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To investigated the effects of spray application of zinc sulfate (ZnSO4) solution on the accumulation of crucial bioactive components and antioxidant activity, and explored the regulatory mechanism of zinc sulfate on glucosinolates in broccoli flower buds and seedlings at the molecular level, seven-day-old broccoli sprouts were treated with one of four concentrations (1, 2, 4, and 8 mmol/L) of aqueous ZnSO4 solutions or water (0 mmol/L ZnSO4 as the control). The effects of each ZnSO4 treatment on the bioactive components of broccoli sprouts were investigated by determining the contents of chlorophyll, ascorbic acid, total flavonoids, total phenols, and total glucosinolates, as well as myrosinase activity and DPPH free-radical scavenging rate. The expression levels of glucosinolate synthesis structural genes in the sprouts were analyzed using a real-time quantitative PCR detection system. The results showed that the chlorophyll content of all ZnSO4 treatments was lower than that of the control. The ascorbic acid content tended to be elevated with an increase in ZnSO4 concentration and was higher than that of the control for all ZnSO4 treatments. The total polyphenol content was the highest in response to 8 mmol/L ZnSO4 treatment and was significantly (P

Abstract Background Cancer development is intricately linked with metabolic dysregulation, including lactic acid metabolism, which plays a pivotal role in tumor progression and immune evasion. However, its specific implications in gastric adenocarcinoma (STAD) remain unclear. This study introduces a novel methodology to evaluate lactic acid metabolism comprehensively in STAD, aiming to elucidate its prognostic significance and impact on immunotherapy efficacy. Targeted therapies directed at key lactic acid metabolism genes (LMGs) identified within the tumor microenvironment (TME) hold promise for personalized treatment strategies. Methods Lactic acid metabolism patterns were assessed in 415 STAD patients using a panel of 21 LMGs. Cox regression and Lasso regression analyses were employed to develop a predictive risk model based on differentially expressed genes (DEGs). Validation of the model was conducted using independent cohorts from the GEO and TCGA databases, as well as additional datasets focused on immunotherapy responses. Further investigations into TME dynamics of lactic acid metabolism included functional assays targeting SLC16A3, a pivotal gene identified through our analyses. Results Patients were stratified into distinct risk groups based on their lactic acid metabolism profiles. Low-risk patients exhibited attenuated lactic acid metabolism, correlating with favorable clinical outcomes characterized by prolonged survival and enhanced responsiveness to immunotherapy. Notably, tumor cells within the TME demonstrated heightened levels of active lactic acid metabolism, particularly impacting tumor-infiltrating lymphocytes such as CD8 + T cells and regulatory T cells. Mechanistically, SLC16A3 emerged as a critical regulator promoting STAD cell proliferation, invasion, and migration while modulating the metabolic landscape. Conclusion This study underscores the prognostic value of a lactic acid metabolism-based model in STAD, providing insights into its potential as a predictive biomarker for patient stratification and therapeutic targeting. The findings highlight SLC16A3 as a promising candidate for therapeutic intervention aimed at modulating lactic acid metabolism in the TME, thereby advancing personalized treatment strategies in gastric cancer management.

Abstract Background This study aims to analyse the clinical characteristics of different types of odontogenic jawbone cysts (OJCs) and to provide a theoretical basis for prevention and clinical treatment. Methods Data from 1,038 patients with OJCs were collected, and relevant information, such as sex, age, clinical symptoms and signs, imaging data, number of lesions, lesion location, pathological diagnosis, clinical treatment and prognosis, was statistically analysed. Results According to the World Health Organization (WHO) classification of OJCs in 2022, the highest incidence rate was observed for radicular cysts (RCs), accounting for 58.96% of the total number of cases, followed by dentigerous cysts (DCs), accounting for approximately 23.22% of cases. lateral periodontal cysts (LPCs) and calcifying odontogenic cysts (COCs) comprised the lowest number of cases. The age distribution of patients was between 4 and 89 years, and the high incidence age group was youth and middle age, accounting for 66.67% of the total number of cases. The male-to-female ratio of patients was 1.51:1, and there was a statistically significant difference between the sexes(p

Abstract Background and Aim Remnant cholesterol (RC) is substantially related to negative outcomes in cardiac patients. Patients with coexisting hypertension and heart failure (HF) often develop left ventricular hypertrophy (LVH) and have poor prognoses. This study investigated baseline RC levels and LV remodelling and patients’ prognoses. Methods and results Six hundred thirty consecutive individuals with hypertension and HF participated in this prospective trial from October 2018 to August 2020. Based on left ventricular mass index (LVMI), 560 those eligible were separated into LVH and non-LVH groups. Multiple linear regression and receiver operating characteristic (ROC) curves examined the RC and LV relationship. A Cox regression analysis was conducted to examine the predictive value of RC for clinical outcomes. The LVH group presented significantly elevated values of RC, triglyceride, and cholesterol and decreased high-density lipoprotein cholesterol (HDLC). The optimal cutoff value for RC to predict LV remodelling was 0.49. The subjects were observed for a median of 58 months, and 104 participants met the primary endpoint. The risk models involving the two Cox models were adjusted to incorporate confounding factors, which revealed that those with elevated baseline levels of RC were more susceptible to cardiovascular mortality, as shown by an increased hazard ratio. (HR: 1.91, 95% CI: 1.62–2.26 vs. HR: 1.75, 95% CI: 1.43–2.16, P

Abstract Cophylogeny has been identified between gut bacteria and their animal host and is highly relevant to host health, but little research has extended to gut bacteriophages. Here we use bee model to investigate host specificity and cophylogeny in the “animal-gut bacteria-phage” tripartite system. Through metagenomic sequencing upon different bee species, the gut phageome revealed a more variable composition than the gut bacteriome. Nevertheless, the bacteriome and the phageome showed a significant association of their dissimilarity matrices, indicating a reciprocal interaction between the two kinds of communities. Most of the gut phages were host generalist at the viral cluster level but host specialist at the viral OTU level. While the dominant gut bacteria Gilliamella and Snodgrassella exhibited matched phylogeny with bee hosts, most of their phages showed a diminished level of cophylogeny. The evolutionary rates of the bee, the gut bacteria and the gut phages showed a remarkably increasing trend, including synonymous and non-synonymous substitution and gene content variation. For all of the three codiversified tripartite members, however, their genes under positive selection and genes involving gain/loss during evolution simultaneously enriched the functions into metabolism of nutrients, therefore highlighting the tripartite coevolution that results in an enhanced ecological fitness for the whole holobiont.

Abstract Background Helicobacter pylori (H. pylori) infection is critical in the development and occurrence of gastric cancer. H. pylori secretes gamma-glutamyl transferase (GGT), which affects energy metabolism and histone methylation in mesenchymal stem cells. However, its effect on human gastric epithelial cells remains unclear. This study aimed to investigate the effects of GGT on energy metabolism and histone methylation in gastric epithelial cells and determine its role in the development and progression of H. pylori-induced gastric cancer. Methods A GGT knockout H. pylori strain and mouse gastric cancer model were constructed, and alpha-ketoglutarate (α-KG) was added. The underlying mechanism was investigated using proteomics, immunohistochemistry, Western blotting, and other experimental assays. Results H. pylori can colonize the host’s stomach and destroy the gastric epithelium. GGT secreted by H. pylori decreased the concentration of glutamine in the stomach and increased H3K9me3 and H3K27me3 expression, which promoted the proliferation and migration of gastric epithelial cells. Additionally, α-KG reversed this effect. GGT increased the tumorigenic ability of nude mice. GGT, secreted by H. pylori, promoted the expression of ribosomal protein L15 (RPL15), while GGT knockout and supplementation with α-KG and trimethylation inhibitors reduced RPL15 expression and Wnt signaling pathway expression. Conclusions H. pylori secreted GGT decreased the expression of glutamine and α-KG in gastric epithelial cells, increased the expression of histones H3K9me3 and H3K27me3, and activated the Wnt signaling pathway through RPL15 expression, ultimately changing the biological characteristics of the gastric epithelium and promoting the occurrence of gastric cancer. Altered energy metabolism and histone hypermethylation are important factors involved in this process. Graphical abstract

Abstract Renal dysfunction (RD) often characterizes the worse course of patients with advanced heart failure (AHF). Many prognosis assessments are hindered by researcher biases, redundant predictors, and lack of clinical applicability. In this study, we enroll 1736 AHF/RD patients, including data from Henan Province Clinical Research Center for Cardiovascular Diseases (which encompasses 11 hospital subcenters), and Beth Israel Deaconess Medical Center. We developed an AI hybrid modeling framework, assembling 12 learners with different feature selection paradigms to expand modeling schemes. The optimized strategy is identified from 132 potential schemes to establish an explainable survival assessment system: AIHFLevel. The conditional inference survival tree determines a probability threshold for prognostic stratification. The evaluation confirmed the system’s robustness in discrimination, calibration, generalization, and clinical implications. AIHFLevel outperforms existing models, clinical features, and biomarkers. We also launch an open and user-friendly website www.hf-ai-survival.com , empowering healthcare professionals with enhanced tools for continuous risk monitoring and precise risk profiling.

