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Aims: Atractylodes macrocephala is a traditional Chinese medicine with a variety of pharmacological activities. This study aimed to evaluate its anti-hyperuricemia and antiinflammatory effects on gout, and to preliminarily explore its mechanism. Methods: The hyperuricemia rat model was established by intraperitoneal injection of oteracil potassium and intragastric gavage of yeast powder solution. And the acute gouty arthritis (GA) model was established by injecting monosodium urate (MSU) suspension. In the study of the antihyperuricemia effect of Atractylodes macrocephala, the healthy male Sprague-Dawley rats were randomly divided into the blank group, hyperuricemia group allopurinol group as well as low, moderate and high dose groups of Atractylodes macrocephala decoction (N=8 rats in each group). Serum, liver and kidney tissue samples were collected from each group. Serum uric acid (UA), adenosine deaminase (ADA) and xanthine oxidase (XOD) levels in each group were detected by enzyme-linked immunosorbent assay (ELISA). Protein levels of ADA and XOD in liver tissues were detected by Western blot, and renal histological changes were observed by Hematoxylin-eosin (H＆E) and Masson staining. In order to investigate the anti-inflammatory effect of Atractylodes macrocephala, the healthy male Sprague-Dawley rats were randomly divided into the blank group, GA group, colchicine group, high, moderate and low dose groups of Atractylodes macrocephala decoction (N=8 rats in each group), and serum and synovial tissue of each group were collected. Then the level of serum interleukin (IL)-1β and tumor necrosis factor (TNF)-α was observed by ELISA, and the histological changes of synovial tissue were observed by H＆E staining. Besides, the expression of adenosine monophosphate- activated protein kinase (AMPK) /silent information regulator (SIRT) 1/ nuclear factor kappa B (NF-κB) protein in synovial tissue was observed by Western blot and immunohistochemistry. The markers of M1 and M2 macrophages, inducible nitric oxide synthase (iNOS) and arginase-1 (ARG1) were observed by Western blot and immunofluorescence. Results: Atractylodes macrocephala could reduce the production of UA by inhibiting the level of ADA and XOD, and could improve renal injury and fibrosis. In addition, Atractylodes macrophages could reduce the levels of IL-1β and TNF-α, activate AMPK/SIRT1 signaling pathway, and inhibit the activation of NF-κB and the polarization of macrophages to a pro-inflammatory phenotype. Conclusion: Atractylodes macrocephala shows good anti-hyperuricemic and anti-inflammatory effects, and its anti-inflammation pharmacological activity may be related to the inhibition of M1 macrophage polarization and NF-κB activation through activating AMPK/SIRT1.

Objective: To analyze the perioperative outcomes of neoadjuvant hormone therapy (NHT) before laparoscopic and robot-assisted surgery for localized high-risk prostate cancer in a Chinese cohort. Methods: The clinical data of 385 patients with localized high-risk prostate cancer who underwent radical prostatectomy (RP) in our hospital from January 2019 to June 2021 were analyzed retrospectively, including 168 patients with preoperative NHT and 217 patients with simple surgery. Clinical characteristics were compared in the above two groups, the laparoscopic RP (LRP) cohort (n = 234) and the robot-assisted laparoscopic radical prostatectomy (RALP) cohort (n = 151), respectively. Results: In the overall cohort, compared with the control group, the NHT group had a shorter operative time, less blood loss, a lower positive surgical margin rate, and a higher proportion of Gleason score (GS) downgrading after the operation (p < 0.05). However, there was no significant difference in hospitalization time, biochemical recurrence, urine leakage, urinary continence, or prostate-specific antigen (PSA) progression-free survival (p > 0.05). In the LRP cohort, it was found that the NHT group also had shorter operative time, less blood loss, lower positive surgical margin rate, a higher proportion of GS downgrading after the operation, and faster recovery of urinary control than the control group (p < 0.05). There was no marked difference in hospitalization time, biochemical recurrence, urinary leakage, or PSA progression-free survival. However, in the RALP cohort, the NHT group had a significant difference in the GS downgrading after the operation compared with the control group (p < 0.05). In the overall cohort, multiple analyses showed that initial PSA level, GS at biopsy, clinical T stage, lymph node invasion, use of NHT, and surgical methods were significantly associated with positive surgical margin (p < 0.05) while NHT did not account for biochemical recurrence (p > 0.05). Conclusions: NHT can lower the difficulty of surgery, reduce positive surgical margin rate, and help recovery in short-term urinary control in patients with high-risk prostate cancer after LRP. However, we do not have evidence on the benefit of NHT in high-risk PCa patients treated with RALP. For these patients, surgery can be performed as early as possible.

