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Abstract Non-small cell lung cancer (NSCLC) constitutes a significant portion of lung cancers and cytotoxic drugs (e.g. cisplatin) are currently the first-line treatment. However, NSCLC has developed resistance to this drug, which limits the therapeutic effect and thus affects prognosis. NSCLC sc-RNA-seq data were downloaded from the GEO database and Ku Leuven Laboratory for Functional Epigenetics, and bulk RNA-seq data were obtained from the TCGA database. The “Seurat” package was employed for scRNA-seq data processing, and the uniform manifold approximation and projection (UMAP) were applied for downscaling and cluster identification. Use the FindAllMarkers function to find differential genes (DEGs) for tumor stem cells. Then, we performed univariate regression analyses on the DEGs to identify potential prognostic genes. We created a machine learning framework based on potential prognostic genes, which combines 10 machine learning methods and their 101 combinations to get the optimal prognostic risk model. The model was evaluated in the training set and validation set. A nomogram was developed to provide physicians with a quantitative tool for prognosis prediction. Finally, we evaluated the expression and functionality of SLC2A1. We discovered 22 cell clusters containing 218379 cells by examining single-cell RNA sequencing datasets (GSE148071, KU_lom, GSE131907, GSE136246, GSE127465). Tumour cells were isolated for subpopulation analysis and 162 differential genes from SOX2_cancer were obtained. After univariate Cox analysis, we found 23 genes with prognostic potential prognostic value and utilized them to develop 101‑combination machine learning computational framework. We eventually picked the best performing ‘StepCox[both] + RSF’, which includes 8 genes. The model has a relatively high prediction accuracy in both TCGA and GEO datasets. In in vitro investigations, targeted suppression of the SLC2A1 gene resulted in significant reductions in proliferation, invasion and migration in A549 cells. In addition, a significant reduction in cisplatin resistance was seen in A549/DDP cells. The outcomes demonstrated the precision and credibility of the prognostic model for NSCLC, highlighting its potential significance in the treatment and prognosis of individuals affected by this disease. SLC2A1 may become a promising prognostic marker and a potential therapeutic target, offering valuable insights to inform clinical treatment decisions.

Abstract Purpose Lung adenocarcinoma (LUAD) significantly contributes to cancer-related mortality worldwide. The heterogeneity of the tumor immune microenvironment in LUAD results in varied prognoses and responses to immunotherapy among patients. Consequently, a clinical stratification algorithm is necessary and inevitable to effectively differentiate molecular features and tumor microenvironments, facilitating personalized treatment approaches. Methods We constructed a comprehensive single-cell transcriptional atlas using single-cell RNA sequencing data to reveal the cellular diversity of malignant epithelial cells of LUAD and identified a novel signature through a computational framework coupled with 10 machine learning algorithms. Our study further investigates the immunological characteristics and therapeutic responses associated with this prognostic signature and validates the predictive efficacy of the model across multiple independent cohorts. Results We developed a six-gene prognostic model (MYO1E, FEN1, NMI, ZNF506, ALDOA, and MLLT6) using the TCGA-LUAD dataset, categorizing patients into high- and low-risk groups. This model demonstrates robust performance in predicting survival across various LUAD cohorts. We observed distinct molecular patterns and biological processes in different risk groups. Additionally, analysis of two immunotherapy cohorts (N = 317) showed that patients with a high-risk signature responded more favorably to immunotherapy compared to those in the low-risk group. Experimental validation further confirmed that MYO1E enhances the proliferation and migration of LUAD cells. Conclusion We have identified malignant cell-associated ligand–receptor subtypes in LUAD cells and developed a robust prognostic signature by thoroughly analyzing genomic, transcriptomic, and immunologic data. This study presents a novel method to assess the prognosis of patients with LUAD and provides insights into developing more effective immunotherapies.

Background: Osteocytes are the main stress-sensing cells in bone. The substances secreted by osteocytes under mechanical loading play a crucial role in maintaining body homeostasis. Osteocytes have recently been found to release exosomes into the circulation, but whether they are affected by mechanical loading or participate in the regulation of systemic homeostasis remains unclear. Methods: We used a tail-suspension model to achieve mechanical unloading on osteocytes. Osteocyte-specific CD63 reporter mice were used for osteocyte exosome tracing. Exosome detection and inhibitor treatment were performed to confirm the effect of mechanical loading on exosome secretion by osteocytes. Co-culture, GW4869 and exosome treatment were used to investigate the biological functions of osteocyte-derived exosomes on brown adipose tissue (BAT) and primary brown adipocytes. Osteocyte-specific Dicer KO mice were used to screen for loading-sensitive miRNAs. Dual luciferase assay was performed to validate the selected target gene. Results: Firstly, we found the thermogenic activity was increased in BAT of mice subjected to tail suspension, which is due to the effect of unloaded bone on circulating exosomes. Further, we showed that the secretion of exosomes from osteocytes is regulated by mechanical loading, and osteocyte-derived exosomes can reach BAT and affect thermogenic activity. More importantly, we confirmed the effect of osteocyte exosomes on BAT both in vivo and in vitro. Finally, we discovered that let-7e-5p contained in exosomes is under regulation of mechanical loading and regulates thermogenic activity of BAT by targeting Ppargc1a. Conclusion: Exosomes derived from osteocytes are loading-sensitive, and play a vital role in regulation on BAT, suggesting that regulation of exosomes secretion can restore homeostasis. The translational potential of this article: This study provides a biological rationale for using osteocyte exosomes as potential agents to modulate BAT and even whole-body homeostasis. It also provides a new pathological basis and a new treatment approach for mechanical unloading conditions such as spaceflight.

Abstract Purpose Recent studies have increasingly linked Ephrin receptor B2 (EPHB2) to cancer progression. However, comprehensive investigations into the immunological roles and prognostic significance of EPHB2 across various cancers remain lacking. Methods We employed various databases and bioinformatics tools to investigate the impact of EPHB2 on prognosis, immune infiltration, genome instability, and response to immunotherapy. Validation of the correlation between EPHB2 expression and M2 macrophages included analyses using bulk and single-cell transcriptomic datasets, spatial transcriptomics, and multi-fluorescence staining. Moreover, we performed cMap web tool to screen for EPHB2-targeted compounds and assessed their potential through molecular docking and dynamics simulations. Additionally, in vitro validation using lung adenocarcinoma (LUAD) cell lines was conducted to confirm the bioinformatics predictions about EPHB2. Results EPHB2 dysregulation was observed across multiple cancer types, where it demonstrated significant diagnostic and prognostic value. Gene Set Enrichment Analysis (GSEA) indicated that EPHB2 is involved in enhancing cellular proliferation, invasiveness of cancer cells, and modulation of the anti-cancer immune response. Furthermore, it is emerged as a pan-cancer marker for M2 macrophage infiltration, supported by integrated analyses of transcriptomics and multiple fluorescence staining. In LUAD cells, knockdown of EPHB2 expression led to a decrease in both cell proliferation and migratory activity. Conclusion EPHB2 expression may serve as a pivotal indicator of M2 macrophage infiltration, offering vital insights into tumor dynamics and progression across various cancers, including lung adenocarcinoma, highlighting its significant prognostic and therapeutic potential for further exploration.

Highlights Fundamental principles for designing synergistic composite catalysts are reviewed. The synergistic effect between various active sites, promoting electrocatalytic performance in different reactions are highlighted. The challenges and perspectives for multiple active sites catalysts are discussed.

Abstract Introduction Dichoptic training has emerged as a promising rehabilitation approach for improving binocular visual function in patients with strabismus. A prospective observational study design was employed to assess the effectiveness of online video game-based dichoptic training in rehabilitating binocular visual function in patients who had undergone an operation for intermittent exotropia. Methods A total of 64 patients who had undergone an operation for intermittent exotropia were recruited and divided into the training group and the control group based on whether they would receive the dichoptic training. The dichoptic training was conducted for 3 months in the training group and the control group would not accept any form of orthoptic therapy. Assessments of binocular visual functions and deviation were conducted at baseline, 3-month and 6-month follow-up. Results Twenty-nine participants in the training group (mean 9.69 ± 2.66 years old) and 26 participants in the control group (mean 8.41 ± 2.64 years old) completed follow-up. At both 3- and 6-month follow-ups, the training group showed superior distance stereopsis compared to the control group, with near stereopsis only showing significant difference at the 6-month follow-up. Additionally, the training group exhibited significantly less distance exo-deviation drift than the control group at these times, and no significant difference was observed in near exo-deviation drift between the groups. The control group had a significantly higher rate of suboptimal surgical outcomes at both the 3- and 6-month follow-up. However, no significant differences were observed in simultaneous perception and fusion functions between the two groups. Conclusions Online video game-based dichoptic training has the potential to become a novel postoperative rehabilitation strategy for patients with intermittent exotropia.

