Search

Background Although cardiovascular mortality continued declining from 2000 to 2019, the rate of this decrease decelerated. We aimed to assess the trends and disparities in risk factor control and treatment among US adults with atherosclerotic cardiovascular disease to find potential causes of the deceleration. Methods and Results A total of 55 ,021 participants, aged ≥20 years, from the 1999 to 2018 National Health and Nutrition Examination Survey were included, of which 5717 were with atherosclerotic cardiovascular disease. Risk factor control was defined as hemoglobin A1c

BackgroundAdrenocortical carcinoma (ACC) is an aggressive endocrine malignancy with limited therapeutic options. Treating advanced ACC with mitotane, the cornerstone therapy, remains challenging, thus underscoring the significance to predict mitotane response prior to treatment and seek other effective therapeutic strategies.ObjectiveWe aimed to determine the efficacy of mitotane via an in vitro assay using patient-derived ACC cells (PDCs), identify molecular biomarkers associated with mitotane response and preliminarily explore potential agents for ACC.MethodsIn vitro mitotane sensitivity testing was performed in 17 PDCs and high-throughput screening against 40 compounds was conducted in 8 PDCs. Genetic features were evaluated in 9 samples using exomic and transcriptomic sequencing.ResultsPDCs exhibited variable sensitivity to mitotane treatment. The median cell viability inhibition rate was 48.4% (IQR: 39.3-59.3%) and -1.2% (IQR: -26.4-22.1%) in responders (n=8) and non-responders (n=9), respectively. Median IC50 and AUC were remarkably lower in responders (IC50: 53.4 µM vs 74.7 µM, P

The dynamics of excited‐state charge transfer (CT) in second near‐infrared (NIR‐II) fluorophore proceeds with conformational change that govern fluorescence. Unveiling the relationship between CT dynamics and conformational change in excited state is of great fundamental significance in optimizing NIR‐II fluorescence but remains less explored. This study unveils the pivotal role of large conformational change in promoting the evolution of emissive CT state into nonemissive CT state in NIR‐II fluorophore (CA‐BBT). Spectroscopic and computational results reveal that large conformational rotation/twisting leads to a pronounced increase in the CT character in excited state. This heightened CT character in CA‐BBT enables a rapid evolution of emissive CT state into a nonemissive CT state within 1.4 ps, as observed by ultrafast spectroscopy. Subsequently, this nonemissive state dominates nonradiative decay, resulting in very low NIR‐II fluorescence. Preventing such detrimental evolution by constraining the conformational dynamics of CA‐BBT results in a 10‐fold enhancement of NIR‐II fluorescence, enabling high‐resolution dynamic visualization of vasculature within osteosarcoma. This study offers a profound understanding of the relationship between CT dynamics and conformational changes in NIR‐II fluorescence, presenting innovative perspectives to develop bright NIR‐II fluorophores.

Summary: Despite the promising outcomes of immune checkpoint inhibitors (ICIs), resistance to ICI presents a new challenge. Therefore, selecting patients for specific ICI applications is crucial for maximizing therapeutic efficacy. Herein, we curated 69 human esophageal squamous cell cancer (ESCC) patients’ tumor microenvironment (TME) single-cell transcriptomic datasets to subtype ESCC. Integrative analyses of the cellular network and transcriptional signatures of T cells and myeloid cells define distinct ESCC subtypes characterized by T cell exhaustion, and interleukin (IL) and interferon (IFN) signaling. Furthermore, this approach classifies ESCC patients into ICI responders and non-responders, as validated by whole tumor transcriptomes and liquid biopsy-based single-cell transcriptomes of anti-PD-1 ICI responders and non-responders. Our study stratifies ESCC patients based on TME transcriptional network, providing novel insights into tumor niche remodeling and potentially predicting ICI responses in ESCC patients.

Introduction: Different land use patterns affect access to ecosystem services as well as differences in farmers’ well-being. Methods: The Loess Plateau, which has a fragile ecological environment, was chosen as the study region. On the one hand, information on the importance and accessibility of ESs, as well as the subjective and objective well-being of farmers, was obtained through semistructured interviews and questionnaires. On the other hand, regional differences for the impact of ESs on the well-being of farmers were explored based on a structural equation model.Results: The results indicated that the impact of ESs on farmers’ well-being was crucial for all three village types, but the extent of the impact varied, and the key ESs varied across villages. For hilly villages and gully villages, crops and firewood were the two common ES types that were important. However, water conservation was the key ES type for farmers in hilly villages, and pest control was the key ES type for farmers in gully villages. Fruits, water conservation and recreation were the key ES types in the river villages.Discussion: A focus on the regional differences for the impacts of ESs on farmers’ well-being is meaningful for policy practice. We can contribute to farmers’ well-being by optimising land use allocation and land ecological control to improve their ES access in different regions, which is effective in differentiating and enhancing the sustainability of different regions.

Ginsenoside Rg1 (Rg1), a monomer saponin component, is one of the components with the highest content in total saponins of Panaxnotoginseng. It had various pharmacological effects. The bioavailability of oral tablets is only 1–20 %, and it is eliminated quickly in the blood. The development of new dosage forms and new routes of administration of ginsenoside Rg1 with sustained release and high bioavailability has become a significant problem to be solved. The Rg1 liposomes study used a thin film dispersion ultrasound method for its preparation. This study focused the pharmacokinetic parameters of ginsenoside Rg1 liposomes in rats through the lung perfusion method. Ginsenoside Rg1 liposomes were round and uniform in shape, the particle size was 2–3 μm, and the encapsulation efficiency of ginsenoside Rg1 liposome was 51.2 %. Results showed that, after pulmonary administration of ginsenoside Rg1, the time of ginsenoside Rg1 detected by Rg1 liposomes was longer than that of Rg1 solution, the relative bioavailability of ginsenoside Rg1 liposome lung administration AUC liposome/AUC solution = 122.67 %. These results provided the scientific theoretical and experimental basis for further development of new dosage forms and new routes of administration of ginsenoside Rg1.

ABSTRACT: The processing and analysis of massive high-dimensional datasets are important issues in precision livestock farming (PLF). This study explored the use of multivariate analysis tools to analyze environmental data from multiple sensors located throughout a broiler house. An experiment was conducted to collect a comprehensive set of environmental data including particulate matter (TSP, PM10, and PM2.5), ammonia, carbon dioxide, air temperature, relative humidity, and in-cage and aisle wind speeds from 60 locations in a typical commercial broiler house. The dataset was divided into 3 growth phases (wk 1–3, 4–6, and 7–9). Spearman's correlation analysis and principal component analysis (PCA) were used to investigate the latent associations between environmental variables resulting in the identification of variables that played important roles in indoor air quality. Three cluster analysis methods; k-means, k-medoids, and fuzzy c-means cluster analysis (FCM), were used to group the measured parameters based on their environmental impact in the broiler house. In general, the Spearman and PCA results showed that the in-cage wind speed, aisle wind speed, and relative humidity played critical roles in indoor air quality distribution during broiler rearing. All 3 clustering methods were found to be suitable for grouping data, with FCM outperforming the other 2. Using data clustering, the broiler house spaces were divided into 3, 2, and 2 subspaces (clusters) for wk 1 to 3, 4 to 6, and 7 to 9, respectively. The subspace in the center of the house had a poorer air quality than other subspaces.