With the rise of the upsurge of exploiting marine resources and the rapid development of the research of terrestrial wireless sensor networks, the research of underwater wireless sensor networks (UWSNs,) has become a new research hotspot. In UWSNs, routing protocol plays a very important role as one of the important components. This paper introduces several classical routing algorithms from the following categories: location, sector, reinforcement learning, and intelligent optimization algorithms. Finally, the paper also summarizes and prospects the research direction of underwater wireless sensor network routing algorithms.

Doxorubicin (DOX) is a class of effective chemotherapeutic agents widely used in clinical practice. However, its use has been limited by cardiotoxicity. The mechanism of DOX-induced cardiotoxicity (DIC) is complex, involving oxidative stress, Ca2+ overload, inflammation, pyroptosis, ferroptosis, apoptosis, senescence, etc. Exosomes (EXOs), as extracellular vesicles (EVs), play an important role in the material exchange and signal transmission between cells by carrying components such as proteins and RNAs. More recently, there has been a growing number of publications focusing on the protective effect of EXOs on DIC. Here, this review summarized the main mechanisms of DIC, discussed the mechanism of EXOs in the treatment of DIC, and further explored the value of EXOs as diagnostic biomarkers and therapeutic strategies for DIC.

Exploring novel growth regulators for premature senescence regulation is important for tobacco production. In the present study, chlorine dioxide (ClO

Purpose: Establishment of Sister Chromatid Cohesion N-Acetyltransferase 2 (ESCO2) is a member of histone acetyltransferases. ESCO2 is involved in the mitotic S-phase adhesins acetylation and is responsible for bridging two sister chromatids. However, present ESCO2 cancer research is limited to a few cancers. No systematic pan-cancer analysis has been conducted to investigate its role in diagnosis, prognosis, and effector function. Methods:We thoroughly examined the ESCO2 carcinogenesis in pan-cancer by combining public databases such as The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression Project (GTEx), cBioPortal, Human Protein Atlas (HPA), UALCAN, Xena Shiny databases, and TISCH. The analysis includes differential expression analysis, survival analysis, cellular effector function, gene mutation, single cell analysis, and tumor immune cell infiltration. Furthermore, we confirmed ESCO2's impacts on ccRCC cells' proliferative and invasive capacities in vitro. Results: In our study, ESCO2 is overexpressed in most cancers and has prognostic, predictive ability in various cancers. Moreover, ESCO2 expression positively correlates with tumor stage, grade, and tumor metastasis in several cancers. Function analysis revealed that ESCO2 participates in mitosis, cell cycle, DNA damage repair, and other processes. CDK1 was identified as a downstream gene regulated by ESCO2. Furthermore, ESCO2 might also be implicated in immune cell infiltration. Finally, ESCO2'S knockdown significantly inhibited the A498 cells' proliferation, invasion, and migration. Conclusions: In conclusion, ESCO2 is a possible pan-cancer biomarker and oncogene that can reliably predict the prognosis of cancer patients. ESCO2 was also implicated in the cell cycle and proliferation regulation. In a nutshell, ESCO2 is a therapeutically viable and dependable target.