Abstract Background Advanced maternal age may affect the intrauterine environment and increase the risk of neurodevelopmental disorders in offspring. Thyroid hormones are critical for fetal neurological development but whether maternal age influences fetal thyroid hormone levels in euthyroid mothers is unknown. Objective This study evaluated the association between cord blood thyroid hormones and maternal age, fetal sex, maternal thyroid function, and other perinatal factors. Methods The study population consisted of 203 healthy women with term singleton pregnancies who underwent elective cesarean section. Maternal levels of free T3 (fT3), free T4 (fT4) and TSH before delivery, and cord levels of fT3, fT4 and TSH were measured. Spearman’s correlation coefficient and multiple linear regression analyses were performed to determine the correlation between cord thyroid hormone parameters and maternal characteristics. Results There were no significant differences in maternal serum or cord blood thyroid hormone levels between male and female births. In multivariate linear regression analysis, maternal age and maternal TSH values were negatively associated with the cord blood levels of fT3 in all births, after adjusting for confounding factors. Maternal age was more closely associated with the cord blood levels of fT3 in female than in male births. Conclusion The inverse association between maternal age and cord blood levels of fT3 in euthyroid pregnant women suggested an impact of maternal aging on offspring thyroid function.

Abstract The convergence of cultural and aesthetic elements in timber structures from China’s Tang Dynasty (618–907 AD) and traditional Japanese architecture provides a rich tapestry of architectural evolution and cross-cultural exchanges. Addressing the challenge of distinguishing and understanding the intricate styles of these structures is significant for both historical comprehension and preservation efforts. This research introduces an innovative approach by integrating the Multi-Head Attention (MHA) mechanism into the YOLOv8 model, enhancing the detection of architectural features with improved precision and recall. Our novel YOLOv8-MHA model not only demonstrates a notable improvement in recognizing intricate architectural details but also significantly advances the state of the art in object detection within complex settings. Quantitative results underscore the model’s effectiveness, achieving a precision of 95.6%, a recall of 85.6%, and a mean Average Precision (mAP@50) of 94% across various Intersection over Union (IoU) thresholds. These metrics highlight the model’s superior capability to accurately identify and classify architectural elements, especially within environments rich with nuanced details, utilizing the enhanced YOLOv8-MHA algorithm. The application of our model extends beyond mere architectural analysis; it offers new insights into the intricate interplay of cultural identity and adaptability inherent in East Asian architectural heritage. The study establishes a solid foundation for the meticulous classification and analysis of architectural styles in timber structures within an expansive cultural and historical context, thereby enriching our understanding and preservation of these traditions.

Abstract DEAD-box helicase 17 (DDX17) is a typical member of the DEAD-box family with transcriptional cofactor activity. Although DDX17 is abundantly expressed in the myocardium, its role in heart is not fully understood. We generated cardiomyocyte-specific Ddx17-knockout mice (Ddx17-cKO), cardiomyocyte-specific Ddx17 transgenic mice (Ddx17-Tg), and various models of cardiomyocyte injury and heart failure (HF). DDX17 is downregulated in the myocardium of mouse models of heart failure and cardiomyocyte injury. Cardiomyocyte-specific knockout of Ddx17 promotes autophagic flux blockage and cardiomyocyte apoptosis, leading to progressive cardiac dysfunction, maladaptive remodeling and progression to heart failure. Restoration of DDX17 expression in cardiomyocytes protects cardiac function under pathological conditions. Further studies showed that DDX17 can bind to the transcriptional repressor B-cell lymphoma 6 (BCL6) and inhibit the expression of dynamin-related protein 1 (DRP1). When DDX17 expression is reduced, transcriptional repression of BCL6 is attenuated, leading to increased DRP1 expression and mitochondrial fission, which in turn leads to impaired mitochondrial homeostasis and heart failure. We also investigated the correlation of DDX17 expression with cardiac function and DRP1 expression in myocardial biopsy samples from patients with heart failure. These findings suggest that DDX17 protects cardiac function by promoting mitochondrial homeostasis through the BCL6-DRP1 pathway in heart failure.

Abstract Background Thesium chinense Turcz. (Named as Bai Rui Cao in Chinese) and its preparations (e.g., Bairui Granules) have been used to treat inflammatory diseases, such as acute mastitis, lobar pneumonia, tonsillitis, coronavirus disease 2019 (COVID-19), and upper respiratory tract infection. However, the material basis, pharmacological efficiency, and safety have not been illustrated. Methods Anti-inflammatory activity-guided isolation of constituents has been performed using multiple column chromatography, and their structures were elucidated by NMR spectroscopy and ECD calculations. The inhibitory effects on lung inflammation and safety of the crude ethanol extract (CE), Bairui Granules (BG), and the purified active constituents were evaluated using lipopolysaccharide (LPS)-stimulated acute lung inflammation (ALI) mice model or normal mice. Results Seven new compounds (1–7) and fifty-six known compounds (8–63) were isolated from T. chinense, and fifty-four were reported from this plant for the first time. The new flavonoid glycosides 1–2, new fatty acids 4–5, new alkaloid 7 as well as the known constituents including flavonoid aglycones 8–11, lignans 46–54, alkaloids 34 and 45, coumarins 57, phenylpropionic acids 27, and simple aromatic compounds 39, 44 and 58 exhibited anti-inflammatory activity. Network pharmacology analysis indicated that anti-inflammation of T. chinense was attributed to flavonoids and alkaloids by regulating inflammation-related proteins (e.g., TNF, NF-κB, TGF-β). Furthermore, constituents of T. chinense including kaempferol-3-O-glucorhamnoside (KN, also named as Bairuisu I, 19), astragalin (AG, Bairuisu II, 12), and kaempferol (KF, Bairuisu III, 8), as well as CE and BG could alleviate lung inflammation caused by LPS in mice by preventing neutrophils infiltration and the expression of the genes for pro-inflammatory cytokines NLRP3, caspase-1, IL-1β, and COX-2. After a 28-day subacute toxicity test, BG at doses of 4.875 g/kg and 9.750 g/kg (equivalent to onefold and twofold the clinically recommended dose) and CE at a dose of 11.138 g/kg (equivalent to fourfold the clinical dose of BG) were found to be safe and non-toxic. Conclusions The discovery of sixty-three constituents comprehensively illustrated the material basis of T. chinense. T. chinense and Bairui Granules could alleviate lung inflammation by regulating inflammation-related proteins and no toxicity was observed under the twofold of clinically used doses.

Abstract Wogonin is a natural flavone compound from the plant Scutellaria baicalensis, which has a variety of pharmacological activities such as anti-cancer, anti-virus, anti-inflammatory, and immune regulation. However, the potential mechanism of wogonin remains unknown. This study was to confirm the molecular mechanism of wogonin for acute monocytic leukemia treatment, known as AML-M5. The potential action targets between wogonin and acute monocytic leukemia were predicted from databases. The compound-target-pathway network and protein-protein interaction network (PPI) were constructed. The enrichment analysis of related targets and molecular docking were performed. The network pharmacological results of wogonin for AML-M5 treatment were verified using the THP-1 cell line. 71 target genes of wogonin associated with AML-M5 were found. The key genes TP53, SRC, AKT1, RELA, HSP90AA1, JUN, PIK3R1, and CCND1 were preliminarily found to be the potential central targets of wogonin for AML-M5 treatment. The PPI network analysis, GO analysis and KEGG pathway enrichment analysis demonstrated that the PI3K/AKT signaling pathway was the significant pathway in the wogonin for AML-M5 treatment. The antiproliferative effects of wogonin on THP-1 cells of AML-M5 presented a dose-dependent and time-dependent manner, inducing apoptosis, blocking the cell cycle at the G2/M phase, decreasing the expressions of CCND1, CDK2, and CyclinA2 mRNA, as well as AKT and p-AKT proteins. The mechanisms of wogonin on AML-M5 treatment may be associated with inhibiting cell proliferation and regulating the cell cycle via the PI3K/AKT signaling pathway.

