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Abstract This study aimed to identify predictive factors for the prognosis of acute-on-chronic liver disease (AoCLD) due to both hepatitis B virus (HBV) and alcohol and to develop prognostic models to improve treatment management. AoCLD patients with HBV and alcohol as etiological factors were selected from two multicenter prospective cohorts (NCT02457637,NCT03641872) and included in separate training and validation cohorts (n = 180 and n = 148). In the training cohort, the CATCH-LIFE A nomogram (based on age, bilirubin, international normalized ratio, serum sodium, and hepatic encephalopathy score) and CATCH-LIFE B nomogram (based on age, bilirubin, international normalized ratio, serum albumin, white blood cell, platelet count, and hepatic encephalopathy score) had discriminatory ability for predicting 28-day (c-indexes of 0.910 and 0.899) and 90-day mortality (c-indexes of 0.878 and 0.887, respectively). The area under the curve values for 28-day and 90-day mortality prediction by the CATCH-LIFE A nomogram were 0.922 (95% CI : 0.874, 0.971) and 0.905 (0.856, 0.956), respectively, while those for the CATCH-LIFE B nomogram were 0.916(0.861,0.972) and 0.915 (0.866,0.964), respectively. Similar performance results were observed in the validation cohort. Optimal cut-off scores for each nomogram could be used for patient stratification in high- and low-risk groups, and the high-risk groups showed shorter survival times than the low-risk groups in both the training and validation cohorts. Two nomograms constructed from the first short-term follow-up data from patients with AoCLD due to combined HBV infection and alcohol exposure showed good predictive performance for 28-day and 90-day mortality and might be used to guide clinical management.

Abstract This study aimed to examine the correlation between post-traumatic growth (PTG), spiritual well-being (SWB), perceived social support (PSS), and demographic and clinical factors in Chinese gynecological cancer patients. Through convenience sampling, we conducted a cross-sectional study of 771 adult patients with gynecological cancer. The European Organization for Research and Treatment for Cancer Quality of Life Questionnaire-Spiritual Well-being 32 (EORTC QLQ-SWB32), Post-traumatic Growth Inventory (PTGI), and Multidimensional Scale of Perceived Social Support (MSPSS) were used to measure SWB, PTG, and PSS. A Multiple Linear Regression Model was used to determine the possible factors contributing to PTG. The subscale with the highest centesimal score in the PTGI was the Appreciation of Life Scale, and the lowest was New Possibility. Gynecologic cancer patients with younger ages (B = − 0.313, P = 0.002), perceived more family support (B = 1.289, P

Abstract In the construction industry, ensuring the safety performance of a project relies heavily on the effective allocation of safety resources. As the importance of mental health in the construction industry increases, evolutionary game theory can be used to analyze the interaction mechanism of various factors affecting safety performance during the construction phase. The objective of this paper is to construct an analytical model that combines evolutionary game theory with genetic algorithms from the perspective of Leader-Member Exchange Ambivalence. The model aims to quantify and compare the various factors that influence achieving the expected safety state and identify the specific necessary constraints. Initially, we analyzed the relationships among construction site employees, divided them into superiors and subordinates, and established a game model and payoff matrix based on the research background. Next, we introduced genetic algorithms into the model via the replicator dynamic equation for optimization. We adjusted the coefficients of safety risk level, psychological expected return, moral identity, and other factors to simulate various construction site scenarios. Simulation and optimization results indicate that genetic algorithms provide more accurate reference values for safety resource allocation compared to preset or manually assigned values.

Abstract The evaluation index system is constructed based on the connotation and characteristics of health tourism. Using the entropy method, Thiel index, exploratory spatial data analysis method, spatial Markov chain and spatial econometric model, research is carried out around the development index, difference status, spatial-temporal pattern, dynamic evolution and influencing factors of health tourism. The following results were drawn: (1) The development index of health tourism in China is low, but the development speed is fast. The inter-regional development index shows an eastern China > central China > western China pattern, and the development speed exhibits a western China > central China > eastern China situation. (2) In the overall difference in China’s health tourism development, the intra-regional difference is consistently higher than the inter-regional difference. Among the three major regions, the overall difference between eastern China and western China is always higher than that of central China. (3) The development of health tourism in China is positively correlated in the global space, with some local spatial clustering. (4) The dynamic evolution of health tourism development in China shows part of the “Matthew effect” characteristics, with an obvious spatial spillover effect. (5) Various influencing factors produced widely varying direct, indirect and total effects on health tourism development in China, eastern China, central China and western China. Finally, based on the results of the above empirical analysis, policy recommendations to promote the development of health tourism in China are proposed.