Bone defects and non-union are prevalent in clinical orthopedy, and the outcomes of current treatments are often suboptimal. Bone tissue engineering offers a promising approach to treating these conditions effectively. Bone morphogenetic protein 9 (BMP9) can commit mesenchymal stem cells to osteogenic lineage, and a knowledge of the underlying mechanisms may help advance the field of bone tissue engineering. Leucine-rich repeats containing G protein-coupled receptor 4 (LGR4), a member of G protein-coupled receptors, is essential for modulating bone development. This study is aimed at investigating the impact of LGR4 on BMP9-induced osteogenesis in mesenchymal stem cells as well as the underlying mechanisms. Bone marrow stromal cells from BMP9-knockout mice exhibited diminished LGR4 expression, and exogenous LGR4 clearly restored the impaired osteogenic potency of the bone marrow stromal cells. Furthermore, LGR4 expression was increased by BMP9 in C3H10T1/2 cells. LGR4 augmented the benefits of BMP9-induced osteogenic markers and bone formation, whereas LGR4 inhibition restricted these effects. Meanwhile, the BMP9-induced lipogenic markers were increased by LGR4 inhibition. The protein levels of Raptor and p-Stat3 were elevated by BMP9. Raptor knockdown or p-Stat3 suppression attenuated the osteoblastic markers and LGR4 expression brought on by BMP9. LGR4 significantly reversed the blocking effect of Raptor knockdown or p-Stat3 suppression on the BMP9-induced osteoblastic markers. Raptor interacts with p-Stat3, and p-Stat3 activates the LGR4 promoter activity. In conclusion, LGR4 boosts BMP9 osteoblastic potency in mesenchymal stem cells, and BMP9 may up-regulate LGR4 via the mTORC1/Stat3 signal activation.

Abstract Climate warming is causing an increase in the frequency and severity of heatwaves and extreme precipitation events, posing a threat to both socioeconomic stability and human lives. In 2022, five record-breaking heatwaves and floods occurred, it is suggested a new concurrent extreme feature in the study, which is linked to the three Poles warming and ice/snow melting. The independent and synergistic effect of the three Poles warming has led to the poleward movement of westerly jet streams and amplified Rossby waves, as well as the expansion of subtropical highs in both poleward and zonal directions. Consequently, these have intensified heatwaves in the mid-high latitudes and extreme precipitation events in the Asian regions, combined with a southward displacement and weakening of the inter-hemispheric Asian summer monsoon circulations, which are response to poleward of the mid-latitude and subtropical circulations and the hydrothermal effects of the three Poles. This study highlights the significance of three Poles warming in driving amplified concurrent extremes across tropical and extratropical regions, particularly in 2022. These findings provide valuable insights into the role of snow/ice melting and related hydrothermal factors in global climate predictions and disaster prevention efforts.

Abstract Unpredictable rainfall frequently results in excess moisture, which is detrimental to the landscape because it interferes with the genetic, morphological, and physiological processes of plants, even though the majority of urban landscapes frequently experience moisture shortages. A study was conducted to analyze the effects of a 36-day waterlogging phase and a subsequent 12-day recovery period on the morpho-physiological responses of 17 Crassulaceae species with the goal of identifying those which were more tolerant of the conditions. Results revealed that waterlogging stress has an impact on all morpho-physiological parameters. Sensitive materials (S7, Hylotelephium telephium ‘Purple Emperor’ and S15, S. sexangulare) showed severe ornamental quality damage, mortality, decreases in total dry biomass, root-shoot ratio, and chlorophyll content, as well as higher MDA concentrations. Lower reductions in these parameters, along with improved antioxidant enzyme activities and greater recovery capabilities after drainage, were observed in the most tolerant materials S2 (H. spectabile ‘Brilliant’), S3 (H. spectabile ‘Carl’), and S5 (H. telephium ‘Autumn Joy’). Furthermore, with the exception of early death materials (S7 and S15), all materials showed varying intensities of adventitious root formation in response to waterlogging. The 17 species were divided into 4 clusters based on the comprehensive evaluation value. The first group included S1-S3, S5-S6, S8-S12, which were waterlogged tolerant with the highest values (0.63–0.82). S14 belongs to the intermediate waterlogging tolerant. S4, S13, S16, and S17 were clustered into the low waterlogging-tolerant group. S7 and S15 were the most susceptible to waterlogging. The survival and success of Crassulaceae species (especially, the first and second cluster), throughout this prolonged period of waterlogging (36 days) and recovery were attributed to a combination of physiological and morphological responses, indicating that they are an appealing species for the creation of rain gardens or obstructed drainage locations.

Abstract Background The influence of chronic hepatitis B infection (CBI) on hepatic steatosis, necroinflammation, and fibrosis in nonalcoholic fatty liver disease (NAFLD) population was unclear. We aimed to investigate the effect of CBI on hepatic steatosis and assess the association between NAFLD co-existed CBI and hepatic injury in NAFLD pediatric population. Methods Consecutive hospitalized children with biopsy-proven NAFLD with or without CBI were included. Hepatic steatosis, necroinflammation and fibrosis were evaluated by NASH CRN system and/or METAVIR scoring system, appropriately. Using multivariate logistic analysis, we identified variables associated with hepatic steatosis and liver injury. Results Of 223 biopsy-proven NAFLD children, 161 were NAFLD without CBI, and 62 were NAFLD co-existed CBI. Grouped by mild, moderate and severe hepatic steatosis, there was an inverse association between CBI and the severity of hepatic steatosis [odd ratio (OR) 0.037, 95% confidence interval (CI) 0.014–0.098]. In addition, we explored the relationship between CBI and hepatic necroinflammation and fibrosis in NAFLD children. Hepatic necroinflammation and fibrosis, respectively, were divided into two groups according to severity. And CBI was positively associated with hepatic necroinflammation (OR 6.125, 95%CI 1.958–19.158). However, there was no statistically independent association between CBI and significant hepatic fibrosis. Conclusions CBI was inverse associated with the grade of steatosis and positively associated with severe hepatic necroinflammation, and does not appear to affect significant hepatic fibrosis in pediatric NAFLD children.

Abstract HEI10 is a conserved E3 ubiquitin ligase involved in crossover formation during meiosis, and is thus essential for both male and female gamete development. Here, we have discovered a novel allele of HEI10 in rice that produces a truncated HEI10 protein missing its N-terminal RING domain, namely sh1 (shorter hei10 1). Unlike previously reported hei10 null alleles that are completely sterile, sh1 exhibits complete male sterility but retains partial female fertility. The causative sh1 mutation is a 76 kb inversion between OsFYVE4 and HEI10, which breaks the integrity of both genes. Allelic tests and complementation assays revealed that the gamete developmental defects of sh1 were caused by disruption of HEI10. Further studies demonstrated that short HEI10 can correctly localise to the nucleus, where it could interact with other proteins that direct meiosis; expressing short HEI10 in hei10 null lines partially restores female fertility. Our data reveal an intriguing mutant allele of HEI10 with differential effects on male and female fertility, providing a new tool to explore similarities and differences between male and female meiosis.

Abstract Objective To evaluate the effectiveness of different non-pharmacological interventions for pain management in preterm infants and provide high-quality clinical evidence. Methods Randomized controlled trials (RCTs) of various non-pharmacological interventions for pain management in preterm infants were searched from PubMed, Web of Science, Embase, and the Cochrane Library from 2000 to the present (updated March 2023). The primary outcome was pain score reported as standardized mean difference (SMD). The secondary outcomes were oxygen saturation and heart rate reported as the same form. Results Thirty five RCTs of 2134 preterm infants were included in the meta-analysis, involving 6 interventions: olfactory stimulation, combined oral sucrose and non-nutritive sucking (OS + NNS), facilitated tucking, auditory intervention, tactile relief, and mixed intervention. Based on moderate-quality evidence, OS + NNS (OR: 3.92, 95% CI: 1.72, 6.15, SUCRA score: 0.73), facilitated tucking (OR: 2.51, 95% CI: 1.15, 3.90, SUCRA score: 0.29), auditory intervention (OR: 2.48, 95% CI: 0.91, 4.10, SUCRA score: 0.27), olfactory stimulation (OR: 1.80, 95% CI: 0.51, 3.14, SUCRA score: 0.25), and mixed intervention (OR: 2.26, 95% CI: 0.10, 4.38, SUCRA score: 0.14) were all superior to the control group for pain relief. For oxygen saturation, facilitated tucking (OR: 1.94, 95% CI: 0.66, 3.35, SUCRA score: 0.64) and auditory intervention (OR: 1.04, 95% CI: 0.22, 2.04, SUCRA score: 0.36) were superior to the control. For heart rate, none of the comparisons between the various interventions were statistically significant. Conclusion This study showed that there are notable variations in the effectiveness of different non-pharmacological interventions in terms of pain scores and oxygen saturation. However, there was no evidence of any improvement in heart rate.