Developing antibacterial and biocompatible coatings remains a challenge. A promising new thought is addressed to control the ion release behaviors through the design of the phases, structures, and morphologies of the products. In this study, TiO2-CuxO/Ag coatings were prepared via co-sputtering combined with annealing, the changes brought by different annealing temperatures were characterized, the physical, electrochemical, and antibacterial properties were concerned, and the relationship between biological performance and ions-releasing behavior was investigated. Results showed that the TiO2-CuxO/Ag coatings performed the morphologies of larger Ag particles seeded like islands on the dense CuO, such features improved the hydrophilicity and corrosion resistance of the Ti substrate. Rising the annealing temperature further enhanced the surface roughness, hydrophilicity, Young's modulus, hardness, and dynamic coefficient of friction of the coatings. Within the initial 3 h of soaking, both metal ions contributed to playing the antibacterial roles and produced a synergistic enhancement effect to satisfy the reduction of Staphylococcus aureus to 99.9%. In the next 3–120 h soaking, Ag releasing was suppressed and Cu releasing maintained its slow and stable trend, which made sure that the MC3T3-E1 cells could proliferate and adhere on the negatively charged coating surface, and copper oxide nanoparticles were phagocytized by cells. Among all TiO2-CuxO/Ag coatings, the M500 sample displayed superior performances on the surface roughness, contact angle, mechanical Young's modulus, and surface hardness, also realized the aim of killing pathogenic bacteria first and then activating cell survival, which enabled it to be potentially applied in clinical treatment.

Background: Glycoprotein non-metastatic melanoma B (GPNMB)/osteoactivin was first identified in the human melanoma cell lines. GPNMB plays a key role in the anti-inflammatory and antioxidative functions as well as osteoblast differentiation, cancer progression, and tissue regeneration. Recently, GPNMB was used as an anti-aging vaccine for mice. The present study aimed to investigate the potential of biofluid GPNMB as an aging biomarker in humans using serum and urine samples from an aging Chinese population. Methods: We analyzed RNA-sequencing data (GSE132040) from 17 murine organs across different ages to assess the gene expression of potential ageing biomarkers. Spearman's correlation coefficients were used to evaluate the relationship between gene expression and age. Meanwhile, a cross-sectional population study was conducted, which included 473 participants (aged 25–91 years), a representative subset of participants from the Peng Zu Study on Healthy Ageing in China (Peng Zu Cohort). Biofluid GPNMB levels were measured by ELISA. The associations of serum and urine GPNMB levels with various clinical and anthropometrical indices were assessed using ANOVA, Kruskal-Wallis H test, and univariate and multivariate linear regression analyses. Results: In mice, the Gpnmb mRNA expression levels showed a significant positive association with age in multiple organs in mice (P

Organoids, characterized by their high physiological attributes, effectively preserve the genetic characteristics, physiological structure, and function of the simulated organs. Since the inception of small intestine organoids, other organoids for organs including the liver, lungs, stomach, and pancreas have subsequently been developed. However, a comprehensive summary and discussion of research findings on gastrointestinal tract (GIT) organoids as disease models and drug screening platforms is currently lacking. Herein, in this review, we address diseases related to GIT organoid simulation and highlight the notable advancements that have been made in drug screening and pharmacokinetics, as well as in disease research and treatment using GIT organoids. Organoids of GIT diseases, including inflammatory bowel disease, irritable bowel syndrome, necrotizing enterocolitis, and Helicobacter pylori infection, have been successfully constructed. These models have facilitated the study of the mechanisms and effects of various drugs, such as metformin, Schisandrin C, and prednisolone, in these diseases. Furthermore, GIT organoids have been used to investigate viruses that elicit GIT reactions, including Norovirus, SARS-CoV-2, and rotavirus. Previous studies by using GIT organoids have shown that dasabuvir, gemcitabine, and imatinib possess the capability to inhibit viral replication. Notably, GIT organoids can mimic GIT responses to therapeutic drugs at the onset of disease. The GIT toxicities of compounds like gefitinib, doxorubicin, and sunset yellow have also been evaluated. Additionally, these organoids are instrumental for the study of immune regulation, post-radiation intestinal epithelial repair, treatment for cystic fibrosis and diabetes, the development of novel drug delivery systems, and research into the GIT microbiome. The recent use of conditioned media as a culture method for replacing recombinant hepatocyte growth factor has significantly reduced the cost associated with human GIT organoid culture. This advancement paves the way for large-scale culture and compound screening of GIT organoids. Despite the ongoing challenges in GIT organoid development (e.g., their inability to exist in pairs, limited cell types, and singular drug exposure mode), these organoids hold considerable potential for drug screening. The use of GIT organoids in this context holds great promises to enhance the precision of medical treatments for patients living with GIT diseases.

Pregnancy-induced hypertension syndrome (PIH), a prevalent and critical condition, has garnered increasing attention due to its significant impact on maternal and fetal health outcomes. The conventional treatment approaches rely on magnesium sulfate and various antihypertensive drugs; however, the clinical efficacy of these treatments is limited, highlighting the need to explore alternative avenues for improvement. Recently, a growing number of clinical studies have investigated the use of Salvia-ligustrazine or Ligustrazine in combination with conventional therapy. A comprehensive synthesis and critical analysis of these studies is necessary to evaluate the efficacy and safety of Salvia-ligustrazine or Ligustrazine in treating PIH. We sought all articles published prior to December 2, 2023, from seven databases to identify randomized controlled trials (RCTs) that involved traditional Chinese medicine Salvia-ligustrazine or Ligustrazine in combination with Western medicines for the conventional treatment of PIH, according to predefined inclusion criteria. The studies were assessed using the Cochrane Risk of Bias tool (ROB2.0), and meta-analyses were conducted using Stata 15.0 statistical software. We analyzed 47 RCTs encompassing 4,517 patients. The results demonstrated that combining Salvia-ligustrazine or Ligustrazine with Western medications was more efficacious than using Western medications alone. This combination improved the overall response rate, reduced the incidence of adverse pregnancy outcomes for mothers and infants, and decreased the occurrence of side effects associated with PIH treatment. While we evaluated the efficacy of traditional Chinese medicine injections of Salvia-ligustrazine or Ligustrazine alongside conventional Western treatments, our conclusions must be considered provisional due to potential bias and the limited availability of RCTs.

Natural fractures are widely developed and distributed in tight sandstone reservoirs in the western Sichuan Basin, China, influenced by complex tectonic movements and diagenetic processes. These natural fractures serve as the main flow channels and important storage spaces in such reservoirs. Understanding their development characteristics and controlling factors determines the positioning and production efficiency of tight gas wells. This paper first explores the types and characteristics of natural fractures in the second member of the Upper Triassic Xujiahe Formation (T3x2) in the western Sichuan Basin through outcrop investigations, core observations, image log interpretations, and thin section analyses. We study the main influencing factors controlling the development and distribution of tectonic shear fractures by combining the characterization of fracture attributes. The results show that the tight sandstone reservoirs in the T3x2 mainly contain tectonic fractures, diagenetic fractures, and overpressure fractures, among which shear fractures in tectonic fractures are the main types. There are four sets of tectonic shear fractures in the study area, oriented in the E-W, N-S, NE-SW, and NW-SE directions. The dip angles of fractures range from 20° to 80°, with an average dip angle of 53°, predominantly consisting of high-angle fractures. The lengths of fractures are mainly within 100 cm, with a mean value of 47.53 cm, and 76.2% of fractures have lengths less than 50 cm. Fracture apertures range from 2.51 to 163.19 μm, with an average of 30.54 μm. The proportion of effective fractures in tectonic shear fractures reaches 85.7%. The development of tectonic shear fractures is primarily influenced by lithology, rock mechanics stratigraphy, diagenetic processes and facies, and faults. Lithology stands as the fundamental factor influencing the degree of fracture development in tight reservoirs. In rocks with the same composition, as the grain size decreases, the degree of fracture development increases. Simultaneously, with the rise in muddy content, fracture development diminishes in siltstone, argillaceous siltstone, silty mudstone, and mudstone. The formation and distribution of fractures are governed by rock mechanics stratigraphy, primarily manifested as a decrease in fracture density with an increase in rock mechanics layer thickness within a certain range. Diagenetic processes affect the abundance of tectonic shear fractures by influencing the mechanical properties of rocks. The stronger the compaction and cementation, the poorer the physical properties, and the higher the brittleness of the rocks, resulting in a greater abundance of tectonic shear fractures. Rocks with strong compaction and robust cementation facies exhibit high brittleness, rendering them more susceptible to fracturing under identical stress conditions. Fractures are highly prevalent in both the hanging wall and footwall of faults. As the distance from the fault increases, the linear density of fractures significantly decreases, owing to the uneven distribution of stress perturbation caused by fault activity.