The bacterial communities in rhizosphere soil interact with the roots of plants. This interaction is beneficial for both the bacteria and the plants, which makes it very important to identify the structure of these bacterial communities for plant growth and development. However, the composition characteristics of bacterial communities in rhizosphere soil of 2-year and 5-year Haloxylon ammodendron have not been clearly defined. The purpose of this study was to identify the diverse composition of 2-year and 5-year Haloxylon ammodendron in Turpan, Xinjiang. Thus, rhizosphere soil bacteria were analyzed by isolating, purifying, and identifying the species through high-throughput sequencing technology. The bacterial strains in the rhizosphere soil of Haloxylon ammodendron were isolated with the dilution coating method, resulting in 37 isolated strains. The selective media were used to screen the growth-promoting characteristics of the rhizosphere soil isolates of Haloxylon ammodendron. The results of high-throughput amplification sequencing showed that the rhizosphere bacteria in the 2-year rhizosphere soil belonged to 45 phyla, 109 classes, 288 orders, 451 families, 826 genera, and 404 species, and those in the 5-year rhizosphere soil belonged to 56 phyla, 148 classes, 369 orders, 601 families, 1062 genera, and 671 species. Among them, Firmicutes, Proteobacteria, Actinobacteriota, Bacteroidota, Crenarchaeota, and so on are the dominant bacteria. There were 12206 and 14,053 OTUs in the 2-year-old and 5-year-old rhizosphere soil bacteria, respectively, and 3329 OTUs in the 2-year- and 5-year-old rhizosphere soil, accounting for 16.98% of the total number of OTUs. The results showed that three strains, sg16, sg21, and ss4, had the highest inorganic phosphorus solubility index (1.58). The isolated strain did not have the ability to dissolve organophosphorus and potassium, while the screened strain sg16 had the ability to fix nitrogen. Two strains with a good iron-bearing capacity, Sg9F and Sg1, were screened, among which Sg9F had the highest D/d value and Sg9F had the strongest iron-bearing capacity. The results showed that 37 strains of rhizosphere soil bacteria belonged to six genera. They are Bacillus, Corynebacterium, Phyllobacterium, Lysinibacillus, Sinorhizobium meliloti, and Streptomyces levis. Among them are sg21 (Bacillus sp.), sg1 (Bacillus sp.), sg9F (Streptomyces levis), sg16 (Phyllobacterium phragmitis), and ss4 (Sinorhizobium meliloti). This study provides a particular research basis for the influence of Haloxylon ammodendron rhizosphere bacteria on soil nutrient release and depicts a solution for improving the yield and quality of cistanche deserticola indirectly through isolating, screening, and identifying rhizosphere soil bacteria, including screening strains with growth-promoting functions and analyzing the population structure of rhizosphere bacteria in 2- and 5-year soil in combination with high-throughput sequencing technology.

In the realm of precision cattle health monitoring, this paper introduces the development and evaluation of a novel wearable continuous health monitoring device designed for cattle. The device integrates a sustainable solar-powered module, real-time signal acquisition and processing, and a storage module within an animal ergonomically designed curved casing for non-invasive cattle health monitoring. The curvature of the casing is tailored to better fit the contours of the cattle’s neck, significantly enhancing signal accuracy, particularly in temperature signal acquisition. The core module is equipped with precision temperature sensors and inertial measurement units, utilizing the Arduino MKR ZERO board for data acquisition and processing. Field tests conducted on a cohort of ten cattle not only validated the accuracy of temperature sensing but also demonstrated the potential of machine learning, particularly the Support Vector Machine algorithm, for precise behavior classification and step counting, with an average accuracy of 97.27%. This study innovatively combines real-time temperature recognition, behavior classification, and step counting organically within a self-powered device. The results underscore the feasibility of this technology in enhancing cattle welfare and farm management efficiency, providing clear direction for future research to further enhance these devices for large-scale applications.

The receptive field (RF) plays a crucial role in convolutional neural networks (CNNs) because it determines the amount of input information that each neuron in a CNN can perceive, which directly affects the feature extraction ability. As the number of convolutional layers in CNNs increases, there is a decay of the RF according to the two-dimensional Gaussian distribution. Thus, an effective receptive field (ERF) can be used to characterize the available part of the RF. The ERF is calculated by the kernel size and layer number within the neural network architecture. Currently, ERF calculation methods are typically applied to single-channel input data that are both independent and identically distributed. However, such methods may result in a loss of effective information if they are applied to more general (i.e., multi-channel) datasets. Therefore, we proposed a multi-channel ERF calculation method. By conducting a series of numerical experiments, we determined the relationship between the ERF and the convolutional kernel size in conjunction with the layer number. To validate the new method, we used the recently published global wave surrogate model for climate simulation (GWSM4C) and its accompanying dataset. According to the newly established relationship, we refined the kernel size and layer number in each neural network of the GWSM4C to produce the same ERF but lower RF attenuation rates than those of the original version. By visualizing the gradient map at several points in West African and East Pacific areas, the high gradient value regions confirmed the known swell sources, which indicated effective feature extraction in these areas. Furthermore, the new version of the GWSM4C yielded better prediction accuracy for significant wave height in global swell pools. The root mean square errors in the West African and East Pacific regions reduced from approximately 0.3 m, in the original model to about 0.15 m, in the new model. Moreover, these improvements were attributed to the higher efficiency of the newly modified neural network structure that allows the inclusion of more historical winds while maintaining acceptable computational consumption.

Tumor metastasis is responsible for 90 % of cancer-associated deaths, and its early detection may decrease the likelihood of mortality. Studies have demonstrated that metastasis results from the interaction between “seeds” (tumor cells) and “soil” (pre-metastatic niche, PMN). As the first and most abundant immune cells to be recruited to PMN, neutrophils play a key role in the ultimate formation of metastatic foci through mechanisms such as supporting tumor cell growth, promoting angiogenesis, and shaping an immune-suppressive microenvironment. In this study, two distinct types of sialic acid (SA)-modified liposomes were prepared to target and regulate pro-metastatic neutrophils through the l-selectin receptor. One of these liposomes, named ICG@SAL, was used to encapsulate indocyanine green (ICG) and was specifically designed for the early detection of cancer metastasis. The other liposome, referred to as ABE/Cur@SAL, co-loaded abemaciclib (ABE) and curcumin (Cur), with the intention of suppressing the progression of metastatic tumor. Fluorescence imaging results from the mouse spontaneous metastasis model indicated that ICG@SAL demonstrated faster targeting and stronger accumulation in the metastatic organs than unmodified ICG liposomes (ICG@CL). This suggested that ICG@SAL could detect tumor metastasis at an early stage. The therapy with co-loaded liposomes in the mouse experimental lung metastasis model indicated that ABE/Cur@SAL could inhibit regulatory T (Treg) cell proliferation, enhance effector T cell activity and reduce tumorigenic factor release, implying that ABE/Cur@SAL could inhibit tumor metastasis. Overall, our work provided a sensitive and convenient approach to early diagnosis and treatment of tumor metastasis. ICG@SAL could be employed for the early detection of tumor metastasis, while ABE/Cur@SAL could be used to inhibit the development of tumor metastasis when early metastasis was identified.

