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Abstract Background Gastroesophageal reflux disease (GERD) is among the most common complications of bariatric surgery. This study aimed to analyse the risk factors affecting the worsening of GERD symptoms after laparoscopic sleeve gastrectomy (LSG), and to establish and validate a related nomogram model. Methods The study recruited 236 participants and randomly divided them into training and validation sets in a ratio of 7:3. LASSO regression technique was used to select the optimal predictive features, and multivariate logistic regression was used to construct the column line graphs. The performance of the nomogram was evaluated and validated by analyzing the area under the receiver operating characteristic (ROC) curve, calibration curve, and decision curve. Results In this study, Lasso-logistic regression was applied to select 5 predictors from the relevant variables, which were body mass index (BMI), diabetes, hiatal hernia, GERD, and triglyceride levels. These 5 predictor variables constructed a model with moderate predictive power, with an area under the ROC curve of 0.779 for the training set and 0.796 for the validation set. Decision curve analysis showed that in external validation, if the risk thresholds were between 4 and 98% and 14–95%, then the nomogram can be applied to the clinic. Conclusions We have developed and validated a nomogram that effectively predicts the risk of worsening gastroesophageal reflux symptoms following LSG.

Abstract Objectives To develop an innovative magnetic resonance imaging (MRI)-based PUMCH (Peking Union Medical College Hospital) classification system aimed at standardising the diagnosis of congenital cervical malformations (CCMs) by identifying their distinctive MRI features. Methods Seventy-nine consecutive patients with CCM underwent pre-treatment pelvic MRI; three experienced gynaecological radiologists retrospectively analysed these images. Qualitative assessments included Rock et al’s classification; PUMCH classification; haematometra; cervical signal features; ovarian endometriosis; haematosalpinx; and uterine, vaginal, urinary, and musculoskeletal malformations. Quantitative assessments involved the uterine volume, sagittal cervical length, and maximum ovarian cross-sectional area. The surgical treatment types were also recorded. Statistical methods were used to incorporate differences in clinical features and surgical methods into our classification. Results Morphologically, CCMs were categorised into three types: type I (53%) was characterised by the presence of a cervix with visible cervical canals; type II (23%) featured an existing cervix with concealed cervical canals; and type III (24%) indicated cervical aplasia, which involves a blind end in the lower part of the uterine corpus. Haematometra was significantly more prevalent in patients with type I CCM than in those with type II (p

Abstract Background Spinal tuberculosis (STB) is a local manifestation of systemic infection caused by Mycobacterium tuberculosis, accounting for a significant proportion of joint tuberculosis cases. This study aimed to explore the diagnostic value of MRI combined with mannose-binding lectin (MBL) for STB. Methods 124 patients suspected of having STB were collected and divided into STB and non-STB groups according to their pathological diagnosis. Serum MBL levels were measured using ELISA and a Pearson analysis was constructed to determine the correlation between MBL and STB. ROC was plotted to analyze their diagnostic value for STB. All the subjects included in the study underwent an MRI. Results The sensitivity of MRI for the diagnosis of STB was 84.38% and specificity was 86.67%. The serum MBL levels of the patients in the STB group were significantly lower than the levels in the non-STB group. ROC analysis results indicated that serum MBL’s area under the curve (AUC) for diagnosis of STB was 0.836, with a sensitivity of 82.3% and a specificity was 77.4%. The sensitivity of MRI combined with MBL diagnosis was 96.61%, and the specificity was 92.31%, indicating that combining the two diagnostic methods was more effective than using either one alone. Conclusions Both MRI and MBL had certain diagnostic values for STB, but their combined use resulted in a diagnostic accuracy than either one alone.

ABSTRACT Introduction As a common malignancy, lung cancer has a relatively poor prognosis and a low survival rate. In recent years, ferroptosis, as an emerging filed, has great promise in the potential treatment of cancer. Brucea javanica oil (BJO) is often used to treat various cancers. Oleic acid (OA) is the main ingredient of BJO. In this study, we investigated the role and molecular mechanism of OA in lung cancer treatment by promoting ferroptosis. Methods In this study, A549 cells and H1299 cells were used for in vitro experiments, and a CCK‐8 test, scratch test, and MTT experiment were carried out. We examined reactive oxygen species (ROS), the JC‐1 probe, glutathione (GSH) expression, lipid peroxidation, SDC4 mRNA levels, and ACSL4, SLC7A11, GPX4, and SDC4 protein levels. Results The results showed that OA could inhibit the proliferation and migration of A549 cells and H1299 cells, SDC4 was a potential therapeutic target of OA against lung cancer, and OA treatment significantly inhibited the expression of SDC4 in A549 cells and H1299 cells. OA induces ferroptosis in A549 cells and H1299 cells, decreases GSH levels, increases lipid peroxidation levels, and decreases SDC4 mRNA expression; in addition, OA upregulates ACSL4 expression and decreases SLC7A11, GPX4, and SDC4 expression. Conclusion This study confirmed that OA could inhibit SDC4 expression and promote the occurrence of ferroptosis in A549 cells and H1299 cells through the GPX4/ACSL4 pathway, providing an effective basis for the use of drugs targeting ferroptosis in lung cancer treatment.