Abstract Liver cancer is a major malignant tumor, which seriously threatens human health and increases the economic burden on patients. At present, gene therapy has been comprehensively studied as an excellent therapeutic measure in liver cancer treatment. Oncolytic virus (OV) is a kind of virus that can specifically infect and kill tumor cells. After being modified by genetic engineering, the specificity of OV infection to tumor cells is increased, and its influence on normal cells is reduced. To date, OV has shown its effectiveness and safety in experimental and clinical studies on a variety of tumors. Thus, this review primarily introduces the current status of different genetically engineered OVs used in gene therapy for liver cancer, focuses on the application of OVs and different target genes for current liver cancer therapy, and identifies the problems encountered in OVs-based combination therapy and the corresponding solutions, which will provide new insights into the treatment of liver cancer.

This study presents an innovative approach for estimating the azimuth cutoff wavelength ($\lambda _{c}$) using a multipolarization combination technique to enhance the retrieval of significant wave height (SWH) and wind speed (WS) from Gaofen-3 (GF-3) SAR wave mode data. The study identifies distinct advantages of copolarization for low to moderate sea states and cross-polarization for high sea states in the $\lambda _{c}$ estimation. Consequently, a suite of dual and quad-polarization combination methods is proposed, with the VV+VH combination demonstrating superior cost-efficiency, reducing the root mean square error (RMSE) of $\lambda _{c}$ estimation by around 20% compared with VV polarization. Correlation analysis between $\lambda _{c}$ at various polarizations, particularly VV+VH, and factors such as SWH, WS, wind direction, wave direction, and incidence angle, indicates a strong positive relationship with SWH and WS, and a moderate relationship with incidence angle. This insight informs the development of three $\lambda _{c}$-based SWH and WS retrieval models: single linear regression, multiple linear regression (MLR), and Gaussian process regression (GPR). The MLR and GPR models integrate normalized radar cross section (NRCS) and incidence angle to improve retrieval accuracy. The GPR model achieves more accurate estimation of SWH and WS compared with existing $\lambda _{c}$-based algorithms, with an RMSE of 0.485 m for SWH retrieval and 1.390 m/s for WS retrieval. Despite the performance gap with state-of-the-art algorithms, the GPR model offers exceptional cost-effectiveness and surpasses NRCS-based models for WS retrieval without requiring wind direction input.

Transient simulation in power engineering is crucial as it models the dynamic behavior of power systems during sudden events like faults or short circuits. Electromagnetic transient simulations involve multiple coordinated tasks. Traditional simulations are centralized and struggle to meet scalability requirements. To achieve these goals, distributed electromagnetic transient simulation has emerged as a new trend. Nevertheless, the distributed electromagnetic transient simulation introduces network communication. Achieving real-time simulation across distributed nodes poses the challenge of minimizing communication costs. In this paper, our proposal focuses on optimizing the task orchestration to reduce communication costs. Specifically, in the electromagnetic transient simulation, these tasks has certain communication pattern where the communicated objects of each task are pre-defined. We represent the pattern as a graph, with tasks represented as nodes and communications as edges. Furthermore, we propose to use graph partition with the objective of minimal communication costs and fine tune the partitions with the resource requirements of each distributed node. The experimental results demonstrate that our proposal has strength in achieving high-performance electromagnetic transient simulation.

Synthetic aperture radar (SAR) is widely used for observing sea surfaces and retrieving 2-D wave spectra. However, existing methods for retrieving directional wave spectra from SAR imagettes face challenges due to the complex nonlinear SAR-wave imaging relationship and the limitation of first-guess spectra. This study proposes a novel two-stage machine learning strategy for retrieving 2-D directional wave spectra from Chinese Gaofen-3 SAR wave mode products. We achieve the generation of complete 2-D wave spectra and several wave parameters solely from GF-3 SAR data without necessitating any additional inputs. In the first stage, we employ the Energy Attention Conditional Generative Adversarial Network (EA-CGAN) to retrieve the normalized wave spectrum. The generator of the EA-CGAN establishes a nonlinear transformation from normalized SAR cross spectra to normalized wave spectra to enhance the capabilities. In the second stage, the XGBoost model retrieves the intensity of the wave spectrum. The EA-CGAN and XGBoost models were trained on an extensive dataset that consists of about 11 000 Gaofen-3 SAR wave mode imagettes and 2-D wave spectra from the fifth-generation reanalysis (ERA-5) of the European Centre for Medium-Range Weather Forecasts. The results of the evaluation using test samples reveal high consistency between the retrieved wave spectra and ERA-5 wave spectra in terms of spectral similarity, peak period, peak direction, significant wave height, and mean wave periods. Compared to the traditional methods, our approach offers enhanced effectiveness, demonstrating the potential of advanced deep learning in high-precision SAR wave spectrum inversion.

The present study utilizes the backward propagation neural network (BPNN) and the dual-branch convolutional neural network (DB-CNN) algorithms to construct models for estimating significant wave height (SWH) from polarimetric Gaofen-3 SAR wave mode data, using a dataset of 11 164 images that are collocated with SWH from the European Centre for Medium-Range Weather Forecasts fifth generation reanalysis (ERA5). The models are assessed and compared across nine polarizations [vertical–vertical (VV), horizontal–horizontal (HH), RL, vertical–horizontal (VH), horizontal-vertical (HV), RR, 45° linear, RV, and RH] and various sea states using the SAR-ERA5 test samples as well as buoy and altimeter SWH observations. The results demonstrate the robust performance of BPNN models, with RMSEs around 0.30–0.32 m on SAR-ERA5 test data, 0.32–0.48 m on buoy data, 0.40–0.48 m on Jason-3 data, and 0.36–0.42 m on SARAL data. By comparison, the DB-CNN models, which additionally include two-dimensional (2-D) image spectra as input, only exhibit improved performance at VV, 45° linear, and RL polarizations, while showing negligible improvement at HH, RV, and RH polarizations and a notable degradation at VH, HV, and RR polarizations. Furthermore, the DB-CNN models generally fail to improve the overestimation (underestimation) in low (high) seas, and they even aggravate the overestimation (underestimation) under most polarizations. Additionally considering the heightened complexity, increased vulnerability to overfitting, and training times that are more than 200 times longer, the use of complex deep learning network structures to incorporate 2-D spectral information appears to be operationally limited for SAR SWH retrieval.

A convolutional neural network combined with long short-term memory (CNN-LSTM) phase compensation method (PCM) is proposed and demonstrated, where CNN is employed to extract spatial features, and LSTM is used to capture temporal features and realize the long-term predictions of residual phase fluctuations. This is the first-time machine learning (ML) has been used to mitigate the effects of optical path asymmetry caused by temperature variations on radio frequency (RF) transmission systems. The performance is verified by experiments on a unidirectional two-way RF transmission system, in which both the two 259-km-long separate fibers are coupled into one optical cable. The results demonstrate the CNN-LSTM model presents better prediction performance than the other eight previously proposed ML models. When the prediction duration is 40,000 s and the ambient temperature variation range is 14.38 °C, the coefficient of determination (R Squared, R2) between the predicted value and the actual value is higher than 0.99. In addition, compared to the phase locked loop (PLL) PCM, the proposed CNN-LSTM PCM can reduce the root-mean-square (RMS) phase jitter of the received signal from 219 ps to 19.72 ps, and improve the frequency stability of the system at 10,000 s by 84.5%. Overall, the proposed CNN-LSTM PCM can effectively compensate for residual phase fluctuations generated by the optical path asymmetry, providing a potential option for achieving stable RF transmission in telecommunication networks.