BackgroundImage-guided surgical navigation systems are widely regarded as the benchmark for computer-assisted surgical robotic platforms, yet a persistent challenge remains in addressing intraoperative image drift and mismatch. It can significantly impact the accuracy and precision of surgical procedures. Therefore, further research and development are necessary to mitigate this issue and enhance the overall performance of these advanced surgical platforms.ObjectiveThe primary objective is to improve the precision of image guided puncture navigation systems by developing a computed tomography (CT) and structured light imaging (SLI) based navigation system. Furthermore, we also aim to quantifying and visualize intraoperative image drift and mismatch in real time and provide feedback to surgeons, ensuring that surgical procedures are executed with accuracy and reliability.MethodsA CT-SLI guided orthopedic navigation puncture system was developed. Polymer bandages are employed to pressurize, plasticize, immobilize and toughen the surface of a specimen for surgical operations. Preoperative CT images of the specimen are acquired, a 3D navigation map is reconstructed and a puncture path planned accordingly. During surgery, an SLI module captures and reconstructs the 3D surfaces of both the specimen and a guiding tube for the puncture needle. The SLI reconstructed 3D surface of the specimen is matched to the CT navigation map via two-step point cloud registrations, while the SLI reconstructed 3D surface of the guiding tube is fitted by a cylindrical model, which is in turn aligned with the planned puncture path. The proposed system has been tested and evaluated using 20 formalin-soaked lower limb cadaver specimens preserved at a local hospital.ResultsThe proposed method achieved image registration RMS errors of 0.576 ± 0.146 mm and 0.407 ± 0.234 mm between preoperative CT and intraoperative SLI surface models and between preoperative and postoperative CT surface models. In addition, preoperative and postoperative specimen surface and skeletal drifts were 0.033 ± 0.272 mm and 0.235 ± 0.197 mm respectively.ConclusionThe results indicate that the proposed method is effective in reducing intraoperative image drift and mismatch. The system also visualizes intraoperative image drift and mismatch, and provides real time visual feedback to surgeons.

The primary method for harvesting Lycium barbarum L. (L. barbarum) is manual labor, making it one of the most labor-intensive fruit crops in the Northwest region of China. Due to the decrease of labor supply and the increase of labor cost, the cost of harvesting has become a major hindrance to the development of the L. barbarum industry. Therefore, it is important to achieving mechanized harvesting of L. barbarum. In this study, a vibration harvesting machine was designed. Plackett-Burman experiment was conducted to assess the correlation between the picking rate of ripe fruit and various parameters. It was found that the significant factors were vibration amplitude, vibration frequency, and spacing of the vibrating rods. Based on the response surface methodology (RSM), parameter experiment was conducted to analyze the impact of these factors on picking rate of ripe fruit, picking rate of unripe fruit, and damage rate of ripe fruit. The optimal harvesting parameters were determined to be: vibration amplitude of 44 mm, spacing of the vibrating rods of 24 mm, and vibration frequency of 9Hz. The verification experiment showed that the picking rate of ripe fruit was 86.44%, the picking rate of unripe fruit was 6.81%, and the damage rate of ripe fruit was 5.54%. This study provides a design basis for realizing mechanized harvesting.

BackgroundPulmonary fibrosis (PF) emerges as a significant pulmonary sequelae in the convalescent phase of coronavirus disease 2019 (COVID-19), with current strategies neither specifically preventive nor therapeutic. Licoricesaponin G2 (LG2) displays a spectrum of natural activities, including antibacterial, anti-inflammatory, and antioxidant properties, and has been effectively used in treating various respiratory conditions. However, the potential protective effects of LG2 against PF remain underexplored.MethodsNetwork analysis and molecular docking were conducted in combination to identify the core targets and pathways through which LG2 acts against PF. In the model of bleomycin (BLM)-induced C57 mice and transforming growth factor-β1 (TGF-β1)-induced A549 and MRC5 cells, techniques such as western blot (WB), quantitative Real-Time PCR (qPCR), Immunohistochemistry (IHC), Immunofluorescence (IF), and Transwell migration assays were utilized to analyze the expression of Epithelial-mesenchymal transition (EMT) and inflammation proteins. Based on the analysis above, we identified targets and potential mechanisms underlying LG2’s effects against PF.ResultsNetwork analysis has suggested that the mechanism by which LG2 combats PF may involve the TNF-α pathway. Molecular docking studies have demonstrated a high binding affinity of LG2 to TNF-α and MMP9. Observations from the study indicated that LG2 may mitigate PF by modulating EMT and extracellular matrix (ECM) remodeling. It is proposed that the therapeutic effect is likely arises from the inhibition of inflammatory expression through regulation of the TNF-α pathway.ConclusionLG2 mitigates PF by suppressing TNF-α signaling pathway activation, modulating EMT, and remodeling the ECM. These results provide compelling evidence supporting the use of LG2 as a potential natural therapeutic agent for PF in clinical trials.

Abstract Respiratory diseases, marked by structural changes in the airways and lung tissues, can lead to reduced respiratory function and, in severe cases, respiratory failure. The side effects of current treatments, such as hormone therapy, drugs, and radiotherapy, highlight the need for new therapeutic strategies. Traditional Chinese Medicine (TCM) offers a promising alternative, leveraging its ability to target multiple pathways and mechanisms. Active compounds from Chinese herbs and other natural sources exhibit anti-inflammatory, antioxidant, antitumor, and immunomodulatory effects, making them valuable in preventing and treating respiratory conditions. Ferroptosis, a unique form of programmed cell death (PCD) distinct from apoptosis, necrosis, and others, has emerged as a key area of interest. However, comprehensive reviews on how natural products influence ferroptosis in respiratory diseases are lacking. This review will explore the therapeutic potential and mechanisms of natural products from TCM in modulating ferroptosis for respiratory diseases like acute lung injury (ALI), asthma, pulmonary fibrosis (PF), chronic obstructive pulmonary disease (COPD), lung ischemia–reperfusion injury (LIRI), pulmonary hypertension (PH), and lung cancer, aiming to provide new insights for research and clinical application in TCM for respiratory health.

Abstract Seawater electrolysis offers a renewable, scalable, and economic means for green hydrogen production. However, anode corrosion by Cl- pose great challenges for its commercialization. Herein, different from conventional catalysts designed to repel Cl- adsorption, we develop an atomic Ir catalyst on cobalt iron layered double hydroxide (Ir/CoFe-LDH) to tailor Cl- adsorption and modulate the electronic structure of the Ir active center, thereby establishing a unique Ir-OH/Cl coordination for alkaline seawater electrolysis. Operando characterizations and theoretical calculations unveil the pivotal role of this coordination state to lower OER activation energy by a factor of 1.93. The Ir/CoFe-LDH exhibits a remarkable oxygen evolution reaction activity (202 mV overpotential and TOF = 7.46 O2 s−1) in 6 M NaOH+2.8 M NaCl, superior over Cl--free 6 M NaOH electrolyte (236 mV overpotential and TOF = 1.05 O2 s−1), with 100% catalytic selectivity and stability at high current densities (400-800 mA cm−2) for more than 1,000 h.