Rearranged during transfection (RET), an oncogenic driver, has been found in multiple tumor types and is thus a promising anticancer therapeutic target. Novel selective RET inhibitors (RETi) that can overcome V804 gatekeeper mutations, endowing resistance to multikinase inhibitors (MKI) and, in particular, achieving KDR selectivity, are needed. In addition, the mechanisms underlying RET–inhibition-induced antiproliferative effects in the context of RET addiction are incompletely understood. This study describes a novel selective RETi, SYHA1815, which inhibited the kinase activity of RET wild type and V804 mutant with an IC50 in the subnanomolar to nanomolar range. Notably, SYHA1815 exhibited approximately 20-fold selectivity for RET over KDR, almost equivalent to that of the launched selective inhibitor pralsetinib. SYHA1815 had only a marginal inhibitory effect on cellular KDR signaling at a high (200 nmol/L) concentration, confirming the selectivity over KDR. In addition, SYHA1815 exhibited a favorable selectivity profile, with greater than 100-fold selectivity for RET over 347 other kinases. It exhibited potent antitumor efficacy and overcame V804 mutations in vitro and in vivo by targeting RET. Then, using SYHA1815 as a probe, we found that RET inhibition suppressed RET-driven cell proliferation via G1 cell-cycle arrest through downregulating c-Myc. Furthermore, disruption of c-Myc upon Brd4 inhibitor treatment led to G1 cell-cycle arrest and overrode RET-driven cell proliferation. Moreover, consistent with the marked in vivo efficacy of RET inhibition, the intratumoral c-Myc level was significantly decreased. In summary, SYHA1815 is a promising RETi for RET-aberrant cancer treatment that is currently in a phase I trial.

Supplementary Data

Background: Through the analysis of the relevant data of industrial equipment, faults diagnosis is helpful for system maintenance and reducing economic losses. Objective: The aim is to reduce the influence of irrelevant features and efficiently train the FIR-XgBoost model. Methods: An Extreme Gradient Boosting (XgBoost) approach based on feature importance ranking (FIR) is proposed in this article for fault classification of high- dimensional complex industrial systems. Gini index is applied to rank the importance of the features, and feature selection is implemented based on their position in the ranking. Results: The dataset from the PHM 2021 data challenge, which is related to the process of fuse thermal imaging, is used. The classification accuracy of FIR-XgBoost reaches 99.63%, outperforming other existing algorithms. A case study is presented to show that excellent fault classification can be achieved through ensemble learning and feature selection. Conclusion: Data-driven machine learning methods are proposed for solving high-dimensional fault classification problems on the dataset of the PHM2021 Data Challenge. An FIR-XgBoost method is proposed, the core of which is to retain important features and to reduce redundancy of sensor data. Consequently, feature selection based on FIR has better interpretability than other algorithms. Furthermore, the FIR- XgBoost algorithm retaining the 50 most important features achieves the best fault classification performance among the compared algorithms and can be implemented in specific industrial processes.

Background: The evaluation of the quality of professional master's degree theses has attracted less attention. This paper analyzes the content based on the expert review opinions of the engineering sampling theses in Tianjin in 2017. Methods: This paper focuses on the review opinions of unqualified theses, and extracts three core characteristics of unqualified theses: lack of innovativeness, academic inadequacy, and poor standardization. Results: Lacking in innovativeness means topics, method, and applied value are outdated. Academic inadequacy means the author does not possess adequate knowledge, does not pay sufficient intellectual effort and the dissertation's design is unreasonable with the content irrelevant to the subject. Poor standardization is manifested in poor writing, inappropriate disciplinary paradigm, and academic misconduct. Conclusions: Therefore, strictly monitoring the quality of theses from the system, setting up standardized courses for theses, and improving the academic quality of graduate students have become the priority to the cultivation of engineering masters.

