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Abstract Background Lupus nephritis (LN) is a severe complication of systemic lupus erythematosus (SLE) with poor treatment outcomes. The role and underlying mechanisms of ferroptosis in LN remain largely unknown. We aimed to explore ferroptosis-related molecular subtypes and assess their prognostic value in LN patients. Methods Molecular subtypes were classified on the basis of differentially expressed ferroptosis-related genes (FRGs) via the Consensus ClusterPlus package. The enriched functions and pathways, immune infiltrating levels, immune scores, and immune checkpoints were compared between the subgroups. A scoring algorithm based on the subtype-specific feature genes identified by artificial neural network machine learning, referred to as the NeuraLN, was established, and its immunological features, clinical value, and predictive value were evaluated in patients with LN. Finally, immunohistochemical analysis was performed to validate the expression and role of feature genes in glomerular tissues from LN patients and controls. Results A total of 10 differentially expressed FRGs were identified, most of which showed significant correlation. Based on the 10 FRGs, LN patients were classified into two ferroptosis subtypes, which exhibited significant differences in immune cell abundances, immune scores, and immune checkpoint expression. A NeuraLN-related protective model was established based on nine subtype-specific genes, and it exhibited a robustly predictive value in LN. The nomogram and calibration curves demonstrated the clinical benefits of the protective model. The high-NeuraLN group was closely associated with immune activation. Clinical specimens demonstrated the alterations of ALB, BHMT, GAMT, GSTA1, and HAO2 were in accordance with bioinformatics analysis results, GSTA1 and BHMT were negatively correlated with the severity of LN. Conclusion The classification of ferroptosis subtypes and the establishment of a protective model may form a foundation for the personalized treatment of LN patients.

Objective: To develop a deep learning model to predict lymph node (LN) status in clinical stage IA lung adenocarcinoma patients. Methods: This diagnostic study included 1,009 patients with pathologically confirmed clinical stage T1N0M0 lung adenocarcinoma from two independent datasets (699 from Cancer Hospital of Chinese Academy of Medical Sciences and 310 from PLA General Hospital) between January 2005 and December 2019. The Cancer Hospital dataset was randomly split into a training cohort (559 patients) and a validation cohort (140 patients) to train and tune a deep learning model based on a deep residual network (ResNet). The PLA Hospital dataset was used as a testing cohort to evaluate the generalization ability of the model. Thoracic radiologists manually segmented tumors and interpreted high-resolution computed tomography (HRCT) features for the model. The predictive performance was assessed by area under the curves (AUCs), accuracy, precision, recall, and F1 score. Subgroup analysis was performed to evaluate the potential bias of the study population. Results: A total of 1,009 patients were included in this study; 409 (40.5%) were male and 600 (59.5%) were female. The median age was 57.0 years (inter-quartile range, IQR: 50.0–64.0). The deep learning model achieved AUCs of 0.906 (95% CI: 0.873–0.938) and 0.893 (95% CI: 0.857–0.930) for predicting pN0 disease in the testing cohort and a non-pure ground glass nodule (non-pGGN) testing cohort, respectively. No significant difference was detected between the testing cohort and the non-pGGN testing cohort (P = 0.622). The precisions of this model for predicting pN0 disease were 0.979 (95% CI: 0.963–0.995) and 0.983 (95% CI: 0.967–0.998) in the testing cohort and the non-pGGN testing cohort, respectively. The deep learning model achieved AUCs of 0.848 (95% CI: 0.798–0.898) and 0.831 (95% CI: 0.776–0.887) for predicting pN2 disease in the testing cohort and the non-pGGN testing cohort, respectively. No significant difference was detected between the testing cohort and the non-pGGN testing cohort (P = 0.657). The recalls of this model for predicting pN2 disease were 0.903 (95% CI: 0.870–0.936) and 0.931 (95% CI: 0.901–0.961) in the testing cohort and the non-pGGN testing cohort, respectively. Conclusions: The superior performance of the deep learning model will help to target the extension of lymph node dissection and reduce the ineffective lymph node dissection in early-stage lung adenocarcinoma patients.

Abstract The oxidative potential (OP) of particulate matter (PM) is a major driver of PM-associated health effects. In India, the emission sources defining PM-OP, and their local/regional nature, are yet to be established. Here, to address this gap we determine the geographical origin, sources of PM, and its OP at five Indo-Gangetic Plain sites inside and outside Delhi. Our findings reveal that although uniformly high PM concentrations are recorded across the entire region, local emission sources and formation processes dominate PM pollution. Specifically, ammonium chloride, and organic aerosols (OA) from traffic exhaust, residential heating, and oxidation of unsaturated vapors from fossil fuels are the dominant PM sources inside Delhi. Ammonium sulfate and nitrate, and secondary OA from biomass burning vapors, are produced outside Delhi. Nevertheless, PM-OP is overwhelmingly driven by OA from incomplete combustion of biomass and fossil fuels, including traffic. These findings suggest that addressing local inefficient combustion processes can effectively mitigate PM health exposure in northern India.

Abstract Background Prediction of lymph node metastasis (LNM) is critical for individualized management of papillary thyroid carcinoma (PTC) patients to avoid unnecessary overtreatment as well as undesired under-treatment. Artificial intelligence (AI) trained by thyroid ultrasound (US) may improve prediction performance. Methods From September 2017 to December 2018, patients with suspicious PTC from the first medical center of the Chinese PLA general hospital were retrospectively enrolled to pre-train the multi-scale, multi-frame, and dual-direction deep learning (MMD-DL) model. From January 2019 to July 2021, PTC patients from four different centers were prospectively enrolled to fine-tune and independently validate MMD-DL. Its diagnostic performance and auxiliary effect on radiologists were analyzed in terms of receiver operating characteristic (ROC) curves, areas under the ROC curve (AUC), accuracy, sensitivity, and specificity. Results In total, 488 PTC patients were enrolled in the pre-training cohort, and 218 PTC patients were included for model fine-tuning (n = 109), internal test (n = 39), and external validation (n = 70). Diagnostic performances of MMD-DL achieved AUCs of 0.85 (95% CI: 0.73, 0.97) and 0.81 (95% CI: 0.73, 0.89) in the test and validation cohorts, respectively, and US radiologists significantly improved their average diagnostic accuracy (57% vs. 60%, P = 0.001) and sensitivity (62% vs. 65%, P

Abstract Background The identification of survival predictors is crucial for early intervention to improve outcome in acute myeloid leukemia (AML). This study aim to identify chest computed tomography (CT)-derived features to predict prognosis for acute myeloid leukemia (AML). Methods 952 patients with pathologically-confirmed AML were retrospectively enrolled between 2010 and 2020. CT-derived features (including body composition and subcutaneous fat features), were obtained from the initial chest CT images and were used to build models to predict the prognosis. A CT-derived MSF nomogram was constructed using multivariate Cox regression incorporating CT-based features. The performance of the prediction models was assessed with discrimination, calibration, decision curves and improvements. Results Three CT-derived features, including myosarcopenia, spleen_CTV, and SF_CTV (MSF) were identified as the independent predictors for prognosis in AML (P

Abstract Radiotherapy effectiveness in breast cancer is limited by radioresistance. Nevertheless, the mechanisms behind radioresistance are not yet fully understood. RUVBL1 and RUVBL2, referred to as RUVBL1/2, are crucial AAA+ ATPases that act as co-chaperones and are connected to cancer. Our research revealed that RUVBL1, also known as pontin/TIP49, is excessively expressed in MMTV-PyMT mouse models undergoing radiotherapy, which is considered a murine spontaneous breast-tumor model. Our findings suggest that RUVBL1 enhances DNA damage repair and radioresistance in breast cancer cells both in vitro and in vivo. Mechanistically, we discovered that DTL, also known as CDT2 or DCAF2, which is a substrate adapter protein of CRL4, promotes the ubiquitination of RUVBL1 and facilitates its binding to RUVBL2 and transcription cofactor β-catenin. This interaction, in turn, attenuates its binding to acetyltransferase Tat-interacting protein 60 (TIP60), a comodulator of nuclear receptors. Subsequently, ubiquitinated RUVBL1 promotes the transcriptional regulation of RUVBL1/2-β-catenin on genes associated with the non-homologous end-joining (NHEJ) repair pathway. This process also attenuates TIP60-mediated H4K16 acetylation and the homologous recombination (HR) repair process. Expanding upon the prior study’s discoveries, we exhibited that the ubiquitination of RUVBL1 by DTL advances the interosculation of RUVBL1/2-β-catenin. And, it then regulates the transcription of NHEJ repair pathway protein. Resulting in an elevated resistance of breast cancer cells to radiation therapy. From the aforementioned, it is evident that targeting DTL-RUVBL1/2-β-catenin provides a potential radiosensitization approach when treating breast cancer.

