat trauma models. In vivo evaluation in a full-thickness skin incision model also demonstrates the ability of DNAgel for promoting wound healing. Overall, the DNAgel band-aid with great hemostatic capacity is a promising candidate for rapid hemostasis and wound healing.

Abstract Photodynamic therapy is a highly recommended alternative treatment for solid tumors, such as cutaneous or luminal tumors, in clinical practice. However, conventional photosensitizers (PSs) often induce undesirable phototoxic effects because of their normal tissue distribution and a reduction in antitumor effects resulting from aggregation‐caused quenching effects. The present study developed a novel nano‐formulated aggregation‐induced emission (AIE)‐characteristic PS, nab‐TTVPHE, which is composed of human serum albumin as a carrier and TTVPHE as a therapeutic agent, as a more effective cancer treatment with lower phototoxic effects. Notably, the reactive oxygen species generated by TTVPHE were shielded by the nanoaggregate structure, and the photodynamic activity was after nanostructure dissociation. Nab‐TTVPHE was actively internalized in tumor cells via secreted protein, acidic and rich in cysteine and released to form nanoaggregates. TTVPHE accumulated in mitochondria, where it triggered mitochondrial damage under light irradiation via its photodynamic activity and induced pyroptosis via the caspase‐3/gasdermin E (GSDME) signaling pathway to kill tumor cells. Therefore, this nano‐formulated AIE‐characteristic PS provides an innovative strategy for cancer treatment with lower phototoxic effect and the ability to boost potential antitumor immunity via GSDME‐mediated pyroptosis.

ABSTRACTBackground: Nurses in emergency departments are at a high risk of experiencing secondary traumatic stress because of their frequent exposure to trauma patients and high-stress environments.Objective: This systematic review and meta-analysis aimed to determine the overall prevalence of secondary traumatic stress among emergency nurses and to identify the contributing factors.Method: We conducted a systematic search for cross-sectional studies in databases such as PubMed, Web of Science, Embase, CINAHL, Wanfang Database, and China National Knowledge Internet up to October 21, 2023. The Joanna Briggs Institute’s appraisal checklists for prevalence and analytical cross-sectional studies were used for quality assessment. Heterogeneity among studies was assessed using Cochrane’s Q test and the I2 statistic. A random effects model was applied to estimate the pooled prevalence of secondary traumatic stress, and subgroup analyses were performed to explore sources of heterogeneity. Descriptive analysis summarized the associated factors.Results: Out of 345 articles retrieved, 14 met the inclusion criteria, with 11 reporting secondary traumatic stress prevalence. The pooled prevalence of secondary traumatic stress among emergency nurses was 65% (95% CI: 58%–73%). Subgroup analyses indicated the highest prevalence in Asia (74%, 95% CI: 72%–77%), followed by North America (59%, 95% CI: 49%–72%) and Europe (53%, 95% CI: 29%–95%). Nine studies identified associated factors, including personal, work-related, and social factors. In the subgroup of divided by recruitment period, emergency department nurses in the COVID-19 outbreak period had a higher prevalence of secondary traumatic stress (70%, 95% CI: 62%–78%).Conclusions: Secondary traumatic stress prevalence is notably high among emergency department nurses, with significant regional variations and period differences. The factors affecting secondary traumatic stress also varied across studies. Future research should focus on improving research designs and sample sizes to pinpoint risk factors and develop prevention strategies.Registration: PROSPERO CRD42022301167.

BackgroundColorectal cancer is a prevalent malignancy of the digestive system, with an increasing incidence. Lower extremity deep vein thrombosis (DVT) is a frequent postoperative complication, occurring in up to 40% of cases.ObjectiveThis research aims to develop and validate a machine learning model (ML) to predict the risk of lower limb deep vein thrombosis in patients with colorectal cancer, facilitating preventive and therapeutic measures to enhance recovery and ensure safety.MethodsIn this retrospective cohort study, we collected data from 429 colorectal cancer patients from January 2021 to January 2024. The medical records included age, blood test results, body mass index, underlying diseases, clinical staging, histological typing, surgical methods, and postoperative complications. We employed the Synthetic Minority Oversampling Technique to address imbalanced data and split the dataset into training and validation sets in a 7:3 ratio. Feature selection was performed using Random Forest (RF), XGBoost, and Least Absolute Shrinkage and Selection Operator algorithms (LASSO). We then trained six machine learning models: Logistic Regression (LR), Naive Bayes (NB), Gaussian Process (GP), Random Forest, XGBoost, and Multilayer Perceptron (MLP). The model’s performance was evaluated using metrics such as area under the Receiver Operating Characteristic curve, accuracy, sensitivity, specificity, F1 score, and confusion matrix. Additionally, SHAP and LIME were used to enhance the interpretability of the results.ResultsThe study combined Random Forest, XGBoost algorithms, and LASSO regression with univariate regression analysis to identify significant predictive factors, including age, preoperative prealbumin, preoperative albumin, preoperative hemoglobin, operation time, PIKVA2, CEA, and preoperative neutrophil count. The XGBoost model outperformed other ML algorithms, achieving an AUC of 0.996, an accuracy of 0.9636, a specificity of 0.9778, and an F1 score of 0.9576. Moreover, the SHAP method identified age and preoperative prealbumin as the primary determinants influencing ML model predictions. Finally, the study employed LIME for more precise prediction and interpretation of individual predictions.ConclusionThe machine learning algorithms effectively predicted postoperative lower limb deep vein thrombosis in colorectal cancer patients. The XGBoost model demonstrated strong potential for improving early detection and treatment in clinical settings.