Background & Aims: Currently, there is limited knowledge on the clinical profile of drug-induced liver injury (DILI) in Chinese children. We aimed to assess the clinical characteristics, suspected drugs, and outcomes associated with pediatric DILI in China. Methods: This nationwide, multicenter, retrospective study, conducted between 2012 and 2014, analyzed 25,927 cases of suspected DILI at 308 medical centers using the inpatient medical register system. Utilizing the Roussel Uclaf causality assessment method score, only patients with scores ≥6 or diagnosed with DILI by three experts after scoring

Abstract Climate change is a serious environmental issue appearing in China. As a public service institution operating around the clock, the negative impact of hospitals on the environment is evident, promoting their workers’ pro-environmental behavior (PEB) through increasing climate change health risk perception (CHRP) is an effective method to protect the environment and achieve sustainable development. This study investigates how CHRP shapes pro-environmental attitude (PEA), pro-environmental intention (PEI), and pro-environmental behavior (PEB) among hospital workers. Using structural equation modeling (SEM) to determine the chain of causation from CHRP to PEB among hospital workers. The result shows that CHRP positively affects PEA and PEI, and PEI positively affects their PEB. In addition, although CHRP has no significant direct effect on PEB, it can play a crucial indirect effect through the mediating role of PEI. Moreover, the result of multiple regression shows that there are significant differences regarding PEA, PEI, and PEB.

Abstract More researchers are proposing artificial intelligence algorithms for Internet of Things (IoT) devices and applying them to themes such as smart cities and smart transportation. In recent years, relevant research has mainly focused on data processing and algorithm modeling, and most have shown good prediction results. However, many algorithmic models often adjust parameters for the corresponding datasets, so the robustness of the models is weak. When different types of data face other model parameters, the prediction performance often varies a lot. Thus, this work starts from the perspective of data processing and algorithm models. Taking traffic data as an example, we first propose a new data processing method that processes traffic data with different attributes and characteristics into a dataset that is more common for most models. Then we will compare different types of datasets from the perspective of multiple model parameters, and further analyze the precautions and changing trends of different traffic data in machine learning. Finally, different types of data and ranges of model parameters are explored, together with possible reasons for fluctuations in forecast results when data parameters change.

Abstract Background Dendrobium officinale is a medicinal plant with high commercial value. The Dendrobium officinale market in Yunnan is affected by the standardization of medicinal material quality control and the increase in market demand, mainly due to the inappropriate harvest time, which puts it under increasing resource pressure. In this study, considering the high polysaccharide content of Dendrobium leaves and its contribution to today’s medical industry, (Fourier Transform Infrared Spectrometer) FTIR combined with chemometrics was used to combine the yields of both stem and leaf parts of Dendrobium officinale to identify the different harvesting periods and to predict the dry matter content for the selection of the optimal harvesting period. Results The Three-dimensional correlation spectroscopy (3DCOS) images of Dendrobium stems to build a (Split-Attention Networks) ResNet model can identify different harvesting periods 100%, which is 90% faster than (Support Vector Machine) SVM, and provides a scientific basis for modeling a large number of samples. The (Partial Least Squares Regression) PLSR model based on MSC preprocessing can predict the dry matter content of Dendrobium stems with Factor = 7, RMSE = 0.47, R2 = 0.99, RPD = 8.79; the PLSR model based on SG preprocessing can predict the dry matter content of Dendrobium leaves with Factor = 9, RMSE = 0.2, R2 = 0.99, RPD = 9.55. Conclusions These results show that the ResNet model possesses a fast and accurate recognition ability, and at the same time can provide a scientific basis for the processing of a large number of sample data; the PLSR model with MSC and SG preprocessing can predict the dry matter content of Dendrobium stems and leaves, respectively; The suitable harvesting period for D. officinale is from November to April of the following year, with the best harvesting period being December. During this period, it is necessary to ensure sufficient water supply between 7:00 and 10:00 every day and to provide a certain degree of light blocking between 14:00 and 17:00.

Abstract Patients treated with RET protein tyrosine kinase inhibitors (TKIs) selpercatinib or pralsetinib develop RET TKI resistance by secondary RET mutations or alterative oncogenes, of which alterative oncogenes pose a greater challenge for disease management because of multiple potential mechanisms and the unclear tolerability of drug combinations. A patient with metastatic medullary thyroid carcinoma (MTC) harboring a RET activation loop D898_E901del mutation was treated with selpercatinib. Molecular alterations were monitored with tissue biopsies and cfDNA during the treatment. The selpercatinib-responsive MTC progressed with an acquired ETV6::NTRK3 fusion, which was controlled by selpercatinib plus the NTRK inhibitor larotrectinib. Subsequently, tumor progressed with an acquired EML4::ALK fusion. Combination of selpercatinib with the dual NTRK/ALK inhibitor entrectinib reduced the tumor burden, which was followed by appearance of NTRK3 solvent-front G623R mutation. Preclinical experiments validated selpercatinib plus larotrectinib or entrectinib inhibited RET/NTRK3 dependent cells, whereas selpercatinib plus entrectinib was necessary to inhibit cells with RET/NTRK3/ALK triple alterations or a mixture of cell population carrying these genetic alterations. Thus, RET-altered MTC adapted to selpercatinib and larotrectinib with acquisition of ETV6::NTRK3 and EML4::ALK oncogenes can be managed by combination of selpercatinib and entrectinib providing proof-of-concept of urgency of incorporating molecular profiling in real-time and personalized N-of-1 care transcending one-size-fits-all approach.

Abstract The rendering of urban 3D scenes involves a large number of models. In order to render scenes more efficiently, the main solution is to build a level of detail model (LOD). This may have the problem of building fragmentation, while relying on building a level of detail model (LOD) alone cannot meet the accuracy and fluency of large‐scale scene visualisation. Effective and reasonable data organisation has important research significance for the authors to achieve accurate and fast rendering of scenes. Therefore, the authors propose a large‐scale city model data organisation method considering building distribution to solve the above problems. This method first classifies the buildings in the scene at macro‐, meso‐ and microscales and records the classification using R‐trees. Then an adaptive quadtree is used to construct the data index of the city model. Finally, the data organisation of the large‐scale 3D city model is achieved by using the information of each node of the R‐tree as a constraint and combining with the adaptive quadtree. The results show that the method not only ensures the integrity of the user's area of interest but also can improve the efficiency of 3D scene construction.

Abstract In recent years, due to the shortage of blood products, some extensive burn patients were forced to adopt an “ultra-restrictive” transfusion strategy, in which the hemoglobin levels of RBC transfusion thresholds were 0.05). Among the ultra-restrictive transfusion group, patients with RBC transfusion threshold

Abstract Background Gene variants are responsible for more than half of hearing loss, particularly in nonsyndromic hearing loss (NSHL). The most common pathogenic variant in SLC26A4 gene found in East Asian populations is c.919-2A > G followed by c.2168A > G (p.H723R). This study was to evaluate their variant frequencies in patients with NSHL from special education schools in nine different areas of Southwest China’s Yunnan. Methods We performed molecular characterization by PCR-products directly Sanger sequencing of the SLC26A4 c.919-2AG and c.2168 A > G variants in 1167 patients with NSHL including 533 Han Chinese and 634 ethnic minorities. Results The SLC26A4 c.919-2A > G variant was discovered in 8 patients with a homozygous state (0.69%) and twenty-five heterozygous (2.14%) in 1167 patients with NSHL. The total carrier rate of the c.919-2A > G variant was found in Han Chinese patients with 4.50% and ethnic minority patients with 1.42%. A significant difference existed between the two groups (P G allele variant frequency was ranged from 3.93% in Kunming to zero in Lincang and Nvjiang areas of Yunnan. We further detected the SLC26A4 c.2168 A > G variant in this cohort with one homozygotes (0.09%) and seven heterozygotes (0.60%), which was detected in Baoshan, Honghe, Licang and Pu`er areas. Between Han Chinese group (0.94%) and ethnic minority group (0.47%), there was no statistical significance (P > 0.05). Three Han Chinese patients (0.26%) carried compound heterozygosity for c.919-2A > G and c.2168 A > G. Conclusion These data suggest that the variants in both SLC26A4 c.919-2A > G and c.2168 A > G were relatively less frequencies in this cohort compared to the average levels in most regions of China, as well as significantly lower than that in Han-Chinese patients. These results broadened Chinese population genetic information resources and provided more detailed information for regional genetic counselling for Yunnan.