Apple detection plays a critical role in enabling the functionality of harvesting robots within natural orchard environments. To address challenges related to low detection accuracy, slow inference speed, and high parameter count, we present PcMNet, a lightweight detection model based on an improved YOLOv8 network. Initially, we employed Partial Convolution (Pconv) to construct a PR module, forming the Pconv-block, which subsequently replaced the original C2f feature extraction module within the YOLOv8n backbone. This replacement led to improvements in both detection accuracy and speed, while simultaneously reducing computational complexity (FLOPs), parameter count, and model size. Furthermore, the Cross-Scale Feature Fusion (CCFF) module was refined into Faster-Cross-Scale Feature Fusion (Faster-CCFF) with the integration of Pconv-block, significantly enhancing the model's feature extraction and fusion capabilities. Additionally, we introduced Mixed Local Channel Attention (MLCA) to further strengthen the model's capacity to capture essential features while effectively suppressing background noise. Experimental results demonstrate that PcMNet achieved a detection accuracy of 92.8 % and an mAP@0.5 of 95.5 %, representing improvements of 1.4 and 0.7 percentage points, respectively, over YOLOv8n. Moreover, PcMNet successfully reduced FLOPs, parameter count, and model size to 5.1 G, 1.4 M, and 3.2 MB, respectively. The per-image detection time was reduced to 2.3 ms, indicating reductions of 37.80 %, 53.33 %, 49.21 %, and 56.60 % in FLOPs, parameters, model size, and detection time compared to YOLOv8n. When deployed on edge computing devices with TensorRT acceleration, PcMNet achieved a detection rate of 92 FPS. Field validation experiments conducted in natural orchard environments confirmed PcMNet's superior ability to detect apples under challenging conditions, such as occlusions and varying lighting conditions. Its lightweight design and rapid detection capabilities provide a valuable reference for achieving real-time apple detection in automated and intelligent harvesting robots, thereby contributing to advancements in smart agriculture.

Predicting nearshore significant wave heights (SWHs) with high accuracy is of great importance for coastal engineering activities, marine and coastal resource studies, and related operations. In recent years, the prediction of SWHs in two-dimensional fields based on deep learning has been gradually emerging. However, predictions for nearshore areas still suffer from insufficient resolution and poor accuracy. This paper develops a NS (NearShore) model based on the GWSM4C model (Global Wave Surrogate Model for Climate simulations). In the training area, the GWSM4C -NS model achieved a correlation coefficient (CC) of 0.977, with a spatial Root Mean Square Error (RMSE), annual mean spatial relative error (MAPE), and annual mean spatial absolute error (MAE) of 0.128 m, 10.7%, and 0.103 m, respectively. Compared to the GWSM4C model’s predictions, the RMSE and MAE decreased by 59% and 60% respectively, demonstrating the model’s effectiveness in enhancing nearshore SWH predictions. Additionally, applying this model to untrained sea areas to further validate its learning capability in wave energy propagation resulted in a CC of 0.951, with RMSE, MAPE, and MAE of 0.161m, 12.9%, and 0.137m, respectively. The RMSE and MAE were 43% and 39% lower than the GWSM4C model’s interpolated predictions. The results shown above suggest that the newly proposed model can effectively improve the performance of GWSM4C in nearshore areas.

IntroductionTo enhance the precision of evaluating the impact of urban environments on resident health, this study introduces a novel fuzzy intelligent computing model designed to address health risk concerns using multi-media environmental monitoring data.MethodsThree cities were selected for the study: Beijing (B City), Kunming (K City), and Wuxi (W City), representing high, low, and moderate pollution levels, respectively. The study employs a Fuzzy Inference System (FIS) as the chosen fuzzy intelligent computing model, synthesizing multi-media environmental monitoring data for the purpose of urban health risk assessment.Results(1) The model reliably estimates health risks across diverse cities and environmental conditions. (2) There is a positive correlation between PM2.5 concentrations and health risks, though the impact of noise levels varies by city. In cities B, K, and W, the respective correlation coefficients are 0.65, 0.55, and 0.7. (3) The Root Mean Square Error (RMSE) values for cities B, K, and W, are 0.0132, 0.0125, and 0.0118, respectively, indicating that the model has high accuracy. The R2 values for the three cities are 0.8963, 0.9127, and 0.9254, respectively, demonstrating the model’s high explanatory power. The residual values for the three cities are 0.0087, 0.0075, and 0.0069, respectively, indicating small residuals and demonstrating robustness and adaptability. (4) The model’s p-values for the Indoor Air Quality Index (IAQI), Thermal Comfort Index (TCI), and Noise Pollution Index (NPI) all satisfy p < 0.05 for the three cities, affirming the model’s credibility in estimating health risks under varied urban environments.DiscussionThese results showcase the model’s ability to adapt to diverse geographical conditions and aid in the accurate assessment of existing risks in urban settings. This study significantly advances environmental health risk assessment by integrating multidimensional data, enhancing the formulation of comprehensive environmental protection and health management strategies, and providing scientific support for sustainable urban planning.

Abstract The aim of this study is to provide effective solutions to promote the transition of resource-based cities to low carbon and sustainable development. Firstly, this study investigates the background of low-carbon transformation of resource-based cities. Secondly, it introduces the application method of Chat Generative Pre-trained Transformer (ChatGPT) in detail. Finally, this study proposes a comprehensive application of ChatGPT and artificial bee colony (ABC) algorithm. The results show that the average energy utilization efficiency improvement index of the group using ChatGPT is 0.11. The average energy efficiency improvement index of the group using ABC algorithm is 0.02 higher than that of the control group. The integrated application of ChatGPT and ABC algorithm can further improve the low-carbon transformation effect of resource-based cities and achieve the goal of green development.

Abstract Extracellular vesicles (EVs) have been identified as promising biomarkers for the noninvasive diagnosis of various diseases. However, challenges in separating EVs from soluble proteins have resulted in variable EV recovery rates and low purities. Here, we report a high-yield ( > 90%) and rapid (

Homomorphic encryption (HE) is a promising method in privacy-preserving cloud computing. Applying HE on feedforwad neural networks has been frequently reported recently but the research on recurrent neural networks is still insufficient. In previous studies, HE-based GRU is built with bootstrapping due to the changeable input length and large number of required multiplications, which is not time-efficient. In this study, we give a guideline of building bootstrapping-free HE-based GRU for text classification tasks. We discuss the methods of pre-processing of texts to decrease the input sequence length but keep the accuracy in a comparable level as the original GRU. The architecture of GRU is designed with flexibility to process the input sequence with different lengths while fixing the number of recurrent steps. At last, the HE parameter selection is discussed. We analyze the noise raised from HE operations and select the parameters that ensure the results from encrypted data are the same as that on plaintexts. The proposed model is evaluated on 6 popular text datasets, and the results show that the accuracy is only lower than the original GRU by at most 4.2%. Despite the complicated calculations in GRU, the proposed model is light-weighted and the fastest inference among our implementation costs only 10 minutes. We show the potential of applying HE schemes on complex models without bootstrapping to achieve fast encrypted computations.

Abstract Transient receptor potential melastatin 8 (TRPM8) is a cold sensory receptor in primary sensory neurons that regulates various neuronal functions. Substance P (SP) is a pro-inflammatory neuropeptide secreted by the neurons, and it aggravates colitis. However, the regulatory role of TRPM8 in SP release is still unclear. Our study aimed to investigate TRPM8’s role in SP release from primary sensory neurons during colitis and clarify the effect of SP on colonic epithelium. We analyzed inflammatory bowel disease patients’ data from the Gene Expression Omnibus dataset. Dextran sulfate sodium (DSS, 2.5%)-induced colitis in mice, mouse dorsal root ganglion (DRG) neurons, ND7/23 cell line, and mouse or human colonic organoids were used for this experiment. Our study found that TRPM8, TAC1 and WNT3A expression were significantly correlated with the severity of ulcerative colitis in patients and DSS-induced colitis in mice. The TRPM8 agonist (menthol) and the SP receptor antagonist (Aprepitant) can attenuate colitis in mice, but the effects were not additive. Menthol promoted calcium ion influx in mouse DRG neurons and inhibited the combination and phosphorylation of PKAca from the cAMP signaling pathway and GSK-3β from the Wnt/β-catenin signaling pathway, thereby inhibiting the effect of Wnt3a-driven β-catenin on promoting SP release in ND7/23 cells. Long-term stimulation with SP inhibited proliferation and enhanced apoptosis in both mouse and human colonic organoids. Conclusively, TRPM8 inhibits SP release from primary sensory neurons by inhibiting the interaction between PKAca and GSK-3β, thereby inhibiting the role of SP in promoting colonic epithelial apoptosis and relieving colitis.