Objective: Blood-stasis syndrome (BSS), an important syndrome in Type 2 diabetes mellitus(T2DM), is associated with the pathophysiological mechanisms underlying diabetic vascular complications. However, BSS has not been fully characterized as of yet. Due to the strong correlation between BSS and vasculopathy, we hypothesized that the metabolic characteristics of BSS in T2DM (T2DM BSS) are highly specific. By combining untargeted metabolomics and pseudotargeted lipidomics approaches, this study aimed to comprehensively elucidate the metabolic traits of T2DM BSS, thereby providing novel insights into the vascular complications of diabetes and establishing a foundation for precision medicine. Methods: The survey was conducted in Haidian District of Beijing from October 2021 to November 2021, and data collection was completed in January 2022. Liquid chromatography-mass spectrometry (LC-MS) based untargeted metabolomics and liquid chromatography-tandem mass spectrometry (LC-MS/MS) based pseudotargeted lipidomics were performed to detect metabolites and lipids. Multivariate, univariate, and pathway analyses were utilized to investigate metabolic changes. The unique metabolites of BSS were obtained by inter-group comparisons and screening. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic accuracy of metabolites. Results: A total of 1189 participants completed the survey, of which 120 participants were recruited in this study and further divided into a discovery cohort (n = 90) and a validation cohort (n = 30). Among these, 21 participants were selected for psuedotargeted lipidomics analysis. 81 metabolites, mainly involving glycerophospholipids, were identified as unique metabolites of T2DM BSS, while fatty acyls (FAs) were identified as unique lipids. T2DM BSS was associated with significant dysregulation in glycerophospholipid metabolism and choline metabolism within cancer pathways as major metabolic disturbances. Furthermore, analyses of both the discovery and validation cohorts, indicated that LysoPC (20:5(5Z,8Z,11Z,14Z,17Z)/0:0) and LysoPC (15:0) had the greatest impact on distinguishing BSS. Conclusion: Altered levels of glycerophospholipids and FAs have been associated with T2DM BSS. These results provide valuable mechanistic insights linked with the development of BSS in T2DM subjects.

Abstract Textile wastewater is among the most polluted types of industrial waste. Wastewater treatment in the textile industry is notoriously difficult because of the use of complex chemicals and dyes in the textile production steps, and conventional methods are not enough to treat these. Textile wastewater, known for its high pollution levels, poses challenges for treatment due to complex chemicals and dyes. A comparative study was conducted on simple ozonation and catalytic ozonation (CO) using alum to degrade methylene blue (MB). The authors analyzed various factors like time, pH, catalyst loading and ozone dosages during the study. Results showed that catalytic activity in ozonation depends on pH and ozone flow. The maximum MB elimination was achieved at pH 6.6 and 200 V ozone flow. Scanning electron microscopy (SEM) was used to characterize the surface morphology of the catalyst, Fourier transform infrared spectroscopy (FTIR) was used to identify the important functional groups, and energy dispersive X-ray spectroscopy (EDS) was used to characterize the catalyst's elemental composition. Compared to simple ozonation, CO showed higher removal in the initial phase. Real textile wastewater analysis confirmed the effectiveness of alum catalysts in achieving significant removal of MB (87%) through this novel cost-efficient process. Graphical Abstract

Objective To understand the impact of chronic disease comorbidity on healthcare institution-seeking behavior of elderly people in China, and to provide a theoretical basis for optimizing healthcare order and enhancing the management capacity of primary care for chronic diseases. MethodsThe data on 4 508 elderly people aged ≥ 60 years in 25 provincial-level administrative divisions across China were collected from the China Family Panel Studies (CFPS) conducted in 2020. The Stata 17.0 statistical software was used to analyze the impact of chronic disease comorbidity on healthcare facility-seeking behavior of elderly people in China using the probit regression model and the propensity score matching (PSM) method. ResultsOf the 4 508 elderly people surveyed, 748 (16.59%) reported having chronic disease comorbidity (suffering from two or more chronic diseases); 2 536 (56.26%) and 1 972 (43.74%) reported seeking medical treatment at primary and non-primary healthcare institutions during past six months. After adjusting for predisposing factors (gender, age, education level, marital status), enabling factors (household registration type, monthly retirement pension, satisfaction with medical treatment, participation in medical insurance, emotional relationship with children, financial support from children, caregiving provided by children), and need factors (self-rated health status, changes in health conditions, physical discomfort in the past two weeks, the ability to take care of oneself), the results of probit regression analysis showed that the elderly people with comorbid chronic diseases were more inclined to seek medical treatment at non-primary healthcare institutions (β = – 0.130, 95% confidence interval: – 0.237 – – 0.022). The estimated values of average treatment effect on the treated (ATT) of chronic disease comorbidity on the healthcare institution selection of the elderly people were – 0.052, – 0.048, and – 0.047, respectively, based on matched propensity scores of predisposing, enabling, and need factors with K-nearest neighbor, kernel, and radius matching (all P < 0.05), indicating that chronic disease comorbidity can reduce the probability of elderly people seeking medical treatment at primary healthcare institutions. The heterogeneity test results showed that the impact of chronic disease comorbidity on females (ATT = – 0.078 – – 0.063, all P < 0.05) and the elderly people with a junior high school education or below (ATT = – 0.058 – – 0.052, all P < 0.05) was more significant, but there was no statistically significant difference in the impact on males and elderly people with a high school education or above (all P > 0.05). ConclusionChronic disease comorbidity may affect the selection of medical institutions for elderly people in China to some extent, and its influence differs by gender and education level of the elderly.