Computational biomechanical analysis plays a pivotal role in understanding and improving human movements and physical functions. Although physics-based modeling methods can interpret the dynamic interaction between the neural drive to muscle dynamics and joint kinematics, they suffer from high computational latency. In recent years, data-driven methods have emerged as a promising alternative due to their fast execution speed, but label information is still required during training, which is not easy to acquire in practice. To tackle these issues, this paper presents a novel physics-informed deep learning method to predict muscle forces without any label information during model training. In addition, the proposed method could also identify personalized muscle-tendon parameters. To achieve this, the Hill muscle model-based forward dynamics is embedded into the deep neural network as the additional loss to further regulate the behavior of the deep neural network. Experimental validations on the wrist joint from six healthy subjects are performed, and a fully connected neural network (FNN) is selected to implement the proposed method. The predicted results of muscle forces show comparable or even lower root mean square error (RMSE) and higher coefficient of determination compared with baseline methods, which have to use the labeled surface electromyography (sEMG) signals, and it can also identify muscle-tendon parameters accurately, demonstrating the effectiveness of the proposed physics-informed deep learning method.

Abstract Objectives To explore the characteristics of myocardial involvement in Wilson Disease (WD) patients by cardiac magnetic resonance (CMR). Methods We prospectively included WD patients and age- and sex-matched healthy population. We applied CMR to analyze cardiac function, strain, T1 maps, T2 maps, extracellular volume fraction (ECV) maps, and LGE images. Subgroup analyzes were performed for patients with WD with predominantly neurologic manifestations (WD‐neuro +) or only hepatic manifestations (WD‐neuro −). Results Forty-one WD patients (age 27.9 ± 8.0 years) and 40 healthy controls (age 25.4 ± 2.9 years) were included in this study. Compared to controls, the T1, T2, and ECV values were significantly increased in the WD group (T1 1085.1 ± 39.1 vs. 1046.5 ± 33.1 ms, T2 54.2 ± 3.3 ms vs. 51.5 ± 2.6 ms, ECV 31.8 ± 3.6% vs. 24.3 ± 3.7%) (all p

Abstract The synthesis of three-dimensional covalent organic frameworks with highly connected building blocks presents a significant challenge. In this study, we report two 3D COFs with the nia topology, named JUC-641 and JUC-642, by introducing planar hexagonal and triangular prism nodes. Notably, our adsorption studies and breakthrough experiments reveal that both COFs exhibit exceptional separation capabilities, surpassing previously reported 3D COFs and most porous organic polymers, with a separation factor of up to 2.02 for benzene and cyclohexane. Additionally, dispersion-corrected density functional theory analysis suggests that the good performance of these 3D COFs can be attributed to the incorporation of highly aromatic building blocks and the presence of extensive pore structures. Consequently, this research not only expands the diversity of COFs but also highlights the potential of functional COF materials as promising candidates for environmentally-friendly separation applications.

Abstract Background Non-small cell lung cancer (NSCLC) accounts for the vast majority of lung cancers. Early detection is crucial to reduce lung cancer-related mortality. Aberrant DNA methylation occurs early during carcinogenesis and can be detected in blood. It is essential to investigate the dysregulated blood methylation markers for early diagnosis of NSCLC. Methods NSCLC-associated methylation gene folate receptor gamma (FOLR3) was selected from an Illumina 850K array analysis of peripheral blood samples. Mass spectrometry was used for validation in two independent case–control studies (validation I: n = 2548; validation II: n = 3866). Patients with lung squamous carcinoma (LUSC) or lung adenocarcinoma (LUAD), normal controls (NCs) and benign pulmonary nodule (BPN) cases were included. FOLR3 methylations were compared among different populations. Their associations with NSCLC clinical features were investigated. Receiver operating characteristic analyses, Kruskal–Wallis test, Wilcoxon test, logistics regression analysis and nomogram analysis were performed. Results Two CpG sites (CpG_1 and CpG_2) of FOLR3 was significantly lower methylated in NSCLC patients than NCs in the discovery round. In the two validations, both LUSC and LUAD patients presented significant FOLR3 hypomethylations. LUSC patients were highlighted to have significantly lower methylation levels of CpG_1 and CpG_2 than BPN cases and LUAD patients. Both in the two validations, CpG_1 methylation and CpG_2 methylation could discriminate LUSC from NCs well, with areas under the curve (AUCs) of 0.818 and 0.832 in validation I, and 0.789 and 0.780 in validation II. They could also differentiate LUAD from NCs, but with lower efficiency. CpG_1 and CpG_2 methylations could also discriminate LUSC from BPNs well individually in the two validations. With the combined dataset of two validations, the independent associations of age, gender, and FOLR3 methylation with LUSC and LUAD risk were shown and the age-gender-CpG_1 signature could discriminate LUSC and LUAD from NCs and BPNs, with higher efficiency for LUSC. Conclusions Blood-based FOLR3 hypomethylation was shown in LUSC and LUAD. FOLR3 methylation heterogeneity between LUSC and LUAD highlighted its stronger associations with LUSC. FOLR3 methylation and the age-gender-CpG_1 signature might be novel diagnostic markers for the early detection of NSCLC, especially for LUSC.

Abstract Dysregulation of wild-type p53 turnover is a key cause of hepatocellular carcinoma (HCC), yet its mechanism remains poorly understood. Here, we report that WD repeat and SOCS box containing protein 2 (WSB2), an E3 ubiquitin ligase, is an independent adverse prognostic factor in HCC patients. WSB2 drives HCC tumorigenesis and lung metastasis in vitro and in vivo. Mechanistically, WSB2 is a new p53 destabilizer that promotes K48-linked p53 polyubiquitination at the Lys291 and Lys292 sites in HCC cells, leading to p53 proteasomal degradation. Degradation of p53 causes IGFBP3-dependent AKT/mTOR signaling activation. Furthermore, WSB2 was found to bind to the p53 tetramerization domain via its SOCS box domain. Targeting mTOR with everolimus, an oral drug, significantly blocked WSB2-triggered HCC tumorigenesis and metastasis in vivo. In clinical samples, high expression of WSB2 was associated with low wild-type p53 expression and high p-mTOR expression. These findings demonstrate that WSB2 is overexpressed and degrades wild-type p53 and then activates the IGFBP3-AKT/mTOR axis, leading to HCC tumorigenesis and lung metastasis, which indicates that targeting mTOR could be a new therapeutic strategy for HCC patients with high WSB2 expression and wild-type p53.

Abstract Background Currently, the value of oral selective estrogen receptor degraders (SERDs) for hormone receptor-positive (HR+) and human epidermal growth factor receptor 2-negative (HER2-) advanced breast cancer (aBC) after progression on ≥ 1 line of endocrine therapy (ET) remains controversial. We conducted a meta-analysis to evaluate progression-free survival (PFS) and safety benefits in several clinical trials. Materials and methods Cochrane Library, Embase, PubMed, and conference proceedings (SABCS, ASCO, ESMO, and ESMO Breast) were searched systematically and comprehensively. Random effects models or fixed effects models were used to assess pooled hazard ratios (HRs) and 95% confidence intervals (CIs) for treatment with oral SERDs versus standard of care. Results A total of four studies involving 1,290 patients were included in our analysis. The hazard ratio (HR) of PFS showed that the oral SERD regimen was better than standard of care in patients with HR+/HER2- aBC after progression on ≥ 1 line of ET (HR: 0.75, 95% CI: 0.62-0.91, p = 0.004). In patients with ESR1 mutations, the oral SERD regimen provided better PFS than standard of care (HR: 0.58, 95% CI: 0.47-0.71, p < 0.00001). Regarding patients with disease progression following previous use of CDK4/6 inhibitors, PFS benefit was observed in oral SERD-treatment arms compared to standard of care (HR: 0.75, 95% CI: 0.64-0.87, p = 0.0002). Conclusions The oral SERD regimen provides a significant PFS benefit compared to standard-of-care ET in patients with HR+/HER2- aBC after progression on ≥ 1 line of ET. In particular, we recommend oral SERDs as a preferred choice for those patients with ESR1m, and it could be a potential replacement for fulvestrant. The oral SERD regimen is also beneficial after progression on CDK4/6 inhibitors combined with endocrine therapy.