Survey/review study Deep Learning Attention Mechanism in Medical Image Analysis: Basics and Beyonds Xiang Li 1, Minglei Li 1, Pengfei Yan 1, Guanyi Li 1, Yuchen Jiang 1, Hao Luo 1,*, and Shen Yin 2 1 Department of Control Science and Engineering, Harbin Institute of Technology, Harbin 150001, China 2 Department of Mechanical and Industrial Engineering, Faculty of Engineering, Norwegian University of Science and Technology, Trondheim 7034, Norway * Correspondence: hao.luo@hit.edu.cn Received: 16 October 2022 Accepted: 25 November 2022 Published: Abstract: With the improvement of hardware computing power and the development of deep learning algorithms, a revolution of "artificial intelligence (AI) + medical image" is taking place. Benefiting from diversified modern medical measurement equipment, a large number of medical images will be produced in the clinical process. These images improve the diagnostic accuracy of doctors, but also increase the labor burden of doctors. Deep learning technology is expected to realize an auxiliary diagnosis and improve diagnostic efficiency. At present, the method of deep learning technology combined with attention mechanism is a research hotspot and has achieved state-of-the-art results in many medical image tasks. This paper reviews the deep learning attention methods in medical image analysis. A comprehensive literature survey is first conducted to analyze the keywords and literature. Then, we introduce the development and technical characteristics of the attention mechanism. For its application in medical image analysis, we summarize the related methods in medical image classification, segmentation, detection, and enhancement. The remaining challenges, potential solutions, and future research directions are also discussed.

In the fields of deep learning and computer vision,the security of object detection models has received extensive attention. Revealing the security vulnerabilities resulting from adversarial attacks has become one of the most important research directions. Existing studies show that object detection models can also be threatened by adversarial examples,just like other deep neural networks based models,e.g.,those for classification. In this paper,we propose a bidirectional adversarial attack method. Firstly,the added perturbation pushes the prediction results given by the object detectors far away from the groundtruth class while getting close to the background class. Secondly,a condence loss function is designed for the region proposal network to reduce the foreground scores. Thirdly,the adversarial examples are generated by a pre-trained autoencoder,and the model is trained using an adversarial approach,which can enhance the similarity between the adversarial examples and the original image and speed up algorithm convergence. The proposed method was verified on the most popular two-stage detection framework (Faster R-CNN),and 55.1% mAP-drop were obtained. In addition,the adversarial examples have superior transferability,migrating which to the common one-stage detection framework (YOLOv3) gets a 39.5% mAP-drop.

Federated learning (FL) enables various organizations to jointly train one single model without revealing their private data to each other. The FL can be classified as horizontal federated learning (HFL) and vertical federated learning (VFL) according to the distribution of overlap samples and overlap features in the dataset. VFL allows various organizations to share machine learning based on the overlap samples, each one of which has the same identity. However, VFL suffers from insufficient number of overlap samples among all participants. Hence, the shortage of overlap data results in a worse performance of the global model. In this article, we propose a data augmentation method, FedDA, which is based on the generative adversarial network (GAN) to increase the number of training data. We generate more overlap data by learning the features of finite overlap data and many locally existing nonoverlap data, which expand the availability for training the overlap dataset. A series of experiments were executed on both MNIST and CIFAR-10. The results show that FedDA can efficiently utilize nonoverlap samples to enhance the effect of the data augmentation. It can generate high-quality overlap samples and expand the set of overlap samples. Thus, when the VFL is short of overlap samples, FedDA can provide abundant training data to improve the performance of the VFL model.

Hierarchical porous carbon is synthesized through a one-step carbonization and activation method from coal tar pitch using CaO as the hard template.

Skeletal fluorosis is a form of endemic disease caused by the excessive intake of fluoride. Bone deformation and periosteal calcification are the typical manifestations that can be observed in the images and are usually served as a basis of pathological grading. In the current medical systems, the diagnosis of skeletal fluorosis fully relies on doctors’ knowledge and experience, and no research effort has been made in automatic image information diagnostic systems. According to the image information, the shape of the lesion is irregular, the boundary is unclear and the lesion related pixels only occupy a small part of the image. We take the lead in proposing a two-stage scheme that can achieve automated X-ray image diagnosis and severity grading. In the first stage, a Dense U-Net is proposed for reliable lesion determination, and a multitype feature fusion approach passes effective and comprehensive features to the subsequent stage. In the second stage, a novel classifier is designed with the integration of ensemble learning and multiple instance learning, which can ensure classification accuracy in case that the feature for diagnosis only takes up a small proportion of the whole image. Through plenty of experiments on the actual data collected from the hospitals, it is verified that the proposed strategy can achieve satisfactory results on skeletal fluorosis image diagnosis and severity grading.