Abstract Background Post-hepatectomy liver failure (PHLF) is a serious complication after hepatectomy and a major cause of death. The current criteria for PHLF diagnosis (ISGLS consensus) require laboratory data of elevated INR level and hyperbilirubinemia on or after postoperative day 5. This study aims to propose a new indicator for the early clinical prediction of PHLF. Methods The peri-operative arterial lactate concentration level ratios were derived from time points within the 3 days before surgery and within POD1, the patients were divided into two groups: high lactate ratio group (≥ 1) and low lactate ratio group (

Abstract Background Quitting support from smokers’ partners can predict quit attempts and smoking abstinence but research on factors that predict such support has been limited. To add more evidence for partner support and the improved interventions for smoking cessation, we analyzed some new potential predictors of quitting support from smokers’ spouses. Method This cross-sectional study was conducted in in 2022 and 2023, selecting the students’ families in which fathers smoked and mothers didn’t smoke from grade 1–5 of 13 primary schools in Qingdao, China. Parents who met the criteria completed the online questionnaires and 1018 families were included in the analysis. We measured personal information related to smokers and their spouses such as age, education and nicotine dependence, and variables related to family and marital relationship such as family functioning, perceived responsiveness and power in decision-making of quitting smoking. Quitting support from smokers’ spouses was measured by Partner Interaction Questionnaire and generalized linear model was used to explore the potential predictors of partner support. Results In this study, the mean age of smokers was 39.97(SD = 5.57) and the mean age of smokers’ spouses was 38.24(SD = 4.59). The regression analysis showed that for smokers and their spouses, the older age groups showed the lower ratio of positive/negative support(P

Background: The Juan-Bi decoction (JBD) is a classic traditional Chinese medicines (TCMs) prescription for the treatment of rheumatoid arthritis (RA). However, the active compounds of the JBD in RA treatment remain unclear.Aim: The aim of this study is to screen effective compounds in the JBD for RA treatment using systems pharmacology and experimental approaches.Method: Botanical drugs and compounds in the JBD were acquired from multiple public TCM databases. All compounds were initially screened using absorption, distribution, metabolism, excretion, and toxicity (ADMET) and physicochemical properties, and then a target prediction was performed. RA pathological genes were acquired from the DisGeNet database. Potential active compounds were screened by constructing a compound–target–pathogenic gene (C-T-P) network and calculating the cumulative interaction intensity of the compounds on pathogenic genes. The effectiveness of the compounds was verified using lipopolysaccharide (LPS)-induced RAW.264.7 cells and collagen-induced arthritis (CIA) mouse models.Results: We screened 15 potentially active compounds in the JBD for RA treatment. These compounds primarily act on multiple metabolic pathways, immune pathways, and signaling transduction pathways. Furthermore, in vivo and in vitro experiments showed that bornyl acetate (BAC) alleviated joint damage, and inflammatory cells infiltrated and facilitated a smooth cartilage surface via the suppression of the steroid hormone biosynthesis.Conclusion: We screened potential compounds in the JBD for the treatment of RA using systems pharmacology approaches. In particular, BAC had an anti-rheumatic effect, and future studies are required to elucidate the underlying mechanisms.

IntroductionTuberculosis, caused by Mycobacterium tuberculosis complex (MTBC), remains a global health concern in both human and animals. However, the absence of rapid, accurate, and highly sensitive detection methods to differentiate the major pathogens of MTBC, including M. tuberculosis, M. bovis, and BCG, poses a potential challenge.MethodsIn this study, we have established a triplex droplet digital polymerase chain reaction (ddPCR) method employing three types of probe fluorophores, with targets M. tuberculosis (targeting CFP-10-ESAT-6 gene of RD1 and Rv0222 genes of RD4), M. bovis (targeting CFP-10-ESATs-6 gene of RD1), and BCG (targeting Rv3871 and Rv3879c genes of ΔRD1), respectively.ResultsBased on optimization of annealing temperature, sensitivity and repeatability, this method demonstrates a lower limit of detection (LOD) as 3.08 copies/reaction for M. tuberculosis, 4.47 copies/reaction for M. bovis and 3.59 copies/reaction for BCG, without cross-reaction to Mannheimia haemolytica, Mycoplasma bovis, Haemophilus parasuis, Escherichia coli, Pasteurella multocida, Ochrobactrum anthropi, Salmonella choleraesuis, Brucella melitensis, and Staphylococcus aureus, and showed repeatability with coefficients of variation (CV) lower than 10%. The method exhibits strong milk sample tolerance, the LOD of detecting in spike milk was 5 × 103 CFU/mL, which sensitivity is ten times higher than the triplex qPCR. 60 clinical DNA samples, including 20 milk, 20 tissue and 20 swab samples, were kept in China Animal Health and Epidemiology Center were tested by the triplex ddPCR and triplex qPCR. The triplex ddPCR presented a higher sensitivity (11.67%, 7/60) than that of the triplex qPCR method (8.33%, 5/60). The positive rates of M. tuberculosis, M. bovis, and BCG were 1.67, 10, and 0% by triplex ddPCR, and 1.67, 6.67, and 0% by triplex qPCR, with coincidence rates of 100, 96.7, and 100%, respectively.DiscussionOur data demonstrate that the established triplex ddPCR method is a sensitive, specific and rapid method for differentiation and identification of M. tuberculosis, M. bovis, and BCG.

This work investigates the nonlinear response of a conical premixed flame to dual-frequency excitation through experiments and G-equation analysis. In addition to the responses at excitation frequencies, f1 and f2, a notable flame response can also be identified at the difference frequency, fd. The fd response is particularly significant in the high-frequency regime, characterized by multiple wavelengths of both f1 and f2 on the flame front. This distinct behavior of difference-frequency response can be attributed to the spatial superposition of the intensified flame fluctuation waves at the two forcing frequencies. As the excitation enhances at either f1 or f2, the nonlinear effect suppresses the responses of the upper-part flame at both f1 and f2 while promoting the bulk flame response at fd. Based on a third-order expansion of the G-equation, it is shown that the nonlinear flame dynamics necessarily generate the observed interaction component when the flame is perturbed by dual-frequency excitations. The present results emphasize the importance of nonlinear flame dynamics in inducing flame responses at interaction frequencies, offering insights into thermoacoustic oscillations of multiple frequencies.