Abstract Background Nonalcoholic fatty liver disease (NAFLD) is a major cause of chronic liver diseases and has emerged as the leading factor in the pathogenesis of hepatocellular carcinoma (HCC). MyD88 contributes to the development of HCC. However, the underlying mechanism by which MyD88 in myofibroblasts regulates NAFLD-associated liver cancer development remains unknown. Results Myofibroblast MyD88-deficient (SMAMyD88−/−) mice were protected from diet-induced obesity and developed fewer and smaller liver tumors. MyD88 deficiency in myofibroblasts attenuated macrophage M2 polarization and fat accumulation in HCC tissues. Mechanistically, MyD88 signaling in myofibroblasts enhanced CCL9 secretion, thereby promoting macrophage M2 polarization. This process may depend on the CCR1 receptor and STAT6/ PPARβ pathway. Furthermore, liver tumor growth was attenuated in mice treated with a CCR1 inhibitor. CCLl5 (homologous protein CCL9 in humans) expression was increased in myofibroblasts of HCC and was associated with shorter survival of patients with HCC. Thus, our results indicate that MyD88 in myofibroblasts promotes NAFLD-related HCC progression and may be a promising therapeutic target for HCC treatment. Conclusion This study demonstrates that MyD88 in myofibroblasts can promote nonalcoholic fatty liver disease-related hepatocarcinogenesis by enhancing macrophage M2 polarization, which might provide a potential molecular therapeutic target for HCC. Graphical Abstract

Abstract Background Fine motor skills are closely related to cognitive function. However, there is currently no comprehensive assessment of fine motor movement and how it corresponds with cognitive function. To conduct a complete assessment of fine motor and clarify the relationship between various dimensions of fine motor and cognitive function. Methods We conducted a cross-sectional study with 267 community-based participants aged ≥ 60 years in Beijing, China. We assessed four tests performance and gathered detailed fine motor indicators using Micro-Electro-Mechanical System (MEMS) motion capture technology. The wearable MEMS device provided us with precise fine motion metrics, while Chinese version of the Montreal Cognitive Assessment (MoCA) was used to assess cognitive function. We adopted logistic regression to analyze the relationship between fine motor movement and cognitive function. Results 129 (48.3%) of the participants had cognitive impairment. The vast majority of fine motor movements have independent linear correlations with MoCA-BJ scores. According to logistic regression analysis, completion time in the Same-pattern tapping test (OR = 1.033, 95%CI = 1.003–1.063), Completion time of non-dominant hand in the Pieces flipping test (OR = 1.006, 95%CI = 1.000-1.011), and trajectory distance of dominant hand in the Pegboard test (OR = 1.044, 95%CI = 1.010–1.068), which represents dexterity, are related to cognitive impairment. Coordination, represented by lag time between hands in the Same-pattern tapping (OR = 1.663, 95%CI = 1.131–2.444), is correlated with cognitive impairment. Coverage in the Dual-hand drawing test as an important indicator of stability is negatively correlated with cognitive function (OR = 0.709, 95%CI = 0.6501–0.959). Based on the above 5-feature model showed consistently high accuracy and sensitivity at the MoCA-BJ score (ACU = 0.80–0.87). Conclusions The results of a comprehensive fine-motor assessment that integrates dexterity, coordination, and stability are closely related to cognitive functioning. Fine motor movement has the potential to be a reliable predictor of cognitive impairment.

Stabilizing the CMOS-compatible low-temperature Au-free GaN Ohmic contact is a critical work that determines the performance and yield of GaN power HEMTs in mass production. The instability of this contact has been puzzling the industry and academia for years. In this work, an overlooked factor, fluorine injection, is unambiguously verified to widely exist during dielectric etching and can easily destroy the low-temperature GaN Ohmic contact formation. The injection depth is confirmed to be over 30 nm with a fluorine peak concentration of 1023 at/cm3 in vicinity of the surface. Traditional method of partial AlGaN recessing with a pretty tiny processing window is proven unfriendly for production and vulnerable to the fluorine injection. Two methods to get rid of the fluorine are proposed. The first one is to over-etch the AlGaN barrier to the GaN channel to fully remove the fluorine ions. The second is to deposit an etch-stop blocking layer of Al2O3, which is also compatible with CMOS process.

This paper studies the distributed bipartite output formation control problem of heterogeneous discrete-time linear multi-agent systems (MASs) via cooperative output regulation theory. To construct the bipartite formation, under the structurally-balanced augmented directed signed graph, the followers of two antagonistic subgroups are supposed to respectively keep the desired relative positions with the leader, also called as the exosystem, in the same magnitude but the opposite sign. Since the information of exosystem can not be directly obtained by all followers, the distributed exosystem observer based on the discrete-time algebraic Riccati equality (ARE) is presented and the distributed state feedback controller is further designed. Moreover, in the case where the states of followers are not available, the distributed output feedback controller is proposed by introducing the state estimator. Finally, two numerical examples are given to demonstrate the effectiveness of analytic results.

As an important mechanism in multi-agent interaction, communication can make agents form complex team relationships rather than constitute a simple set of multiple independent agents. However, the existing communication schemes can bring much timing redundancy and irrelevant messages, which seriously affects their practical application. To solve this problem, this paper proposes a targeted multi-agent communication algorithm based on state control (SCTC). The SCTC uses a gating mechanism based on state control to reduce the timing redundancy of communication between agents and determines the interaction relationship between agents and the importance weight of a communication message through a series connection of hard- and self-attention mechanisms, realizing targeted communication message processing. In addition, by minimizing the difference between the fusion message generated from a real communication message of each agent and a fusion message generated from the buffered message, the correctness of the final action choice of the agent is ensured. Our evaluation using a challenging set of StarCraft II benchmarks indicates that the SCTC can significantly improve the learning performance and reduce the communication overhead between agents, thus ensuring better cooperation between agents.

Retinal disease detection and diagnosis relying solely on artificial retinal diseases will put great pressure on doctors and increase the rate of misdiagnosis. Therefore, the development of computer vision technology has brought the possibility for ophthalmologists to use computer-aided diagnosis. In recent years, most models for retinal disease recognition have been based on deep learning, which has the disadvantage of requiring large amounts of data and training time. It is also partly based on broad learning and its disadvantages are that feature extraction ability is limited and poor scalability. To overcome these limitations, we propose a novel artificial intelligence-based approach for the automatic assessment of retinal disease images called a multi-view deep-broad learning network (MDBL-Net), which absorbs the advantages of deep learning and broad learning. MDBL-Net comprises a Multi-view and Multi-scale Feature Extraction (MMFE) module and a Multi-scale Aggregation (MA) block. The MMFE module extracts features of different scales by learning feature representations from multiple views, while the MA block fully aggregates multi-scale deep-broad features from low-level to high-level representations. Experiments were conducted on two public datasets, UCSD and OCT2017. Experiments were conducted on two public datasets, UCSD and OCT2017, and results demonstrate that MDBL-Net achieves high accuracy even with limited training data (only 1%) and significantly reduces training time compared to traditional deep learning models. Specifically, MDBL-Net achieved an accuracy of 96.93% on the UCSD dataset and 99.90% on the OCT2017 dataset, outperforming state-of-the-art models in both cases. These findings suggest that the proposed MDBL-Net approach holds great promise for the task of retinal disease screening and recognition.

Multitemporal change detection (CD) plays a crucial role in the remote sensing application field. In recent years, supervised deep learning methods have shown excellent performance in detecting changes in very-high-resolution (VHR) images. However, these methods require a large number of labeled samples for training, making the process time-consuming and labor-intensive. Unsupervised approaches are more attractive in practical applications since they can produce a CD map without relying on any ground reference or prior knowledge. In this article, we propose a novel unsupervised CD approach, named transformer-based multivariate alteration detection (trans-MAD). It utilizes a pre-detection strategy that combines the compressed change vector analysis and the iteratively reweighted multivariate alteration detection (IR-MAD) to generate reliable pseudotraining samples. More accurate and robust CD results can be achieved by leveraging the IR-MAD to detect insignificant changes and by incorporating the transformer-based attention mechanism to model the difference or similarity between two distant pixels in an image. The proposed trans-MAD approach was validated on two VHR bitemporal satellite remote sensing datasets, and the obtained experimental results demonstrated its superiority comparing with the state-of-the-art unsupervised CD methods.