Encouraging and astonishing developments have recently been achieved in image-based diagnostic technology. Modern medical care and imaging technology are becoming increasingly inseparable. However, the current diagnosis pattern of signal to image to knowledge inevitably leads to information distortion and noise introduction in the procedure of image reconstruction (from signal to image). Artificial intelligence (AI) technologies that can mine knowledge from vast amounts of data offer opportunities to disrupt established workflows. In this prospective study, for the first time, we develop an AI-based signal-to-knowledge diagnostic scheme for lung nodule classification directly from the computed tomography (CT) raw data (the signal). We find that the raw data achieves almost comparable performance with CT, indicating that it is possible to diagnose diseases without reconstructing images. Moreover, the incorporation of raw data through three common convolutional network structures greatly improves the performance of the CT models in all cohorts (with a gain ranging from 0.01 to 0.12), demonstrating that raw data contains diagnostic information that CT does not possess. Our results break new ground and demonstrate the potential for direct signal-to-knowledge domain analysis.

Recent years have seen the publication of numerous papers on the application of three-dimensional (3D) printing in plastic surgery. Despite this growing interest, a comprehensive bibliometric analysis of the field has yet to be conducted. To address this gap, we undertook a bibliometric study to map out the knowledge structure and identify research hotspots related to 3D printing in plastic surgery. We analyzed publications from 1995 to 2024, found in the Web of Science Core Collection (WoSCC), utilizing tools such as VOSviewer, CiteSpace, and the R package “bibliometrix”. Our analysis included 1,057 documents contributed by 5,545 authors from 1,620 organizations across 71 regions, and these were published in 400 journals. We observed a steady growth in annual publications, with Europe, Asia, North America, and Oceania leading in research output. Notably, Shanghai Jiao Tong University emerged as a primary research institution in this domain. The Journal of Craniofacial Surgery and Journal of Oral and Maxillofacial Surgery have made significant contributions to the field, with Thieringer, Florian M being the most prolific and frequently cited author. Key areas of focus include medical education and surgical procedures, with “3D printing”, “virtual surgical planning” and “reconstructive/orthognathic surgery” highlighted as future research hotspots. Our study provides a detailed bibliometric analysis, revealing the evolution and progress of 3D printing technologies in plastic surgery. As these technologies continue to advance, their impact on clinical practice and patient lives is expected to be profound.

Summary: Both concurrent chemoradiotherapy (CCRT) and induction chemotherapy (ICT) followed by CCRT are standard care of advanced nasopharyngeal carcinoma (NPC). However, tailoring personalized treatment is lacking. Herein, we established a radiogenomic clinical decision support system to classify patients into three subgroups according to their predicted disease-free survival (DFS) with CCRT and ICT response. The CCRT-preferred group was suitable for CCRT since they achieved good survival with CCRT, which could not be improved by ICT. The ICT-preferred group was suitable for ICT plus CCRT since they had poor survival with CCRT; additional ICT could afford an improved DFS. The clinical trial–preferred group was suitable for clinical trials since they exhibited poor survival regardless of receiving CCRT or ICT plus CCRT. These findings suggest that our radiogenomic clinical decision support system could identify optimal candidates for CCRT, ICT plus CCRT, and clinical trials, and may thus aid in personalized management of advanced NPC.

Summary: Background: Surgery is crucial for glioma treatment, but achieving complete tumour removal remains challenging. We evaluated the effectiveness of a probe targeting monocarboxylate transporter 4 (MCT4) in recognising gliomas, and of near-infrared window II (NIR-II) fluorescent molecular imaging and photothermal therapy as treatment strategies. Methods: We combined an MCT4-specific monoclonal antibody with indocyanine green to create the probe. An orthotopic mouse model and a transwell model were used to evaluate its ability to guide tumour resection using NIR-II fluorescence and to penetrate the blood–brain barrier (BBB), respectively. A subcutaneous tumour model was established to confirm photothermal therapy efficacy. Probe specificity was assessed in brain tissue from mice and humans. Finally, probe effectiveness in photothermal therapy was investigated. Findings: MCT4 was differentially expressed in tumour and normal brain tissue. The designed probe exhibited precise tumour targeting. Tumour imaging was precise, with a signal-to-background (SBR) ratio of 2.8. Residual tumour cells were absent from brain tissue postoperatively (SBR: 6.3). The probe exhibited robust penetration of the BBB. Moreover, the probe increased the tumour temperature to 50 °C within 5 min of laser excitation. Photothermal therapy significantly reduced tumour volume and extended survival time in mice without damage to vital organs. Interpretation: These findings highlight the potential efficacy of our probe for fluorescence-guided surgery and therapeutic interventions. Funding: Jilin Province Department of Science and Technology (20200403079SF), Department of Finance (2021SCZ06) and Development and Reform Commission (20200601002JC); National Natural Science Foundation of China (92059207, 92359301, 62027901, 81930053, 81227901, U21A20386); and CAS Youth Interdisciplinary Team (JCTD-2021-08).

Summary: Background: The diagnosis of hepatocellular carcinoma (HCC) often experiences latency, ultimately leading to unfavorable patient outcomes due to delayed therapeutic interventions. Our study is designed to develop and validate a model that employs triple-phase computerized tomography (CT)-based deep learning radiomics and clinical variables for early warning of HCC in patients with cirrhosis. Methods: We studied 1858 patients with cirrhosis primarily from the PreCar cohort (NCT03588442) between June 2018 and January 2020 at 11 centres, and collected triple-phase CT images and laboratory results 3–12 months prior to HCC diagnosis or non-HCC final follow-up. Using radiomics and deep learning techniques, early warning model was developed in the discovery cohort (n = 924), and then validated in an internal validation cohort (n = 231), and an external validation cohort from 10 external centres (n = 703). Findings: We developed a hybrid model, named ALARM model, which integrates deep learning radiomics with clinical variables, enabling early warning of the majority of HCC cases. The ALARM model effectively predicted short-term HCC development in cirrhotic patients with area under the curve (AUC) of 0.929 (95% confidence interval 0.918–0.941) in the discovery cohort, 0.902 (0.818–0.987) in the internal validation cohort, and 0.918 (0.898–0.961) in the external validation cohort. By applying optimal thresholds of 0.21 and 0.65, the high-risk (n = 221, 11.9%) and medium-risk (n = 433, 23.3%) groups, which covered 94.4% (84/89) of the patients who developed HCC, had significantly higher rates of HCC occurrence compared to the low-risk group (n = 1204, 64.8%) (24.3% vs 6.4% vs 0.42%, P