Considerable progress has been made in recent years to the development of sustainable polymers from bio-based feedstocks. In this study, 100% bio-based nylons were prepared via an integrated chemical and biological process from lignocellulose. These novel nylons were obtained by the melt polymerization of 3-propyladipic acid derived from lignin and 1,5-pentenediamine/1,4-butanediamine derived from carbohydrate sugar. Central to the concept is a three-step noble metal free catalytic chemical funnelling sequence (Raney Ni mediated reductive catalytic fractionation - reductive funnelling - oxidative funnelling), which allowed for obtaining a single component 3-propyladipic acid from lignin with high efficiency. The structural and thermodynamic properties of the obtained nylons have been systematically investigated, and thus obtained transparent bio-based nylons exhibited higher Mw (>32,000) and excellent thermal stability (Td5% > 265 °C). Considering their moderate Tg and good melt strength, these transparent bio-based nylons could serve as promising functional additives or temperature-responsive materials.

ABSTRACTCoarse – and fine-grained bimodal-structures in a Al-Mg base alloy with rare earth elements of Sc/Zr is produced due to the ultrafast nonequilibrium solidification occurs in laser-induced molten pools during laser powder bed fusion (LPBF) additive manufacturing. A novel high-fidelity cellular automaton (CA) algorithm incorporating numerical calculations of molt-pool temperature fields elucidates the formation and evolution of the bimodal-structure. Subsequent heat treatment induces precipitation of Al3(Sc/Zr) particles within the grains, synergistically enhancing strength and plasticity of the LPBF-processed alloy. The crystal plastic finite element method (CPFEM) is used to reveal the synergistic effect between the strength and plasticity during the material tensile procedure. The bimodal-structure exhibits good fatigue resistance but intriguing anisotropy under low stress cyclic loading. It is proved that differentiated distribution patterns relative to the principal stress direction of the bimodal-structure have a significant influence on its fatigue performance. Numerical evolutionary of the bimodal grain deformation reflects this phenomenon.

Background: Colon adenocarcinoma (COAD) is one of the most common malignant tumors. The interplay involving ferroptosis between tumor and immune cells plays a crucial in cancer progression. However, the biological basis of this interplay in COAD development remains elusive. Methods: Transcriptome data of COAD samples were obtained from The Cancer Genome Atlas and National Center for Biotechnology Information databases. Using single-sample gene set enrichment analysis, we calculated the ferroptosis score (FS) and immune cell infiltration levels for each sample, leveraging the expression levels of genes related to ferroptosis and various immune cell types. Samples with FSs greater than the 75th percentile were classified into the high-FS subgroup, while those below the 25th percentile were categorized as the low-FS subgroup. Moreover, tumor tissue samples and adjacent normal tissue samples were collected from twenty colon patients. Using real-time quantitative polymerase chain reaction, we validated the expression of certain genes in these samples. Results: The COAD samples with high FSs experienced favorable survival probability and heightened sensitivity to anticancer drugs, with FSs negatively associated with the pathological stages. Moreover, the up-regulated genes in high-FS subgroup exhibited enrichment in immune-related pathways, suggesting a correlation between immunity and ferroptosis. Importantly, we discovered a key lncRNA-mRNA co-expression network linking tumor cell ferroptosis and immune infiltration (e.g., neutrophil) in the progression and classification of COAD. Further analysis identified several ferroptosis-related lncRNAs (e.g., RP11-399O19.9) within this network, indicating their potential roles in COAD progression and deserving in-depth study. Conclusions: Our findings provide novel insights into the underlying biological basis, particularly involving lncRNAs, at gene expression level associated with ferroptosis in COAD and cancer therapy. Nevertheless, further analysis and validation are required to expand the findings.

Non-Small Cell Lung Cancer (NSCLC) is a prevalent and deadly form of lung cancer worldwide with a low 5-year survival rate. Current treatments have limitations, particularly for advanced-stage patients. P21, a protein that inhibits the CCND1-CDK4 complex, plays a crucial role in cell proliferation. Computer-Aided Drug Design (CADD) based on pharmacophores can screen and design PPI inhibitors targeting the CCND1-CDK4 complex. By analyzing known inhibitors, key pharmacophores are identified, and computational methods are used to screen potential PPI inhibitors. Molecular docking, pharmacophore matching, and structure-activity relationship studies optimize the inhibitors. This approach accelerates the discovery of CCND1-CDK4 PPI inhibitors for NSCLC treatment. Molecular dynamics simulations of CCND1-CDK4-P21 and CCND1-CDK4 complexes showed stable behavior, comprehensive sampling, and P21’s impact on complex stability and hydrogen bond formation. A pharmacophore model facilitated virtual screening, identifying compounds with favorable binding affinities. Further simulations confirmed the stability and interactions of selected compounds, including 513457. This study demonstrates the potential of CADD in optimizing PPI inhibitors targeting the CCND1-CDK4 complex for NSCLC treatment. Extended simulations and experimental validations are necessary to assess their efficacy and safety.

BackgroundPrevious studies have highlighted the crucial role of immune cells in lung cancer development; however, the direct link between immunophenotypes and lung cancer remains underexplored.MethodsWe applied two-sample Mendelian randomization (MR) analysis, using genetic variants as instruments to determine the causal influence of exposures on outcomes. This method, unlike traditional randomized controlled trials (RCTs), leverages genetic variants inherited randomly at conception, thus reducing confounding and preventing reverse causation. Our analysis involved three genome-wide association studies to assess the causal impact of 731 immune cell signatures on lung cancer using genetic instrumental variables (IVs). We initially used the standard inverse variance weighted (IVW) method and further validated our findings with three supplementary MR techniques (MR–Egger, weighted median, and MR-PRESSO) to ensure robustness. We also conducted MR–Egger intercept and Cochran’s Q tests to assess heterogeneity and pleiotropy. Additionally, reverse MR analysis was performed to explore potential causality between lung cancer subtypes and identified immunophenotypes, using R software for all statistical calculations.ResultsOur MR analysis identified 106 immune signatures significantly associated with lung cancer. Notably, we found five suggestive associations across all sensitivity tests (P

Introduction: This study bridges traditional remedies and modern pharmacology by exploring the synergy between natural compounds and Ceritinib in treating Non-Small Cell Lung Cancer (NSCLC), aiming to enhance efficacy and reduce toxicities.Methods: Using a combined approach of computational analysis, machine learning, and experimental procedures, we identified and analyzed PD173074, Isoquercitrin, and Rhapontin as potential inhibitors of fibroblast growth factor receptor 3 (FGFR3). Machine learning algorithms guided the initial selection, followed by Quantitative Structure-Activity Relationship (QSAR) modeling and molecular dynamics simulations to evaluate the interaction dynamics and stability of Rhapontin. Physicochemical assessments further verified its drug-like properties and specificity.Results: Our experiments demonstrate that Rhapontin, when combined with Ceritinib, significantly suppresses tumor activity in NSCLC while sparing healthy cells. The molecular simulations and physicochemical evaluations confirm Rhapontin’s stability and favorable interaction with FGFR3, highlighting its potential as an effective adjunct in NSCLC therapy.Discussion: The integration of natural compounds with established cancer therapies offers a promising avenue for enhancing treatment outcomes in NSCLC. By combining the ancient wisdom of natural remedies with the precision of modern science, this study contributes to evolving cancer treatment paradigms, potentially mitigating the side effects associated with current therapies.