Abstract Objectives Histological transformation to small cell lung cancer (SCLC) has been identified as a mechanism of TKIs resistance in EGFR-mutant non-small cell lung cancer (NSCLC). We aim to explore the prevalence of transformation in EGFR-wildtype NSCLC and the mechanism of SCLC transformation, which are rarely understood. Methods We reviewed 1474 NSCLC patients to investigate the NSCLC-to-SCLC transformed cases and the basic clinical characteristics, driver gene status and disease course of them. To explore the potential functional genes in SCLC transformation, we obtained pre- and post-transformation specimens and subjected them to a multigene NGS panel involving 416 cancer-related genes. To validate the putative gene function, we established knocked-out models by CRISPR-Cas 9 in HCC827 and A549-TP53-/- cells and investigated the effects on tumor growth, drug sensitivity and neuroendocrine phenotype in vitro and in vivo. We also detected the expression level of protein and mRNA to explore the molecular mechanism involved. Results We firstly reported an incidence rate of 9.73% (11/113) of SCLC transformation in EGFR-wildtype NSCLC and demonstrated that SCLC transformation is irrespective of EGFR mutation status (P = 0.16). We sequenced 8 paired tumors and identified a series of mutant genes specially in transformed SCLC such as SMAD4, RICTOR and RET. We firstly demonstrated that SMAD4 deficiency can accelerate SCLC transition by inducing neuroendocrine phenotype regardless of RB1 status in TP53-deficient NSCLC cells. Further mechanical experiments identified the SMAD4 can regulate ASCL1 transcription competitively with Myc in NSCLC cells and Myc inhibitor acts as a potential subsequent treatment agent. Conclusions Transformation to SCLC is irrespective of EFGR status and can be accelerated by SMAD4 in non-small cell lung cancer. Myc inhibitor acts as a potential therapeutic drug for SMAD4-mediated resistant lung cancer. Video Abstract

Abstract Background Body mass index (BMI) is an important risk factor for hypertension in diabetic patients. However, the underlying mechanisms remain poorly understood. Although liver-derived biological intermediates may play irreplaceable roles in the pathophysiology of diabetes, few studies have explored them in the association between BMI and hypertension in diabetes. Objective To investigate the role of liver enzymes in mediating the relationship between BIM exposure and hypertension in type 2 diabetes mellitus (T2DM). Methods We included a total of 1765 participants from the China National Diabetic Chronic Complications Study Cohort. Associations between liver enzymes and hypertension were estimated using multivariable regression models. The function of liver indicators in the relationship between BMI and hypertension was assessed using mediation analysis. Mediation analysis was conducted, taking into account age, diabetes duration, current smoking, fasting plasma glucose level, glycated hemoglobin, anti-diabetic therapy, and family history of diseases, including diabetes, hypertension, obesity, and hyperlipidemia. Results For men, the association of BMI with hypertension was partially mediated by alanine aminotransferase (ALT), with a proportion of mediation was 68.67%, by aspartate aminotransferase (AST) was 27.02%, and by γ-glutamyltransferase (GGT) was 38.58%, by AST/ALT was 63.35%; for women, the proportion mediated by ALT was 36.93%, and by AST was 37.47%, and GGT was 44.60%, and AST/ALT was 43.73% for BMI (all P

Abstract Recent studies have reported the role of the M3 muscarinic acetylcholine receptor (M3R), a member of the G‐protein coupled receptor superfamily, encoded by the CHRM3 gene, in cardiac function and the regulation of blood pressure (BP). The aim of this study was to investigate the associations of CHRM3 genetic variants with salt sensitivity, longitudinal BP changes, and the development of hypertension in a Chinese population. We conducted a chronic dietary salt intervention experiment in a previously established Chinese cohort to analyze salt sensitivity of BP. Additionally, a 14‐year follow‐up was conducted on all participants in the cohort to evaluate the associations of CHRM3 polymorphisms with longitudinal BP changes, as well as the incidence of hypertension. The single nucleotide polymorphism (SNP) rs10802811 within the CHRM3 gene displayed significant associations with low salt‐induced changes in systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean arterial pressure (MAP), while rs373288072, rs114677844, and rs663148 exhibited significant associations with SBP and MAP responses to a high‐salt diet. Furthermore, the SNP rs58359377 was associated with changes in SBP and pulse pressure (PP) over the course of 14 years. Additionally, the 14‐year follow‐up revealed a significant association between the rs619288 polymorphism and an increased risk of hypertension (OR = 1.74, 95% CI: 1.06‐2.87, p = .029). This study provides evidence that CHRM3 may have a role in salt sensitivity, BP progression, and the development of hypertension.

The as-cast (Fe50Mn30Co10Cr10)97C2Mo1 HEA (high entropy alloy) was cold rolled to 90 % (CR90 %), followed by annealing at different temperatures (400 °C, 500 °C, 600 °C). Their microstructure and mechanical properties have been systematically investigated. The results show that this HEA has a single FCC structure, and that annealing at 500 °C and 600 °C leads to the formation of Cr-rich σ phase. The CR90 % HEA exhibits the tensile strength of 1926 MPa and 4 % elongation. However, annealing at 400 °C and 500 °C makes the tensile strength improve to ∼2078 MPa and ∼2060 MPa, respectively, while their plasticity does not change significantly. Annealing at 600 °C results in a significant reduction in the strength (∼1650 MPa) while a slight increase in plasticity. The element segregation and the formation of nano-sized (Fe, Mn)-rich precipitates are contributed to the significant increase in tensile strength of the HEA annealed at 400 °C. Nevertheless, annealing at 600 °C leads to a decrease in the density of defects (e.g. dislocations) caused by cold rolling and the increase in the grain size, which finally resulting in a reduction in strength.

Abstract Background Cinnamomum camphora (L.) Presl (C. camphora) is an evergreen broad-leaved tree cultivated in subtropical China. The use of C. camphora as clonal cuttings for coppice management has become popular recently. However, little is known about the relationship between soil core microbiota and ecosystem multi-functionality under tree planting. Particularly, the effects of soil core microbiota on maintaining ecosystem multi-functionality under C. camphora coppice planting remained unclear. Materials and methods In this study, we collected soil samples from three points (i.e., the abandoned land, the root zone, and the transition zone) in the C. camphora coppice planting to investigate whether core microbiota influences ecosystem multi-functions. Results The result showed a significant difference in soil core microbiota community between the abandoned land (AL), root zone (RZ), and transition zone (TZ), and soil ecosystem multi-functionality of core microbiota in RZ had increased significantly (by 230.8%) compared to the AL. Soil core microbiota played a more significant influence on ecosystem multi-functionality than the non-core microbiota. Moreover, the co-occurrence network demonstrated that the soil ecosystem network consisted of five major ecological clusters. Soil core microbiota within cluster 1 were significantly higher than in cluster 4, and there is also a higher Copiotrophs/Oligotrophs ratio in cluster 1. Our results corroborated that soil core microbiota is crucial for maintaining ecosystem multi-functionality. Especially, the core taxa within the clusters of networks under tree planting, with the same ecological preferences, had a significant contribution to ecosystem multi-functionality. Conclusion Overall, our results provide further insight into the linkage between core taxa and ecosystem multi-functionality. This enables us to predict how ecosystem functions respond to the environmental changes in areas under the C. camphora coppice planting. Thus, conserving the soil microbiota, especially the core taxa, is essential to maintaining the multiple ecosystem functions under the C. camphora coppice planting.