Background: The transplantation of endothelial progenitor cells (EPCs) has been shown to reduce neointimal hyperplasia following arterial injury. However, the efficacy of this approach is hampered by limited homing of EPCs to the injury site. Additionally, the in vivo recruitment and metabolic activity of transplanted EPCs have not been continuously monitored. Methods: EPCs were labeled with indocyanine green (ICG)-conjugated superparamagnetic iron oxide nanoparticles (SPIONs) and subjected to external magnetic field targeting to enhance their delivery to a carotid balloon injury (BI) model in Sprague–Dawley rats. Magnetic particle imaging (MPI)/ fluorescence imaging (FLI) multimodal in vivo imaging, 3D MPI/CT imaging and MPI/FLI ex vivo imaging was performed after injury. Carotid arteries were collected and analyzed for pathology and immunofluorescence staining. The paracrine effects were analyzed by enzyme-linked immunosorbent assay. Results: The application of a magnetic field significantly enhanced the localization and retention of SPIONs@PEG-ICG-EPCs at the site of arterial injury, as evidenced by both in vivo continuous monitoring and ex vivo by observation. This targeted delivery approach effectively inhibited neointimal hyperplasia and increased the presence of CD31-positive cells at the injury site. Moreover, serum levels of SDF-1α, VEGF, IGF-1, and TGF-β1 were significantly elevated, indicating enhanced paracrine activity. Conclusions: Our findings demonstrate that external magnetic field-directed delivery of SPIONs@PEG-ICG-EPCs to areas of arterial injury can significantly enhance their therapeutic efficacy. This enhancement is likely mediated through increased paracrine signaling. These results underscore the potential of magnetically guided SPIONs@PEG-ICG-EPCs delivery as a promising strategy for treating arterial injuries.

BackgroundAortic stenosis (AS) in combination with left ventricular outflow tract obstruction (LVOTO) has occasionally been reported. However, making a precise diagnosis and successfully treating this combination is challenging due to the hemodynamic interaction between the two conditions.Case summaryA 56-year-old male patient who had been diagnosed with severe AS and asymmetric left ventricular hypertrophy underwent aortic valve replacement (AVR) and a conventional septal myectomy. Immediately after the procedure, significant systolic anterior motion and mitral regurgitation developed, necessitating a surgical mitral edge-to-edge repair. Ten days after the procedure, the patient developed hematuria and LVOTO, which was confirmed by echocardiography. Because the LVOTO might have been the cause of the hematuria, the patient underwent alcohol septal ablation, but this had little effect. Three months later, a transapical beating-heart septal myectomy (TA-BSM) was performed in our hospital. Postoperatively, the LVOTO had been significantly ameliorated and the hematuria had resolved.ConclusionFor patients with AS and LVOTO due to a hypertrophic interventricular septum, inadequate amelioration of the LVOTO after AVR may lead to severe hemolytic hematuria. TA-BSM is a minimally invasive, safe, and effective surgical procedure for ameliorating LVOTO in patients with aortic valve prostheses.

Introduction: Traditional Chinese medicine (TCM) is gaining worldwide popularity as a complementary and alternative medicine. The isolation and characterization of active ingredients from TCM has become optional strategies for drug development. In order to overcome the inherent limitations of these natural products such as poor water solubility and low bioavailability, the combination of nanotechnology with TCM has been explored. Taking advantage of the benefits offered by the nanoscale, various drug delivery systems have been designed to enhance the efficacy of TCM in the treatment and prevention of diseases.Methods: The manuscript aims to present years of research dedicated to the application of nanotechnology in the field of TCM.Results: The manuscript discusses the formulation, characteristics and therapeutic effects of nano-TCM. Additionally, the formation of carrier-free nanomedicines through self-assembly between active ingredients of TCM is summarized. Finally, the paper discusses the safety behind the application of nano-TCM and proposes potential research directions.Discussion: Despite some achievements, the safety of nano-TCM still need special attention. Furthermore, exploring the substance basis of TCM formulas from the perspective of nanotechnology may provide direction for elucidating the scientific intension of TCM formulas.

Electromagnetic detection is one of the reliable means of detecting mining wire rope defects. Based on the electromagnetic method of mining wire rope defect detection, there is a problem of unclear characterization law of defects and signal properties, and there are irrelevant signal properties in quantitative identification, which affects the accuracy of identification. The characterization relationship between the leakage signal characteristics and defect changes is summarized by using the method of three-dimensional magnetic dipole theory calculation, Maxwell simulation and experimental mutual verification. Firstly, the three-dimensional magnetic dipole theoretical model of steel wire rope under magnetic field environment is established, and the model is applied to the standard defects at the magnetic field theoretical calculations and Maxwell analog simulation; the peak/valley absolute value, wave width, peak/valley difference, peak/valley width of the theoretical and simulated values are extracted respectively, and the characterization analysis of the defects and the signals is carried out; finally, the design of defects is experimentally verified, and the experimental values of the Spearman correlation coefficients are obtained. The experimental results show that: the width and depth of defects are positively correlated with three signal eigenvalues of peak/valley absolute value, wave width and peak/valley difference, and there is no obvious correlation with peak/valley width; the length of defects shows a trend of increasing and then decreasing with peak/valley absolute value, and positively correlates with wave width, peak/valley difference and peak/valley width. And it is found that the width defect signal amplitude is the strongest, the length defect signal amplitude is the weakest, the depth signal amplitude is in the middle, and the peak/valley difference and peak/valley absolute value have the highest correlation with the defect. This study has certain engineering guidance value.

Anti‐aging and tumorigenesis share common genes and pathways, and thus targeting these genes as part of anti‐aging interventions carries the risk of tumorigenesis. It is essential to understand the gene signatures that balance tumorigenesis and aging. To achieve this goal, we analyzed RNA‐sequencing data from three non‐tumorigenic immortalized cell lines that spontaneously escaped from senescence. By single sample gene set enrichment assay (ssGSEA) and GSEA analysis, we found that both cell growth signaling (E2F targets, MYC targets) and tumor surveillance mechanisms (DNA repair, G2M checkpoint, mitotic spindle) were up‐regulated in all three cell lines, suggesting that these genes are potential signatures for non‐tumorigenic immortalization. Further analysis revealed that the 182 commonly up‐regulated genes in these three cell lines overlapped with the DREAM/G2M pathway, which is known to be the upstream regulator of E2F, Myc targets, DNA repair, G2M checkpoint and mitotic spindle pathways in its cell cycle activation or inhibitory form. By western blotting, quantitative PCR and co‐immunoprecipitation, we verified that both forms of the DREAM pathway are up‐regulated in all three cell lines; this pathway facilitates control of cell cycle progression, supporting a new mechanism for non‐tumorigenic immortalization. Thus, we propose that the DREAM/G2M pathway plays important dual roles with respect to preventing tumorigenesis in the process of immortalization. Our data might serve as the basis for the identification of new signature pathways or gene biomarkers for non‐tumorigenic immortalization, and may aid in the discovery of new targets for tumor‐free anti‐aging drug screening.