Transferring abundant, inexpensive, and nontoxic carbon dioxide (CO2) into biodegradable polymers is one of the ideal ways to promote sustainable development. Although a great deal of preeminent researches has been reported in the last decade, including well-designed organometallic complexes, Lewis pairs, etc. The moisture- and air-sensitive nature of these extensively used catalysts preclude their use in industrial applications. Herein, we report a novel stable catalyst system of commercial zinc glutarate (ZnGA) with a supported metal for the synthesis of poly­(ester-b-carbonate). The special supported microstructure facilitates efficient polymerizations via a plausible heterometal coordination mechanism. Notably, the resulted biodegradable CO2-based copolymer showed strong tensile strength (>40 MPa), improved elongation (45% versus 7%), excellent transmittance, and low water vapor permeability (WVP) (1.7 × 10–11 g m–1 s–1 Pa–1). Moreover, the supported ZnGA catalyst is recyclable, and its simple and low-cost preparation process is compatible with the manufacturing and processing methods of the existing infrastructure.

Abstract Prostate cancer (PCa) is the most common cancer among men in the United States and the leading cause of cancer-related death. The Solute Carrier Family 14 Member 1 (SLC14A1) is a member of urea transporters which are important for the regulation of urine concentration. However, the physiological significance of SLC14A1 in PCa still remains unclear. In the present study, via bioinformatics analysis and experiments, we found that expression of SLC14A1 is significantly decreased in PCa progression, which could be attributed to hypermethylation on SLC14A1 promoter region. Moreover, its low expression and hypermethylation on SLC14A1 promoter are closely related to the poor prognosis of PCa patients. On the other hand, overexpression of SLC14A1 inhibited cell proliferation and metastasis while its overexpression also suppressed CDK1/CCNB1 pathway and mTOR/MMP-9 signaling pathway. Additionally, SLC14A1 expression is enriched in prostate basal-type cells. In summary, our study indicates that its low expression level and promoter hypermethylation of SLC14A1 may represent novel indicators for PCa progression and prognosis, and SLC14A1 could inhibit the progression of PCa.

Abstract Background Tumor-associated macrophages (TAMs) constitute a substantial part of human hepatocellular carcinoma (HCC). The present study was devised to explore TAM diversity and their roles in HCC progression. Methods Through the integration of multiple 10 × single-cell transcriptomic data derived from HCC samples and the use of consensus nonnegative matrix factorization (an unsupervised clustering algorithm), TAM molecular subtypes and expression programs were evaluated in detail. The roles played by these TAM subtypes in HCC were further probed through pseudotime, enrichment, and intercellular communication analyses. Lastly, vitro experiments were performed to validate the relationship between CD63, which is an inflammatory TAM expression program marker, and tumor cell lines. Results We found that the inflammatory expression program in TAMs had a more obvious interaction with HCC cells, and CD63, as a marker gene of the inflammatory expression program, was associated with poor prognosis of HCC patients. Both bulk RNA-seq and vitro experiments confirmed that higher TAM CD63 expression was associated with the growth of HCC cells as well as their epithelial-mesenchymal transition, metastasis, invasion, and the reprogramming of lipid metabolism. Conclusions These analyses revealed that the TAM inflammatory expression program in HCC is closely associated with malignant tumor cells, with the hub gene CD63 thus representing an ideal target for therapeutic intervention in this cancer type.