In this study, a novel coarse-to-fine image registration algorithm based on feature matching using the Gabor filters is provided for repeat-pass SAR interferometry (InSAR). In the coarse registration, we propose a feature-based algorithm developed using the Gabor odd filter ratio-based operator (GOFRO), and the innovative feature based on the GOFRO (GOFRO-F) consists of the improved nonlinear scale space and the descriptor. The establishment of nonlinear scale spaces informed by the GOFRO enhances the stability of feature detection. Also, an improved 64-dimensional descriptor based on the GOFRO is presented to optimize the capability of feature matching. In the fine registration, we employ the normalized correlation coefficient algorithm (NCCA) and the random consensus sample (RANSAC) for determining offsets and the sub-pixel projection model calculation. The datasets of interferograms are derived from the 0.3 THz stepped-frequency radar. Compared to other algorithms, registration experiments of both simple and complex scatters show that GOFRO-F is more suitable for high precision image registration because of its enhanced robustness to the speckle noise. Furthermore, the three-dimensional (3D) reconstruction is conducted based on our proposed algorithm. The results demonstrate that our proposed algorithm facilitates the attainment of millimeter-level spatial resolutions for InSAR in THz band (THz InSAR).

Abstract Background Triangular fibrocartilage complex (TFCC) injury is a frequent soft tissue injury that has been observed to accompany distal radius fractures (DRFs) with concomitant changes in radiologic parameters. The aim of this study was to investigate the relevance of distal radial radiologic parameters associated with DRF and traumatic TFCC injury. Methods A total of 172 patients with distal radius fractures who underwent X-ray, CT, and MRI before undergoing volar locking plate or external splint fixation between October 2021 and December 2022 were included in this study. An analysis of various radiologic parameters and the classification of fracture type and TFCC injuries by CT and MRI was performed. All patients were divided into the TFCC uninjured group and the injured group. The incidence and relevant radiologic parameters were compared. Results This study included 76 males and 96 females with a mean age of 56.1 years. Among all patients, 33 (19.2%), 40 (23.2%), and 99 (57.6%) had DRF with A, B, and C fractures, respectively, according to the AO/OTA classification. In patients with fractures, the TFCC was found to be injured in 54.1% (93/172) of patients (type 1A in 21, 1B in 46, 1C in 39, and 1D in 35) but uninjured in 45.9% (79/172). There were significant differences between the TFCC injured and uninjured groups regarding the radius length (p = 0.044) and DRUJ distance (p = 0.040) of radiologic parameters that changed with DRF, although there were no differences between the two groups regarding gender, age, injured side, intra- and extra-articular, radius inclination and palmer tilt angle, or sagittal translation. Within the TFCC injured group, the radius length and DRUJ distance were 4.83 mm and 2.95 mm less or wider than 7.19 mm and 1.83 mm of the uninjured group. Moreover, shorter radius length was related to type lB TFCC injury (p = 0.041). Both radius length (AUC = 0.658) and DRUJ distance (AUC = 0.582) had no convincing predictive value for TFCC injury in DRF. Conclusion 1B TFCC injury is most common in patients with DRF and concomitant TFCC injury. Both radius length and DRUJ distance have a significant statistical correlation with TFCC injury, and patients with TFCC injury tend to have a shortened radius and wider DRUJ distance, although they have no predictive value for TFCC injury in DRF. In addition, a shorter radius length was related to type lB TFCC injury.

Aiming at the problems of poor passability of a tracked chassis due to small plots, complicated road conditions and steep slopes during mechanized potato harvesting in hilly and mountainous areas. To design a potato combine harvester and take the tracked chassis of the harvester as the research object to study its driving performance under typical road conditions in mountainous areas. Firstly, mechanical analysis and theoretical calculation are carried out on the tracked chassis to obtain the relevant parameters of key components, and secondly, its driving performance is analyzed to obtain the driving limit values of passing performance under different working conditions; RecurDyn software was used to establish the dynamics simulation and analysis model of the whole machine, and the driving limit values of the harvester were determined under five different road conditions through simulation. The results show the following: the driving limit gradient angle is 20° in cross-gradient conditions, 25° in longitudinal up-gradient conditions, 25° in longitudinal down-gradient conditions, the limiting height of the chassis that can be overpassed in obstacle-crossing conditions is 450 mm, and the limiting width of the chassis that can be spanned in trench-crossing conditions is 1200 mm. The simulation results were verified through field tests, and the results of the field tests showed that the harvester met the requirements of stable travelling on longitudinal slopes of 24°, climbed over a 450 mm straight wall and crossed over a 1200 mm trench, which were similar to the simulation results, indicating that the simulation results were accurate and feasible, and met the design requirements for the travelling passage of the crawler potato harvester. This study provides an in-depth understanding of the tracked chassis of the potato combine harvester in hilly mountainous areas, with a view to providing reference for the design of tracked harvesters for other crops in hilly mountainous areas.

This paper explores methodologies to enhance the integration of a green supply chain circular economy within smart cities by incorporating machine learning technology. To refine the precision and effectiveness of the prediction model, the gravitational algorithm is introduced to optimize parameter selection in the support vector machine model. A nationwide prediction model for green supply chain economic development efficiency is meticulously constructed by leveraging public economic, environmental, and demographic data. A comprehensive empirical analysis follows, revealing a noteworthy reduction in mean squared error and root mean squared error with increasing iterations, reaching a minimum of 0.007 and 0.103, respectively—figures that are the lowest among all considered machine learning models. Moreover, the mean absolute percentage error value is remarkably low at 0.0923. The data illustrate a gradual decline in average prediction error and standard deviation throughout the model optimization process, indicative of both model convergence and heightened prediction accuracy. These results underscore the significant potential of machine learning technology in optimizing supply chain and circular economy management. The paper provides valuable insights for decision-makers and researchers navigating the landscape of sustainable development.

Osteoarthritis (OA) is a chronic joint disease, characterized by degenerative destruction of articular cartilage. Chondrocytes, the unique cell type in cartilage, mediate the metabolism of extracellular matrix (ECM), which is mainly constituted by aggrecan and type II collagen. A disintegrin and metalloproteinase with thrombospondin 5 (ADAMTS5) is an aggrecanase responsible for the degradation of aggrecan in OA cartilage. CCAAT/enhancer binding protein β (C/EBPβ), a transcription factor in the C/EBP family, has been reported to mediate the expression of ADAMTS5. Our previous study showed that 5,7,3’,4’-tetramethoxyflavone (TMF) could activate the Sirt1/FOXO3a signaling in OA chondrocytes. However, whether TMF protected against ECM degradation by down-regulating C/EBPβ expression was unknown. In this study, we found that aggrecan expression was down-regulated, and ADAMTS5 expression was up-regulated. Knockdown of C/EBPβ could up-regulate aggrecan expression and down-regulate ADAMTS5 expression in IL-1β-treated C28/I2 cells. TMF could compromise the effects of C/EBPβ on OA chondrocytes by activating the Sirt1/FOXO3a signaling. Conclusively, TMF exhibited protective activity against ECM degradation by mediating the Sirt1/FOXO3a/C/EBPβ pathway in OA chondrocytes.

Abstract Background Evidence from observational studies and clinical trials suggests an association between periodontal disease and Alzheimer's disease (AD). However, the causal relationship between periodontal disease and AD remains to be determined. Methods We obtained periodontal disease data from the FinnGen database and two sets of AD data from the IEU consortium and PGC databases. Subsequently, we conducted a two‐sample Mendelian randomization (MR) analysis to investigate the causal relationship between periodontal disease and AD. Results The results of the random‐effects IVW analysis revealed no evidence of a genetic causal relationship between periodontal disease and AD, regardless of whether the AD data from the IEU consortium or the AD data from the PGC database were utilized. No heterogeneity, multiple effects of levels, or outliers were observed in this study. Conclusions Our findings indicate that there is no causal relationship between periodontal disease and AD at the genetic level.