Abstract Purpose Opioids are currently the most frequently prescribed analgesics in clinical practice. However, their effect on cancer progression remains a topic of debate. Opioid receptors (ORs) are present in various types of tumor cells and their expression levels vary depending on the type of tumor. This study aims to explore and preliminarily characterize the association between four different ORs (μ, δ, κ, and nociception/orphanin FQ peptide receptor) and the prognosis of different types of tumors for comparison, with a focus on nociception/ orphanin FQ peptide receptor. Methods The expression levels of four ORs in normal tissues and immune cells were obtained from Human Protein Atlas (HPA) RNA-seq dataset, Monaco dataset, and Consensus dataset. Pan-cancer analysis was performed using the The Cancer Genome Atlas (TCGA) dataset, which included the expression of four ORs in different cancer types, significant copy-number alterations (sCNA), gene mutations of the four ORs, survival analysis, co-expression genes analysis, functional enrichment analyses, and correlations between ORs and immune cell infiltration levels. Based on the results of bioinformatic analysis, we selected 10 cancer cell lines for validation in vitro using specific agonists for the four ORs. Results OPRL1 (opioid related nociceptin receptor 1 gene) exhibited the highest abundance across different types of cancers, while OPRM1 (opioid receptor mu 1 gene) and OPRD1 (opioid receptor delta 1 gene) were barely detectable in multiple cancer types. Pan-cancer survival analysis revealed the overall worse/better prognosis of the four ORs in certain cancer types. Elevated levels of OPRM1 appear to be associated with poorer outcomes in breast invasive carcinoma and kidney renal clear cell carcinoma. Elevated OPRD1 levels are connected to worsen outcomes in kidney renal clear cell carcinoma and liver hepatocellular carcinoma, but better prognosis in bladder urothelial carcinoma. Increased OPRK1 (opioid receptor kappa 1 gene) expression is linked to a poorer prognosis in kidney renal papillary cell carcinoma. Furthermore, high OPRL1 expression relates to worse outcomes in bladder urothelial carcinoma and liver hepatocellular carcinoma, but better outcomes in breast invasive carcinoma and pancreatic adenocarcinoma. Functional enrichment analyses found that immune-related pathways were enriched in OPRK1 and OPRL1, with OPRL1 exhibiting the highest correlation with immune cell infiltration. Different effects on cell growth, migration, and invasion were observed in different cancer types upon the administration of agonists for the four ORs. Conclusion OPRL1 may play a vital role in monocytes and regulating the immune response and tumor-infiltrating macrophages. Due to its high abundance in different types of tumors, it may hold greater clinical significance for oncology patients. OPRK1 also participates in immune-related pathways. OPRL1 could potentially serve as therapeutic targets for different types of cancers. Graphical Abstract

Abstract This study aimed to comprehensively evaluate non-contrast computed tomography (CT)-based radiomics for predicting early outcomes in patients with severe atherosclerotic renal artery stenosis (ARAS) after percutaneous transluminal renal angioplasty (PTRA). A total of 52 patients were retrospectively recruited, and their clinical characteristics and pretreatment CT images were collected. During a median follow-up period of 3.7 mo, 18 patients were confirmed to have benefited from the treatment, defined as a 20% improvement from baseline in the estimated glomerular filtration rate. A deep learning network trained via self-supervised learning was used to enhance the imaging phenotype characteristics. Radiomics features, comprising 116 handcrafted features and 78 deep learning features, were extracted from the affected renal and perirenal adipose regions. More features from the latter were correlated with early outcomes, as determined by univariate analysis, and were visually represented in radiomics heatmaps and volcano plots. After using consensus clustering and the least absolute shrinkage and selection operator method for feature selection, five machine learning models were evaluated. Logistic regression yielded the highest leave-one-out cross-validation accuracy of 0.780 (95%CI: 0.660–0.880) for the renal signature, while the support vector machine achieved 0.865 (95%CI: 0.769–0.942) for the perirenal adipose signature. SHapley Additive exPlanations was used to visually interpret the prediction mechanism, and a histogram feature and a deep learning feature were identified as the most influential factors for the renal signature and perirenal adipose signature, respectively. Multivariate analysis revealed that both signatures served as independent predictive factors. When combined, they achieved an area under the receiver operating characteristic curve of 0.888 (95%CI: 0.784–0.992), indicating that the imaging phenotypes from both regions complemented each other. In conclusion, non-contrast CT-based radiomics can be leveraged to predict the early outcomes of PTRA, thereby assisting in identifying patients with ARAS suitable for this treatment, with perirenal adipose tissue providing added predictive value.

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

Vehicular Edge Computing (VEC) is a promising technique to accommodate the computation-intensive and delay-sensitive tasks through offloading the tasks to the RoadSide-Unit (RSU) equipped with edge computing servers or neighboring vehicles. Nevertheless, the limited computation resources of edge computing servers and the mobility of vehicles make the offloading policy design very challenging. In this context, through considering the potential transmission gains brought by the mobility of vehicles, we propose an efficient computation offloading and resource allocation scheme in VEC networks with two kinds of offloading modes, i.e., Vehicle to Vehicle (V2V) and Vehicle to RSU (V2R). We define a new cost function for vehicular users by incorporating the vehicles’ offloading delay, energy consumption, and expenses with a differentiated pricing strategy, as well as the transmission gain. An optimization problem is formulated to minimize the average cost of all the task vehicles under the latency and computation capacity constraints. A distributed iterative algorithm is proposed by decoupling the problem into two subproblems for the offloading mode selection and the resource allocation. Matching theory-based and Lagrangian-based algorithms are proposed to solve the two subproblems, respectively. Simulation results show the proposed algorithm achieves low complexity and significantly improves the system performance compared with three benchmark schemes.

Fibrotic scarring and impaired myocardial calcium homeostasis serve as the two main factors in the pathology of heart failure following myocardial infarction (MI), leading to poor prognosis and death in patients. Serca2a is a target of interest in gene therapy for MI-induced heart failure via the regulation of intracellular calcium homeostasis and, subsequently, enhancing myocardial contractility. A recent study also reported that Serca2a ameliorates pulmonary fibrosis by blocking nuclear factor kB (NF-kB)/interleukin-6 (IL-6)-induced (SMAD)/TGF-β signaling activation, while the effect in MI-induced myocardial fibrosis remains to be addressed. Here, we loaded Serca2a plasmids into type 1 collagen-targeting nanoparticles to synthesize the GKWHCTTKFPHHYCLY-Serca2a-Liposome (GSL-NPs) for targeted treatment of myocardial infarction. We showed that GSL-NPs were effectively targeted in the scar area in MI-induced mice within tail-vein delivery for 48 h. Treatment with GSL-NPs improved cardiac functions and shrank fibrotic scars after MI in mice by up-regulating Serca2a. In cardiac fibroblasts, GSL-NPs alleviated hypoxia-induced fibrotic progression partly by inhibiting NF-kB activation. Furthermore, treatment with GSL-NPs protected cardiomyocyte calcium homeostasis and enhanced myocardial contractility during hypoxia. Together, we demonstrate that type I collagen-targeted liposome delivery of Serca2a may benefit patients with myocardial infarction by inhibiting fibrotic scarring as well as modulation of calcium homeostasis.