ABSTRACT Current microbiome signatures for chronic diseases such as diabetic kidney disease (DKD) are mainly based on low-resolution taxa such as genus or phyla and are often inconsistent among studies. In microbial ecosystems, bacterial functions are strain specific, and taxonomically different bacteria tend to form co-abundance functional groups called guilds. Here, we identified guild-level signatures for DKD by performing in-depth metagenomic sequencing and conducting genome-centric and guild-based analysis on fecal samples from 116 DKD patients and 91 healthy subjects. Redundancy analysis on 1,543 high-quality metagenome-assembled genomes (HQMAGs) identified 54 HQMAGs that were differentially distributed among the young healthy control group, elderly healthy control group, early-stage DKD patients (EDG), and late-stage DKD patients (LDG). Co-abundance network analysis classified the 54 HQMAGs into two guilds. Compared to guild 2, guild 1 contained more short-chain fatty acid biosynthesis genes and fewer genes encoding uremic toxin indole biosynthesis, antibiotic resistance, and virulence factors. Guild indices, derived from the total abundance of guild members and their diversity, delineated DKD patients from healthy subjects and between different severities of DKD. Age-adjusted partial Spearman correlation analysis showed that the guild indices were correlated with DKD disease progression and with risk indicators of poor prognosis. We further validated that the random forest classification model established with the 54 HQMAGs was also applicable for classifying patients with end-stage renal disease and healthy subjects in an independent data set. Therefore, this genome-level, guild-based microbial analysis strategy may identify DKD patients with different severity at an earlier stage to guide clinical interventions.IMPORTANCETraditionally, microbiome research has been constrained by the reliance on taxonomic classifications that may not reflect the functional dynamics or the ecological interactions within microbial communities. By transcending these limitations with a genome-centric and guild-based analysis, our study sheds light on the intricate and specific interactions between microbial strains and diabetic kidney disease (DKD). We have unveiled two distinct microbial guilds with opposite influences on host health, which may redefine our understanding of microbial contributions to disease progression. The implications of our findings extend beyond mere association, providing potential pathways for intervention and opening new avenues for patient stratification in clinical settings. This work paves the way for a paradigm shift in microbiome research in DKD and potentially other chronic kidney diseases, from a focus on taxonomy to a more nuanced view of microbial ecology and function that is more closely aligned with clinical outcomes.

Major depressive disorder (MDD) is a debilitating mental health condition that poses significant risks and burdens. Resting-state functional magnetic resonance imaging (fMRI) has emerged as a promising tool in investigating the neural mechanisms underlying MDD. However, a comprehensive bibliometric analysis of resting-state fMRI in MDD is currently lacking. Here, we aimed to thoroughly explore the trends and frontiers of resting-state fMRI in MDD research. The relevant publications were retrieved from the Web of Science database for the period between 1998 and 2022, and the CiteSpace software was employed to identify the influence of authors, institutions, countries/regions, and the latest research trends. A total of 1501 publications met the search criteria, revealing a gradual increase in the number of annual publications over the years. China contributed the largest publication output, accounting for the highest percentage among all countries. Particularly, the University of Electronic Science and Technology of China, Capital Medical University, and Harvard Medical School were identified as key institutions that have made substantial contributions to this growth. Neuroimage, Biological Psychiatry, Journal of Affective Disorders, and Proceedings of the National Academy of Sciences of the United States of America are among the influential journals in the field of resting-state fMRI research in MDD. Burst keywords analysis suggest the emerging research frontiers in this field are characterized by prominent keywords such as dynamic functional connectivity, cognitive control network, transcranial brain stimulation, and childhood trauma. Overall, our study provides a systematic overview into the historical development, current status, and future trends of resting-state fMRI in MDD, thus offering a useful guide for researchers to plan their future research.

Abstract Background Off-targeted distribution of chemotherapeutic drugs causes severe side effects, further leading to poor prognosis and patient compliance. Ligand/receptor-mediated targeted drug delivery can improve drug accumulation in the tumor but it always attenuated by protein corona barriers. Results To address these problems, a radically different strategy is proposed that can leave the off-targeted drugs inactive but activate the tumor-distributed drugs for cancer-targeting therapy in a tumor microenvironment-independent manner. The feasibility and effectiveness of this strategy is demonstrated by developing an ultrasound (US)-activated prodrug-loaded liposome (CPBSN38L) comprising the sonosensitizer chlorin e6 (Ce6)-modified lipids and the prodrug of pinacol boronic ester-conjugated SN38 (PBSN38). Once CPBSN38L is accumulated in the tumor and internalized into the cancer cells, under US irradiation, the sonosensitizer Ce6 rapidly induces extensive production of intracellular reactive oxygen species (ROS), thereby initiating a cascade amplified ROS-responsive activation of PBSN38 to release the active SN38 for inducing cell apoptosis. If some of the injected CPBSN38L is distributed into normal tissues, the inactive PBSN38 exerts no pharmacological activity on normal cells. CPBSN38L exhibited strong anticancer activity in multiple murine tumor models of colon adenocarcinoma and hepatocellular carcinoma with no chemotherapy-induced side effects, compared with the standard first-line anticancer drugs irinotecan and topotecan. Conclusions This study established a side-effect-evitable, universal, and feasible strategy for cancer-targeting therapy. Graphical Abstract