In a paced aircraft final assembly line, some disturbances can be collected timely on the basis of the cyber-physical production system. In order to reduce the execution deviation, some workers need to switch among stations after a fixed period. Thus, a worker allocation problem with the multistage workstation is introduced first. Then, an integer programming formulation is presented to formulate the problem with the objective of shortest workstation cycle and the workload balance of both stations and workers. Moreover, a modified nondominated sorting genetic algorithm (NSGA-IV) is proposed to solve it, which tradeoff the convergence and the population diversity in the decision space. Finally, the NSGA-IV algorithm compares with five multiobjective evolutionary algorithms in a real-world case. Compared with manual allocation, the takt time of an aircraft final assembly line is reduced by 20.86% by using the NSGA-IV algorithm.

Background: Bioresorbable scaffold (BVS) provides a transient supporting force for blocked vessels and allows them to return to previous physiological characteristics. After the verification with twists and turns, it has been acknowledged as an emerging revolution of percutaneous coronary intervention (PCI), which expresses the current concept of intervention without placement. Through this bibliometric study, we organize the knowledge structure of bioresorbable scaffolds and attempt to predict future research hotspots in this field. Methods: 12371 articles were retrieved from the WEB OF SCIENCE CORE COLLECTION (WoSCC) database from 2000-2022. Then, we utilize CiteSpace 6.1.R2 and VOSviewer 1.6.18 to analyze the data visually. Results: Firstly, according to the spatial analysis, the quantity of annual publications shows an approximately increasing trend over the past two decades. The USA, the People’s Republic of China, and GERMANY published the most articles about bioresorbable scaffolds. Secondly, SERRUYS P ranked first for his most prolific work and the highest cited frequency in this domain. Thirdly, the hotspots in this field can be inferred from the keyword distribution; they were the fabrication technique based on tissue engineering, the factors to be optimized of bioresorbable scaffolds such as mechanical property, degradation, and implantation, and the common adverse effects of bioresorbable scaffolds such as thrombosis. Most importantly, in terms of burst detection, we could speculate that cutting-edge technology for manufacturing scaffolds represented by 3D printing constitutes the future hotspots in bioresorbable scaffold development. Conclusion: We review the growing trend of bioresorbable scaffolds since their introduction and present the early prosperity, the subsequently questioned safety, and the resultantly advanced techniques in recent years. In brief, this review, the first visualized bibliometric analysis on BVS, enrolls extensive literature in this field and offers more guidance for future research from a panoramic view.

Industrial Metaverse is a novel ecological system for modern industry. One of the most inspiring parts of Industrial Metaverse lies in the potential to restructure the industrial chain and to provide added value to all the stakeholders therein. It is being enabled by the deep intertwining of SOTA ICT (information & communication technology) and the real industrial economy. In this paper, we envision how the industrial metaverses form a higher-dimensional world on top of the physical world we live in and unfold discussion along the dimensions of “the enabling technologies” and “potential industrial application scenarios”. We will report some notable observations of pioneering projects from today’s industry/market, and then discuss the industrial metaverses from the perspectives of developers and users, respectively showing the technical requirements and the main benefits. A summary is given regarding the growing interest of industrial metaverses.