Summary: Background: In order to address the low compliance and dissatisfied specificity of low-dose computed tomography (LDCT), efficient and non-invasive approaches are needed to complement its limitations for lung cancer screening and management. The ASCEND-LUNG study is a prospective two-stage case–control study designed to evaluate the performance of a liquid biopsy-based comprehensive lung cancer screening and post-screening pulmonary nodules management system. Methods: We aimed to develop a comprehensive lung cancer system called Peking University Lung Cancer Screening and Management System (PKU-LCSMS) which comprises a lung cancer screening model to identify specific populations requiring LDCT and an artificial intelligence-aided (AI-aided) pulmonary nodules diagnostic model to classify pulmonary nodules following LDCT. A dataset of 465 participants (216 cancer, 47 benign, 202 non-cancer control) were used for the two models’ development phase. For the lung cancer screening model development, cancer participants were randomly split at a ratio of 1:1 into the train and validation cohorts, and then non-cancer controls were age-matched to the cancer cases in a 1:1 ratio. Similarly, for the AI-aided pulmonary nodules model, cancer and benign participants were also randomly divided at a ratio of 2:1 into the train and validation cohorts. Subsequently, during the model validation phase, sensitivity and specificity were validated using an independent validation cohort consisting of 291 participants (140 cancer, 25 benign, 126 non-cancer control). Prospectively collected blood samples were analyzed for multi-omics including cell-free DNA (cfDNA) methylation, mutation, and serum protein. Computerized tomography (CT) images data was also obtained. Paired tissue samples were additionally analyzed for DNA methylation, DNA mutation, and messenger RNA (mRNA) expression to further explore the potential biological mechanisms. This study is registered with ClinicalTrials.gov, NCT04817046. Findings: Baseline blood samples were evaluated for the whole screening and diagnostic process. The cfDNA methylation-based lung cancer screening model exhibited the highest area under the curve (AUC) of 0.910 (95% CI, 0.869–0.950), followed by the protein model (0.891 [95% CI, 0.845–0.938]) and lastly the mutation model (0.577 [95% CI, 0.482–0.672]). Further, the final screening model, which incorporated cfDNA methylation and protein features, achieved an AUC of 0.963 (95% CI, 0.942–0.984). In the independent validation cohort, the multi-omics screening model showed a sensitivity of 99.2% (95% CI, 0.957–1.000) at a specificity of 56.3% (95% CI, 0.472–0.652). For the AI-aided pulmonary nodules diagnostic model, which incorporated cfDNA methylation and CT images features, it yielded a sensitivity of 81.1% (95% CI, 0.732–0.875), a specificity of 76.0% (95% CI, 0.549–0.906) in the independent validation cohort. Furthermore, four differentially methylated regions (DMRs) were shared in the lung cancer screening model and the AI-aided pulmonary nodules diagnostic model. Interpretation: We developed and validated a liquid biopsy-based comprehensive lung cancer screening and management system called PKU-LCSMS which combined a blood multi-omics based lung cancer screening model incorporating cfDNA methylation and protein features and an AI-aided pulmonary nodules diagnostic model integrating CT images and cfDNA methylation features in sequence to streamline the entire process of lung cancer screening and post-screening pulmonary nodules management. It might provide a promising applicable solution for lung cancer screening and management. Funding: This work was supported by Science, Science, Technology & Innovation Project of Xiongan New Area, Beijing Natural Science Foundation, CAMS Innovation Fund for Medical Sciences (CIFMS), Clinical Medicine Plus X-Young Scholars Project of Peking University, the Fundamental Research Funds for the Central Universities, Research Unit of Intelligence Diagnosis and Treatment in Early Non-small Cell Lung Cancer, Chinese Academy of Medical Sciences, National Natural Science Foundation of China, Peking University People's Hospital Research and Development Funds, National Key Research and Development Program of China, and the fundamental research funds for the central universities.

BackgroundNutritional anemia is highly prevalent and has triggered a globally recognized public health concern worldwide.ObjectiveTo better understand the prevalence of anemia and the state of nutritional health in developed countries to inform global nutritional health and better manage the disease.MethodWe employed the Healthcare Cost and Utilization Project (HCUP)-2020 National Inpatient Health Care Data (NIS), administered by The Agency for Healthcare Research and Quality. Nutritional anemia was diagnosed according to the International Classification of Diseases, 10th Revision (ICD-10). Matching analysis and multivariate regression were used to adjust for patient and hospital characteristics. Controls were obtained by stratifying and matching for age and sex.ResultsThe 2020 HCUP-NIS database encompassed a survey over 6.4 million hospitalized patients, among which 1,745,350 patients diagnosed with anemia, representing approximately 26.97% of the hospitalized population, over 310,000 were diagnosed with nutritional anemia, and 13,150 patients were hospitalized for nutritional anemia as primary diagnosis. Hospitalization rate for nutritional anemia exhibited an increased age-dependent increase nationwide, especially among females, who displayed 1.87 times higher than males. Notably, in comparison to the control group, individuals of the Black race exhibit a higher prevalence of nutritional anemia (case group: 21.7%, control group: 13.0%, p

Glioma is a common tumor originating in the brain that has a high mortality rate. Temozolomide (TMZ) is the first-line treatment for high-grade gliomas. However, a large proportion of gliomas are resistant to TMZ, posing a great challenge to their treatment. In the study, the specific functions and mechanism(s) by which cortistatin (CORT) regulates TMZ resistance and glioma progression were evaluated. The decreased expression of CORT was detected in glioma tissues, and highly expressed CORT was associated with a better survival rate in patients with glioma. CORT overexpression notably decreased the capacity of glioma cells to proliferate and migrate in vitro and to form tumors in vivo. CORT overexpression also markedly suppressed the viability and enhanced the apoptosis of TMZ-resistant U251 cells by regulating MGMT, p21, and Puma expression. Importantly, CORT overexpression reduced the resistance of gliomas to TMZ in vivo. CORT expression was negatively correlated with MGMT expression in both glioma tissues and cells, and it was found that CORT inhibited NF-κB pathway activation in glioma cells, thereby inhibiting MGMT expression. In conclusion, CORT regulates glioma cell growth, migration, apoptosis, and TMZ resistance by weakening the activity of NF-κB/p65 and thereby regulating MGMT expression. The CORT/NF-κB/MGMT axis might be regarded as a molecular mechanism contributing to the resistance of glioma to TMZ. Our data also suggest that CORT regulates the viability and metastatic potential of glioma cells, independent of its effects on TMZ resistance, providing evidence of novel therapeutic targets for glioma that should be evaluated in further studies.

Among the various manifestations of COVID-19, the neurological implications of SARS-CoV-2 infection are of significant concern. Marchiafava–Bignami disease (MBD), a neurodegenerative disorder, exhibits a clinical spectrum ranging from mild progressive dementia in its chronic form to states of acute coma and varied mortality rates. Acute MBD primarily occurs in chronic alcoholics and malnourished individuals and is characterized by sudden loss of consciousness, seizures, confusion, and psychosis. We herein report a case of MBD presenting as acute loss of consciousness after the development of COVID-19. The patient presented with a history of fever and upper respiratory infection and was diagnosed with SARS-CoV-2 infection. He developed a neurological syndrome characterized by altered consciousness and convulsions, and brain magnetic resonance imaging revealed abnormal signals in the corpus callosum and frontoparietal lobes. Considering his alcohol intake history and the absence of other differential diagnoses, we diagnosed him with acute MBD triggered by COVID-19. After high-dose vitamin B1 and corticosteroid therapy, his clinical symptoms improved. In this case, we observed a temporal sequence between the development of COVID-19 and acute exacerbation of MBD. This case adds to the mounting evidence suggesting the potential effect of SARS-CoV-2 on the neurological system.

Abstract Increased pulmonary vascular permeability is a characteristic feature of lung injury. However, there are no established methods that allow the three‐dimensional visualization and quantification of pulmonary vascular permeability in vivo. Evans blue extravasation test and total protein test of bronchoalveolar lavage fluid (BALF) are permeability assays commonly used in research settings. However, they lack the ability to identify the spatial and temporal heterogeneity of endothelial barrier disruption, which is typical in lung injuries. Magnetic resonance (MR) and near‐infrared (NIR) imaging have been proposed to image pulmonary permeability, but suffer from limited sensitivity and penetration depth, respectively. In this study, we report the first use of magnetic particle imaging (MPI) to assess pulmonary vascular leakage noninvasively in vivo in mice. A dextran‐coated superparamagnetic iron oxide (SPIO), synomag®, was employed as the imaging tracer, and pulmonary SPIO extravasation was imaged and quantified to evaluate the vascular leakage. Animal models of acute lung injury and pulmonary fibrosis (PF) were used to validate the proposed method. MPI sensitively detected the SPIO extravasation in both acutely injured and fibrotic lungs in vivo, which was confirmed by ex vivo imaging and Prussian blue staining. Moreover, 3D MPI illustrated the spatial heterogeneity of vascular leakage, which correlated well with CT findings. Based on the in vivo 3D MPI images, we defined the SPIO extravasation index (SEI) to quantify the vascular leakage. A significant increase in SEI was observed in the injured lungs, in consistent with the results obtained via ex vivo permeability assays. Overall, our results demonstrate that 3D quantitative MPI serves as a useful tool to examine pulmonary vascular integrity in vivo, which shows promise for future clinical translation.