Renewable energy promotes the sustainable development of the environmental protection industry (EPI) and the economy. Meanwhile, the EPI provides various technologies and services for renewable energy, which is an important guarantee for the renewable energy market. This paper combines the DY and BK spillover indexes to discuss the cross-market risk contagion effect between EPI and renewable energy market from the perspective of time-frequency spillover. The results show that: (1) The spillover effect between the EPI and renewable energy market is cyclical from the perspective of frequency domain, and the total market spillover is dominated by high-frequency spillover. And the risk contagion has the characteristics of time-varying and vulnerability from the perspective of time domain. (2) The net spillover effect shows that the EPI plays the role of a net exporter in different frequency domains, the solar energy and the hydro market play more roles as risk receivers, and the role of the wind energy market is frequently converted. (3) The risk spillover network structure shows that EPI acts as an important node in the cross-market risk contagion path. Driven by EPI, the short-term risk transmission follows the path of “EPI-biomass energy-other four renewable energy markets”, and the medium and long-term follows the path of “EPI-wind energy-other four renewable energy markets”.

In recent years, UAV object tracking has provided technical support across various fields. Most existing work relies on convolutional neural networks (CNNs) or visual transformers. However, CNNs have limited receptive fields, resulting in suboptimal performance, while transformers require substantial computational resources, making training and inference challenging. Mountainous and jungle environments-critical components of the Earth's surface and key scenarios for UAV object tracking-present unique challenges due to steep terrain, dense vegetation, and rapidly changing weather conditions, which complicate UAV tracking. The lack of relevant datasets further reduces tracking accuracy. This article introduces a new tracking framework based on a state-space model called TrackingMamba, which uses a single-stream tracking architecture with Vision Mamba as its backbone. TrackingMamba not only matches transformer-based trackers in global feature extraction and long-range dependence modeling but also maintains computational efficiency with linear growth. Compared to other advanced trackers, TrackingMamba delivers higher accuracy with a simpler model framework, fewer parameters, and reduced FLOPs. Specifically, on the UAV123 benchmark, TrackingMamba outperforms the baseline model OSTtrack-256, improving AUC by 2.59% and Precision by 4.42%, while reducing parameters by 95.52% and FLOPs by 95.02%. The article also evaluates the performance and shortcomings of TrackingMamba and other advanced trackers in the complex and critical context of jungle environments, and it explores potential future research directions in UAV jungle object tracking.

With the rapid advancement of remote sensing technology, bitemporal remote sensing change detection (CD) techniques have also seen significant progress. However, existing CD tasks still face two challenges: 1) Variations in lighting and seasonal factors complicate imaging conditions, causing pseudovariation interference, and 2) the spatial distribution and shapes of building are diverse, leading to difficulties in extracting and utilizing effective change features. In this article, we propose the spatial-temporal evolution guided change detection network (STEGNet) to capture and fully utilize rich spatial-temporal information. Specifically, we develop the chrono colorizer to mitigate pseudovariant interference by standardizing color styles and enriching time series information. In addition, we introduce the temporal-spatial guidance module, which combines spatial-temporal information to guide the decoding operation and mitigate information loss during spatial-temporal fusion, resulting in finer prediction results. Experimental results on three benchmark datasets demonstrate that STEGNet effectively suppresses pseudovariation interference and significantly improves the integrity of detection boundaries.

Recently, hybrid networks, combining graph convolutional networks (GCNs) and convolutional neural networks into a unified framework, have garnered significant attention in hyperspectral image (HSI) classification. However, existing hybrid networks have the following limitations. 1) Existing methods primarily utilize simple fusion strategies such as concatenation or direct addition, resulting in the ineffective utilization of advantageous features. 2) Traditional GCNs only consider the relationship between pairs of vertices, limiting their ability to capture complex high-order and long-range correlations. In this work, a novel differential feature fusion network (DF2Net) is proposed for HSI classification. Specifically, DF2Net utilizes two subnetworks to learn features at different abstraction levels: 1) the spectral–spatial hypergraph convolutional network for capturing complex high-order and long-range correlations, and the spectral–spatial convolution network for pixel-level local information extraction. Subsequently, we introduce an advantageous feature differential enhancement fusion module, in which mutual enhancement of advantageous features from different network structures is performed, thereby improving the classification robustness of different regions in HSI. The experiments on four HSI benchmark datasets demonstrate that our DF2Net exhibits superior advantages over state-of-the-art models, particularly when the training samples are limited.

Multispectral point cloud, with spatial and multiple-band spectral information, provides the data basis for finer land cover 3-D classification. However, spectral information is not well utilized by traditional methods of point cloud classification. Benefiting from the excellent performance of graph neural networks on non-Euclidean data, it is well suited to the joint use of spatial and spectral information from multispectral point clouds to achieve better classification performance. However, existing graph-based methods for point cloud classification rely on manual experience to construct input graph and cannot adapt to the complexity of remote sensing scenes. In this article, we propose a novel multikernel graph structure learning (MKGSL) framework for multispectral point cloud classification. Specifically, we explore the high-dimensional feature distribution properties of multispectral point clouds in Hilbert space through the use of kernel method. An innovative multiple-kernel learning mechanism is embedded into our network, which allows to obtain better mappings adaptively. Simultaneously, a series of prior constraints designed based on land cover distribution characteristics are imposed on the network training process, which leads the learned graph of the multispectral point cloud to facilitate better classification. Our method is dedicated to adaptively constructing task-oriented graph structures to improve the performance of multispectral point cloud classification. Experimental comparisons demonstrate that the proposed MKGSL performs better than several state-of-the-art methods on two real multispectral point cloud datasets.

Abstract This study aims to promote the protection and inheritance of cultural heritage in private gardens in the Jiangnan area of China. By establishing a precise visual labeling system and accelerating the construction of a database for private garden features, we deepen the understanding of garden design philosophy. To this end, we propose an improved Jiangnan private garden recognition model based on You Only Look Once (YOLO) v8. This model is particularly suitable for processing garden environments with characteristics such as single or complex structures, rich depth of field, and cluttered targets, effectively enhancing the accuracy and efficiency of object recognition. This design integrates the Diverse Branch Block (DBB), Bidirectional Feature Pyramid Network (BiFPN), and Dynamic Head modules (DyHead) to optimize model accuracy, feature fusion, and object detection representational capability, respectively. The enhancements elevated the model's accuracy by 8.7%, achieving a mean average precision (mAP@0.5) value of 57.1%. A specialized dataset, comprising 4890 images and encapsulating various angles and lighting conditions of Jiangnan private gardens, was constructed to realize this. Following manual annotation and the application of diverse data augmentation strategies, the dataset bolsters the generalization and robustness of the model. Experimental outcomes reveal that, compared to its predecessor, the improved model has witnessed increments of 15.16%, 3.25%, and 11.88% in precision, mAP0.5, and mAP0.5:0.95 metrics, respectively, demonstrating exemplary performance in the accuracy and real-time recognition of garden target elements. This research not only furnishes robust technical support for the digitization and intelligent research of Jiangnan private gardens but also provides a potent methodological reference for object detection and classification research in analogous domains.

Abstract Chronic obstructive pulmonary disease (COPD) is a severe condition that leads to premature mortality and places a significant financial burden on healthcare systems. An adjunctive therapy in COPD includes the simultaneous administration of astragalus injection and ambroxol hydrochloride. Despite its widespread use, the effectiveness of this combined approach in COPD treatment has not been systematically evaluated. Thus, we conducted a systematic review and meta-analysis to assess the efficacy of combining astragalus injection with ambroxol hydrochloride as an adjuvant treatment for COPD. Six electronic databases were used to search for relevant randomized controlled trials, and data analysis was conducted using Review Manager 5.4. A total of 14 randomized controlled trials were included, involving 1070 patients who met the criteria. The results of the meta-analysis indicated that the combination of astragalus injection with ambroxol hydrochloride as an adjuvant treatment can improve various clinical parameters in patients with COPD compared to conventional treatment alone. These parameters include the clinical effective rate (OR = 5.44, 95% CI 3.51–8.43, I2 = 0%), partial pressure of oxygen in artery (MD = 1.12, 95% CI 0.87–1.36, I2 = 5%), partial pressure of carbon dioxide in artery (MD = − 1.43, 95% CI − 1.65 to − 1.21, I2 = 0%), forced expiratory volume in one second (MD = 0.30, 95% CI 0.18–0.42, I2 = 0%), percentage of forced expiratory volume in one second (MD = 16.18, 95% CI 12.60–19.76, I2 = 82%), forced vital capacity (MD = 0.33, 95% CI 0.21–0.45, I2 = 36%), hemoglobin (MD = − 16.17, 95% CI − 20.84 to − 11.51, I2 = 29%), and the ratio of forced expiratory volume in one second to forced vital capacity (MD = 2.51, 95% CI − 0.05 to 5.06, I2 = 0%). The combination of astragalus injection and ambroxol hydrochloride could be a selection of COPD patients as an adjuvant treatment. However, further validation is required to evaluate the effectiveness of combining astragalus injection and ambroxol hydrochloride as an adjunctive treatment for patients with COPD.