Abstract Background Clinical characteristics of patients with pulmonary thromboembolism have been described in previous studies. Although very old patients with pulmonary thromboembolism are a special group based on comorbidities and age, they do not receive special attention. Objective This study aims to explore the clinical characteristics and mortality predictors among very old patients with pulmonary thromboembolism in a relatively large population. Design and participants The study included a total of 7438 patients from a national, multicenter, registry study, the China pUlmonary thromboembolism REgistry Study (CURES). Consecutive patients with acute pulmonary thromboembolism were enrolled and were divided into three groups. Comparisons were performed between these three groups in terms of clinical characteristics, comorbidities and in-hospital prognosis. Mortality predictors were analyzed in very old patients with pulmonary embolism. Key results In 7,438 patients with acute pulmonary thromboembolism, 609 patients aged equal to or greater than 80 years (male 354 (58.1%)). There were 2743 patients aged between 65 and 79 years (male 1313 (48%)) and 4095 patients aged younger than 65 years (male 2272 (55.5%)). Patients with advanced age had significantly more comorbidities and worse condition, however, some predisposing factors were more obvious in younger patients with pulmonary thromboembolism. PaO2

In the context of global warming, the lake surface water temperature (LSWT) exhibits a general upward trend. As an indicator of climate change, the surface water temperature of plateau lakes is particularly sensitive to climate warming. In this study, the machine learning model and physical process model are combined to predict water temperature in lake systems: The Air2water model and the long short term memory (LSTM) model are combined to form a prediction model for the inversion and prediction of 11 typical plateau lakes in the Yunnan–Guizhou Plateau. The modeling results show that the Air2water model performs best, followed by the LSTM model. Overall, the Air2water model and the LSTM model effectively reproduce the monthly, seasonal, and interannual variations of LSWT dynamics in the 11 lakes. The research results show that the LSWT dataset from 2021 to 2025 is constructed based on the forecast result, and it is found that six lakes, including Caohai, Dianchi, Erhai, Qionghai, Xingyun Lake, and Yangzonghai, showed significant warming in the next five years (the maximum warming rate was less than +0.2 °C/year), while the remaining five lakes showed no significant changes, and the size classification of LSWT data in different spaces on the interannual scale was consistent with the classification of altitudes. By conducting extensive exploration and research on the inversion of LSWT using physical process models and machine learning models, this study offers novel solutions for LSWT prediction and inversion. The study also provides a solid theoretical and practical foundation for future LSWT research, thereby holding practical significance and research value.

Background: Many colorectal cancer (CRC) patients are initially diagnosed at an advanced stage. Some evidence indicating that TNM staging may not be entirely reliable in forecasting survival outcomes. The research aims to develop accurate prediction models that can determine the survival outcomes of patients with clinically locally advanced colorectal cancer (LACRC). The study aims to provide valuable insights for clinical professionals to make right clinical decisions. Methods: We retrospectively analyzed 636 LACRC patients admitted to the General Hospital of Western Theater Command between Jan 1, 2017, and Dec 31, 2019. Significant factors determining the prognosis of LACRC patients screened by lasso regression and multifactorial cox regression analysis. These factors were utilized to develop a nomogram model, which serves as a valuable predictive tool for estimating the overall survival (OS) of LACRC patients. Next, we validated the nomogram in the validation group. Results: The nomogram emphasized that the TNM stage holds the most prognostic value, followed by CA19–9 levels, smoking history, and MLR. Multiple assessments confirmed the superior discriminatory ability of the nomograms. Conclusions: We have developed and verified nomograms that can predict OS cohort 635 LACRC patients. The survival nomogram may assist clinicians in better strategizing the therapeutic options for patients with LACRC.

In recent years, deep learning has received tremendous attention in the field of hyperspectral unmixing (HU) due to its powerful learning capabilities. Particularly, the unsupervised unmixing method based on an autoencoder (AE) has become a research hotspot. Most of the current AE unmixing networks mainly focus on information about pixels and their neighborhoods in images. However, they make insufficient use of information about spatial heterogeneity and spectral differences of endmembers in hyperspectral image (HSI) data. To this end, an AE HU network with the name of SSF-Net is proposed for fusing the spatial–spectral features. The network first extracts pseudoendmember information from the HSI using a regional vertex component analysis algorithm. Then, a dual-branch feature fusion module incorporating a spatial–spectral attention mechanism is constructed to make full use of the information in the HSI data, thereby improving the network's unmixing performance. It is worth stating that SSF-Net can fuse spatial–spectral information and utilize different attention maps to obtain more significant spectral difference information and more discriminative spatial difference information about the scene. The experimental results on synthetic and real datasets demonstrate that the proposed SSF-Net outperforms state-of-the-art unmixing algorithms.

Multi-layer commingled production is the main feature of gas well development in Sulige tight sandstone gas reservoir, Ordos Basin. Identifying potential interference between layers and devising methods for their characterization are crucial considerations for optimizing the development of gas reservoirs. To address these issues, we designed a physical simulation experiment process and interlayer commingled mining schemes, implementing various interlayer combination modes. The results show that a common occurrence in the process of multi-layer commingled production of tight gas and water layers, whether it involves gas layers production alone or simultaneous production of gas and water layers. This phenomenon involves the crossflow of gas and water between layers, resulting in interlayer interference and a subsequent reduction in gas reservoir recovery. Based on these observations, the concept of an interlayer interference index in multi-layer commingled production in tight sandstone gas reservoirs is proposed. The interference index model is obtained by fitting the multiple linear regression method, showcasing its correlation with the physical properties of the reservoir. High water saturation and a significant permeability ratio of the water layer to the gas layer (exceeding the critical value of 1) can result in the early occurrence of interlayer interference and yield a higher interference index. Furthermore, based on the interference index model, a novel method for productivity evaluation of gas wells in tight gas reservoirs is established. The calculations demonstrate that the interference index curve effectively characterizes the interlayer interference performance of gas wells. The productivity and production performance predictions derived from this model align closely with historical production data, affirming the model's effectiveness and accuracy. Therefore, the interference index model emerges as a valuable tool for predicting the productivity and production performance of gas wells in Sulige tight sandstone gas reservoirs. The research results have important theoretical guidance and practical significance for the efficient development of Sulige tight sandstone gas reservoirs.

Abstract Although prognostic prediction of nasopharyngeal carcinoma (NPC) remains a pivotal research area, the role of dynamic contrast-enhanced magnetic resonance (DCE-MR) has been less explored. This study aimed to investigate the role of DCR-MR in predicting progression-free survival (PFS) in patients with NPC using magnetic resonance (MR)- and DCE-MR-based radiomic models. A total of 434 patients with two MR scanning sequences were included. The MR- and DCE-MR-based radiomics models were developed based on 289 patients with only MR scanning sequences and 145 patients with four additional pharmacokinetic parameters (volume fraction of extravascular extracellular space (v e ), volume fraction of plasma space (v p ), volume transfer constant (K trans ), and reverse reflux rate constant (k ep ) of DCE-MR. A combined model integrating MR and DCE-MR was constructed. Utilizing methods such as correlation analysis, least absolute shrinkage and selection operator regression, and multivariate Cox proportional hazards regression, we built the radiomics models. Finally, we calculated the net reclassification index and C-index to evaluate and compare the prognostic performance of the radiomics models. Kaplan-Meier survival curve analysis was performed to investigate the model’s ability to stratify risk in patients with NPC. The integration of MR and DCE-MR radiomic features significantly enhanced prognostic prediction performance compared to MR- and DCE-MR-based models, evidenced by a test set C-index of 0.808 vs 0.729 and 0.731, respectively. The combined radiomics model improved net reclassification by 22.9%–52.6% and could significantly stratify the risk levels of patients with NPC (p = 0.036). Furthermore, the MR-based radiomic feature maps achieved similar results to the DCE-MR pharmacokinetic parameters in terms of reflecting the underlying angiogenesis information in NPC. Compared to conventional MR-based radiomics models, the combined radiomics model integrating MR and DCE-MR showed promising results in delivering more accurate prognostic predictions and provided more clinical benefits in quantifying and monitoring phenotypic changes associated with NPC prognosis.