Abstract Recent studies challenge the dogma that a 21-mer phosphopeptide P140 protects against direct cell damage in the phase-III clinical trial (NCT02504645) for lupus, involving reactive oxygen species (ROS)-dependent release of citrullinated histone H3 (H3cit)-linked neutrophil extracellular traps. An open question is the cellular location of ROS production and H3cit formation in lupus. In this study, we examined the effects of P140 peptides on ROS production and H3cit location in lupus with in vivo and situ fluorescence imaging with subcellular resolution. We developed a mouse model of the B6 strain harbouring a bioluminescent reporter under the control of the Lysozyme M promoter. Based on the imiquimod-induced disease model of B6 mice, we used bioluminescent imaging, flow cytometry analysis, and immunohistology staining to study the effects of P140 peptides in lupus. We found a profound accumulation of CX3CR1-positive macrophages in the lungs of lupus mice after the application of P140, accompanied by lung fibrosis formation. The defined P140-mediated macrophage responses were associated with an increase of H3cit in the cytosol, interleukin-1 receptor type 1 on the extracellular membrane, and intracellular production of ROS. Of interest, the disease of imiquimod-induced lupus was prevented with an antioxidant drug apocynin. This study shows that P140 peptides play a role in aggravated murine lupus in a manner dependent on ROS production and H3cit upregulation through pulmonary macrophages.

Abstract Background umor cells, immune cells and stromal cells jointly modify tumor development and progression. We aim to explore the potential effects of tumor purity on the immune microenvironment, genetic landscape and prognosis in prostate cancer (PCa). Methods Tumor purity of prostate cancer patients was extracted from The cancer genome atlas (TCGA). Immune cellular proportions were calculated by the CIBERSORT. To identify critical modules related to tumor purity, we used weighted gene co-expression network analysis (WGCNA). Using STRING and Cytoscape, protein–protein interaction (PPI) networks were constructed and analyzed. A Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, Disease Ontology (DO), and Gene Set Enrichment Analysis (GSEA) enrichment analysis of identified modules was conducted. To identify the expression of key genes at protein levels, we used the Human Protein Atlas (HPA) platform. Results A model of tumor purity score (TPS) was constructed in the gene expression omnibus series (GSE) 116,918 cohort. TCGA cohort served as a validation set and was employed to validate the TPS. TPS model, as an independent prognostic factor of distant metastasis‐free survival (DMFS) in PCa. Patients had higher tumor purity and better prognosis in the low-TPS group. Tumor purity was related to the infiltration of mast cells and macrophage cells positively, whereas related to the infiltration of dendritic cells, T cells and B cells negatively in PCa. The nomogram based on TPS, Age, Gleason score and T stage had a good predictive value and could evaluate the prognosis of PCa metastasis. GO and KEGG enrichment analyses showed that hub genes mainly participate in T cell activation and T-helper lymphocytes (TH) differentiation. Hub genes were mainly enriched in primary immunodeficiency disease, according to DO analysis. SLAMF8 was identified as the most critical gene by Cytoscape and HPA analysis. Conclusions Dynamic changes in the immune microenvironment associated with tumor purity could correlate with a poor DMFS of low-purity PCa. The TPS can predict the DMFS of PCa. In addition, prostate cancer metastases may be related to immunosuppression caused by a disorder of the immune microenvironment.

Abstract Background Post-radiation nasopharyngeal necrosis (PRNN) is a severe adverse event following re-radiotherapy for patients with locally recurrent nasopharyngeal carcinoma (LRNPC) and associated with decreased survival. Biological heterogeneity in recurrent tumors contributes to the different risks of PRNN. Radiomics can be used to mine high-throughput non-invasive image features to predict clinical outcomes and capture underlying biological functions. We aimed to develop a radiogenomic signature for the pre-treatment prediction of PRNN to guide re-radiotherapy in patients with LRNPC. Methods This multicenter study included 761 re-irradiated patients with LRNPC at four centers in NPC endemic area and divided them into training, internal validation, and external validation cohorts. We built a machine learning (random forest) radiomic signature based on the pre-treatment multiparametric magnetic resonance images for predicting PRNN following re-radiotherapy. We comprehensively assessed the performance of the radiomic signature. Transcriptomic sequencing and gene set enrichment analyses were conducted to identify the associated biological processes. Results The radiomic signature showed discrimination of 1-year PRNN in the training, internal validation, and external validation cohorts (area under the curve (AUC) 0.713–0.756). Stratified by a cutoff score of 0.735, patients with high-risk signature had higher incidences of PRNN than patients with low-risk signature (1-year PRNN rates 42.2–62.5% vs. 16.3–18.8%, P

Abstract Purpose Dexmedetomidine (Dexmed) is a highly selective alpha 2 adrenoceptor (α2-AR) agonist with excellent sedation and analgesic effects and is frequently used in breast cancer surgery. However, the exact impact of Dexmed on breast cancer prognosis is still unclear. The primary objective of this pilot study was to explore study feasibility (recruitment and dropout rates) for future large-scale randomized controlled trial (RCT) to test the hypothesis that intraoperative Dexmed reduced recurrence-free survival (RFS) and overall survival (OS) in patients after breast cancer surgery. Methods Interviews with patients were performed during the anesthetic preoperative visit for informed consent. Adult females scheduled for a mastectomy due to primary breast cancer were 1:1 randomised to saline (Group Control) or Dexmed (Group Dexmed) treatment groups. The primary outcomes were descriptions of study feasibility (recruitment and dropout rates). We also performed a preliminary analysis of RFS (time from surgery to the earliest date of recurrence/metastasis) and OS (time from surgery to the date of all-cause death) and collected data on percentages/numbers of circulating immune cells at pre- and 24 h post-operation. Results A total of 964 patients were screened; 40% (385/964) met the inclusion criteria, among which 39% (150/385) were enrolled and randomly assigned to either Group Control (n = 75) or Group Dexmed (n = 75). The median follow-up duration was 49 months (interquartile range (IQR): 34–58 months) for Group Control and 48 months (IQR: 33–60 months) for Group Dexmed. Five percent (5%, 8/150) patients were lost to follow-up and 1% (2/150) died. There was no significant difference in RFS and OS. The percentage/number of natural killer (NK), B and T-cell subsets and the CD4+/CD8+ ratio were similar between groups at 24 h post-operation. Conclusion The pilot study was feasible to deliver. In a future definitive trial, the lower recruitment rate may be improved by increasing the number of anesthesiologists involved in the study. The study about the effects of Dexmed on long-term prognoses of breast cancer patients that is planned to follow this pilot study is a large-scaled randomized control study with the aim of providing evidence-based guidelines for rational use of Dexmed in patients undergoing breast cancer surgery. Trial registration Registered at ClinicalTrials.gov on October 20, 2016 (ID: NCT03109990). Graphical Abstract