IntroductionThis paper aims to explore the intersection of corporate social responsibility (CSR) and public health within the context of digital platforms. Specifically, the paper explores the impact of digital platforms on the sustainable development practices of enterprises, seeking to comprehend how these platforms influence the implementation of environmental protection policies, resource management, and social responsibility initiatives.MethodsTo assess the impact of digital platforms on corporate environmental behavior, we conducted a questionnaire survey targeting employees in private enterprises. This survey aimed to evaluate the relationship between the adoption of digital platforms and the implementation of environmental protection policies and practices.ResultsAnalysis of the survey responses revealed a significant positive correlation between the use of digital platforms and the environmental protection behavior of enterprises (r=0.523;p

Immunotherapy is a promising approach for preventing postoperative tumor recurrence and metastasis. However, inflammatory neutrophils, recruited to the postoperative tumor site, have been shown to exacerbate tumor regeneration and limit the efficacy of cancer vaccines. Consequently, addressing postoperative immunosuppression caused by neutrophils is crucial for improving treatment outcomes. This study presents a combined chemoimmunotherapeutic strategy that employs a biocompatible macroporous scaffold-based cancer vaccine (S-CV) and a sialic acid (SA)-modified, doxorubicin (DOX)-loaded liposomal platform (DOX@SAL). The S-CV contains whole tumor lysates as antigens and imiquimod (R837, Toll-like receptor 7 activator)-loaded PLGA nanoparticles as immune adjuvants for cancer, which enhance dendritic cell activation and cytotoxic T cell proliferation upon localized implantation. When administered intravenously, DOX@SAL specifically targets and delivers drugs to activated neutrophils in vivo, mitigating neutrophil infiltration and suppressing postoperative inflammatory responses. In vivo and vitro experiments have demonstrated that S-CV plus DOX@SAL, a combined chemo-immunotherapeutic strategy, has a remarkable potential to inhibit postoperative local tumor recurrence and distant tumor progression, with minimal systemic toxicity, providing a new concept for postoperative treatment of tumors.

ABSTRACTTigecycline and colistin were referred to as the “last resort” antibiotics in defending against carbapenem-resistant, Gram-negative bacterial infections, and are currently widely used in clinical treatment. However, the emergence and prevalence of plasmid-mediated tet(X4) and mcr-1 genes pose a serious threat to the therapeutic application of tigecycline and colistin, respectively. In this research, a tigecycline- and colistin-resistant bacteria resensitization system was developed based on efficient and specific DNA damage caused by Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-Associated Protein 9 (Cas9) nucleases. A conjugation method was used to deliver the resensitization system, which harbors two single-guide RNAs targeting tet(X4) and mcr-1 genes and constitutively expressed Cas9. The conjugation efficiency was nearly 100% after conjugation condition optimization in vitro, and the resensitivity efficiency for clinical isolates was over 90%. In addition, when performing resensitization in vivo, the resistance marker was replaced with a glutamate-based, chromosomal, plasmid-balanced lethal system to prevent the introduction of additional resistance genes in clinical settings, making this strategy a therapeutic approach to combat the in vivo spread of antibiotic resistance genes (ARGs) among bacterial pathogens. As a proof of concept, this resensitive system can significantly decrease the counts of tigecycline- and colistin-resistant bacteria to 1% in vivo. Our study demonstrates the efficacy and adaptability of CRISPR-Cas systems as powerful and programmable antimicrobials in resensitizing tet(X4)- and mcr-1-mediated, tigecycline- and colistin-resistant strains, and opens up new pathways for the development of CRISPR-based tools for selective bacterial pathogen elimination and precise microbiome composition change.IMPORTANCEThe emergence of plasmid-encoded tet(X4) and mcr-1 isolated from human and animal sources has affected the treatment of tigecycline and colistin, and has posed a significant threat to public health. Tigecycline and colistin are considered as the “last line of defense” for the treatment of multidrug-resistant (MDR) Gram-negative bacterial infections, so there is an urgent need to find a method that can resensitize tet(X4)-mediated tigecycline-resistant and mcr-1-mediated colistin-resistant bacteria. In this study, we developed a glutamate-based, chromosomal, plasmid-balanced lethal conjugative CRISPR/Cas9 system, which can simultaneously resensitize tet(X4)-mediated tigecycline-resistant and mcr-1-mediated colistin-resistant Escherichia coli. The counts of tigecycline- and colistin-resistant bacteria decreased to 1% in vivo after the resensitization system was administered. This study opens up new pathways for the development of CRISPR-based tools for selective bacterial pathogen elimination and precise microbiome composition change.

In the era of information technology advancement, big data analysis has emerged as a crucial tool for government governance. Despite this, corruption remains a challenge at the grass-roots level, primarily attributed to information asymmetry. To enhance the efficacy of corruption prevention and control in grass-roots government, this study introduces the concept of data platform management and integrates it with the “5W” (Who, What, When, Where, Why) analysis framework. The research is motivated by the observation that existing studies on corruption prevention primarily concentrate on the formulation of laws and regulations, neglecting the potential improvement in actual effectiveness through the utilization of data platforms and analytical frameworks. The research employs methodologies grounded in the Strengths, Weaknesses, Opportunities, Threats (SWOT) analysis framework, the Plan, Do, Check, Act (PDCA) cycle analysis framework, and the 5W analysis framework. Throughout the iterative process of implementing data platform management, various timeframes are established, and the impact of the three models is evaluated using indicators such as public participation and government satisfaction. The research reveals that the SWOT framework can formulate targeted strategies, the PDCA framework continuously optimizes work processes, and the 5W framework profoundly explores the root causes of corruption. The outcomes indicate a 10.76% increase in the public participation level score with the 5W model, rising from 71.67%, and a 23.24% increase in the governance efficiency score, reaching 66.12%. The SWOT model excels in case handling prescription and corruption reporting rate. The synergistic application of the three models demonstrates a positive impact. In conclusion, the amalgamation of data platform management and a multi-model approach effectively enhances the corruption prevention capabilities of grass-roots governments, offering insights for the establishment of transparent and efficient grass-roots governance.

Innovation and sustainability are two important determinants to promote the competitiveness of construction projects. In order to improve the production efficiency, environmental regulation is an indispensable element. Nevertheless, few studies have discussed the complex relationships between innovation, sustainability, environmental regulation and competitiveness at the project level. Motivated by this, the paper aims to explore the interaction between innovation and sustainability, and the impacts on competitiveness, while incorporating the mediating role of sustainability and moderating role of environmental regulation. Based on structural equation modeling method, a novel conceptual model was established. By processing and analyzing 184 valid questionnaires, the findings indicate that innovation has a positive effect on sustainability and competitiveness. Sustainability is also significantly associated with competitiveness. Moreover, the relationship between innovation and competitiveness is partially mediated by sustainability. When construction projects carry out innovation activities or implement sustainability practices, the greater the degree of environmental regulation, the higher the competitiveness. These findings provide theoretical contributions to the existing studies, revealing the intertwined relationships between innovation sustainability environmental regulation and competitiveness at the project level. In practice, project managers can improve project sustainability and competitiveness by adopting sustainability-rooted practice and innovation-based activities.

Abstract. Background:. Gallbladder cancer (GBC) is the most common malignant tumor of biliary tract. Isoliquiritigenin (ISL) is a natural compound with chalcone structure extracted from the roots of licorice and other plants. Relevant studies have shown that ISL has a strong anti-tumor ability in various types of tumors. However, the research of ISL against GBC has not been reported, which needs to be further investigated. Methods:. The effects of ISL against GBC cells in vitro and in vivo were characterized by cytotoxicity test, RNA-sequencing, quantitative real-time polymerase chain reaction, reactive oxygen species (ROS) detection, lipid peroxidation detection, ferrous ion detection, glutathione disulphide/glutathione (GSSG/GSH) detection, lentivirus transfection, nude mice tumorigenesis experiment and immunohistochemistry. Results:. ISL significantly inhibited the proliferation of GBC cells in vitro. The results of transcriptome sequencing and bioinformatics analysis showed that ferroptosis was the main pathway of ISL inhibiting the proliferation of GBC, and HMOX1 and GPX4 were the key molecules of ISL-induced ferroptosis. Knockdown of HMOX1 or overexpression of GPX4 can reduce the sensitivity of GBC cells to ISL-induced ferroptosis and significantly restore the viability of GBC cells. Moreover, ISL significantly reversed the iron content, ROS level, lipid peroxidation level and GSSG/GSH ratio of GBC cells. Finally, ISL significantly inhibited the growth of GBC in vivo and regulated the ferroptosis of GBC by mediating HMOX1 and GPX4. Conclusion:. ISL induced ferroptosis in GBC mainly by activating p62-Keap1-Nrf2-HMOX1 signaling pathway and down-regulating GPX4 in vitro and in vivo. This evidence may provide a new direction for the treatment of GBC.