The tricarboxylic acid (TCA) cycle is a central route for generating cellular energy and precursors for biosynthetic pathways. Emerging evidences have shown that the aberrations of metabolic enzymes which affect the integrity of TCA cycle are implicated in various tumour pathological processes. Interestingly, several TCA enzymes exhibit the characteristics of RNA binding properties, and their long non-coding RNA (lncRNA) partners play critical regulatory roles in regulating the function of TCA cycle and tumour progression. In this review, we will discuss the functional roles of RNA binding proteins and their lncRNA partners in TCA cycle, with emphasis placed on the cancer progression. A further understanding of RNA binding proteins and their lncRNA partners in TCA cycle, as well as their molecular mechanisms in oncogenesis, will aid in developing novel layers of metabolic targets for cancer therapy in the near future. Abbreviations: CS: citrate synthase. AH: aconitase, including ACO1, and ACO2. IDH: isocitrate dehydrogenase, including IDH1, IDH2, and IDH3. KGDHC: α-ketoglutarate dehydrogenase complex, including OGDH, DLD, and DLST. SCS: succinyl-CoA synthase, including SUCLG1, SUCLG2, and SUCLA2. SDH: succinate dehydrogenase, including SDHA, SDHB, SDHC, and SDHD. FH: fumarate hydratase. MDH: malate dehydrogenase, including MDH1 and MDH2. PC: pyruvate carboxylase. ACLY: ATP Citrate Lyase. NIT: nitrilase. GAD: glutamate decarboxylase. ABAT: 4-aminobutyrate aminotransferase. ALDH5A1: aldehyde dehydrogenase 5 family member A1. ASS: argininosuccinate synthase. ASL: adenylosuccinate synthase. DDO: D-aspartate oxidase. GOT: glutamic-oxaloacetic transaminase. GLUD: glutamate dehydrogenase. HK: hexokinase. PK: pyruvate kinase. LDH: lactate dehydrogenase. PDK: pyruvate dehydrogenase kinase. PDH: pyruvate dehydrogenase complex. PHD: prolyl hydroxylase domain protein.

Abstract Prescribing appropriate exercise is an important means to improve the safety and efficacy of cardiac rehabilitation. Improper exercise may induce an increased cardiovascular risk in older persons with coronary heart disease. Cardiopulmonary exercise testing (CPET)-guided cardiac rehabilitation could be helpful for providing clinical evidence for cardiac rehabilitation therapy in older persons after percutaneous coronary intervention (PCI). We retrospectively included older persons who underwent PCI and cardiac rehabilitation based on CPET at the Cardiac Rehabilitation Center of Peking University Third Hospital from January 2014 to December 2019. Patients’ baseline and follow-up clinical data were collected. A total of 403 older persons after PCI were included in the study. The mean age was 80.5 ± 4.3. The mean follow-up time was 12 ± 2 months. During the follow-up period, no significant exercise-related adverse events occurred, and the peak oxygen uptake (VO2peak) increased compared with baseline (15.5 ± 3.8 ml/min/kg vs. 17.3 ± 4.1 ml/min/kg). Among the 90 patients (22.2%) without exercise habits at baseline who started regular exercise during follow-up, the improvement in VO2peak was most significant, at 3.2 ± 0.4 ml/min/kg. Cardiac rehabilitation based on CPET improved exercise habits and exercise tolerance in older persons with coronary heart disease after PCI.

Wheat rusts, including leaf, stripe, and stem rust, have been a threat to global food security due to their devastating impact on wheat yields. In recent years, significant strides have been made in understanding wheat rusts, focusing on disease spread mechanisms, the discovery of new host resistance genes, and the molecular basis of rust pathogenesis. This review summarizes the latest approaches and studies in wheat rust research that provide a comprehensive understanding of disease mechanisms and new insights into control strategies. Recent advances in genetic resistance using modern genomics techniques, as well as molecular mechanisms of rust pathogenesis and host resistance, are discussed. In addition, innovative management strategies, including the use of fungicides and biological control agents, are reviewed, highlighting their role in combating wheat rust. This review also emphasizes the impact of climate change on rust epidemiology and underscores the importance of developing resistant wheat varieties along with adaptive management practices. Finally, gaps in knowledge are identified and suggestions for future research are made. This review aims to inform researchers, agronomists, and policy makers, and to contribute to the development of more effective and sustainable wheat rust control strategies.

As semiconductor chip manufacturing technology advances, chip structures are becoming more complex, leading to an increased likelihood of void defects in the solder layer during packaging. However, identifying void defects in packaged chips remains a significant challenge due to the complex chip background, varying defect sizes and shapes, and blurred boundaries between voids and their surroundings. To address these challenges, we present a deep-learning-based framework for void defect segmentation in chip packaging. The framework consists of two main components: a solder region extraction method and a void defect segmentation network. The solder region extraction method includes a lightweight segmentation network and a rotation correction algorithm that eliminates background noise and accurately captures the solder region of the chip. The void defect segmentation network is designed for efficient and accurate defect segmentation. To cope with the variability of void defect shapes and sizes, we propose a Mamba model-based encoder that uses a visual state space module for multi-scale information extraction. In addition, we propose an interactive dual-stream decoder that uses a feature correlation cross gate module to fuse the streams’ features to improve their correlation and produce more accurate void defect segmentation maps. The effectiveness of the framework is evaluated through quantitative and qualitative experiments on our custom X-ray chip dataset. Furthermore, the proposed void defect segmentation framework for chip packaging has been applied to a real factory inspection line, achieving an accuracy of 93.3% in chip qualification.

Chemotherapy-induced diarrhea (CID) is a potentially serious side effect that often occurs during anticancer therapy and is caused by the toxic effects of chemotherapeutic drugs on the gastrointestinal tract, resulting in increased frequency of bowel movements and fluid contents. Among these agents, irinotecan (CPT-11) is most commonly associated with CID. Hesperidin (HPD), a flavonoid glycoside found predominantly in citrus fruits, has anti-oxidation properties and anti-inflammation properties that may benefit CID management. Nevertheless, its potential mechanism is still uncertain. In this study, we firstly evaluated the pharmacodynamics of HPD for the treatment of CID in a mouse model, then used network pharmacology and molecular docking methods to excavate the mechanism of HPD in relieving CID, and finally further proved the predicted mechanism through molecular biology experiments. The results demonstrate that HPD significantly alleviated diarrhea, weight loss, colonic pathological damage, oxidative stress, and inflammation in CID mice. In addition, 74 potential targets for HPD intervention in CID were verified by network pharmacology, with the top 10 key targets being AKT1, CASP3, ALB, EGFR, HSP90AA1, MMP9, ESR1, ANXA5, PPARG, and IGF1. The Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis revealed that the PI3K–Akt pathway, FoxO pathway, MAPK pathway, TNF pathway, and Ras pathway were most relevant to the HPD potential treatment of CID genes. The molecular docking results showed that HPD had good binding to seven apoptosis-related targets, including AKT1, ANXA5, CASP3, HSP90AA1, IGF1, MMP9, and PPARG. Moreover, we verified apoptosis by TdT-mediated dUTP nick-end labeling (TUNEL) staining and immunohistochemistry, and the hypothesis about the proteins above was further verified by Western blotting in vivo experiments. Overall, this study elucidates the potential and underlying mechanisms of HPD in alleviating CID.