Abstract Objective CalliSpheres® is a microsphere that is already widely used for primary liver cancer treatment; however, its application in colorectal cancer liver metastasis (CRLM) is limited. The current study aimed to investigate the efficacy of CalliSpheres® drug-eluting bead (DEB) transarterial chemoembolization (TACE) therapy versus (vs.) conventional cTACE therapy in treating refractory CRLM (RCRLM) patients. Methods Twenty-two RCRLM patients who underwent CalliSpheres® DEB-TACE therapy (n = 11) or cTACE therapy (n = 11) were retrospectively analyzed. Data on clinical response, progression-free survival (PFS) and overall survival (OS) were retrieved. Results The objective response rate (36.4% vs. 18.2%, P = 0.338) and disease control rate (81.8% vs. 54.4%, P = 0.170) were both numerically (but not statistically) higher in the DEB-TACE group than in the cTACE group. Meanwhile, PFS was prolonged in the DEB-TACE group compared with the cTACE group [median: 12.0 (95% CI: 5.6–18.4) vs. 4.0 (95% CI: 0.9–7.1) months, P = 0.018]; OS was also longer in the DEB-TACE group compared with the cTACE group [median: 24.0 (95% CI: 18.3–29.7) vs. 14.0 (95% CI: 7.1–20.9) months, P = 0.040]. In addition, after adjustment by multivariate Cox analyses, DEB-TACE was superior to cTACE independently regarding PFS (HR: 0.110, 95% CI: 0.026–0.463, P = 0.003) and OS (HR: 0.126, 95% CI: 0.028–0.559, P = 0.006). Conclusion CalliSpheres® DEB-TACE therapy may prolong survival profile than cTACE therapy in RCRLM patients, while further validation is still needed.

A method is proposed to fuse the velocity data of the global navigation satellite system (GNSS) and leveling height via combined adjustment with constraints. First, stable GNSS–leveling points are uniformly selected, and the constraints of the geodetic height change velocity and normal height change velocity are given. Then, the GNSS vertical velocities and leveling height difference are used as observations of combined adjustment, and robust least-squares estimation are used to estimate the velocities of the unknown points. Finally, a vertical movement model is established with the GNSS vertical velocities and leveling vertical velocities obtained via combined adjustment. Data from the second-order leveling network and GNSS control points in Shandong Province are taken as test data, and eight calculation schemes are used for discussion. One of the schemes, the bifactor robust combined adjustment method based on variance component estimation with two kinds of vertical velocity constraints achieves the optimal results. The method applied in the scheme can be recommended for data fusion of GNSS and leveling, further improving the reliability of vertical crustal movement in Shandong Province.

N6-methyladenosine (m6A) RNA modification is widely perceived as the most abundant and common modification in transcripts. This modification is dynamically regulated by specific m6A “writers”, “erasers” and “readers” and is reportedly involved in the occurrence and development of many diseases. Since m6A RNA modification was discovered in the 1970s, with the progress of relevant research technologies, an increasing number of functions of m6A have been reported, and a preliminary understanding of m6A has been obtained. In this review, we summarize the mechanisms through which m6A RNA modification is regulated from the perspectives of expression, posttranslational modification and protein interaction. In addition, we also summarize how external and internal environmental factors affect m6A RNA modification and its functions in tumors. The mechanisms through which m6A methylases, m6A demethylases and m6A-binding proteins are regulated are complicated and have not been fully elucidated. Therefore, we hope to promote further research in this field by summarizing these mechanisms and look forward to the future application of m6A in tumors.

Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) have attracted attention in the field of regenerative medicine due to their potential ability to repair damaged hearts. However, the immature phenotype of these cells limits their clinical application. Cardiomyocyte maturation is accompanied by changes in mitochondrial quality. PTEN-induced putative kinase 1 (PINK1) has been linked to mitochondrial quality control. However, whether the changes in mitochondrial quality in hiPSC-CMs are associated with PINK1, and the impact of PINK1 on hiPSC-CMs development are not clear. In this study, we found that knockdown of PINK1 in hiPSC-CMs resulted in mitochondrial fragmentation and impaired mitochondrial functions such as mitophagy and mitochondrial biogenesis. PINK1 deletion also inhibited the maturation of hiPSC-CMs, reverting them to a naive structural and functional state. We found that restoring the mitochondrial structure did not completely rescue the mitochondrial dysfunction caused by PINK1 deletion, while activation of PINK1 kinase activity using kinetin promoted mitochondrial fusion, increased the mitochondrial membrane potential and ATP production, and maintained the development and maturation of hiPSC-CMs. In conclusion, PINK1 regulates the mitochondrial structure and function of hiPSC-CMs, and is essential for the maturation of hiPSC-CMs.

The incorporation and application of SAGD rapid preheating technology effectively solve the problem of the long preheating cycle in the SAGD steam cycle. The thermal properties of reservoir rocks are an important factor affecting the heat transfer law governing their formation during the rapid preheating process of SAGD. During the rapid preheating process of SAGD, the expansion of the reservoir and the steam cycle process will cause changes in the pore permeability, oil-water saturation, and temperature of the reservoir rocks, which will inevitably lead to differences in the changes that occur in the thermal properties of the reservoir rocks, compared to those under the influence of a single factor. In this study, experiments were conducted to determine the thermal properties of reservoir rocks under the combined influence of pore permeability, oil-water saturation, and temperature, quantitatively characterizing the changes in the thermal properties of reservoir rocks. Using the orthogonal method to design and carry out experiments for determining the thermal properties of reservoir rocks, the main factors affecting the thermal properties of reservoir rocks and the significance of each factor’s impact on the thermal properties of reservoir rocks were determined through intuitive analysis and variance analysis of the experimental results. Finally, a regression equation that can characterize changes in the thermal properties of reservoir rocks under the influence of multiple factors was obtained through multiple nonlinear regressions of the experimental results.

In this paper, we make use of the simultaneous decomposition of eight quaternion matrices to study the solvability conditions and general solutions to a system of two-sided coupled Sylvester-type quaternion matrix equations AiXiCi+BiXi+1Di=Ωi,i=1,2,3,4. We design an algorithm to compute the general solution to the system and give a numerical example. Additionally, we consider the application of the system in the encryption and decryption of color images.

In thiosulfate leaching of gold, the copper-ammonia complex serves as an oxidant and catalyst. This study examined the impact of magnetizing the copper-ammonia thiosulphate lixiviant solution on gold leaching from refractory oxidized ores. Magnetization reduced surface tension, improved wettability and infiltration, and enhanced the diffusion of leaching agents. It also increased dissolved oxygen content and boosted the catalytic efficiency of copper-ammonia complexes. These changes led to more efficient gold extraction, with column leaching showing a 4.74% improvement in extraction rates compared to non-magnetized methods and a 3.67% improvement over cyanide processes. These findings suggest that magnetized copper-ammonia thiosulphate lixiviant is a promising, environmentally friendly alternative to cyanide for refractory oxidized gold ores.

Global warming and waterlogging stress due to climate change are expected to continue influencing agricultural production worldwide. In the field, two or more environmental stresses usually happen simultaneously, inducing more complex responses in plants compared with individual stresses. Our aim was to clarify how the two key factors (temperature and water) interacted and influenced physiological response and plant growth in tomatoes under ambient temperature, moderately elevated temperature, waterlogging stress, and moderately elevated temperature and waterlogging stress. The results showed that leaf photosynthesis was inhibited by waterlogging stress but enhanced by elevated temperature, as shown by both the light- and temperature-response curves. The elevated temperature decreased leaf water-use efficiency, but enhanced plant growth and fresh and dry weights of plants under both normal water supply and waterlogging stress conditions. Elevated temperature generally decreased the anthocyanin and flavonol index in tomato leaves compared with the control temperature, regardless of water status. The increase in the optimal temperature was more pronounced in plants under normal irrigation than under waterlogging stress. Waterlogging stress significantly inhibited the root length, and leaf number and area, while the moderately elevated temperature significantly enhanced the leaf number and area. Overall, the moderately elevated temperature offset the effects of waterlogging stress on tomato plants, as shown by leaf gas exchange, plant size, and dry matter accumulation. Our study will improve the understanding of how tomatoes respond to increasing temperature and excess water.