Mechanized harvesting is a crucial step in the agricultural production of foxtail millet (Setaria italica), as its peduncles are susceptible to bending and breaking during the harvesting process, leading to yield losses and deterioration in grain quality. To evaluate the suitability of foxtail millet for mechanical harvesting, this study comprehensively analyzed the biomechanical properties of the peduncles and related biological morphological characteristics of 116 foxtail millet accessions, establishing a system for indicator screening and comprehensive evaluation. Using partial correlation analysis and R-type cluster analysis, four biomechanical and seven related morphological indices of the peduncle were screened from 22 candidate indicators, with their coefficient of variation ranging from 6% to 80%. The entropy method was used to assign weights to the selected indices, with biomechanical factors contributing 47.4%, peduncle morphology 20.2%, spike morphology 27.6%, and plant height 4.8%. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Rank-Sum Ratio (RSR) methods were applied to rank and grade the classification of the 116 foxtail millet varieties into four performance groups: Excellent (8 varieties), Good (50 varieties), Moderate (51 varieties), and Poor (7 varieties). This study provides a scientific basis for the selection and evaluation of foxtail millet varieties.

To enhance the chemical mechanical polishing (CMP) efficiency of flexible display substrates, a polishing slurry based on ferric chloride and oxalic acid was proposed for polishing 304 stainless steel substrates. An orthogonal optimization test was designed to determine the significance order of the influence of abrasive size, abrasive content, oxalic acid content and ferric chloride content on the material removal rate (MRR) and surface roughness (surface arithmetical mean height of area, Sa). The aim was to investigate the effects of ferric chloride and oxalic acid on MRR. An optimized polishing slurry capable of achieving an MRR of over 560 nm/min and a Sa as low as 8 nm after 30 min was obtain. Compared with the conventional polishing slurry, whose MRR is 226.56 nm/min, the novel slurry has doubled its polishing efficiency or more.

Abstract Triple-negative breast cancer (TNBC) is a relatively aggressive breast cancer subtype due to tumor relapse, drug resistance, and multi-organ metastatic properties. Identifying reliable biomarkers to predict prognosis and precisely guide TNBC immunotherapy is still an unmet clinical need. To address this issue, we successfully constructed a novel 25 machine learning (ML) algorithms-based immune infiltrating cell (IIC) associated signature of TNBC (MLIIC), achieved by multiple transcriptome data of purified immune cells, TNBC cell lines, and TNBC entities. The TSI index was employed to determine IIC-RNAs that were accompanied by an expression pattern of upregulation in immune cells and downregulation in TNBC cells. LassoLR, Boruta, Xgboost, SVM, RF, and Pamr were utilized for further obtaining the optimal IIC-RNAs. Following univariate Cox regression analysis, LassoCox, CoxBoost, and RSF were utilized for the dimensionality reduction of IIC-RNAs from a prognostic perspective. RSF, Ranger, ObliqueRSF, Rpart, CoxPH, SurvivalSVM, CoxBoost, GlmBoost, SuperPC, StepwiseCox, Enet, LassoCox, CForest, Akritas, BlackBoost, PlsRcox, SurvReg, GBM, and CTree were used for determining the most potent MLIIC signature. Consequently, this MLIIC signature was correlated significantly with survival status validated by four independent TNBC cohorts. Also, the MLIIC signature had a superior predictive capability for TNBC prognosis, compared with 148 previously reported signatures. In addition, MLIIC signature scores developed by immunofluorescent staining of tissue arrays from TNBC patients showed a substantial prognostic value. In TNBC immunotherapy, the low MLIIC profile demonstrated significant immune-responsive efficacy in a dataset of multiple cancer types. MLIIC signature could also predict m6A epigenetic regulation which controls T cell homeostasis. Therefore, this well-established MLIIC signature is a robust predictive indicator for TNBC prognosis and the benefit of immunotherapy, thus providing an efficient tool for combating TNBC.

Abstract Establishing a valid in vitro model to represent tumor heterogeneity and biology is critical but challenging. Tumor organoids are self‐assembled three‐dimensional cell clusters which are of great significance for recapitulating the histopathological, genetic, and phenotypic characteristics of primary tissues. The organoid has emerged as an attractive in vitro platform for tumor biology research and high‐throughput drug screening in cancer medicine. Organoids offer unique advantages over cell lines and patient‐derived xenograft models, but there are no standardized methods to guide the culture of organoids, leading to confusion in organoid studies that may affect accurate judgments of tumor biology. This review summarizes the shortcomings of current organoid culture methods, presents the latest research findings on organoid standardization, and proposes an outlook for organoid modeling.

Abstract Oxygen evolution reaction (OER) is of crucial importance to sustainable energy and environmental engineering, and layered double hydroxides (LDHs) are among the most active catalysts for OER in alkaline conditions, but the reaction mechanism for OER on LDHs remains controversial. Distinctive types of reaction mechanisms have been proposed for the O-O coupling in OER, yet they compose a coupled reaction network with competing kinetics dependent on applied potentials. Herein, we combine grand-canonical methods and micro-kinetic modeling to unravel that the nature of dominant mechanism for OER on LDHs transitions among distinctive types as a function of applied potential, and this arises from the interplay among applied potential and competing kinetics in the coupled reaction network. The theory-predicted overpotentials, Tafel slopes, and findings are in agreement with the observations of experiments including isotope labelling. Thus, we establish a computational methodology to identify and elucidate the potential-dependent mechanisms for electrochemical reactions.

Large-scale volcanic eruptions always inflict severe damage upon facilities and cause significant environmental pollution. Building damage caused by lava flow and volcanic ash coverage can reflect the infrastructure devastation and victim scope within the affected region. The application of machine learning methods for building damage automated identification from remote sensing imagery typically relies on a large number of training samples. However, labeled data scarcity is a common issue in the field of disasters, particularly in volcanic eruptions. To address this, we propose a two-stage building damage quick mapping workflow, which combines a building localization model trained on prior knowledge and a damage classification model fine-tuned on few-shot volcanic eruption-related samples. The classification model utilizes a CNN-based Siamese network for bi-temporal image feature extraction and comparison, with the backbone initialized with pre-trained weights from ImageNet. We conducted building damage classification tasks for single-disaster scenarios and cross-disaster domain scenarios in the eruptions of Mount Semeru, Tonga, and ST. Vincent; the visual damage level of each building was used as ground truth. The results demonstrate that our model can identify building damage efficiently and accurately in different volcanic eruption scenarios, with an over 93% F1-score on the 2-way 20-shot tasks. Furthermore, though building samples from different volcanic eruption regions present cross-domain challenges, our model can adapt to different feature domains by being supplemented with a few samples of another volcanic eruption disaster. Additionally, in the case of Mount Semeru Eruption, we gain insights into the potential of building damage statistics in post-eruption environmental assessments. To further enhance the model robustness on mixed-domain samples and multi-level damage classification tasks, issues including sample bias of certain disaster sources should be addressed.