Domain generalization (DG) aims to learn knowledge from multiple related domains to achieve a robust generalization performance in unseen target domains, which is an effective approach to mitigate domain shift in remote sensing image classification. Although the sharpness-aware minimization (SAM) method enhances DG capability and improves remote sensing image classification performance by promoting the convergence of the loss minimum to a flatter loss surface, the perturbation loss (maximum loss within the neighborhood of a local minimum) of SAM fails to accurately measure the true sharpness of the loss landscape. Furthermore, its variants often overlook gradient conflicts, thereby limiting further improvement in DG performance. In this paper, we introduce implicit sharpness-aware minimization (ISAM), a novel method that addresses the deficiencies of SAM and mitigates gradient conflicts. Specifically, we demonstrate that the discrepancy in training loss during gradient ascent or descent serves as an equivalent measure of the dominant eigenvalue of the Hessian matrix. This discrepancy provides a reliable measure for sharpness. ISAM effectively reduces sharpness and mitigates potential conflicts between gradients by implicitly minimizing the discrepancy between training losses while ensuring a sufficiently low minimum through minimizing perturbation loss. Extensive experiments and analyses demonstrate that ISAM significantly enhances the model’s generalization ability on remote sensing and DG datasets, outperforming existing state-of-the-art methods.

In the rapid development of urban construction, underground pipelines play a crucial role. However, the current underground pipelines have poor association with relevant management departments, and there are deficiencies in data completeness, accuracy, and information content. Managing and sharing information resources is relatively difficult, transforming the constructed 3D underground pipeline geographic information systems into an ‘Information silo’. This results in redundant construction and resource wastage of underground utilities. The complex distribution characteristics of underground utilities make rapid batch modeling and post-model maintenance challenging. Therefore, researching a 3D spatial data fusion model for urban underground utilities becomes particularly important. Given the above problem, this paper proposes a spatial data fusion model for underground pipeline scene modeling. It elaborates on the geometric, semantic, and temporal characteristics of underground pipelines, encapsulating these features. With underground pipeline objects as the core and pipeline characteristics as the foundation, a spatial data fusion model integrating multiple characteristics of underground pipelines has been constructed. Through software development, the data model designed in this paper facilitates rapid construction of underground pipeline scenes. This further enhances the consistency and integrity of underground pipeline data, enabling shared resources and comprehensive supervision of facility operations on a daily basis.

Abstract Malnutrition is a prevalent and severe issue in hospitalized patients with chronic diseases. However, malnutrition screening is often overlooked or inaccurate due to lack of awareness and experience among health care providers. This study aimed to develop and validate a novel digital smartphone‐based self‐administered tool that uses facial features, especially the ocular area, as indicators of malnutrition in inpatient patients with chronic diseases. Facial photographs and malnutrition screening scales were collected from 619 patients in four different hospitals. A machine learning model based on back propagation neural network was trained, validated, and tested using these data. The model showed a significant correlation (p

Abstract The severity of osteoarthritis (OA) and cartilage degeneration is highly associated with synovial inflammation. Although recent investigations have revealed a dysregulated crosstalk between fibroblast‐like synoviocytes (FLSs) and macrophages in the pathogenesis of synovitis, limited knowledge is available regarding the involvement of exosomes. Here, increased exosome secretion is observed in FLSs from OA patients. Notably, internalization of inflammatory FLS‐derived exosomes (inf‐exo) can enhance the M1 polarization of macrophages, which further induces an OA‐like phenotype in co‐cultured chondrocytes. Intra‐articular injection of inf‐exo induces synovitis and exacerbates OA progression in murine models. In addition, it is demonstrated that inf‐exo stimulation triggers the activation of glycolysis. Inhibition of glycolysis using 2‐DG successfully attenuates excessive M1 polarization triggered by inf‐exo. Mechanistically, HIF1A is identified as the determinant transcription factor, inhibition of which, both pharmacologically or genetically, relieves macrophage inflammation triggered by inf‐exo‐induced hyperglycolysis. Furthermore, in vivo administration of an HIF1A inhibitor alleviates experimental OA. The results provide novel insights into the involvement of FLS‐derived exosomes in OA pathogenesis, suggesting that inf‐exo‐induced macrophage dysfunction represents an attractive target for OA therapy.

Introduction: Drug dosages and combinations are the main factors that affect the efficacy of pleiotropic traditional Chinese medicine (TCM). Coptis chinensis Franch. (CF) is a representative TCM with multiple effects and is often combined with Tetradium ruticarpum (A. Jussieu) T. G. Hartley (TR) to treat cholestasis. The present study assessed the influence of CF dose and its combination with TR on the efficacy of CF in cholestasis treatment, including their effects on fecal metabolism and fecal microorganisms.Methods: Rats with α-naphthylisothiocyanate (ANIT, 50 mg/kg)-induced cholestasis were administered low (0.3 g/kg) and high (0.6 g/kg) doses of CF, as well as CF combined with TR at doses of 0.6 g/kg and 0.9 g/kg, respectively. The anti-cholestatic effects of these treatments were assessed by determining their anti-inflammatory, hypolipidemic, and anti-oxidative stress properties. Additionally, fecal metabolomics and fecal microorganisms were analyzed.Results: Low dose CF had a more potent hypolipidemic effect than high dose CF, whereas high dose CF had more potent anti-inflammatory and anti-oxidative stress effects. Combination with TR enhanced the hypolipidemic effect, but antagonized the anti-inflammatory effect, of CF. Analyses of fecal metabolomics and fecal microorganisms showed differences in the regulation of lipid- and amino acid metabolism-related pathways, including pathways of linoleic acid, tyrosine, and arachidonic acid metabolism, and amino acid biosynthesis between different doses of CF as well as between different doses of CF in combination with TR. These differences may contribute to differences in the anti-cholestatic effects of these preparations.Conclusion: CF dose influences its anti-cholestatic efficacy. The combination with TR had synergistic or antagonistic effects on the properties of CF, perhaps by altering fecal metabolism and fecal microbial homeostasis.

Background: Identifying biomarkers may lead to easier detection and a better understanding of pathogenesis of pancreatic ductal adenocarcinoma (PDAC). Methods: Plasma small extracellular vesicles (sEV) from 106 participants, including 20 healthy controls (HC), 12 chronic pancreatitis (CP) patients, 12 benign pancreatic tumour (BPT) patients, and 58 PDAC patients, were profiled for microRNA (miRNA) sequencing. Three machine learning methods were applied to establish and evaluate the diagnostic model. Results: The plasma sEV miRNA diagnostic signature (d-signature) selected using the three machine learning methods could distinguish PDAC patients from non-PDAC individuals, HC, and benign pancreatic disease (BPD, CP plus BPT) both in training and validation cohort. Combining the d-signature with carbohydrate antigen 19-9 (CA19-9) performed better than with each model alone. Plasma sEV miR-664a-3p was selected by all methods and used to predict PDAC diagnosis with high accuracy combined with CA19-9. Plasma sEV miR-664a-3p was significantly positively associated with the presence of vascular invasion, lower surgery ratio, and poor differentiation. MiR-664a-3p was mainly distributed in the PDAC cancer stroma, including fibers and vessels, and was accompanied by VEGFA expression. Overexpression of miR-664a-3p could promote the epithelial-mesenchymal transition (EMT) and angiogenesis. Conclusion: In conclusion, our study demonstrated the potential utility of the sEV-miRNA d-signature in the diagnosis of PDAC via machine learning methods. A novel sEV biomarker, miR-664a-3p, was identified for the diagnosis of PDAC. It can also potentially promote angiogenesis and metastasis, provide insight into PDAC pathogenesis, and reveal novel regulators of this disease.

Abstract Background Benevolent leadership is common in organizations, including hospitals, and is known to have positive effects on employees. Yet, nursing literature lacks sufficient research on its relationships with nurses’ behavior. Methods In March to April 2022, a cross-sectional study was carried out involving 320 nurses employed across various hospitals in Sichuan Province, China. Benevolent leadership, affective commitment, work engagement, and helping behavior were evaluated using the Benevolent Leadership Scale, Affective Commitment Scale, Work Engagement Scale, and Helping Behavior Questionnaire, respectively. The study employed structural equation model and the bootstrap method to investigate the proposed relationships. Results The SEM analysis results indicated a positive association between benevolent leadership and several outcomes among nurses. Specifically, benevolent leadership was found to be positively associated with nurses’ affective commitment (β = 0.58, p