Objective: To explore the balance of peripheral blood T helper 17 cells/regulatory T cell (Th17/Treg) ratio and the polarization ratio of M1 and M2 macrophages in lower extremity arteriosclerosis obliterans (ASO). Methods: A rat model of lower extremity ASO was established, and blood samples from patients with lower extremity ASO before and after surgery were obtained. ELISA was used to detect interleukin 6 (IL-6), IL-10, and IL-17. Real-time RCR and Western blot analyses were used to detect Foxp3, IL-6, IL-10, and IL-17 expression. Moreover, flow cytometry was applied to detect the Th17/Treg ratio and M1/M2 ratio. Results: Compared with the control group, the iliac artery wall of ASO rats showed significant hyperplasia, and the concentrations of cholesterol and triglyceride were significantly increased (P

Objectives: We intend to develop a dual-modal dynamic contour-based instance segmentation method that is based on carotid artery and jugular vein ultrasound and its optical flow image, then we evaluate its performance in comparison with the classic single-modal deep learning networks. Method: We collected 2432 carotid artery and jugular vein ultrasound images and divided them into training, validation and test dataset by the ratio of 8:1:1. We then used these ultrasound images to generate optical flow images with clearly defined contours. We also proposed a dual-stream information fusion module to fuse complementary features between different levels extracted from ultrasound and optical flow images. In addition, we proposed a learnable contour initialization method that eliminated the need for manual design of the initial contour, facilitating the rapid regression of nodes on the contour to the ground truth points. Results: We verified our method by using a self-built dataset of carotid artery and jugular vein ultrasound images. The quantitative metrics demonstrated a bounding box detection mean average precision of 0.814 and a mask segmentation mean average precision of 0.842. Qualitative analysis of our results showed that our method achieved smoother segmentation boundaries for blood vessels. Conclusions: The dual-modal network we proposed effectively utilizes the complementary features of ultrasound and optical flow images. Compared to traditional single-modal instance segmentation methods, our approach more accurately segments the carotid artery and jugular vein in ultrasound images, demonstrating its potential for reliable and precise medical image analysis.

High-altitude UAVs (HA-UAVs) have emerged as vital components in 6G communication infrastructures, providing stable relays for telecommunications services above terrestrial and aerial disturbances. This paper explores the multifaceted roles of HA-UAVs in remote sensing, data relay, and telecommunication network enhancement. A Large Language Model (LLM) framework is introduced that dynamically predicts optimal HA-UAV connectivity for IoT devices, enhancing network performance and adaptability. The study emphasizes HA-UAVs’ operational efficiency, broad coverage, and potential to transform global communications, particularly in remote and underserved areas. Our proposed satellite-HA-UAV-IoT architecture with LLM optimization demonstrated substantial improvements, including a 25% increase in network throughput (from 20 Mbps to 25 Mbps at a 20 km distance), a 40% reduction in latency (from 25 ms to 15 ms), and a 28% enhancement in energy efficiency (from 0.25 μJ/bit to 0.18 μJ/bit), significantly advancing the performance and adaptability of next-generation IoT networks. These advancements pave the way for unprecedented connectivity and set the stage for future communication technologies.

Through the use of recycled aggregates, the construction industry can mitigate its environmental impact. A key consideration for concrete structural engineers when designing and constructing concrete structures is compressive strength. This study aims to accurately forecast the compressive strength of recycled aggregate concrete (RAC) using machine learning techniques. We propose a simplified approach that incorporates a two-layer stacked ensemble learning model to predict RAC compressive strength. In this framework, the first layer consists of ensemble models acting as base learners, while the second layer utilizes a random forest (RF) model as the meta-learner. A comparative analysis with four other ensemble learning models demonstrates the superior performance of the proposed stacked model in effectively integrating predictions from the base learners, resulting in enhanced model accuracy. The model achieves a low mean absolute error (MAE) of 2.599 MPa, a root mean squared error (RMSE) of 3.645 MPa, and a high R-squared (R2) value of 0.964. Additionally, a Shapley (SHAP) additive explanation analysis reveals the influence and interrelationships of various input factors on the compressive strength of RAC, aiding design and construction professionals in optimizing raw material content during the RAC design and production process.

Abstract Fusarium head blight (FHB) caused by Fusarium species, seriously threatens the safety of wheat (Triticum aestivum) production. Resistant cultivars and fungicides are frequently used to control these FHB pathogens. However, Fusarium species have been adapting the current FHB control approaches in a manner that raises concern for future FHB control strategies, which could lead to a greater risk of FHB outbreaks. In this study, a total of 521 strains of Fusarium were isolated from Sichuan province of China, to investigate the diversity of Fusarium species and the genes associated with their adaptation. Seven species were identified based on molecular markers and morphological analysis. The virulence assays showed that Fusarium asiaticum (Fa) and Fusarium graminearum (Fg) were the two major causal agents of FHB, with high virulence and more frequent isolates. Fungicide resistance analysis showed that four isolates had developed the resistance to carbendazim, and four isolates had developed the resistance to tebuconazole. Of note, two point-mutation variants (F200Y and E198Q) occurred in the β2-tubulin gene, leading to the carbendazim resistance. The landscape of genomic diversity was analyzed through whole-genome sequencing, revealing a total of 182,811 and 430,733 variants (including: single nucleotide polymorphisms, SNP, insertion and deletion, Indel, and structure variation, SV) among the Fa and Fg isolates, respectively. In addition, potential alterations in gene function (15.22%) were predicted among Fg variants. These alterations offer potential helps for the Fusarium species to adapt to various managements of FHB, which may increase risks in developing fungicide-resistant isolates. However, these annotated genetic variants are valuable resources for further genetic and genomic studies, as well as potential markers to assist disease risk assessment.