Lung ischemia–reperfusion injury (IRI) stands as the primary culprit behind primary graft dysfunction (PGD) after lung transplantation, yet viable therapeutic options are lacking. In the present study, we used a murine hilar clamp (1 h) and reperfusion (3 h) model to study IRI. The left lung tissues were harvested for metabolomics, transcriptomics, and single-cell RNA sequencing. Metabolomics of plasma from human lung transplantation recipients was also performed. Lung histology, pulmonary function, pulmonary edema, and survival analysis were measured in mice. Integrative analysis of metabolomics and transcriptomics revealed a marked up-regulation of arachidonate 12-lipoxygenase (ALOX12) and its metabolite 12-hydroxyeicosatetraenoic acid (12-HETE), which played a pivotal role in promoting ferroptosis and neutrophil extracellular trap (NET) formation during lung IRI. Additionally, single-cell RNA sequencing revealed that ferroptosis predominantly occurred in pulmonary endothelial cells. Importantly, Alox12-knockout (KO) mice exhibited a notable decrease in ferroptosis, NET formation, and tissue injury. To investigate the interplay between endothelial ferroptosis and NET formation, a hypoxia/reoxygenation (HR) cell model using 2 human endothelial cell lines was established. By incubating conditioned medium from HR cell model with neutrophils, we found that the liberation of high mobility group box 1 (HMGB1) from endothelial cells undergoing ferroptosis facilitated the formation of NETs by activating the TLR4/MYD88 pathway. Last, the administration of ML355, a targeted inhibitor of Alox12, mitigated lung IRI in both murine hilar clamp/reperfusion and rat left lung transplant models. Collectively, our study indicates ALOX12 as a promising therapeutic strategy for lung IRI.

Introduction. The association between Helicobacter pylori (H. pylori) infection and depression and anxiety has been reported in the literature. The aim of this meta-analysis is to investigate the association of H. pylori infection with depression and anxiety. Methods. A systematic search was conducted in PubMed, Web of Science, Embase, and Chinese databases for observational studies that reported the incidence or prevalence of depression and anxiety in patients with H. pylori infection. We conducted meta-analysis with a random-effects model. Results. A total of twelve studies were included in this meta-analysis. Significant and positive association was found for H. pylori infection with anxiety (OR 2.49, 95% CI 1.83–3.39), but not depression (OR 1.40, 95% CI 0.75–2.58). Subgroup analysis from study design noted a significant positive association between H. pylori infection and anxiety (case-control and cross-sectional study, OR 2.36, 95% CI 1.65–3.38; cohort study, RR 1.72, 95% CI 1.28–2.32). The similar trend among studies used the Self-Rating Anxiety Scale (OR 2.43, 95% CI 1.78–3.32) and other questionnaires (OR 2.41, 95% CI 1.50–3.87) have been observed. However, subgroup analysis of the association between H. pylori infection and depression yields different results. Subgroup analysis from cohort studies noted a significant association between H. pylori infection and depression (RR 1.99, 95% CI 1.33–2.99), but not case-control and cross-sectional studies (OR 1.20, 95% CI 0.59–2.46). A significant positive association has been observed among studies used the Self-Rating Depression Scale (OR 2.03, 95% CI 1.11–3.70), as opposed to studies using other questionnaires (OR 1.03, 95% CI 0.31–3.44). Conclusion. This meta-analysis provide the support that a significant positive association between H. pylori infection and anxiety disorders. However, the association between H. pylori infection and depression appears to be insignificant.

BackgroundPrevious cross-sectional studies have identified multiple potential risk factors for functional dyspepsia (FD). However, the causal associations between these factors and FD remain elusive. Here we aimed to fully examine the causal relationships between these factors and FD utilizing a two-sample MR framework.MethodsA total of 53 potential FD-related modifiable factors, including those associated with hormones, metabolism, disease, medication, sociology, psychology, lifestyle and others were obtained through a comprehensive literature review. Independent genetic variants closely linked to these factors were screened as instrumental variables from genome-wide association studies (GWASs). A total of 8875 FD cases and 320387 controls were available for the analysis. The inverse variance weighted (IVW) method was employed as the primary analytical approach to assess the relationship between genetic variants of risk factors and the FD risk. Sensitivity analyses were performed to evaluate the consistency of the findings using the weighted median model, MR-Egger and MR-PRESSO methods.ResultsGenetically predicted depression (OR 1.515, 95% confidence interval (CI) 1.231 to 1.865, p = 0.000088), gastroesophageal reflux disease (OR 1.320, 95%CI 1.153 to 1.511, p = 0.000057) and years of education (OR 0.926, 95%CI 0.894 to 0.958, p = 0.00001) were associated with risk for FD in univariate MR analyses. Multiple medications, alcohol consumption, poultry intake, bipolar disorder, mood swings, type 1 diabetes, elevated systolic blood pressure and lower overall health rating showed to be suggestive risk factors for FD (all pConclusionsOur comprehensive MR study demonstrated that depression and lower educational attainment were causal factors for FD at the genetic level.

The development of urban digital platforms has changed its entrepreneurial environment and affected regional innovation vitality. We calculated the development index of digital platforms of 294 prefecture-level cities in China between 2013 and 2020 using the principal component analysis method. We used the microdata of enterprise registration information to describe the urban entrepreneurial activity. Digital platforms have promoted urban entrepreneurial activity significantly. Moreover, alleviating labour market distortions, optimizing the urban financial environment, and improving technological innovation are virtual channels for digital platforms to increase urban entrepreneurial activity. Furthermore, digital platforms play a more significant role in promoting urban entrepreneurial activity in the eastern region and cities with better industrial structures. This impact has a nonlinear increasing “marginal effect” in which the faster the development of digital platforms, the more significant the promoting effect on urban entrepreneurial activity.

In today's digital era, households are empowered by digital servitization, which could potentially impact their ability to become entrepreneurs. Using data from the China Family Panel Studies (CFPS) and the National Bureau of Statistics of China, we construct the digital servitization index to analyze the impact of digital servitization on household entrepreneurship. The results show that the utilization of digital servitization by households significantly increases the likelihood of them engaging in entrepreneurial endeavors. This result remains robust after a series of robustness tests. We also find that digital servitization can help households identify entrepreneurial opportunities and access entrepreneurial resources by alleviating information and financial constraints, and increasing social capital. Further, the effect is more among households with rural residences, low material assets, elderly household heads, and household heads with low cognitive abilities, which means that digital servitization can promote underdog entrepreneurship. In conclusion, this paper provides micro-level evidence supporting the idea that digital servitization can foster household entrepreneurship, particularly among underprivileged groups. Ultimately, this paper highlights the potential of digitization as an essential resource to drive economic growth and help households in need become successful entrepreneurs.

Background: Tertiary lymphoid structure (TLS) is a unique organ that carries out tumor cell elimination at tumor sites. It is continuously stimulated by inflammatory tumor signals and has been found to augment immunotherapy response. However, the detailed mechanisms behind it still need to be defined. Methods: To explore and grasp the whole picture of TLS from a pan-cancer view, we collected nine TLS-related genes from previous studies. We performed a comprehensive analysis of 9637 samples across 33 tumor types accessed from The Cancer Genome Atlas (TCGA) database. EdU, Transwell, and flow cytometry were performed on the feature gene PTGDS in U251 cells. The regulatory role of PTGDS on PD-L1 expression and macrophage polarization was verified. Results: Alteration analysis showed that mutations of TLS-related genes were widespread and relatively high. Clustering analysis based on the expression of these nine genes obtained two distinct clusters, with high EIF1AY and PTGDS in cluster 2 and better overall survival in cluster 1. To distinguish the two clusters, we utilized six machine learning algorithms and filtrated EIF1AY, PTGDS, SKAP1, and RBP5 as the characteristic genes, among which the former two genes were proved to be hazardous. PTGDS was found to regulate PD-L1 expression and also promoted the proliferation and migration of U251 cells. The knockdown of PTGDS could reduce the migration of macrophages and inhibit the polarization of macrophages into M2-phenotype. In addition, we established a TLS score to demonstrate patients’ TLS activity. The low TLS-score group overlapped with cluster 1 and displayed a better prognosis. Besides, the low TLS-score group was related to better immunotherapy responses. The HE staining of histopathological sections confirmed that the low TLS-score group exhibited higher infiltration of immune cells. Conclusion: This study reveals broad molecular, tumorigenic, and immunogenic signatures for further functional and therapeutic studies of tertiary lymphoid structure. The TLS score we established effectively predicted immunotherapy response and patients’ survival. Its future application and combination await more research.