Abstract As construction becomes greener, people have higher and higher requirements for engineering project management, which makes it necessary to deeply study the comprehensive optimization of schedule, cost and sustainability level. Adhering to the concept of low carbon and green, the article takes carbon emission factor into the total cost of building construction and improves the traditional cost objective of engineering projects; then quantitatively analyzes the economic, environmental and social impacts of assembled buildings from the perspective of sustainability, and introduces the sustainability objective into the traditional duration-cost problem study, taking the duration of each job in the double code arrow diagram as the independent variable to construct the duration -cost-sustainability level multi-objective optimization model. In order to solve the type effectively, a series of Pareto optimal solutions are obtained using the NSGA-II algorithm, and the efficacy coefficient method is used for program decision making. The results show that the Pareto solution set can provide effective support for the project manager’s decision making, and the NSGA-II algorithm is effective in solving the multi-objective optimization model.

Abstract Medical image segmentation is a fundamental step in medical analysis and diagnosis. In recent years, deep learning networks have been used for precise segmentation. Numerous improved encoder–decoder structures have been proposed for various segmentation tasks. However, high-level features have gained more research attention than the abundant low-level features in the early stages of segmentation. Consequently, the learning of edge feature maps has been limited, which can lead to ambiguous boundaries of the predicted results. Inspired by the encoder–decoder network and attention mechanism, this study investigates a novel multilayer edge attention network (MEA-Net) to fully utilize the edge information in the encoding stages. MEA-Net comprises three major components: a feature encoder module, a feature decoder module, and an edge module. An edge feature extraction module in the edge module is designed to produce edge feature maps by a sequence of convolution operations so as to integrate the inconsistent edge information from different encoding stages. A multilayer attention guidance module is designed to use each attention feature map to filter edge information and select important and useful features. Through experiments, MEA-Net is evaluated on four medical image datasets, including tongue images, retinal vessel images, lung images, and clinical images. The evaluation values of the Accuracy of four medical image datasets are 0.9957, 0.9736, 0.9942, and 0.9993, respectively. The values of the Dice coefficient are 0.9902, 0.8377, 0.9885, and 0.9704, respectively. Experimental results demonstrate that the network being studied outperforms current state-of-the-art methods in terms of the five commonly used evaluation metrics. The proposed MEA-Net can be used for the early diagnosis of relevant diseases. In addition, clinicians can obtain more accurate clinical information from segmented medical images.

Abstract 68Ga-radiolabeled small molecules that specifically target prostate-specific membrane antigen (PSMA) have been extensively investigated, and some of these tracers have been used in the diagnosis of prostate cancer via 68Ga-positron emission tomography (68Ga-PET). Nevertheless, current 68Ga-labeled radiotracers show only fair detection rates for metastatic prostate cancer lesions, especially those with lower levels of prostate specific antigen (PSA), which often occurs in the biochemical recurrence of prostate cancer. The goal of this study was to design and synthesize a new PSMA-targeted radiotracer, 68Ga-SC691, with high affinity for prostate cancer cells and excellent pharmacokinetics. To this end, structural optimization was carried out on the bifunctional group, target motif, and linker while the high affinity targeting scaffold remained. To explore its potential in the clinic, a comparative study was further performed in vitro and in vivo between 68Ga-SC691 and 68Ga-PSMA-11, a clinically approved tracer for PSMA-positive prostate cancer. SC691 was radiolabeled to provide 68Ga-SC691 in 99% radiolabeling yield under mild conditions. High uptake and a high internalization ratio into LNCaP cells were observed in in vitro studies. In vivo studies showed that 68Ga-SC691 had favorable biodistribution properties and could specifically accumulate on PSMA-positive LNCaP xenografts visualized by micro-PET/CT. This radiotracer showed excellent PET imaging quality and comparable, if not higher, uptake in LNCaP xenografts than 68Ga-PSMA-11.