Abstract Background Hepatocyte growth factor (HGF) is a peptide-containing multifunctional cytokine, which is overexpressed and/or activated in multiple malignancies and is reported to be associated with tumor development and inferior survival. At present, the role of HGF in small cell lung cancer (SCLC) has not been fully explored yet. Materials and methods The expression of HGF and its value in predicting survival in SCLC were explored from GEO database and in pan-cancer analysis. Furthermore, we detected the expression of HGF using tumor tissue and paired plasma samples from a validation cohort of 71 SCLC patients at our institute. Correlation between tumor and plasma HGF expression and the prognostic values were analyzed. Results GEO database analysis revealed that tumor tissue had lower HGF expression than paired normal tissue in SCLC. At our institute, immunohistochemical staining showed negative expression of HGF in tumor tissue of SCLC at our institute (47/47, 100%). The average baseline plasma HGF was 1.28 (range,0.42–4.35) ng/ml. However, plasma HGF was higher in SCLC patients with patients with N3, M1, liver metastasis (LM) and bone metastasis (BM) disease compared with those N0 − 2 (1.25 vs. 1.75 ng/mL, P = 0.000), M0 (1.26 vs. 1.63 ng/mL, P = 0.003), non-LM (1.32 vs. 2.06 ng/mL, P = 0.009), and non-BM (1.35 vs. 1.77 ng/mL, P = 0.047), respectively. Multivariate analysis revealed plasma HGF was an independent predictor for LM and prognostic factor of OS. Conclusion Our results revealed that plasma HGF rather than tumor HGF exhibited a potential role in predicting metastasis and survival in SCLC. Plasma HGF might be used as a non-invasive detecting and monitoring tool for SCLC.

Abstract The Schlafen (SLFN)11 gene has been implicated in various biological processes such as suppression of HIV replication, replication stress response, and sensitization of cancer cells to chemotherapy. Due to the rapid diversification of the SLFN family members, it remains uncertain whether a direct ortholog of human SLFN11 exists in mice. Here we show that mSLFN8/9 and hSLFN11 were rapidly recruited to microlaser-irradiated DNA damage tracks. Furthermore, Slfn8/9 expression could complement SLFN11 loss in human SLFN11 −/− cells, and as a result, reduced the growth rate to wild-type levels and partially restored sensitivity to DNA-damaging agents. In addition, both Slfn8/9 and SLFN11 expression accelerated stalled fork degradation and decreased RPA and RAD51 foci numbers after DNA damage. Based on these results, we propose that mouse Slfn8 and Slfn9 genes may share an orthologous function with human SLFN11. This notion may facilitate understanding of SLFN11’s biological role through in vivo studies via mouse modeling.

Abstract Aerial base stations (AeBSs), as crucial components of air-ground integrated networks, are widely employed in cloud computing, disaster relief, and various applications. How to quickly and efficiently deploy multi-AeBSs for higher capacity gain has become a key research issue. In this paper, we address the 3D deployment optimization problem of multi-AeBSs with the objective of maximizing system capacity. To overcome communication overhead and privacy challenges in multi-agent deep reinforcement learning (MADRL), we propose a federated deep deterministic policy gradient (Fed-DDPG) algorithm for the multi-AeBS deployment decision. Specifically, a high-altitude platform (HAP)-assisted multi-AeBS deployment architecture is designed, in which low-altitude AeBS act as the local nodes to train its own deployment decision model, while the HAP acts as the global node to aggregate the weights of local models. In this architecture, AeBSs do not exchange raw data, addressing data privacy concerns and reducing communication overhead. Simulation results show that the proposed algorithm outperforms fully distributed MADRL algorithms and closely approximates the performance of multi-agent deep deterministic policy gradient (MADDPG), which requires global information during training, but with less training time.

Abstract Background and aims Preoperative prediction of microvascular invasion (MVI) using a noninvasive method remain unresolved, especially in HBV-related in intrahepatic cholangiocarcinoma (ICC). This study aimed to build and validate a preoperative prediction model for MVI in HBV-related ICC. Methods Patients with HBV-associated ICC undergoing curative surgical resection were identified. Univariate and multivariate logistic regression analyses were performed to determine the independent risk factors of MVI in the training cohort. Then, a prediction model was built by enrolling the independent risk factors. The predictive performance was validated by receiver operator characteristic curve (ROC) and calibration in the validation cohort. Results Consecutive 626 patients were identified and randomly divided into the training (418, 67%) and validation (208, 33%) cohorts. Multivariate analysis showed that TBIL, CA19-9, tumor size, tumor number, and preoperative image lymph node metastasis were independently associated with MVI. Then, a model was built by enrolling former fiver risk factors. In the validation cohort, the performance of this model showed good calibration. The area under the curve was 0.874 (95% CI: 0.765–0.894) and 0.729 (95%CI: 0.706–0.751) in the training and validation cohort, respectively. Decision curve analysis showed an obvious net benefit from the model. Conclusion Based on clinical data, an easy model was built for the preoperative prediction of MVI, which can assist clinicians in surgical decision-making and adjuvant therapy.