This review systematically analyzes the latest advancements in preprocessing techniques for Electrocardiography (ECG) and Magnetocardiography (MCG) signals over the past decade. ECG and MCG play crucial roles in cardiovascular disease (CVD) detection, but both are susceptible to noise interference. This paper categorizes and compares different ECG denoising methods based on noise types, such as baseline wander (BW), electromyographic noise (EMG), power line interference (PLI), and composite noise. It also examines the complexity of MCG signal denoising, highlighting the challenges posed by environmental and instrumental interference. This review is the first to systematically compare the characteristics of ECG and MCG signals, emphasizing their complementary nature. MCG holds significant potential for improving the precision of CVD clinical diagnosis. Additionally, it evaluates the limitations of current denoising methods in clinical applications and outlines future directions, including the potential of explainable neural networks, multi-task neural networks, and the combination of deep learning with traditional methods to enhance denoising performance and diagnostic accuracy. In summary, while traditional filtering techniques remain relevant, hybrid strategies combining machine learning offer substantial potential for advancing signal processing and clinical diagnostics. This review contributes to the field by providing a comprehensive framework for selecting and improving denoising techniques, better facilitating signal quality enhancement and the accuracy of CVD diagnostics.

This study aimed to assess the associations between sprint force–velocity profile variables with change of direction (COD) performance and to investigate the impact of these variables on asymmetries in COD speed performance. Ninety-nine participants (volleyball players: n = 44, basketball players: n = 55) performed 40 m sprints for Fv relationship calculation, two COD tests (Modified Agility T-test and 505 test). A partial least squares (PLS) regression analysis was conducted to determine the relationships between the variables. The V0 was the most influential variable; it was negatively associated with COD performance variables (β = −0.260, −0.263 and −0.244 for MAT, 505-D and 505-ND, respectively), and F0 (β = 0.169, 0.163) was associated with the COD performance variables (COD deficit D and COD deficit ND, respectively), slightly larger than the effects of Fvslope (β = −0.162, −0.146), DRF (β = −0.159, −0.142) and Pmax (β = −0.162, −0.146). For COD deficit imbalance, the DRF (β = −0.070) was the most influential variable followed by Fvslope (β = −0.068), F0 (β = 0.046) and gender (β = 0.031). V0 and RFmax were the critical variables for improving COD performance that includes linear sprints, while DRF, Fvslope, F0 and Pmax collectively influence 180° COD performance. Meanwhile, DRF and Fvslope were important factors for asymmetries in COD speed performance. It is recommended to use the Fv profile to diagnose different COD movement patterns and then develop training plans accordingly for team sports played on smaller courts, such as basketball and volleyball.

The Insulated Gate Bipolar Transistor (IGBT) is the key power device in the rod control power cabinet of nuclear power plants; its reliable operation is of great significance for ensuring the safe and economical operation of the nuclear power plants. Therefore, it is necessary to conduct fault prediction research on IGBT to achieve better condition-based maintenance and improve its operational reliability. However, power cabinets often operate under multiple, complex working conditions, so predicting IGBT faults from single working condition data usually has limitations and low accuracy. Its failure probability has an important relationship with the actual operating conditions of the cabinet. In order to improve the reliability and maintainability of the control power cabinet in nuclear power plants, this paper takes IGBTs in the rod control power cabinet as the object and makes full use of the data of IGBTs under multiple working conditions to carry out research on the cross-condition fault prediction of IGBTs under multiple-source working conditions. A transfer learning (TL) model based on a bidirectional time convolutional network (BiTCN) combined with attention was proposed to solve the problem of low accuracy of cross-operating fault prediction in a multi-source domain. Firstly, an IGBT fault simulation model was built to collect the life cycle state data of the module under different working conditions. Then, after pre-processing such as removing outliers, kernel principal component analysis (KPCA) was used to integrate all source domain data, obtain source domain characterization data, and train the BiTCN-attention model. Finally, the BiTCN-attention model trained in the source domain was transferred, and the model was fine-tuned according to the target domain data. Simulation results show that the accuracy of the proposed BiTCN-attention transfer learning prediction method can reach more than 99%, which is significantly better than that of the recurrent neural network transfer learning (RNN-TL) model, long short-term memory network transfer learning (LSTM-TL) model, gated cyclic unit transfer learning (GRU-TL) model, and time convolutional network transfer learning (TCN-TL) model. This method can not only reduce the inconsistency of fault characteristic values caused by changes in working conditions but also accurately predict the degradation trend when only early fault data are available, providing an effective solution for IGBT fault prediction across working conditions in multi-source domains.