Abstract Systemic immune-inflammation index (SII) emerged as a biomarker of chronic inflammation and an independent prognostic factor for many cancers. We aimed to investigate the associations of SII level with total and cause-specific mortality risks in the general populations, and the potential modification effects of lifestyle-related factors on the above associations. In this study, we included 30,521 subjects from the Dongfeng-Tongji (DFTJ) cohort and 25,761 subjects from the National Health and Nutrition Examination Survey (NHANES) 1999–2014. Cox proportional hazards regression models were used to estimate the associations of SII with mortality from all-cause, cardiovascular diseases (CVD), cancer and other causes. In the DFTJ cohort, compared to subjects in the low SII subgroup, those within the middle and high SII subgroups had increased risks of total mortality [hazard ratio, HR (95% confidence interval, CI) = 1.12 (1.03–1.22) and 1.26 (1.16–1.36), respectively) and CVD mortality [HR (95%CI) = 1.36 (1.19–1.55) and 1.50 (1.32–1.71), respectively]; those within the high SII subgroup had a higher risk of other causes mortality [HR (95%CI) = 1.28 (1.09–1.49)]. In the NHANES 1999–2014, subjects in the high SII subgroup had higher risks of total, CVD, cancer and other causes mortality [HR (95%CI) = 1.38 (1.27–1.49), 1.33 (1.11–1.59), 1.22 (1.04–1.45) and 1.47 (1.32–1.63), respectively]. For subjects with a high level of SII, physical activity could attenuate a separate 30% and 32% risk of total and CVD mortality in the DFTJ cohort, and a separate 41% and 59% risk of total and CVD mortality in the NHANES 1999–2014. Our study suggested high SII level may increase total and CVD mortality in the general populations and physical activity exerted a beneficial effect on the above associations.

The earliest atherosclerotic lesions preferentially develop in arterial regions experienced disturbed blood flow, which induces endothelial expression of pro-atherogenic genes and the subsequent endothelial dysfunction. Our previous study has demonstrated an up-regulation of DNA methyltransferase 1 (DNMT1) and a global hypermethylation in vascular endothelium subjected to disturbed flow. Here, we determined that DNMT1-specific inhibition in arterial wall ameliorates the disturbed flow-induced atherosclerosis through, at least in part, targeting cell cycle regulator cyclin A and connective tissue growth factor (CTGF). We identified the signaling pathways mediating the flow-induction of DNMT1. Inhibition of the mammalian target of rapamycin (mTOR) suppressed the DNMT1 up-regulation both in vitro and in vivo. Together, our results demonstrate that disturbed flow influences endothelial function and induces atherosclerosis in an mTOR/DNMT1-dependent manner. The conclusions obtained from this study might facilitate further evaluation of the epigenetic regulation of endothelial function during the pathological development of atherosclerosis and offer novel prevention and therapeutic targets of this disease.

Urinary volatile organic compounds (VOCs) analysis for kidney diseases has attracted a large amount of scientific interest recently, and urinary metabolite analysis has already been applied to many diseases. Urine was collected from 15 mesangial proliferative glomerulonephritis (MsPGN) patients, 21 IgA nephropathy (IgAN) patients and 15 healthy controls. Solid phase microextraction-chromatography-mass spectrometry (SPME-GC-MS) was used to analyse the urinary metabolites. The statistical methods principal component analysis (PCA) and orthogonal partial least-squares discriminant analysis (OPLSDA) were performed to process the final data. Five metabolites were significantly greater in the group of MsPGN patients than in the normal control group (P < 0.05) while three metabolites were found at increased levels in the group of IgAN patients compared with the normal controls (P < 0.05). In addition, five metabolites were significantly increased in the group of IgAN patients compared with the MsPGN patients (P < 0.05). These five metabolites may be specific biomarkers for distinguishing between MsPGN and IgAN. The analysis of urinary VOCs appears to have potential clinical applications as a diagnostic tool. [ABSTRACT FROM AUTHOR]