Abstract Acute lung injury (ALI) is an adverse disease of the respiratory system, and one of its prevalent causes is sepsis induction. Cell pyroptosis facilitates the progression of ALI and lncRNAs play critical roles in ALI. Thus, this research seeks to investigate the specific mechanism of NEAT1 in sepsis‐ALI.BEAS‐2B cells were exposed to lipopolysaccharide (LPS) to construct a cell model of sepsis‐induced ALI. The gene and protein expression were assessed using qRT‐PCR and western blot. Cell viability was identified by CCK‐8. Cell death was discovered using PI staining. The secretion of IL‐1β and IL‐18 was examined using ELISA. The interconnections among NEAT1, miR‐26a‐5p, and ROCK1 were confirmed using starbase, luciferase assay, and RIP.LPS treatment augmented NEAT1 and ROCK1 levels while mitigating miR‐26a‐5p level in BEAS‐2B cells. Additionally, LPS treatment facilitated cell death and cell pyroptosis, whereas NEAT1 silencing could reverse these effects in BEAS‐2B cells. Mechanistically, NEAT1 positively mediated ROCK1 expression by targeting miR‐26a‐5p. Furthermore, miR‐26a‐5p inhibitor offset NEAT1 depletion‐mediated suppressive effects on cell death and cell pyroptosis. ROCK1 upregulation decreased the inhibitory impacts produced by miR‐26a‐5p overexpression on cell death and cell pyroptosis. Our outcomes demonstrated NEAT1 could reinforce LPS‐induced cell death and cell pyroptosis by repressing the miR‐26a‐5p/ROCK1 axis, thereby worsening ALI caused by sepsis. Our data indicated NEAT1, miR‐26a‐5p, and ROCK1 might be biomarkers and target genes for relieving sepsis‐induced ALI.

High-molecular-weight glutenin subunits (HMW-GSs) are the most critical grain storage proteins that determine the unique processing qualities of wheat. Although it is a part of the superior HMW-GS pair (Dx5+Dy10), the contribution of the Dy10 subunit to wheat processing quality remains unclear. In this study, we elucidated the effect of Dy10 on wheat processing quality by generating and analyzing a deletion mutant (with the Dy10-null allele), and by elucidating the changes to wheat flour following the incorporation of purified Dy10. The Dy10-null allele was transcribed normally, but the Dy10 subunit was lacking. These findings implied that the Dy10-null allele reduced the glutenin:gliadin ratio and negatively affected dough strength (i.e., Zeleny sedimentation value, gluten index, and dough development and stability times) and the bread-making quality; however, it positively affected the biscuit-making quality. The incorporation of various amounts of purified Dy10 into wheat flour had a detrimental effect on biscuit-making quality. The results of this study demonstrate that the Dy10 subunit is essential for maintaining wheat dough strength. Furthermore, the Dy10-null allele may be exploited by soft wheat breeding programs.

In the context of “dual carbon” goals, governments need accurate carbon accounting results as a basis for formulating corresponding emission reduction policies. Therefore, this study proposes a combined carbon emission prediction method for urban regions, considering micro-level enterprise electricity consumption data and macro-level regional data. Considering the different applicability of prediction methods and the requirements for the data volume, a region-level carbon emission prediction method based on the long short-term memory neural network is proposed, which takes into account the micro-level electricity–carbon coupling relationship. Additionally, a region-level carbon emission prediction method based on the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) is proposed, considering the macro-level economic–carbon coupling relationship. The generalized induced ordered weighted averaging method is employed to assign differential weights to micro- and macro-prediction values, yielding regional carbon emission predictions. An empirical analysis is conducted using a key city in the eastern region as an example, analyzing the main influencing factors and predicting carbon emissions based on relevant data from 2017 to 2021, and the accuracy of the models is analyzed and validated.

Introduction: This study aimed to investigate the impact of a 10-week Core Stability Training (CST) compared to Traditional Strength Training (TST) on the balance abilities of adolescent male basketball players.Methods: Subjects (age: 15.70 ± 0.75, height: 178.4 ± 8.31, weight: 66.55 ± 8.34) were randomly assigned to either the Core Stability Training group or the Traditional Strength Training group. Three selected balance assessment indicators included the Single-leg Standing with Eyes Closed Test, Star Excursion Balance Test, and Core Four-Direction Endurance Test.Results: 1) The scores were significantly different in both groups before and after the Single-leg Standing with Eyes Closed Test; (p < 0.01, d = 1.692, d = 1.837); 2) In the Star Excursion Balance Test, the scores of the experimental group showed significant difference (p < 0.05) or highly significant difference (p < 0.01) with an average effect size of (d = 1.727) when the left or right foot supported in the other directions before and after the training. However, there was no significant difference in scores in the c direction when the left foot supported (p > 0.05, d = 0.954); 3) In the Core Four-Direction Endurance Test, there were no significant differences in scores for the control group before and after training (p > 0.05, d = 0.567), while the experimental group showed significant differences in scores before and after training (p < 0.05, d = 1.889).Discussion: Both CST and TST were effective in enhancing the balance abilities of adolescent basketball players. CST, in particular, demonstrated improvements in dynamic balance and agility across multiple planes. Basketball coaches are encouraged to consider incorporating CST training programs into their overall training plans for optimal balance enhancement.