Background Erosion is one of the most common and basic lesions of oral mucosal diseases. Long-term refractory oral erosions, induced by autoimmune blistering diseases, infectious diseases, malignant diseases, and some rare conditions, may substantially reduce the quality of life of patients or even constitute a life-threatening condition, resulting in a clinical dilemma regarding the accurate diagnosis and precise management of these diseases. As a special type of malignant lymphoma, most lesions of follicular lymphoma (FL) in the oral mucosa present as masses or swelling of the oral mucosa, while emerging novel presentations lead to intractable diagnoses. Hence, diagnostic algorithms for such diseases are clinically required. Case presentation A 55-year-old female patient presented to the clinic with long-lasting oral mucosal erosions and proliferative lesions. Blood tests, pathological examinations of oral lesions including haematoxylin–eosin (HE) staining, and direct immunofluorescence precluded all of the potential diagnoses described previously. Unexpectedly, positron emission tomography/computed tomography (PET/CT) and abdominal CT of the patient revealed a dense mass in the retroperitoneal area, and the final diagnosis of the retroperitoneal mass was FL. After three courses of chemotherapy conducted by the haematologist, the erosion and proliferative lesions in the patient's oral mucosa had significantly improved. HE and immunohistochemical staining results of intraoral lesions also confirmed it as oral FL. The successful diagnosis of FL in this case is of great clinical significance, as the oral and abdominal FL were treated in a timely manner to avoid unfavourable outcomes. Conclusions To the best of our knowledge, this is the first case of FL that exhibited widespread erosions interspersed with proliferative lesions. Clinicians should be aware of oral FL or seek systemic factors in the presence of similar refractory oral erosions when treatment is non-responsive and the diagnosis is intractable.

Background Lung cancer is currently the most frequent cancer in Jiangsu Province, China, and the features of cancer distribution have changed continuously in the last decade. The aim of this study was to analyse the trend of the incidence of lung cancer in Jiangsu from 2009 to 2018 and predict the incidence from 2019 to 2030. Methods Data on lung cancer incidence in Jiangsu from 2009 to 2018 were retrieved from the Jiangsu Cancer Registry. The average annual percentage change (AAPC) was used to quantify the trend of the lung cancer age-standardized rate (ASR) using Joinpoint software. Bayesian age-period-cohort models were used to predict lung cancer incidence up to 2030. Results In Jiangsu, the lung cancer crude rate increased from 45.73 per 100,000 in 2009 to 69.93 per 100,000 in 2018. The lung cancer ASR increased from 29.03 per 100,000 to 34.22 per 100,000 during the same period (AAPC = 2.17%, 95% confidence interval [CI], 1.54%, 2.80%). Between 2019 and 2030, the lung cancer ASR is predicted to decrease slightly to 32.14 per 100,000 (95% highest density interval [HDI], 24.99, 40.22). Meanwhile, the ASR showed a downward trend in males and rural regions while remaining stable in females and urban regions. Conclusion We predict that the incidence of lung cancer in Jiangsu will decrease in the next 12 years, mainly due to the decrease in males and rural areas. Therefore, future lung cancer prevention and control efforts should be focused on females and urban regions.

Because of their extremely broad spectrum and strong biocidal power, nanoparticles of metals, especially silver (AgNPs), have been widely applied as effective antimicrobial agents against bacteria, fungi, and so on. However, the mutagenic effects of AgNPs and resistance mechanisms of target cells remain controversial. In this study, we discover that AgNPs do not speed up resistance mutation generation by accelerating genome-wide mutation rate of the target bacterium Escherichia coli. AgNPs-treated bacteria also show decreased expression in quorum sensing (QS), one of the major mechanisms leading to population-level drug resistance in microbes. Nonetheless, these nanomaterials are not immune to resistance development by bacteria. Gene expression analysis, experimental evolution in response to sublethal or bactericidal AgNPs treatments, and gene editing reveal that bacteria acquire resistance mainly through two-component regulatory systems, especially those involved in metal detoxification, osmoregulation, and energy metabolism. Although these findings imply low mutagenic risks of nanomaterial-based antimicrobial agents, they also highlight the capacity for bacteria to evolve resistance.