Abstract Objective Thromboangiitis obliterans (TAO) is one of the most common types of peripheral arterial disease (PAD). This study aimed to explore the characteristics of the top 100 most cited articles in the TAO. Methods A bibliometric analysis based on the Web of Science (WOS) database was performed. Literature was retrieved and ranked by the citations. Listed below are the top 100 citations, including original articles, reviews, full-length proceeding papers, and case reports that were included for analysis. The type of literature, research areas, and languages were recorded. The trends of citations including the total citations, an analysis of publication and citation numbers were conducted each year. We analyzed citations from highly cited countries, authors, institutions, and journals. Research hotspots were gathered by a visualized analysis of author keywords. Results Most of the highly cited literature was original articles. A rising trend was observed in the number of citations per year. The peaks in the number of highly cited articles appeared in the year 1998 and 2006. The majority of the articles focused on the cardiovascular system and surgery. Journal of Vascular Surgery published most of the highly cited articles. The USA and Japan contributed nearly half the number of highly cited articles. Mayo Clinic and Nagoya University were highly cited institutions. Shionoya S and Olin JW were both the author with the largest number of citations and the most highly cited author in the reference. Articles that were highly cited most often addressed the following topics: “vasculitis”, “autoimmune disease”, and “critical limb ischemia”. Keywords that were mostly used in recent years were “stem cell therapy”, “progenitor therapy”, and “immunoadsorption”. The detection of bursts of author keywords showed the following: “permeability”, “differentiation”, and “critical limb ischemia” are recent keywords that have burst. Conclusions In this study, the highly cited contributors in the field of TAO research were identified. Most cited articles in the top 100 focused on the cardiovascular system and surgery. Treatment and pathophysiology including stem cell therapy, progenitor therapy, genetics, autoimmunity, and inflammation are the hotspots of TAO.

Abstract The beginning of human settlement in the Hami Basin, located in the eastern part of Xinjiang, has been a focal question for the academic community in China. In particular, the thesis that the immigrating population from the Hexi Corridor since the late Neolithic founded the Tianshanbeilu culture has riveted the attention of scholars. Pottery wares, abundantly discovered at the synonymous cemetery of this culture, have played a key role in extrapolating population migration and cultural interaction. This paper aims to test the thesis by characterizing the chemical composition, painting pigment, and carburizing technique of 70 pottery samples from the cemetery with various scientific methods. It shows that the chemical compositions of the coarse pottery in the three colors of red, yellow, and gray, painted and unpainted alike, are remarkably different from those of fine pottery in black and red, indicating that the raw materials for the coarse and fine pottery samples are possibly procured from different sources; the pigments of the red slip and black paint are derived from hematite, black manganese ore, and carbon black; carburizing and polishing techniques are further applied to the gray coarse pottery; In combination with the compositional data of pottery samples from the Yaer cemetery also in the Hami Basin and the Xichengyi settlement in the Hexi Corridor, this paper finds that some pottery wares of the Tianshanbeilu culture were exchanged within the Hami Basin, but each site had its own production facility. No direct exchange of pottery wares with Xichengyi is attested; the similar style of pottery wares between the two sites may have resulted from population migration and technological exchange.

Understanding how human leukocyte antigen (HLA) polymorphism affects both the susceptibility and severity of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection will help to identify individuals at higher risk to better manage and prioritize vaccination at the clinical level and explain the differences in epidemic trends in different regions at the epidemiological level. This study compared the frequencies of HLA class I alleles (HLA-A, B) in 214 coronavirus disease 2019 (COVID-19) patients with different disease severity and 35 healthy controls and analyzed the correlations between specific HLA alleles and disease severity and T cell memory. The results showed no significant difference in HLA allele frequencies between COVID-19 patients and healthy controls (P > 0.05). The allele HLA-B*13:02 was significantly correlated with the disease severity of COVID-19 patients (P = 0.006). After adjustment for age and disease severity, the T cell responses of COVID-19 convalescents with the allele HLA-B*40:01 may be lower at six months (P = 0.044) and 12 months (P = 0.069). Moreover, these results may be due to their rare peptide anchors by analyzing the binding peptide motifs of these HLA alleles. The study may be valuable for investigating the potential association of specific HLA alleles with SARS-CoV-2 infection.

Abstract Background Clinical nurses are susceptible to compassion fatigue when exposed to various types of traumatic events in patients for extended periods of time. However, the developmental process, staging, and psychological responses distinct to each stage of compassion fatigue in nurses are not fully clarified. This study aimed to explore the processes of compassion fatigue and the psychological experiences specific to each phase of compassion fatigue among clinical nurses. Methods Charmaz’s Constructivist Grounded Theory methodology was used in this qualitative research. Semi-structured interviews were conducted with 13 clinical nurses with varying degrees of compassion fatigue from December 2020 to January 2021. Interview data were analyzed using grounded theory processes. Results The data were categorized into five separate categories and 22 sub-categories. This study found that the process of compassion fatigue is dynamic and cumulative, which was classified into five phases: compassion experience period, compassion decrement period, compassion discomfort period, compassion distress period, and compassion fatigue period. Conclusion Clinical nurses who experience compassion fatigue may go through five stages that are stage-specific and predictable. The findings can shed light on local and global applications to better understand the problem of nurses’ compassion fatigue. The interventions for addressing compassion fatigue in clinical nurses should be stage-specific, targeted, and individualized.

Abstract Background German chamomile (Matricaria chamomilla L.) is an important medicinal plant, and the essential oils in the flowers have various biological activities. Genetic transformation systems are important for plant quality improvement and molecular research. To the best of our knowledge, a genetic transformation system has not yet been reported for German chamomile. Results In this study, we developed Agrobacterium-mediated transformation protocols for German chamomile callus tissues. This involved optimizing key parameters, such as hygromycin and cefotaxime concentrations, bacterial density, and infection and co-culture durations. We also performed gas chromatography–mass spectrometry analysis to identify volatile compounds in non-transgenic and transgenic callus and hairy root tissues. Furthermore, to compare and verify the callus transformation system of German chamomile, we transferred McFPS to the hairy roots of German chamomile. The results showed that the optimal conditions for Agrobacterium-mediated callus tissue transformation were as follows: explant, petiole; cefotaxime concentration, 300 mg/L; hygromycin concentration, 10 mg/L; and bacterial solution concentration, OD600 = 0.6; callus transformation efficiency was the highest when the co-culture time was 3 days. Conclusions Establishment of a high-efficiency callus transformation system will lay the foundation for gene function identification in German chamomile.

Abstract Titanium dioxide (TiO2)-based photodynamic antibacterial (PDA) agents present a novel approach for addressing drug-resistant bacterial infections and the associated tissue damage. However, the suboptimal dispersibility, negative charge, and weak photocatalytic activity under visible light of TiO2 hinder its practical applications. This study aimed to address these limitations by developing a highly hydrophilic and dispersed Zn-TiO2/reduced graphene oxide (rGO) (HTGZ) nano-system with exceptional visible light catalytic activity and tissue repair ability. HTGZ produced an antibacterial ratio over 98% within a short time, likely due to the enhanced production of reactive oxygen species under visible light. After being co-cultured for 4 days, L929 cells and BMSCs maintained over 90% activity, indicating that HTGZ had no significant cytotoxicity. Furthermore, the transcriptomic and metabolic analyses revealed that the antibacterial mechanism mainly came from the destruction of cell membranes and the disruption of various metabolic processes, such as purine metabolism and fatty acid biosynthesis. Critically, results of in vivo experiments had authenticated that HTGZ significantly promoted infected tissue regeneration by slaughtering bacteria and release Zn2+. After 14 days, the wound area was only one-third that of the control group. Overall, the enhanced antibacterial efficacy and wound-healing potential position HTGZ as a promising nano-antibacterial medication for the clinical treatment of infectious bacterial diseases.