To alleviate the tense situation of limited passenger service resources in the terminal and to achieve the matching of resource scheduling with the flight support process, the process–resource interdependent network is constructed according to its mapping relationship and the time-varying characteristics of the empirical network and network evolution conditions are analyzed. Then, node capacity, node load, and the cascading failure process are investigated, the impact of average service rate and service quality standard on queue length is considered, the node capacity model is constructed under the condition of resource capacity constraints, and the load-redistribution resource adaptive scheduling method based on cascading failure is proposed. Finally, the method’s effectiveness is verified by empirical analysis, the service efficiency is assessed using the total average service time and variance, and the network robustness is assessed using the proportion of maximum connected subgraph. The results indicate that the resource adaptive scheduling method is effective in improving service efficiency, and the average value of its measurement is smaller than that of the resource average allocation method by 0.069; in terms of the robustness improvement of the interdependent network, the phenomenon of re-failure after the load redistribution is significantly reduced.

In order to solve the problem of great difficulty in detecting the internal damage of wire rope, this paper proposes a method to improve the VGG model to identify the internal damage of wire rope. The short-time Fourier transform method is used to transform the wire rope damage signal into a time-frequency spectrogram as the model input, and then the traditional VGG model is improved from three aspects: firstly, the attention mechanism module is introduced to increase the effective feature weights, which effectively improves the recognition accuracy; and then, the batch normalization layer is added to carry out a uniform normalization of the data, so as to make the model easier to converge. At the same time, the pooling layer and the fully connected layer are improved to solve the redundancy problem of the traditional VGG network model, which makes the model structure more lightweight, greatly saves the computational cost, shortens the training time, and finally adopts the joint-sample uniformly distributed cross-entropy as the loss function to solve the overfitting problem and further improve the recognition rate. The experimental results show that the improved VGG model has an identification accuracy of up to 98.84% for the internal damage spectrogram of the wire rope, which shows a good identification ability. Not only that, but the model is also superior, with less time-consuming training and stronger generalization ability.

A mechanical model for offshore casing cutting based on the field application of a mechanical cutting device in the South China is presented in this paper. The proposed model includes the calculation of the piston acting on the cutter and the calculation of the cutting torque and wellhead driving torque. The influence of structural parameters on cutting extension distance, cutting torque, wellhead driving torque, and the proportion of cutting torque to wellhead driving torque are analyzed. The required extension distance is related to piston displacement and cutter face angle, the cutter face angle and cutting depth (water depth) have obvious influence on the cutting torque and wellhead driving torque, and the drilling revolution affects the inertia torque and wellhead driving torque. Since the proportion of cutting torque to wellhead driving torque decreased with the increase in the cutting depth, we cannot determine whether the casing cutting is completed based on the sudden decrease in the wellhead driving torque with cutting depths greater than 800 m. The numerical simulation results of the cutting tool cutting the casing show that if the revolution speed is too high, the initial cutting melting of the casing may affect the cutting force, and it is recommended to increase the rake angle as much as possible within a certain range. The field example shows the limitation of judging casing cutting by a sudden drop in torque.

In an era marked by escalating demand for water resources and expanding human encroachment, numerous regions worldwide find it very challenging to balance water supply and demand. As a key source of water yield services, ecosystems often fail to spatially satisfy the great demand for water resources in some areas. This study focuses on the role of ecosystems in the supply and demand of water yield services in human social systems and then maps the flow between them. It quantifies their intricate relationship by employing the concept of ecosystem service flow. A notable gap exists in methodologies that intuitively illustrates the 'supply-movement-demand' dynamics in this context. Consequently, this study introduces a comprehensive methodology by integrating multiple models such as 'InVEST-Geoda-Gephi' to investigate the transmission paths and characteristics of surplus and deficit distribution of water yield service flow across various scales. Firstly, the spatiotemporal evolution of water yield service supply and demand within ecosystems is analyzed. Secondly, we scrutinize regional supply and demand alignment spatial patterns utilizing the Geoda model. Subsequently, the Gephi network model simulates water yield service flow from ecosystems to human social systems. The findings corroborate the network model's effectiveness and visual capabilities in representing ecosystem service flow across diverse scales. The primary conclusions drawn are as follows: (a) The spatiotemporal distribution of water yield services around the Wuyi Mountains exhibited a pattern characterized as 'high in the center, low on the edges' from 2000 to 2020, revealing significant spatial heterogeneity between high-supply and high-demand areas; (b) Influenced by the mountainous region's supply, the study area was mainly characterized by a high-low spatial mismatch. High-high spatial match was mainly distributed in provincial capitals and selected coastal economic zones. In contrast, low-low spatial match transitioned from northwest to southwest, and low–high mismatch encircled in areas featuring both high supply and high demand, particularly around provincial capitals and adjacent undeveloped coastal regions; (c) With the advance of urbanization and economic development, deficit nodes in the water yield service flow network tended to be marginalized, which reduced network density and connectivity leading to a decline in the ability of Wuyi Mountains ecosystem to provide water yield services and weakening its flow to human social systems due to external interference. This research provides strategic guidance for rationally allocating water resources in regional ecosystems and promoting ecological protection and collaborative development.

IntroductionInflammatory Bowel Disease (IBD) and Parkinson’s disease (PD) are both chronic, progressive disorders. As such, given the inconclusive results of extensive research on the association between IBD and PD, our study intends to examine this relationship further using the UK Biobank database.MethodsWe conducted a prospective cohort study using the Cox proportional hazards model, analyzing data from the UK Biobank to investigate the relationship between IBD and PD, following subjects until PD diagnosis, loss to follow up, death or study termination on 30 June, 2023.ResultsThe results show that IBD had no effect on the risk of PD (HR: 1.356, 95% CI: 0.941–1.955, p = 0.103), and the effect remained consistent in specific Crohn’s disease, ulcerative colitis or unclassified IBD populations. In addition, after sensitivity analysis using propensity matching scores and excluding patients diagnosed with PD 5 or 10 years after baseline, IBD had no effect on the risk of PD. However, in the subgroup analysis, we found that in females (HR: 1.989, 95% CI: 1.032–3.835, p = 0.040), the polygenic risk score was highest (HR: 2.476, 95% CI: 1.401–4.374, p = 0.002), and having ulcerative colitis without hypertension (HR: 2.042, 95% CI: 1.128–3.697, p = 0.018) was associated with an increased risk of PD.ConclusionIn conclusion, over an average follow-up period of 13.93 years, we found no significant association between IBD and PD.

Pollutants produced by engines are a significant source of environmental pollution, so the study of engine emissions is very important. In this study, with CONVERGE software, a diesel engine model of the engine was produced. To better obtain the characteristic results of the engine, this was coupled with an improved chemical kinetics mechanism. Then, the results of this model were verified experimentally. Additionally, the effects of four different EGR rates on the combustion, performance, and emissions of a dual-fuel diesel engine were investigated by the verified model under different (50 %, 75 %, and 100 %) load conditions. Lastly, the brake specific fuel consumption, NOx emission, and HC emission were optimized by the response surface methodology (RSM). The results show that the pressure, temperature, and NOx emission in the engine's cylinder can all be reduced by raising the EGR at three different loads. Besides, the optimization results show that the engine achieves the best operating conditions at 100 % load, hydrogen fraction of 6.92 %, and EGR rate of 7.68 %.