Rearranged during transfection (RET), an oncogenic driver, has been found in multiple tumor types and is thus a promising anticancer therapeutic target. Novel selective RET inhibitors (RETi) that can overcome V804 gatekeeper mutations, endowing resistance to multikinase inhibitors (MKI) and, in particular, achieving KDR selectivity, are needed. In addition, the mechanisms underlying RET–inhibition-induced antiproliferative effects in the context of RET addiction are incompletely understood. This study describes a novel selective RETi, SYHA1815, which inhibited the kinase activity of RET wild type and V804 mutant with an IC50 in the subnanomolar to nanomolar range. Notably, SYHA1815 exhibited approximately 20-fold selectivity for RET over KDR, almost equivalent to that of the launched selective inhibitor pralsetinib. SYHA1815 had only a marginal inhibitory effect on cellular KDR signaling at a high (200 nmol/L) concentration, confirming the selectivity over KDR. In addition, SYHA1815 exhibited a favorable selectivity profile, with greater than 100-fold selectivity for RET over 347 other kinases. It exhibited potent antitumor efficacy and overcame V804 mutations in vitro and in vivo by targeting RET. Then, using SYHA1815 as a probe, we found that RET inhibition suppressed RET-driven cell proliferation via G1 cell-cycle arrest through downregulating c-Myc. Furthermore, disruption of c-Myc upon Brd4 inhibitor treatment led to G1 cell-cycle arrest and overrode RET-driven cell proliferation. Moreover, consistent with the marked in vivo efficacy of RET inhibition, the intratumoral c-Myc level was significantly decreased. In summary, SYHA1815 is a promising RETi for RET-aberrant cancer treatment that is currently in a phase I trial.

At present, the U.S. Fusion Engineering Systems Study (FESS) considers several cooling/breeding concepts that utilize flowing liquid metals (LMs), Li, or eutectic PbLi alloy as working fluids for i...

As a result of ongoing breakthroughs in cancer therapy, cancer patients' survival rates have grown considerably. However, cardiotoxicity has emerged as the most dangerous toxic side effect of cancer treatment, negatively impacting cancer patients' prognosis. In recent years, the link between non-coding RNAs (ncRNAs) and cancer therapy-induced cardiotoxicity has received much attention and investigation. NcRNAs are non-protein-coding RNAs that impact gene expression post-transcriptionally. They include microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs). In several cancer treatments, such as chemotherapy, radiotherapy, and targeted therapy-induced cardiotoxicity, ncRNAs play a significant role in the onset and progression of cardiotoxicity. This review focuses on the mechanisms of ncRNAs in cancer therapy-induced cardiotoxicity, including apoptosis, mitochondrial damage, oxidative stress, DNA damage, inflammation, autophagy, aging, calcium homeostasis, vascular homeostasis, and fibrosis. In addition, this review explores potential ncRNAs-based biomarkers and therapeutic strategies, which may help to convert ncRNAs research into clinical practice in the future for early detection and improvement of cancer therapy-induced cardiotoxicity.

Snail family transcriptional repressor 2 (SNAI2) is a key regulator of partial epithelial-mesenchymal transition (p-EMT) and is associated with tumorigenesis. Whether SNAI2 promotes oral leukoplakia (OLK) malignant transformation by modulating p-EMT is unclear.This study utilized two clinical datasets (GSE26549 and GSE85195) from the Gene Expression Omnibus database, cytological experiments, and a 4-nitroquinoline 1-oxide-induced mice model to explore the role of SNAI2 in OLK malignant transformation.The clinical cohort found SNAI2, as a risk factor (HR = 2.50, 95% CI: 1.08-5.79, p = 0.033), could promote OLK malignant transformation (p = 0.012). Cytological experiments indicated that SNAI2 overexpression promoted DOK cell proliferation, invasion, migration, and increase the protein expression of p-EMT relative signatures, whereas SNAI2 silencing has opposite effects. Furthermore, the mice model and clinical datasets demonstrated the expression of SNAI2 and p-EMT relative signatures were increased with OLK malignant transformation. And SNAI2 was strongly correlated with p-EMT. Besides, co-expressed genes of SNAI2 were also enriched in p-EMT relative biological processes and signaling pathways.p-EMT plays a significant role in promoting the OLK malignant transformation. As an important regulator of p-EMT, SNAI2 could be a target to block the OLK malignant transformation.