Objective: Vascular cognitive impairment (VCI) accounts for approximately 50%–70% of all dementia cases and poses a significant burden on existing medical systems. Identifying an optimal strategy for preventing VCI and developing efficient symptomatic treatments remains a significant challenge. Syndrome differentiation represents a fundamental approach for personalized diagnosis and treatment in Traditional Chinese Medicine (TCM) and aligns with the principles of precision medicine. The objective of this study was to elucidate the metabolic characteristics of VCI based on TCM syndrome differentiation, thus providing novel insights into the diagnosis and treatment of VCI.Methods: A 2-year cross-sectional cognitive survey was conducted in four communities in Beijing between September 2020 and November 2022. The syndrome differentiation of participants was based on the Kidney-Yang Deficiency Syndrome Scale (KYDSS), which was originally developed by Delphi expert consultation. The identification of serum metabolites was performed by Ultra performance liquid chromatography (UPLC) analysis coupled with an electrospray ionization quadruple time-of-flight mass spectrometer (ESI-QTOF MS). Multivariate, univariate, and pathway analyses were used to investigate metabolic changes. Logistic regression models were also used to construct metabolite panels that were capable of discerning distinct groups. Phospholipase A2 (PLA2) levels were measured by a commercial ELISA kit.Results: A total of 2,337 residents completed the survey, and the prevalence of VCI was 9.84%. Of the patients with VCI, those with Kidney-Yang deficiency syndrome (VCIS) accounted for 70.87% of cases and exhibited more severe cognitive impairments. A total of 80 participants were included in metabolomics study, including 30 with VCIS, 20 without Kidney-Yang deficiency syndrome (VCINS), and 30 healthy control participants (C). Ultimately, 45 differential metabolites were identified when comparing the VCIS group with group C, 65 differential metabolites between the VCINS group and group C, and 27 differential metabolites between the VCIS group and the VCINS group. The downregulation of phosphatidylethanolamine (PE), and phosphatidylcholine (PC) along with the upregulation of lysophosphatidylethanolamine (LPE), lysophosphatidylcholine (LPC), phosphatidic acid (PA) and phospholipase A2 (PLA2) can be considered as the general metabolic characteristics associated with VCI. Dysfunction of glycerophospholipids, particularly LPEs and PCs, was identified as a key metabolic characteristic of VCIS. In particular Glycerophospho-N-Arachidonoyl Ethanolamine (GP-NArE) was discovered for the first time in VCI patients and is considered to represent a potential biomarker for VCIS. The upregulation of PLA2 expression was implicated in the induction of alterations in glycerophospholipid metabolism in both VCIS and VCINS. Moreover, robust diagnostic models were established based on these metabolites, achieving high AUC values of 0.9322, 0.9550, and 0.9450, respectively.Conclusion: These findings contribute valuable information relating to the intricate relationship between metabolic disorders in VCI, neurodegeneration and vascular/neuroinflammation. Our findings also provide a TCM perspective for the precise diagnosis and treatment of VCI in the context of precision medicine.

Idiopathic pulmonary fibrosis (IPF) is a fatal interstitial lung disease with an unclear pathogenesis. This study aimed to elucidate the function and potential mechanisms of TUG1 in IPF progression. Cell viability and migration were detected by CCK-8 and transwell assays. Autophagy, fibrosis, or EMT-related proteins were measured by Western blotting. Pro-inflammatory cytokine levels were assessed by ELISA kits. The subcellular localization of TUG1 was observed by FISH assay. RIP assay detected the interaction between TUG1 and CDC27. TUG1 and CDC27 was up-regulated in TGF-β1-induced RLE-6TN cells. TUG1 depletion suppressed pulmonary fibrosis via attenuating inflammation, EMT, inducing autophagy and inactivating PI3K/Akt/mTOR pathway in vitro and in vivo. TUG1 knockdown prevented CDC27 expression. TUG1 silencing ameliorated pulmonary fibrosis by reducing CDC27 expression and inhibiting PI3K/Akt/mTOR pathway.

Abstract Background Nicotine dependence is a significant public health issue, and understanding the factors associated with nicotine dependence in this population is crucial for developing effective interventions. This study examined the association between family functioning and nicotine dependence levels of smoking fathers based on the McMaster model of family functioning (MMFF), providing evidence for future interventions. Methods In this study, we selected fathers of first- to fifth-grade students from 10 pilot elementary schools in Qingdao whose families smoked. We used the Fagerstrom test to assess nicotine dependence and the Family Assessment Device to evaluate family functioning. We performed univariate analysis to compare differences among those with different levels of nicotine dependence, and we used an ordinal logistic regression analysis to investigate the influences related to nicotine dependence. Results This study included 874 smokers, with 78.5% having mild nicotine dependence, 11.7% having moderate dependence, and 9.84% having severe dependence. Univariate analysis showed that smokers with severe dependence had lower education levels, higher prevalence of chronic diseases, more frequent alcohol consumption, and poorer family functioning compared to those with mild to moderate dependence. Ordinal logistic regression analysis showed that poorer general functioning scores (OR = 1.087, 95% CI: 1.008–1.173, P = 0.030), poorer behavioral control (OR = 1.124, 95% CI: 1.026–1.232, P = 0.012), more quit attempts, frequent alcohol consumption, and longer smoking duration may be associated with a higher likelihood of developing severe nicotine dependence. The older age of starting smoking and higher education level may be associated with a lower likelihood of developing severe nicotine dependence. However, it is important to note that the cross-sectional nature of this study precludes the determination of causal relationships. Conclusions This study finds that heavy nicotine dependence in smoking fathers is associated with risky behaviors and demographics such as longer smoking duration and frequent alcohol consumption. Targeted smoking cessation interventions are crucial for this group, taking these specific factors into consideration. Family functioning, particularly general functioning and behavioral control, may also be linked to nicotine dependence, indicating the need for further research in this area.

Accurate wind power forecasting (WPF) is critical in optimizing grid operations and efficiently managing wind energy resources. Challenges arise from the inherent volatility and non-stationarity of wind data, particularly in short-to-medium-term WPF, which extends to longer forecast horizons. To address these challenges, this study introduces a novel model that integrates Improved Empirical Mode Decomposition (IEMD) with an enhanced Transformer called TransIEMD. TransIEMD begins by decomposing the wind speed into Intrinsic Mode Functions (IMFs) using IEMD, transforming the scalar wind speed into a vector form that enriches the input data to reveal hidden temporal dynamics. Each IMF is then processed with channel attention, embedding, and positional encoding to prepare inputs for an enhanced Transformer. The Direct Embedding Module (DEM) provides an alternative viewpoint on the input data. The distinctive perspectives of IEMD and DEM offer interaction through cross-attention within the encoder, significantly enhancing the ability to capture dynamic wind patterns. By combining cross-attention and self-attention within the encoder–decoder structure, TransIEMD demonstrates enhanced proficiency in detecting and leveraging long-range dependencies and dynamic wind patterns, improving the forecasting precision. Extensive evaluations on a publicly available dataset from the National Renewable Energy Laboratory (NREL) demonstrate that TransIEMD significantly improves the forecasting accuracy across multiple horizons of 4, 8, 16, and 24 h. Specifically, at the 24 h forecast horizon, TransIEMD achieves reductions in the normalized mean absolute error and root mean square error of 4.24% and 4.37%, respectively, compared to the traditional Transformer. These results confirm the efficacy of integrating IEMD with attention mechanisms to enhance the accuracy of WPF.

Introduction Diabetic foot ulcer (DFU) stands as a severe diabetic lower extremity complication, characterized by high amputation rates, mortality, and economic burden. We propose using Mendelian randomization studies to explore shared and distinct risk factors for diabetic lower extremity complications.Research design and methods We selected uncorrelated genetic variants associated with 85 phenotypes in five categories at the genome-wide significance level as instrumental variables. Genetic associations with DFU, diabetic polyneuropathy (DPN), and diabetic peripheral artery disease (DPAD) were obtained from the FinnGen and UK Biobank studies.Results Body mass index (BMI) emerged as the only significant risk factor for DPAD, DPN, and DFU, independent of type 2 diabetes, fasting glucose, fasting insulin, and HbA1c. Educational attainment stood out as the sole significant protective factor against DPAD, DPN, and DFU. Glycemic traits below the type 2 diabetes diagnosis threshold showed associations with DPAD and DPN. While smoking history exhibited suggestive associations with DFU, indicators of poor nutrition, particularly total protein, mean corpuscular hemoglobin, and mean corpuscular volume, may also signal potential DFU occurrence.Conclusions Enhanced glycemic control and foot care are essential for the diabetic population with high BMI, limited education, smoking history, and indicators of poor nutrition. By focusing on these specific risk factors, healthcare interventions can be better tailored to prevent and manage DFU effectively.

With the increasing scarcity of spectrum resources and the continuous exploration of high-frequency spectrum resources, millimeter wave (mmWave) has been introduced to 5G deployment scenarios, one of which is the high-speed railway wireless networks. Because of the characteristics of mmWave, it is suitable for Enhanced Mobile Broadband (eMBB) service in subway tunnel scenarios, in which there are increasing data rate requirement of passengers to be satisfied. To this end, we propose a new scheme of Integrated Access and Backhaul (IAB) plus small stations in the carriage, in which the backhaul link is 5G Non-Standalone (NSA) networking with 4G for master node and 5G mmWave for secondary node, so as to overcome the handover burden and penetration loss of mmWave, that is more severe in the high-frequency range. Based on the existing old 4G equipment, mmWave is applied for 5G user-plane connection in NSA dual connection, while the control plane connection still relies on 4G commercial frequency band, in order that even if the millimeter-wave link quality is seriously degraded, the IAB-node can still maintain the connection with the master node and re-establish the mmWave link through the exchange of control information on the 4G low-frequency band. Unlike previous attempts, the proposed approach applies dual link to the backhaul link and 5G commercial frequency band to the in-vehicle access link of small station with Radio Access Network (RAN) sharing technology, so that the bandwidth shared by the operators can be aggregated in one small station and fully used for user access to match the mmWave large bandwidth of the backhaul link. Field trials showed that although the data rates were somewhat unstable at the handover points, which still need to be improved in the future work, the average end-to-end uplink and downlink data rates were greatly improved, compared to the previous average data rates when only outdoor signal penetrating indoors was used in subway tunnel. In addition, the proposed scheme can reduce the operator’s construction and maintenance costs by making full use of the existing 4G equipment, which provides a reference for upgrading 4G to 5G signal coverage for all tunnel scenarios.

Equine coronavirus (ECoV) was first identified in the USA and has been previously described in several countries. In order to test the presence of ECoV in China, we collected 51 small intestinal samples from donkey foals with diarrhoea from a donkey farm in Shandong Province, China between August 2020 and April 2021. Two samples tested positive for ECoV and full-length genome sequences were successfully obtained using next-generation sequencing, one of which was further confirmed by Sanger sequencing. The two strains shared 100% sequence identity at the scale of whole genome. Bioinformatics analyses further showed that the two Chinese strains represent a novel genetic variant of ECoV and shared the highest sequence identity of 97.05% with the first identified ECoV strain – NC99. In addition, it may be a recombinant, with the recombination region around the NS2 gene. To our knowledge, this is the first documented report of ECoV in China, highlighting its risk to horse/donkey breeding. In addition, its potential risk to public health also warrants further investigation.